| griddap
| Subset
| tabledap
| Make A Graph
| wms
| files
| Title
| Summary
| FGDC
| ISO 19115
| Info
| Background Info
| RSS
| Email
| Institution
| Dataset ID
|
|
| https://erddap.s4raise.it/erddap/tabledap/allDatasets.subset
| https://erddap.s4raise.it/erddap/tabledap/allDatasets
| https://erddap.s4raise.it/erddap/tabledap/allDatasets.graph
|
|
| * The List of All Active Datasets in this ERDDAP *
| This dataset is a table which has a row of information for each dataset currently active in this ERDDAP.\n\ncdm_data_type = Other\nVARIABLES:\ndatasetID (Dataset ID)\naccessible\ninstitution\ndataStructure (Data Structure)\ncdm_data_type (Common Data Model Type)\nclass (ERDDAP Class)\ntitle\nminLongitude (Minimum Longitude, degrees_east)\nmaxLongitude (Maximum Longitude, degrees_east)\nlongitudeSpacing (Average Grid Longitude Spacing, degrees_east)\nminLatitude (Minimum Latitude, degrees_north)\nmaxLatitude (Maximum Latitude, degrees_north)\nlatitudeSpacing (Average Grid Latitude Spacing, degrees_north)\nminAltitude (Minimum Altitude (or negative Depth), m)\nmaxAltitude (Maximum Altitude (or negative Depth), m)\nminTime (Minimum Time, seconds since 1970-01-01T00:00:00Z)\nmaxTime (Maximum Time, seconds since 1970-01-01T00:00:00Z)\ntimeSpacing (Average Grid Time Spacing, seconds)\ngriddap (Base URL of OPeNDAP Grid Service)\nsubset (URL of Subset Web Page)\ntabledap (Base URL of OPeNDAP Table/Sequence Service)\nMakeAGraph (URL of Make-A-Graph Web Page)\nsos (Base URL of SOS Service)\nwcs (Base URL of WCS Service)\nwms (Base URL of WMS Service)\nfiles (Base URL of \"files\" Service)\n... (10 more variables)\n
|
|
| https://erddap.s4raise.it/erddap/info/allDatasets/index.htmlTable
| https://erddap.s4raise.it/erddap
|
|
| ETT S.p.A.
| allDatasets
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_nep_1_1km_01
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_nep_1_1km_01.graph
|
| https://erddap.s4raise.it/erddap/files/unige-dicca_forecast_nep_1_1km_01/
| 2 days 1.1 km resolution forecast over Liguria (01)
| Hourly 3-dimensional atmospheric gridded data, with a temporal coverage of 48 hours and a spatial resolution of 1.1 km. Data cover Central and Eastern Liguria.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][y][x]):\nConvective_precipitation_surface_Mixed_intervals_Accumulation (Convective precipitation (Mixed_intervals Accumulation) @ Ground or water surface, kg.m-2)\nEvaporation_surface_Mixed_intervals_Accumulation (Evaporation (Mixed_intervals Accumulation) @ Ground or water surface, kg.m-2)\nTotal_precipitation_surface_Mixed_intervals_Accumulation (Total precipitation (Mixed_intervals Accumulation) @ Ground or water surface, kg.m-2)\n
|
|
| https://erddap.s4raise.it/erddap/info/unige-dicca_forecast_nep_1_1km_01/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-dicca_forecast_nep_1_1km_01.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-dicca_forecast_nep_1_1km_01&showErrors=false&email=
| UNIGE-DICCA
| unige-dicca_forecast_nep_1_1km_01
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_nep_1_1km_02
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_nep_1_1km_02.graph
|
| https://erddap.s4raise.it/erddap/files/unige-dicca_forecast_nep_1_1km_02/
| 2 days 1.1 km resolution forecast over Liguria (02)
| Hourly 3-dimensional atmospheric gridded data, with a temporal coverage of 48 hours and a spatial resolution of 1.1 km. Data cover Central and Eastern Liguria.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][y][x]):\nConvective_available_potential_energy_surface (Convective available potential energy @ Ground or water surface, J/kg)\nConvective_inhibition_surface (Convective inhibition @ Ground or water surface, J/kg)\nDownward_long_wave_radiation_flux_surface (Downward long-wave radiation flux @ Ground or water surface, W.m-2)\nDownward_short_wave_radiation_flux_surface (Downward short-wave radiation flux @ Ground or water surface, W.m-2)\nFrictional_velocity_surface (Frictional velocity @ Ground or water surface, m/s)\nGeopotential_height_adiabatic_condensation_lifted (Geopotential height @ Level of adiabatic condensation lifted from the surface, gpm)\nGeopotential_height_cloud_base (Geopotential height @ Cloud base level, gpm)\nGeopotential_height_cloud_ceiling (Geopotential height @ Cloud ceiling, gpm)\nGeopotential_height_cloud_tops (Geopotential height @ Level of cloud tops, gpm)\nGeopotential_height_highest_tropospheric_freezing (Geopotential height @ Highest tropospheric freezing level, gpm)\nGeopotential_height_surface (Geopotential height @ Ground or water surface, gpm)\nGeopotential_height_zeroDegC_isotherm (Geopotential height @ Level of 0 °C isotherm, gpm)\nLand_cover_0_sea_1_land_surface (Land cover (0 = sea, 1 = land) @ Ground or water surface)\nLatent_heat_net_flux_surface (Latent heat net flux @ Ground or water surface, W.m-2)\nMSLP_Eta_model_reduction_msl (MSLP (Eta model reduction) @ Mean sea level, Pa)\nPlanetary_boundary_layer_height_surface (Planetary boundary layer height @ Ground or water surface, m)\nPrecipitable_water_surface_layer (Precipitable water @ Ground or water surface layer, kg.m-2)\nPrecipitation_rate_surface (Precipitation rate @ Ground or water surface, kg.m-2.s-1)\nPressure_cloud_base (Pressure @ Cloud base level, Pa)\nPressure_reduced_to_MSL_msl (Pressure reduced to MSL @ Mean sea level, Pa)\nPressure_surface (Pressure @ Ground or water surface, Pa)\nSensible_heat_net_flux_surface (Sensible heat net flux @ Ground or water surface, W.m-2)\nSnow_depth_surface (Snow depth @ Ground or water surface, m)\nSnow_free_albedo_surface (Snow free albedo @ Ground or water surface, percent)\nTemperature_surface (Temperature @ Ground or water surface, K)\nTotal_cloud_cover_surface_layer (Total cloud cover @ Ground or water surface layer, percent)\n... (5 more variables)\n
|
|
| https://erddap.s4raise.it/erddap/info/unige-dicca_forecast_nep_1_1km_02/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-dicca_forecast_nep_1_1km_02.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-dicca_forecast_nep_1_1km_02&showErrors=false&email=
| UNIGE-DICCA
| unige-dicca_forecast_nep_1_1km_02
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_nep_1_1km_03
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_nep_1_1km_03.graph
|
| https://erddap.s4raise.it/erddap/files/unige-dicca_forecast_nep_1_1km_03/
| 2 days 1.1 km resolution forecast over Liguria (03)
| Hourly 3-dimensional atmospheric gridded data, with a temporal coverage of 48 hours and a spatial resolution of 1.1 km. Data cover Central and Eastern Liguria.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][altitude][y][x]):\nu_component_of_wind_height_above_ground (u-component of wind @ Specified height level above ground, m/s)\nv_component_of_wind_height_above_ground (v-component of wind @ Specified height level above ground, m/s)\n
|
|
| https://erddap.s4raise.it/erddap/info/unige-dicca_forecast_nep_1_1km_03/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-dicca_forecast_nep_1_1km_03.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-dicca_forecast_nep_1_1km_03&showErrors=false&email=
| UNIGE-DICCA
| unige-dicca_forecast_nep_1_1km_03
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_nep_1_1km_04
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_nep_1_1km_04.graph
|
| https://erddap.s4raise.it/erddap/files/unige-dicca_forecast_nep_1_1km_04/
| 2 days 1.1 km resolution forecast over Liguria (04)
| Hourly 3-dimensional atmospheric gridded data, with a temporal coverage of 48 hours and a spatial resolution of 1.1 km. Data cover Central and Eastern Liguria.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][altitude][y][x]):\nDewpoint_temperature_height_above_ground (Dewpoint temperature @ Specified height level above ground, K)\nRelative_humidity_height_above_ground (Relative humidity @ Specified height level above ground, percent)\nSpecific_humidity_height_above_ground (Specific humidity @ Specified height level above ground, kg/kg)\n
|
|
| https://erddap.s4raise.it/erddap/info/unige-dicca_forecast_nep_1_1km_04/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-dicca_forecast_nep_1_1km_04.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-dicca_forecast_nep_1_1km_04&showErrors=false&email=
| UNIGE-DICCA
| unige-dicca_forecast_nep_1_1km_04
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_nep_1_1km_05
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_nep_1_1km_05.graph
|
| https://erddap.s4raise.it/erddap/files/unige-dicca_forecast_nep_1_1km_05/
| 2 days 1.1 km resolution forecast over Liguria (05)
| Hourly 3-dimensional atmospheric gridded data, with a temporal coverage of 48 hours and a spatial resolution of 1.1 km. Data cover Central and Eastern Liguria.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][altitude][y][x]):\nTemperature_height_above_ground (Temperature @ Specified height level above ground, K)\n
|
|
| https://erddap.s4raise.it/erddap/info/unige-dicca_forecast_nep_1_1km_05/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-dicca_forecast_nep_1_1km_05.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-dicca_forecast_nep_1_1km_05&showErrors=false&email=
| UNIGE-DICCA
| unige-dicca_forecast_nep_1_1km_05
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_nep_1_1km_06
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_nep_1_1km_06.graph
|
| https://erddap.s4raise.it/erddap/files/unige-dicca_forecast_nep_1_1km_06/
| 2 days 1.1 km resolution forecast over Liguria (06)
| Hourly 3-dimensional atmospheric gridded data, with a temporal coverage of 48 hours and a spatial resolution of 1.1 km. Data cover Central and Eastern Liguria.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][hybrid][y][x]):\nGeopotential_height_hybrid (Geopotential height @ Hybrid level, gpm)\nu_component_of_wind_hybrid (u-component of wind @ Hybrid level, m/s)\nv_component_of_wind_hybrid (v-component of wind @ Hybrid level, m/s)\n
|
|
| https://erddap.s4raise.it/erddap/info/unige-dicca_forecast_nep_1_1km_06/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-dicca_forecast_nep_1_1km_06.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-dicca_forecast_nep_1_1km_06&showErrors=false&email=
| UNIGE-DICCA
| unige-dicca_forecast_nep_1_1km_06
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_nep_1_1km_07
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_nep_1_1km_07.graph
|
| https://erddap.s4raise.it/erddap/files/unige-dicca_forecast_nep_1_1km_07/
| 2 days 1.1 km resolution forecast over Liguria (07)
| Hourly 3-dimensional atmospheric gridded data, with a temporal coverage of 48 hours and a spatial resolution of 1.1 km. Data cover Central and Eastern Liguria.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][isobaric][y][x]):\nGeopotential_height_isobaric (Geopotential height @ Isobaric surface, gpm)\nRelative_humidity_isobaric (Relative humidity @ Isobaric surface, percent)\nTemperature_isobaric (Temperature @ Isobaric surface, K)\nVertical_velocity_pressure_isobaric (Vertical velocity (pressure) @ Isobaric surface, Pa/s)\nu_component_of_wind_isobaric (u-component of wind @ Isobaric surface, m/s)\nv_component_of_wind_isobaric (v-component of wind @ Isobaric surface, m/s)\n
|
|
| https://erddap.s4raise.it/erddap/info/unige-dicca_forecast_nep_1_1km_07/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-dicca_forecast_nep_1_1km_07.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-dicca_forecast_nep_1_1km_07&showErrors=false&email=
| UNIGE-DICCA
| unige-dicca_forecast_nep_1_1km_07
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_nep_1_1km_08
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_nep_1_1km_08.graph
|
| https://erddap.s4raise.it/erddap/files/unige-dicca_forecast_nep_1_1km_08/
| 2 days 1.1 km resolution forecast over Liguria (08)
| Hourly 3-dimensional atmospheric gridded data, with a temporal coverage of 48 hours and a spatial resolution of 1.1 km. Data cover Central and Eastern Liguria.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][isobaric1][y][x]):\nAbsolute_vorticity_isobaric (Absolute vorticity @ Isobaric surface, 1/s)\n
|
|
| https://erddap.s4raise.it/erddap/info/unige-dicca_forecast_nep_1_1km_08/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-dicca_forecast_nep_1_1km_08.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-dicca_forecast_nep_1_1km_08&showErrors=false&email=
| UNIGE-DICCA
| unige-dicca_forecast_nep_1_1km_08
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_nep_1_1km_09
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_nep_1_1km_09.graph
|
| https://erddap.s4raise.it/erddap/files/unige-dicca_forecast_nep_1_1km_09/
| 2 days 1.1 km resolution forecast over Liguria (09)
| Hourly 3-dimensional atmospheric gridded data, with a temporal coverage of 48 hours and a spatial resolution of 1.1 km. Data cover Central and Eastern Liguria.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][pressure_difference_layer][y][x]):\nu_component_of_wind_pressure_difference_layer (u-component of wind @ Level at specified pressure difference from ground to level layer, m/s)\nv_component_of_wind_pressure_difference_layer (v-component of wind @ Level at specified pressure difference from ground to level layer, m/s)\n
|
|
| https://erddap.s4raise.it/erddap/info/unige-dicca_forecast_nep_1_1km_09/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-dicca_forecast_nep_1_1km_09.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-dicca_forecast_nep_1_1km_09&showErrors=false&email=
| UNIGE-DICCA
| unige-dicca_forecast_nep_1_1km_09
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_nep_1_1km_10
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_nep_1_1km_10.graph
|
| https://erddap.s4raise.it/erddap/files/unige-dicca_forecast_nep_1_1km_10/
| 2 days 1.1 km resolution forecast over Liguria (10)
| Hourly 3-dimensional atmospheric gridded data, with a temporal coverage of 48 hours and a spatial resolution of 1.1 km. Data cover Central and Eastern Liguria.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][altitude][y][x]):\nSnow_phase_change_heat_flux_height_above_ground_1_Hour_Average (Snow phase change heat flux (1_Hour Average) @ Specified height level above ground, W.m-2)\n
|
|
| https://erddap.s4raise.it/erddap/info/unige-dicca_forecast_nep_1_1km_10/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-dicca_forecast_nep_1_1km_10.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-dicca_forecast_nep_1_1km_10&showErrors=false&email=
| UNIGE-DICCA
| unige-dicca_forecast_nep_1_1km_10
|
|
| https://erddap.s4raise.it/erddap/tabledap/unige-dicca_waves_animation.subset
| https://erddap.s4raise.it/erddap/tabledap/unige-dicca_waves_animation
| https://erddap.s4raise.it/erddap/tabledap/unige-dicca_waves_animation.graph
|
| https://erddap.s4raise.it/erddap/files/unige-dicca_waves_animation/
| 2 days 1.1 km resolution forecast over Liguria - Animation of waves
| Hourly 3-dimensional atmospheric gridded data, with a temporal coverage of 48 hours. Data cover Central and Eastern Liguria - Animation of waves\n\ncdm_data_type = Grid\nVARIABLES:\nurl\ntime (seconds since 1970-01-01T00:00:00Z)\nname (File Name)\nlastModified (Last Modified, seconds since 1970-01-01T00:00:00Z)\nsize (bytes)\nfileType (File Type)\n
|
|
| https://erddap.s4raise.it/erddap/info/unige-dicca_waves_animation/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-dicca_waves_animation.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-dicca_waves_animation&showErrors=false&email=
| UNIGE-DICCA
| unige-dicca_waves_animation
|
|
| https://erddap.s4raise.it/erddap/tabledap/unige-dicca_wind_animation_1_1km.subset
| https://erddap.s4raise.it/erddap/tabledap/unige-dicca_wind_animation_1_1km
| https://erddap.s4raise.it/erddap/tabledap/unige-dicca_wind_animation_1_1km.graph
|
| https://erddap.s4raise.it/erddap/files/unige-dicca_wind_animation_1_1km/
| 2 days 1.1 km resolution forecast over Liguria - Animation of wind
| Hourly 3-dimensional atmospheric gridded data, with a temporal coverage of 48 hours and a spatial resolution of 1.1 km. Data cover Central and Eastern Liguria - Animation of wind\n\ncdm_data_type = Grid\nVARIABLES:\nurl\ntime (seconds since 1970-01-01T00:00:00Z)\nname (File Name)\nlastModified (Last Modified, seconds since 1970-01-01T00:00:00Z)\nsize (bytes)\nfileType (File Type)\n
|
|
| https://erddap.s4raise.it/erddap/info/unige-dicca_wind_animation_1_1km/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-dicca_wind_animation_1_1km.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-dicca_wind_animation_1_1km&showErrors=false&email=
| UNIGE-DICCA
| unige-dicca_wind_animation_1_1km
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_nep_wrf_apcp
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_nep_wrf_apcp.graph
|
| https://erddap.s4raise.it/erddap/files/unige-dicca_forecast_nep_wrf_apcp/
| 2 days 1.1 km resolution forecast over Liguria - Precipitation
| Hourly 3-dimensional atmospheric gridded data, with a temporal coverage of 48 hours and a spatial resolution of 1.1 km. Data cover Central and Eastern Liguria.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][y][x]):\nTotal_precipitation_surface_3_Hour_Accumulation (Total precipitation (3 Hours Accumulation) @ Ground or water surface, kg.m-2)\nTotal_precipitation_surface_Mixed_intervals_Accumulation (Total precipitation (Mixed_intervals Accumulation) @ Ground or water surface, kg.m-2)\n
|
|
| https://erddap.s4raise.it/erddap/info/unige-dicca_forecast_nep_wrf_apcp/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-dicca_forecast_nep_wrf_apcp.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-dicca_forecast_nep_wrf_apcp&showErrors=false&email=
| UNIGE-DICCA
| unige-dicca_forecast_nep_wrf_apcp
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_son_3_3km_01
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_son_3_3km_01.graph
|
| https://erddap.s4raise.it/erddap/files/unige-dicca_forecast_son_3_3km_01/
| 2 days 3.3 km resolution forecast over Northern and Central Italy (01)
| Hourly 3-dimensional atmospheric gridded data, with a temporal coverage of 48 hours and a spatial resolution of 3.3 km. Data cover Northern and Central Italy.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][y][x]):\nConvective_precipitation_surface_Mixed_intervals_Accumulation (Convective precipitation (Mixed_intervals Accumulation) @ Ground or water surface, kg.m-2)\nEvaporation_surface_Mixed_intervals_Accumulation (Evaporation (Mixed_intervals Accumulation) @ Ground or water surface, kg.m-2)\nTotal_precipitation_surface_Mixed_intervals_Accumulation (Total precipitation (Mixed_intervals Accumulation) @ Ground or water surface, kg.m-2)\n
|
|
| https://erddap.s4raise.it/erddap/info/unige-dicca_forecast_son_3_3km_01/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-dicca_forecast_son_3_3km_01.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-dicca_forecast_son_3_3km_01&showErrors=false&email=
| UNIGE-DICCA
| unige-dicca_forecast_son_3_3km_01
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_son_3_3km_02
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_son_3_3km_02.graph
|
| https://erddap.s4raise.it/erddap/files/unige-dicca_forecast_son_3_3km_02/
| 2 days 3.3 km resolution forecast over Northern and Central Italy (02)
| Hourly 3-dimensional atmospheric gridded data, with a temporal coverage of 48 hours and a spatial resolution of 3.3 km. Data cover Northern and Central Italy.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][y][x]):\nConvective_available_potential_energy_surface (Convective available potential energy @ Ground or water surface, J/kg)\nConvective_inhibition_surface (Convective inhibition @ Ground or water surface, J/kg)\nDownward_long_wave_radiation_flux_surface (Downward long-wave radiation flux @ Ground or water surface, W.m-2)\nDownward_short_wave_radiation_flux_surface (Downward short-wave radiation flux @ Ground or water surface, W.m-2)\nFrictional_velocity_surface (Frictional velocity @ Ground or water surface, m/s)\nGeopotential_height_adiabatic_condensation_lifted (Geopotential height @ Level of adiabatic condensation lifted from the surface, gpm)\nGeopotential_height_cloud_base (Geopotential height @ Cloud base level, gpm)\nGeopotential_height_cloud_ceiling (Geopotential height @ Cloud ceiling, gpm)\nGeopotential_height_cloud_tops (Geopotential height @ Level of cloud tops, gpm)\nGeopotential_height_highest_tropospheric_freezing (Geopotential height @ Highest tropospheric freezing level, gpm)\nGeopotential_height_surface (Geopotential height @ Ground or water surface, gpm)\nGeopotential_height_zeroDegC_isotherm (Geopotential height @ Level of 0 °C isotherm, gpm)\nLand_cover_0_sea_1_land_surface (Land cover (0 = sea, 1 = land) @ Ground or water surface)\nLatent_heat_net_flux_surface (Latent heat net flux @ Ground or water surface, W.m-2)\nMSLP_Eta_model_reduction_msl (MSLP (Eta model reduction) @ Mean sea level, Pa)\nPlanetary_boundary_layer_height_surface (Planetary boundary layer height @ Ground or water surface, m)\nPrecipitable_water_surface_layer (Precipitable water @ Ground or water surface layer, kg.m-2)\nPrecipitation_rate_surface (Precipitation rate @ Ground or water surface, kg.m-2.s-1)\nPressure_cloud_base (Pressure @ Cloud base level, Pa)\nPressure_reduced_to_MSL_msl (Pressure reduced to MSL @ Mean sea level, Pa)\nPressure_surface (Pressure @ Ground or water surface, Pa)\nSensible_heat_net_flux_surface (Sensible heat net flux @ Ground or water surface, W.m-2)\nSnow_depth_surface (Snow depth @ Ground or water surface, m)\nSnow_free_albedo_surface (Snow free albedo @ Ground or water surface, percent)\nTemperature_surface (Temperature @ Ground or water surface, K)\nTotal_cloud_cover_surface_layer (Total cloud cover @ Ground or water surface layer, percent)\n... (5 more variables)\n
|
|
| https://erddap.s4raise.it/erddap/info/unige-dicca_forecast_son_3_3km_02/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-dicca_forecast_son_3_3km_02.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-dicca_forecast_son_3_3km_02&showErrors=false&email=
| UNIGE-DICCA
| unige-dicca_forecast_son_3_3km_02
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_son_3_3km_03
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_son_3_3km_03.graph
|
| https://erddap.s4raise.it/erddap/files/unige-dicca_forecast_son_3_3km_03/
| 2 days 3.3 km resolution forecast over Northern and Central Italy (03)
| Hourly 3-dimensional atmospheric gridded data, with a temporal coverage of 48 hours and a spatial resolution of 3.3 km. Data cover Northern and Central Italy.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][altitude][y][x]):\nu_component_of_wind_height_above_ground (u-component of wind @ Specified height level above ground, m/s)\nv_component_of_wind_height_above_ground (v-component of wind @ Specified height level above ground, m/s)\n
|
|
| https://erddap.s4raise.it/erddap/info/unige-dicca_forecast_son_3_3km_03/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-dicca_forecast_son_3_3km_03.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-dicca_forecast_son_3_3km_03&showErrors=false&email=
| UNIGE-DICCA
| unige-dicca_forecast_son_3_3km_03
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_son_3_3km_04
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_son_3_3km_04.graph
|
| https://erddap.s4raise.it/erddap/files/unige-dicca_forecast_son_3_3km_04/
| 2 days 3.3 km resolution forecast over Northern and Central Italy (04)
| Hourly 3-dimensional atmospheric gridded data, with a temporal coverage of 48 hours and a spatial resolution of 3.3 km. Data cover Northern and Central Italy.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][altitude][y][x]):\nDewpoint_temperature_height_above_ground (Dewpoint temperature @ Specified height level above ground, K)\nRelative_humidity_height_above_ground (Relative humidity @ Specified height level above ground, percent)\nSpecific_humidity_height_above_ground (Specific humidity @ Specified height level above ground, kg/kg)\n
|
|
| https://erddap.s4raise.it/erddap/info/unige-dicca_forecast_son_3_3km_04/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-dicca_forecast_son_3_3km_04.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-dicca_forecast_son_3_3km_04&showErrors=false&email=
| UNIGE-DICCA
| unige-dicca_forecast_son_3_3km_04
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_son_3_3km_05
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_son_3_3km_05.graph
|
| https://erddap.s4raise.it/erddap/files/unige-dicca_forecast_son_3_3km_05/
| 2 days 3.3 km resolution forecast over Northern and Central Italy (05)
| Hourly 3-dimensional atmospheric gridded data, with a temporal coverage of 48 hours and a spatial resolution of 3.3 km. Data cover Northern and Central Italy.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][altitude][y][x]):\nTemperature_height_above_ground (Temperature @ Specified height level above ground, K)\n
|
|
| https://erddap.s4raise.it/erddap/info/unige-dicca_forecast_son_3_3km_05/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-dicca_forecast_son_3_3km_05.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-dicca_forecast_son_3_3km_05&showErrors=false&email=
| UNIGE-DICCA
| unige-dicca_forecast_son_3_3km_05
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_son_3_3km_06
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_son_3_3km_06.graph
|
| https://erddap.s4raise.it/erddap/files/unige-dicca_forecast_son_3_3km_06/
| 2 days 3.3 km resolution forecast over Northern and Central Italy (06)
| Hourly 3-dimensional atmospheric gridded data, with a temporal coverage of 48 hours and a spatial resolution of 3.3 km. Data cover Northern and Central Italy.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][hybrid][y][x]):\nGeopotential_height_hybrid (Geopotential height @ Hybrid level, gpm)\nu_component_of_wind_hybrid (u-component of wind @ Hybrid level, m/s)\nv_component_of_wind_hybrid (v-component of wind @ Hybrid level, m/s)\n
|
|
| https://erddap.s4raise.it/erddap/info/unige-dicca_forecast_son_3_3km_06/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-dicca_forecast_son_3_3km_06.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-dicca_forecast_son_3_3km_06&showErrors=false&email=
| UNIGE-DICCA
| unige-dicca_forecast_son_3_3km_06
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_son_3_3km_07
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_son_3_3km_07.graph
|
| https://erddap.s4raise.it/erddap/files/unige-dicca_forecast_son_3_3km_07/
| 2 days 3.3 km resolution forecast over Northern and Central Italy (07)
| Hourly 3-dimensional atmospheric gridded data, with a temporal coverage of 48 hours and a spatial resolution of 3.3 km. Data cover Northern and Central Italy.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][isobaric][y][x]):\nGeopotential_height_isobaric (Geopotential height @ Isobaric surface, gpm)\nRelative_humidity_isobaric (Relative humidity @ Isobaric surface, percent)\nTemperature_isobaric (Temperature @ Isobaric surface, K)\nVertical_velocity_pressure_isobaric (Vertical velocity (pressure) @ Isobaric surface, Pa/s)\nu_component_of_wind_isobaric (u-component of wind @ Isobaric surface, m/s)\nv_component_of_wind_isobaric (v-component of wind @ Isobaric surface, m/s)\n
|
|
| https://erddap.s4raise.it/erddap/info/unige-dicca_forecast_son_3_3km_07/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-dicca_forecast_son_3_3km_07.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-dicca_forecast_son_3_3km_07&showErrors=false&email=
| UNIGE-DICCA
| unige-dicca_forecast_son_3_3km_07
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_son_3_3km_08
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_son_3_3km_08.graph
|
| https://erddap.s4raise.it/erddap/files/unige-dicca_forecast_son_3_3km_08/
| 2 days 3.3 km resolution forecast over Northern and Central Italy (08)
| Hourly 3-dimensional atmospheric gridded data, with a temporal coverage of 48 hours and a spatial resolution of 3.3 km. Data cover Northern and Central Italy.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][isobaric1][y][x]):\nAbsolute_vorticity_isobaric (Absolute vorticity @ Isobaric surface, 1/s)\n
|
|
| https://erddap.s4raise.it/erddap/info/unige-dicca_forecast_son_3_3km_08/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-dicca_forecast_son_3_3km_08.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-dicca_forecast_son_3_3km_08&showErrors=false&email=
| UNIGE-DICCA
| unige-dicca_forecast_son_3_3km_08
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_son_3_3km_09
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_son_3_3km_09.graph
|
| https://erddap.s4raise.it/erddap/files/unige-dicca_forecast_son_3_3km_09/
| 2 days 3.3 km resolution forecast over Northern and Central Italy (09)
| Hourly 3-dimensional atmospheric gridded data, with a temporal coverage of 48 hours and a spatial resolution of 3.3 km. Data cover Northern and Central Italy.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][pressure_difference_layer][y][x]):\nu_component_of_wind_pressure_difference_layer (u-component of wind @ Level at specified pressure difference from ground to level layer, m/s)\nv_component_of_wind_pressure_difference_layer (v-component of wind @ Level at specified pressure difference from ground to level layer, m/s)\n
|
|
| https://erddap.s4raise.it/erddap/info/unige-dicca_forecast_son_3_3km_09/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-dicca_forecast_son_3_3km_09.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-dicca_forecast_son_3_3km_09&showErrors=false&email=
| UNIGE-DICCA
| unige-dicca_forecast_son_3_3km_09
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_son_3_3km_10
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_son_3_3km_10.graph
|
| https://erddap.s4raise.it/erddap/files/unige-dicca_forecast_son_3_3km_10/
| 2 days 3.3 km resolution forecast over Northern and Central Italy (10)
| Hourly 3-dimensional atmospheric gridded data, with a temporal coverage of 48 hours and a spatial resolution of 3.3 km. Data cover Northern and Central Italy.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][altitude][y][x]):\nSnow_phase_change_heat_flux_height_above_ground_1_Hour_Average (Snow phase change heat flux (1_Hour Average) @ Specified height level above ground, W.m-2)\n
|
|
| https://erddap.s4raise.it/erddap/info/unige-dicca_forecast_son_3_3km_10/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-dicca_forecast_son_3_3km_10.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-dicca_forecast_son_3_3km_10&showErrors=false&email=
| UNIGE-DICCA
| unige-dicca_forecast_son_3_3km_10
|
|
| https://erddap.s4raise.it/erddap/tabledap/unige-dicca_wind_animation_3_3km.subset
| https://erddap.s4raise.it/erddap/tabledap/unige-dicca_wind_animation_3_3km
| https://erddap.s4raise.it/erddap/tabledap/unige-dicca_wind_animation_3_3km.graph
|
| https://erddap.s4raise.it/erddap/files/unige-dicca_wind_animation_3_3km/
| 2 days 3.3 km resolution forecast over Northern and Central Italy - Animation of wind
| Hourly 3-dimensional atmospheric gridded data, with a temporal coverage of 48 hours and a spatial resolution of 3.3 km. Data cover Northern and Central Italy - Animation of wind\n\ncdm_data_type = Grid\nVARIABLES:\nurl\ntime (seconds since 1970-01-01T00:00:00Z)\nname (File Name)\nlastModified (Last Modified, seconds since 1970-01-01T00:00:00Z)\nsize (bytes)\nfileType (File Type)\n
|
|
| https://erddap.s4raise.it/erddap/info/unige-dicca_wind_animation_3_3km/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-dicca_wind_animation_3_3km.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-dicca_wind_animation_3_3km&showErrors=false&email=
| UNIGE-DICCA
| unige-dicca_wind_animation_3_3km
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_son_wrf_apcp
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_son_wrf_apcp.graph
|
| https://erddap.s4raise.it/erddap/files/unige-dicca_forecast_son_wrf_apcp/
| 2 days 3.3 km resolution forecast over Northern and Central Italy - Precipitation
| Hourly 3-dimensional atmospheric gridded data, with a temporal coverage of 48 hours and a spatial resolution of 3.3 km. Data cover Northern and Central Italy.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][y][x]):\nTotal_precipitation_surface_3_Hour_Accumulation (Total precipitation (3 Hours Accumulation) @ Ground or water surface, kg.m-2)\nTotal_precipitation_surface_Mixed_intervals_Accumulation (Total precipitation (Mixed_intervals Accumulation) @ Ground or water surface, kg.m-2)\n
|
|
| https://erddap.s4raise.it/erddap/info/unige-dicca_forecast_son_wrf_apcp/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-dicca_forecast_son_wrf_apcp.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-dicca_forecast_son_wrf_apcp&showErrors=false&email=
| UNIGE-DICCA
| unige-dicca_forecast_son_wrf_apcp
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_fat_10km_01
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_fat_10km_01.graph
|
| https://erddap.s4raise.it/erddap/files/unige-dicca_forecast_fat_10km_01/
| 5 days 10 km resolution forecast over Southern Europe and Mediterranean basin (01)
| Hourly 3-dimensional atmospheric gridded data, with a temporal coverage of 5 day and a spatial resolution of 10 km. Data cover Southern Europe and Mediterranean basin.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][y][x]):\nConvective_precipitation_surface_Mixed_intervals_Accumulation (Convective precipitation (Mixed_intervals Accumulation) @ Ground or water surface, kg.m-2)\nEvaporation_surface_Mixed_intervals_Accumulation (Evaporation (Mixed_intervals Accumulation) @ Ground or water surface, kg.m-2)\nTotal_precipitation_surface_Mixed_intervals_Accumulation (Total precipitation (Mixed_intervals Accumulation) @ Ground or water surface, kg.m-2)\n
|
|
| https://erddap.s4raise.it/erddap/info/unige-dicca_forecast_fat_10km_01/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-dicca_forecast_fat_10km_01.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-dicca_forecast_fat_10km_01&showErrors=false&email=
| UNIGE-DICCA
| unige-dicca_forecast_fat_10km_01
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_fat_10km_02
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_fat_10km_02.graph
|
| https://erddap.s4raise.it/erddap/files/unige-dicca_forecast_fat_10km_02/
| 5 days 10 km resolution forecast over Southern Europe and Mediterranean basin (02)
| Hourly 3-dimensional atmospheric gridded data, with a temporal coverage of 5 day and a spatial resolution of 10 km. Data cover Southern Europe and Mediterranean basin.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][altitude][y][x]):\nSnow_phase_change_heat_flux_height_above_ground_1_Hour_Average (Snow phase change heat flux (1_Hour Average) @ Specified height level above ground, W.m-2)\n
|
|
| https://erddap.s4raise.it/erddap/info/unige-dicca_forecast_fat_10km_02/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-dicca_forecast_fat_10km_02.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-dicca_forecast_fat_10km_02&showErrors=false&email=
| UNIGE-DICCA
| unige-dicca_forecast_fat_10km_02
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_fat_10km_03
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_fat_10km_03.graph
|
| https://erddap.s4raise.it/erddap/files/unige-dicca_forecast_fat_10km_03/
| 5 days 10 km resolution forecast over Southern Europe and Mediterranean basin (03)
| Hourly 3-dimensional atmospheric gridded data, with a temporal coverage of 5 day and a spatial resolution of 10 km. Data cover Southern Europe and Mediterranean basin.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][y][x]):\nConvective_available_potential_energy_surface (Convective available potential energy @ Ground or water surface, J/kg)\nConvective_inhibition_surface (Convective inhibition @ Ground or water surface, J/kg)\nDownward_long_wave_radiation_flux_surface (Downward long-wave radiation flux @ Ground or water surface, W.m-2)\nDownward_short_wave_radiation_flux_surface (Downward short-wave radiation flux @ Ground or water surface, W.m-2)\nFrictional_velocity_surface (Frictional velocity @ Ground or water surface, m/s)\nGeopotential_height_adiabatic_condensation_lifted (Geopotential height @ Level of adiabatic condensation lifted from the surface, gpm)\nGeopotential_height_cloud_base (Geopotential height @ Cloud base level, gpm)\nGeopotential_height_cloud_ceiling (Geopotential height @ Cloud ceiling, gpm)\nGeopotential_height_cloud_tops (Geopotential height @ Level of cloud tops, gpm)\nGeopotential_height_highest_tropospheric_freezing (Geopotential height @ Highest tropospheric freezing level, gpm)\nGeopotential_height_surface (Geopotential height @ Ground or water surface, gpm)\nGeopotential_height_zeroDegC_isotherm (Geopotential height @ Level of 0 °C isotherm, gpm)\nLand_cover_0_sea_1_land_surface (Land cover (0 = sea, 1 = land) @ Ground or water surface)\nLatent_heat_net_flux_surface (Latent heat net flux @ Ground or water surface, W.m-2)\nMSLP_Eta_model_reduction_msl (MSLP (Eta model reduction) @ Mean sea level, Pa)\nPlanetary_boundary_layer_height_surface (Planetary boundary layer height @ Ground or water surface, m)\nPrecipitable_water_surface_layer (Precipitable water @ Ground or water surface layer, kg.m-2)\nPrecipitation_rate_surface (Precipitation rate @ Ground or water surface, kg.m-2.s-1)\nPressure_cloud_base (Pressure @ Cloud base level, Pa)\nPressure_reduced_to_MSL_msl (Pressure reduced to MSL @ Mean sea level, Pa)\nPressure_surface (Pressure @ Ground or water surface, Pa)\nSensible_heat_net_flux_surface (Sensible heat net flux @ Ground or water surface, W.m-2)\nSnow_depth_surface (Snow depth @ Ground or water surface, m)\nSnow_free_albedo_surface (Snow free albedo @ Ground or water surface, percent)\nTemperature_surface (Temperature @ Ground or water surface, K)\nTotal_cloud_cover_surface_layer (Total cloud cover @ Ground or water surface layer, percent)\n... (5 more variables)\n
|
|
| https://erddap.s4raise.it/erddap/info/unige-dicca_forecast_fat_10km_03/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-dicca_forecast_fat_10km_03.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-dicca_forecast_fat_10km_03&showErrors=false&email=
| UNIGE-DICCA
| unige-dicca_forecast_fat_10km_03
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_fat_10km_04
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_fat_10km_04.graph
|
| https://erddap.s4raise.it/erddap/files/unige-dicca_forecast_fat_10km_04/
| 5 days 10 km resolution forecast over Southern Europe and Mediterranean basin (04)
| Hourly 3-dimensional atmospheric gridded data, with a temporal coverage of 5 day and a spatial resolution of 10 km. Data cover Southern Europe and Mediterranean basin.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][altitude][y][x]):\nu_component_of_wind_height_above_ground (u-component of wind @ Specified height level above ground, m/s)\nv_component_of_wind_height_above_ground (v-component of wind @ Specified height level above ground, m/s)\n
|
|
| https://erddap.s4raise.it/erddap/info/unige-dicca_forecast_fat_10km_04/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-dicca_forecast_fat_10km_04.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-dicca_forecast_fat_10km_04&showErrors=false&email=
| UNIGE-DICCA
| unige-dicca_forecast_fat_10km_04
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_fat_10km_05
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_fat_10km_05.graph
|
| https://erddap.s4raise.it/erddap/files/unige-dicca_forecast_fat_10km_05/
| 5 days 10 km resolution forecast over Southern Europe and Mediterranean basin (05)
| Hourly 3-dimensional atmospheric gridded data, with a temporal coverage of 5 day and a spatial resolution of 10 km. Data cover Southern Europe and Mediterranean basin.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][altitude][y][x]):\nDewpoint_temperature_height_above_ground (Dewpoint temperature @ Specified height level above ground, K)\nRelative_humidity_height_above_ground (Relative humidity @ Specified height level above ground, percent)\nSpecific_humidity_height_above_ground (Specific humidity @ Specified height level above ground, kg/kg)\n
|
|
| https://erddap.s4raise.it/erddap/info/unige-dicca_forecast_fat_10km_05/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-dicca_forecast_fat_10km_05.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-dicca_forecast_fat_10km_05&showErrors=false&email=
| UNIGE-DICCA
| unige-dicca_forecast_fat_10km_05
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_fat_10km_06
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_fat_10km_06.graph
|
| https://erddap.s4raise.it/erddap/files/unige-dicca_forecast_fat_10km_06/
| 5 days 10 km resolution forecast over Southern Europe and Mediterranean basin (06)
| Hourly 3-dimensional atmospheric gridded data, with a temporal coverage of 5 day and a spatial resolution of 10 km. Data cover Southern Europe and Mediterranean basin.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][altitude][y][x]):\nTemperature_height_above_ground (Temperature @ Specified height level above ground, K)\n
|
|
| https://erddap.s4raise.it/erddap/info/unige-dicca_forecast_fat_10km_06/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-dicca_forecast_fat_10km_06.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-dicca_forecast_fat_10km_06&showErrors=false&email=
| UNIGE-DICCA
| unige-dicca_forecast_fat_10km_06
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_fat_10km_07
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_fat_10km_07.graph
|
| https://erddap.s4raise.it/erddap/files/unige-dicca_forecast_fat_10km_07/
| 5 days 10 km resolution forecast over Southern Europe and Mediterranean basin (07)
| Hourly 3-dimensional atmospheric gridded data, with a temporal coverage of 5 day and a spatial resolution of 10 km. Data cover Southern Europe and Mediterranean basin.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][hybrid][y][x]):\nGeopotential_height_hybrid (Geopotential height @ Hybrid level, gpm)\nu_component_of_wind_hybrid (u-component of wind @ Hybrid level, m/s)\nv_component_of_wind_hybrid (v-component of wind @ Hybrid level, m/s)\n
|
|
| https://erddap.s4raise.it/erddap/info/unige-dicca_forecast_fat_10km_07/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-dicca_forecast_fat_10km_07.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-dicca_forecast_fat_10km_07&showErrors=false&email=
| UNIGE-DICCA
| unige-dicca_forecast_fat_10km_07
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_fat_10km_08
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_fat_10km_08.graph
|
| https://erddap.s4raise.it/erddap/files/unige-dicca_forecast_fat_10km_08/
| 5 days 10 km resolution forecast over Southern Europe and Mediterranean basin (08)
| Hourly 3-dimensional atmospheric gridded data, with a temporal coverage of 5 day and a spatial resolution of 10 km. Data cover Southern Europe and Mediterranean basin.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][isobaric][y][x]):\nGeopotential_height_isobaric (Geopotential height @ Isobaric surface, gpm)\nRelative_humidity_isobaric (Relative humidity @ Isobaric surface, percent)\nTemperature_isobaric (Temperature @ Isobaric surface, K)\nVertical_velocity_pressure_isobaric (Vertical velocity (pressure) @ Isobaric surface, Pa/s)\nu_component_of_wind_isobaric (u-component of wind @ Isobaric surface, m/s)\nv_component_of_wind_isobaric (v-component of wind @ Isobaric surface, m/s)\n
|
|
| https://erddap.s4raise.it/erddap/info/unige-dicca_forecast_fat_10km_08/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-dicca_forecast_fat_10km_08.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-dicca_forecast_fat_10km_08&showErrors=false&email=
| UNIGE-DICCA
| unige-dicca_forecast_fat_10km_08
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_fat_10km_09
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_fat_10km_09.graph
|
| https://erddap.s4raise.it/erddap/files/unige-dicca_forecast_fat_10km_09/
| 5 days 10 km resolution forecast over Southern Europe and Mediterranean basin (09)
| Hourly 3-dimensional atmospheric gridded data, with a temporal coverage of 5 day and a spatial resolution of 10 km. Data cover Southern Europe and Mediterranean basin.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][isobaric1][y][x]):\nAbsolute_vorticity_isobaric (Absolute vorticity @ Isobaric surface, 1/s)\n
|
|
| https://erddap.s4raise.it/erddap/info/unige-dicca_forecast_fat_10km_09/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-dicca_forecast_fat_10km_09.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-dicca_forecast_fat_10km_09&showErrors=false&email=
| UNIGE-DICCA
| unige-dicca_forecast_fat_10km_09
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_fat_10km_10
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_fat_10km_10.graph
|
| https://erddap.s4raise.it/erddap/files/unige-dicca_forecast_fat_10km_10/
| 5 days 10 km resolution forecast over Southern Europe and Mediterranean basin (10)
| Hourly 3-dimensional atmospheric gridded data, with a temporal coverage of 5 day and a spatial resolution of 10 km. Data cover Southern Europe and Mediterranean basin.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][pressure_difference_layer][y][x]):\nu_component_of_wind_pressure_difference_layer (u-component of wind @ Level at specified pressure difference from ground to level layer, m/s)\nv_component_of_wind_pressure_difference_layer (v-component of wind @ Level at specified pressure difference from ground to level layer, m/s)\n
|
|
| https://erddap.s4raise.it/erddap/info/unige-dicca_forecast_fat_10km_10/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-dicca_forecast_fat_10km_10.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-dicca_forecast_fat_10km_10&showErrors=false&email=
| UNIGE-DICCA
| unige-dicca_forecast_fat_10km_10
|
|
| https://erddap.s4raise.it/erddap/tabledap/unige-dicca_wind_animation_10km.subset
| https://erddap.s4raise.it/erddap/tabledap/unige-dicca_wind_animation_10km
| https://erddap.s4raise.it/erddap/tabledap/unige-dicca_wind_animation_10km.graph
|
| https://erddap.s4raise.it/erddap/files/unige-dicca_wind_animation_10km/
| 5 days 10 km resolution forecast over Southern Europe and Mediterranean basin - Animation of wind
| Hourly 3-dimensional atmospheric gridded data, with a temporal coverage of 5 day and a spatial resolution of 10 km. Data cover Southern Europe and Mediterranean basin - Animation of wind\n\ncdm_data_type = Grid\nVARIABLES:\nurl\ntime (seconds since 1970-01-01T00:00:00Z)\nname (File Name)\nlastModified (Last Modified, seconds since 1970-01-01T00:00:00Z)\nsize (bytes)\nfileType (File Type)\n
|
|
| https://erddap.s4raise.it/erddap/info/unige-dicca_wind_animation_10km/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-dicca_wind_animation_10km.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-dicca_wind_animation_10km&showErrors=false&email=
| UNIGE-DICCA
| unige-dicca_wind_animation_10km
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_fat_wrf_apcp
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_fat_wrf_apcp.graph
|
| https://erddap.s4raise.it/erddap/files/unige-dicca_forecast_fat_wrf_apcp/
| 5 days 10 km resolution forecast over Southern Europe and Mediterranean basin - Precipitation
| Hourly 3-dimensional atmospheric gridded data, with a temporal coverage of 5 day and a spatial resolution of 10 km. Data cover Southern Europe and Mediterranean basin.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][y][x]):\nTotal_precipitation_surface_3_Hour_Accumulation (Total precipitation (3 Hours Accumulation) @ Ground or water surface, kg.m-2)\nTotal_precipitation_surface_Mixed_intervals_Accumulation (Total precipitation (Mixed_intervals Accumulation) @ Ground or water surface, kg.m-2)\n
|
|
| https://erddap.s4raise.it/erddap/info/unige-dicca_forecast_fat_wrf_apcp/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-dicca_forecast_fat_wrf_apcp.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-dicca_forecast_fat_wrf_apcp&showErrors=false&email=
| UNIGE-DICCA
| unige-dicca_forecast_fat_wrf_apcp
|
|
|
| https://erddap.s4raise.it/erddap/tabledap/unige-dicca_dispersion_forecast_ai
| https://erddap.s4raise.it/erddap/tabledap/unige-dicca_dispersion_forecast_ai.graph
|
| https://erddap.s4raise.it/erddap/files/unige-dicca_dispersion_forecast_ai/
| AI-based particle dispersion model
| The particle dispersion model used is Gnome. To drive the dispersion simulations, a neural network is first employed to model the sea current for the next six hours, using the wind and current data from the previous six hours as input. The current signal forecasted by the neural network is then used as a forcing input for Gnome to model the particle dispersion.\n\ncdm_data_type = Point\nVARIABLES:\ntime (time since the beginning of the simulation, seconds since 1970-01-01T00:00:00Z)\nlatitude (latitude of the particle, degrees_north)\nlongitude (longitude of the particle, degrees_east)\nparticle_count (number of particles in a given timestep, 1)\nmass (mass of particle, kilograms)\nstatus_codes (particle status code)\nage (age of particle from time of release, minutes)\ndensity (emulsion density at end of timestep, kg/m^3)\nspill_num (spill to which the particle belongs)\nsurface_concentration (surface concentration of oil, g m-2)\ndepth (particle depth below sea surface, m)\nid (particle ID)\nviscosity (emulsion viscosity at end of timestep, m^2/sec)\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-dicca_dispersion_forecast_ai_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-dicca_dispersion_forecast_ai_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-dicca_dispersion_forecast_ai/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-dicca_dispersion_forecast_ai.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-dicca_dispersion_forecast_ai&showErrors=false&email=
| UNIGE-DICCA
| unige-dicca_dispersion_forecast_ai
|
|
|
| https://erddap.s4raise.it/erddap/tabledap/unige-distav_camogli_buoy_temp_curr_data
| https://erddap.s4raise.it/erddap/tabledap/unige-distav_camogli_buoy_temp_curr_data.graph
|
|
| Camogli in situ buoy sea water temperature and sub surface current data
| The dataset represents data automatically collected and trasmitted in real-time by in situ buoy located in Camogli\n\ncdm_data_type = Other\nVARIABLES:\ntime (Timestamp, seconds since 1970-01-01T00:00:00Z)\nlatitude (degrees_north)\nlongitude (degrees_east)\nsw_temperature_3m (SW Temp 3m, degree_Celsius)\nsw_temperature_6_5m (SW Temp 6.5m, degree_Celsius)\nspeed_mean (Speed, cm/s)\nspeed_std (cm/s)\ndirection_mean (Direction, degrees_north)\ndirection_std (degrees_north)\ntilt\ntilt_std\nread_count\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-distav_camogli_buoy_temp_curr_data_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-distav_camogli_buoy_temp_curr_data_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-distav_camogli_buoy_temp_curr_data/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-distav_camogli_buoy_temp_curr_data.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-distav_camogli_buoy_temp_curr_data&showErrors=false&email=
| UNIGE-DISTAV
| unige-distav_camogli_buoy_temp_curr_data
|
|
|
| https://erddap.s4raise.it/erddap/tabledap/unige-distav_camogli_buoy_wave_wind_data
| https://erddap.s4raise.it/erddap/tabledap/unige-distav_camogli_buoy_wave_wind_data.graph
|
|
| Camogli in situ Camogli buoy wave and wind data
| The dataset represents data automatically collected and trasmitted in real-time by in situ buoy located in Camogli\n\ncdm_data_type = Other\nVARIABLES:\ntime (Timestamp, seconds since 1970-01-01T00:00:00Z)\nlatitude (degrees_north)\nlongitude (degrees_east)\nsignificantWaveHeight (Significant Wave Height, m)\npeakPeriod (Wave Peak Period, s)\nmeanPeriod (Wave Mean Period, s)\npeakDirection (Wave Peak Direction, degrees)\nmeanDirection (Wave Mean Direction, degrees)\npeakDirectionalSpread (Wave Peak Directional Spread, degrees)\nmeanDirectionalSpread (Wave Mean Directional Spread, degrees)\nwind_direction (degrees_north)\nwind_speed (m/s)\nair_pressure (Barometric Pressure, hPa)\nsurfaceTemp (Surface Temp, degree_C)\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-distav_camogli_buoy_wave_wind_data_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-distav_camogli_buoy_wave_wind_data_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-distav_camogli_buoy_wave_wind_data/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-distav_camogli_buoy_wave_wind_data.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-distav_camogli_buoy_wave_wind_data&showErrors=false&email=
| UNIGE-DISTAV
| unige-distav_camogli_buoy_wave_wind_data
|
| https://erddap.s4raise.it/erddap/griddap/cmems_obs_oc_med_bgc_tur-spm-chl_nrt_l3_hr_mosaic_P1D_m
|
|
| https://erddap.s4raise.it/erddap/griddap/cmems_obs_oc_med_bgc_tur-spm-chl_nrt_l3_hr_mosaic_P1D_m.graph
| https://erddap.s4raise.it/erddap/wms/cmems_obs_oc_med_bgc_tur-spm-chl_nrt_l3_hr_mosaic_P1D_m/request
| https://erddap.s4raise.it/erddap/files/cmems_obs_oc_med_bgc_tur-spm-chl_nrt_l3_hr_mosaic_P1D_m/
| CMEMS HR-OC Mediterranean Sea transparency (spm, tur) and geophysical (chl) daily observations mosaic
| CMEMS HR-OC Mediterranean Sea transparency (spm, tur) and geophysical (chl) daily observations mosaic\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][latitude][longitude]):\nCHL (Chlorophyll-a concentration derived from MSI L2R using HR-OC L2W processor, mg m-3)\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/cmems_obs_oc_med_bgc_tur-spm-chl_nrt_l3_hr_mosaic_P1D_m_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/cmems_obs_oc_med_bgc_tur-spm-chl_nrt_l3_hr_mosaic_P1D_m_iso19115.xml
| https://erddap.s4raise.it/erddap/info/cmems_obs_oc_med_bgc_tur-spm-chl_nrt_l3_hr_mosaic_P1D_m/index.htmlTable
| https://marine.copernicus.eu/
| https://erddap.s4raise.it/erddap/rss/cmems_obs_oc_med_bgc_tur-spm-chl_nrt_l3_hr_mosaic_P1D_m.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=cmems_obs_oc_med_bgc_tur-spm-chl_nrt_l3_hr_mosaic_P1D_m&showErrors=false&email=
| Brockmann Consult GmbH, RBINS, VITO for CMEMS, Mercator Ocean
| cmems_obs_oc_med_bgc_tur-spm-chl_nrt_l3_hr_mosaic_P1D_m
|
|
| https://erddap.s4raise.it/erddap/tabledap/algawarning.subset
| https://erddap.s4raise.it/erddap/tabledap/algawarning
| https://erddap.s4raise.it/erddap/tabledap/algawarning.graph
|
|
| collection of algal photos collected by the @lgawarning platform - algal bloom participatory environmental monitoring system
| The @lgawarning platform aims to collect environmental monitoring system for algal blooms, enabling users to transmit reports on the anomalous presence of microalgae in aquatic environments directly from the observation site\n\ncdm_data_type = Point\nVARIABLES:\ntime (seconds since 1970-01-01T00:00:00Z)\nsite_name (Position)\nlatitude (degrees_north)\nlongitude (degrees_east)\nalgae_type (Type)\ndescription\nsample_volume (Volume, ml)\noperator_id (User)\nimage\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/algawarning_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/algawarning_iso19115.xml
| https://erddap.s4raise.it/erddap/info/algawarning/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/algawarning.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=algawarning&showErrors=false&email=
| ETT
| algawarning
|
|
|
| https://erddap.s4raise.it/erddap/tabledap/cesp
| https://erddap.s4raise.it/erddap/tabledap/cesp.graph
|
| https://erddap.s4raise.it/erddap/files/cesp/
| Collection of plastic litter photos collected by the Custodians Earth Solution Platform (CESP)
| The Custodians Earth Solution Platform (CESP) app collects photographic reports of plastic litter as part of a BioDesign Foundation initiative to support environmental cleanup efforts. Developed under the RAISE program and tested in real-world operations, CESP enables the acquisition of georeferenced data that are processed to produce maps illustrating the presence and spatial distribution of plastic litter in urban environments.\n\ncdm_data_type = Other\nVARIABLES:\ntime (seconds since 1970-01-01T00:00:00Z)\nlatitude (degrees_north)\nlongitude (degrees_east)\nevent\nevent_description\ndescription\nimage\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/cesp_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/cesp_iso19115.xml
| https://erddap.s4raise.it/erddap/info/cesp/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/cesp.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=cesp&showErrors=false&email=
| ETT
| cesp
|
|
| https://erddap.s4raise.it/erddap/tabledap/ingv_earthquakes.subset
| https://erddap.s4raise.it/erddap/tabledap/ingv_earthquakes
| https://erddap.s4raise.it/erddap/tabledap/ingv_earthquakes.graph
|
| https://erddap.s4raise.it/erddap/files/ingv_earthquakes/
| Data from a local source.
| Data from a local source.\n\ncdm_data_type = Point\nVARIABLES:\nEventID (Event ID)\ntime (seconds since 1970-01-01T00:00:00Z)\nlatitude (degrees_north)\nlongitude (degrees_east)\nDepth_Km (Depth)\nAuthor\nCatalog\nContributor\nContributorID (Contributor ID)\nMagType (Mag Type)\nMagnitude\nMagAuthor (Mag Author)\nEventLocationName (Event Location Name)\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/ingv_earthquakes_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/ingv_earthquakes_iso19115.xml
| https://erddap.s4raise.it/erddap/info/ingv_earthquakes/index.htmlTable
| ???
| https://erddap.s4raise.it/erddap/rss/ingv_earthquakes.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=ingv_earthquakes&showErrors=false&email=
| ???
| ingv_earthquakes
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_laspezia_20180323
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_laspezia_20180323.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_laspezia_20180323/
| Estimated chlorophyall-a concentration at 60 m spatial resolution (20180323)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_laspezia_20180323_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_laspezia_20180323_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_laspezia_20180323/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_laspezia_20180323.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_laspezia_20180323&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_laspezia_20180323
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_portofino_20180323
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_portofino_20180323.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_portofino_20180323/
| Estimated chlorophyall-a concentration at 60 m spatial resolution (20180323)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_portofino_20180323_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_portofino_20180323_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_portofino_20180323/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_portofino_20180323.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_portofino_20180323&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_portofino_20180323
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_laspezia_20180427
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_laspezia_20180427.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_laspezia_20180427/
| Estimated chlorophyall-a concentration at 60 m spatial resolution (20180427)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_laspezia_20180427_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_laspezia_20180427_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_laspezia_20180427/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_laspezia_20180427.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_laspezia_20180427&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_laspezia_20180427
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_portofino_20180427
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_portofino_20180427.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_portofino_20180427/
| Estimated chlorophyall-a concentration at 60 m spatial resolution (20180427)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_portofino_20180427_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_portofino_20180427_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_portofino_20180427/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_portofino_20180427.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_portofino_20180427&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_portofino_20180427
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_laspezia_20181128
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_laspezia_20181128.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_laspezia_20181128/
| Estimated chlorophyall-a concentration at 60 m spatial resolution (20181128)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_laspezia_20181128_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_laspezia_20181128_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_laspezia_20181128/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_laspezia_20181128.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_laspezia_20181128&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_laspezia_20181128
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_portofino_20181128
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_portofino_20181128.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_portofino_20181128/
| Estimated chlorophyall-a concentration at 60 m spatial resolution (20181128)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_portofino_20181128_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_portofino_20181128_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_portofino_20181128/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_portofino_20181128.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_portofino_20181128&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_portofino_20181128
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_laspezia_20190221
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_laspezia_20190221.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_laspezia_20190221/
| Estimated chlorophyall-a concentration at 60 m spatial resolution (20190221)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_laspezia_20190221_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_laspezia_20190221_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_laspezia_20190221/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_laspezia_20190221.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_laspezia_20190221&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_laspezia_20190221
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_portofino_20190221
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_portofino_20190221.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_portofino_20190221/
| Estimated chlorophyall-a concentration at 60 m spatial resolution (20190221)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_portofino_20190221_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_portofino_20190221_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_portofino_20190221/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_portofino_20190221.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_portofino_20190221&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_portofino_20190221
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_laspezia_20190417
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_laspezia_20190417.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_laspezia_20190417/
| Estimated chlorophyall-a concentration at 60 m spatial resolution (20190417)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_laspezia_20190417_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_laspezia_20190417_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_laspezia_20190417/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_laspezia_20190417.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_laspezia_20190417&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_laspezia_20190417
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_portofino_20190417
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_portofino_20190417.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_portofino_20190417/
| Estimated chlorophyall-a concentration at 60 m spatial resolution (20190417)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_portofino_20190417_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_portofino_20190417_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_portofino_20190417/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_portofino_20190417.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_portofino_20190417&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_portofino_20190417
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_laspezia_20190726
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_laspezia_20190726.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_laspezia_20190726/
| Estimated chlorophyall-a concentration at 60 m spatial resolution (20190726)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_laspezia_20190726_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_laspezia_20190726_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_laspezia_20190726/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_laspezia_20190726.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_laspezia_20190726&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_laspezia_20190726
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_portofino_20190726
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_portofino_20190726.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_portofino_20190726/
| Estimated chlorophyall-a concentration at 60 m spatial resolution (20190726)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_portofino_20190726_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_portofino_20190726_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_portofino_20190726/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_portofino_20190726.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_portofino_20190726&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_portofino_20190726
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_laspezia_20200206
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_laspezia_20200206.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_laspezia_20200206/
| Estimated chlorophyall-a concentration at 60 m spatial resolution (20200206)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_laspezia_20200206_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_laspezia_20200206_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_laspezia_20200206/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_laspezia_20200206.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_laspezia_20200206&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_laspezia_20200206
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_portofino_20200206
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_portofino_20200206.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_portofino_20200206/
| Estimated chlorophyall-a concentration at 60 m spatial resolution (20200206)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_portofino_20200206_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_portofino_20200206_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_portofino_20200206/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_portofino_20200206.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_portofino_20200206&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_portofino_20200206
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_laspezia_20200710
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_laspezia_20200710.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_laspezia_20200710/
| Estimated chlorophyall-a concentration at 60 m spatial resolution (20200710)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_laspezia_20200710_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_laspezia_20200710_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_laspezia_20200710/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_laspezia_20200710.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_laspezia_20200710&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_laspezia_20200710
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_portofino_20200710
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_portofino_20200710.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_portofino_20200710/
| Estimated chlorophyall-a concentration at 60 m spatial resolution (20200710)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_portofino_20200710_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_portofino_20200710_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_portofino_20200710/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_portofino_20200710.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_portofino_20200710&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_portofino_20200710
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_laspezia_20220307
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_laspezia_20220307.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_laspezia_20220307/
| Estimated chlorophyall-a concentration at 60 m spatial resolution (20220307)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_laspezia_20220307_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_laspezia_20220307_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_laspezia_20220307/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_laspezia_20220307.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_laspezia_20220307&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_laspezia_20220307
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_portofino_20220307
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_portofino_20220307.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_portofino_20220307/
| Estimated chlorophyall-a concentration at 60 m spatial resolution (20220307)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_portofino_20220307_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_portofino_20220307_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_portofino_20220307/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_portofino_20220307.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_portofino_20220307&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_portofino_20220307
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_laspezia_20220411
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_laspezia_20220411.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_laspezia_20220411/
| Estimated chlorophyall-a concentration at 60 m spatial resolution (20220411)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_laspezia_20220411_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_laspezia_20220411_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_laspezia_20220411/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_laspezia_20220411.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_laspezia_20220411&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_laspezia_20220411
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_portofino_20220411
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_portofino_20220411.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_portofino_20220411/
| Estimated chlorophyall-a concentration at 60 m spatial resolution (20220411)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_portofino_20220411_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_portofino_20220411_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_portofino_20220411/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_portofino_20220411.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_portofino_20220411&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_portofino_20220411
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_laspezia_20220511
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_laspezia_20220511.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_laspezia_20220511/
| Estimated chlorophyall-a concentration at 60 m spatial resolution (20220511)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_laspezia_20220511_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_laspezia_20220511_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_laspezia_20220511/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_laspezia_20220511.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_laspezia_20220511&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_laspezia_20220511
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_portofino_20220511
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_portofino_20220511.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_portofino_20220511/
| Estimated chlorophyall-a concentration at 60 m spatial resolution (20220511)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_portofino_20220511_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_portofino_20220511_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_portofino_20220511/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_portofino_20220511.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_portofino_20220511&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_portofino_20220511
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_laspezia_20220824
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_laspezia_20220824.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_laspezia_20220824/
| Estimated chlorophyall-a concentration at 60 m spatial resolution (20220824)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_laspezia_20220824_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_laspezia_20220824_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_laspezia_20220824/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_laspezia_20220824.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_laspezia_20220824&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_laspezia_20220824
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_portofino_20220824
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_portofino_20220824.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_portofino_20220824/
| Estimated chlorophyall-a concentration at 60 m spatial resolution (20220824)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_portofino_20220824_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_portofino_20220824_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_portofino_20220824/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_portofino_20220824.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_portofino_20220824&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_portofino_20220824
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_laspezia_20221028
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_laspezia_20221028.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_laspezia_20221028/
| Estimated chlorophyall-a concentration at 60 m spatial resolution (20221028)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_laspezia_20221028_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_laspezia_20221028_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_laspezia_20221028/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_laspezia_20221028.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_laspezia_20221028&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_laspezia_20221028
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_portofino_20221028
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_portofino_20221028.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_portofino_20221028/
| Estimated chlorophyall-a concentration at 60 m spatial resolution (20221028)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_portofino_20221028_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_portofino_20221028_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_portofino_20221028/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_portofino_20221028.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_portofino_20221028&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_portofino_20221028
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_laspezia_20230506
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_laspezia_20230506.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_laspezia_20230506/
| Estimated chlorophyall-a concentration at 60 m spatial resolution (20230506)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_laspezia_20230506_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_laspezia_20230506_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_laspezia_20230506/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_laspezia_20230506.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_laspezia_20230506&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_laspezia_20230506
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_portofino_20230506
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_portofino_20230506.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_portofino_20230506/
| Estimated chlorophyall-a concentration at 60 m spatial resolution (20230506)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_portofino_20230506_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_portofino_20230506_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_portofino_20230506/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_portofino_20230506.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_portofino_20230506&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_portofino_20230506
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_laspezia_20230526
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_laspezia_20230526.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_laspezia_20230526/
| Estimated chlorophyall-a concentration at 60 m spatial resolution (20230526)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_laspezia_20230526_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_laspezia_20230526_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_laspezia_20230526/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_laspezia_20230526.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_laspezia_20230526&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_laspezia_20230526
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_portofino_20230526
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_portofino_20230526.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_portofino_20230526/
| Estimated chlorophyall-a concentration at 60 m spatial resolution (20230526)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_portofino_20230526_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_portofino_20230526_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_portofino_20230526/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_portofino_20230526.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_portofino_20230526&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_portofino_20230526
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_laspezia_20231207
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_laspezia_20231207.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_laspezia_20231207/
| Estimated chlorophyall-a concentration at 60 m spatial resolution (20231207)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_laspezia_20231207_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_laspezia_20231207_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_laspezia_20231207/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_laspezia_20231207.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_laspezia_20231207&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_laspezia_20231207
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_portofino_20231207
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_portofino_20231207.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_portofino_20231207/
| Estimated chlorophyall-a concentration at 60 m spatial resolution (20231207)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_portofino_20231207_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_portofino_20231207_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_portofino_20231207/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_portofino_20231207.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_portofino_20231207&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_portofino_20231207
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_laspezia_20240510
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_laspezia_20240510.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_laspezia_20240510/
| Estimated chlorophyall-a concentration at 60 m spatial resolution (20240510)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_laspezia_20240510_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_laspezia_20240510_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_laspezia_20240510/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_laspezia_20240510.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_laspezia_20240510&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_laspezia_20240510
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_portofino_20240510
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_portofino_20240510.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_portofino_20240510/
| Estimated chlorophyall-a concentration at 60 m spatial resolution (20240510)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_portofino_20240510_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_portofino_20240510_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_portofino_20240510/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_portofino_20240510.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_portofino_20240510&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_portofino_20240510
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_laspezia_20240604
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_laspezia_20240604.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_laspezia_20240604/
| Estimated chlorophyall-a concentration at 60 m spatial resolution (20240604)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_laspezia_20240604_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_laspezia_20240604_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_laspezia_20240604/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_laspezia_20240604.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_laspezia_20240604&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_laspezia_20240604
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_portofino_20240604
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_portofino_20240604.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_portofino_20240604/
| Estimated chlorophyall-a concentration at 60 m spatial resolution (20240604)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_portofino_20240604_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_portofino_20240604_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_portofino_20240604/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_portofino_20240604.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_portofino_20240604&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_portofino_20240604
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_laspezia_20240719
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_laspezia_20240719.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_laspezia_20240719/
| Estimated chlorophyall-a concentration at 60 m spatial resolution (20240719)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_laspezia_20240719_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_laspezia_20240719_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_laspezia_20240719/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_laspezia_20240719.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_laspezia_20240719&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_laspezia_20240719
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_portofino_20240719
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_portofino_20240719.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_portofino_20240719/
| Estimated chlorophyall-a concentration at 60 m spatial resolution (20240719)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_portofino_20240719_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_portofino_20240719_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_portofino_20240719/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_portofino_20240719.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_portofino_20240719&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_portofino_20240719
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_laspezia_20240729
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_laspezia_20240729.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_laspezia_20240729/
| Estimated chlorophyall-a concentration at 60 m spatial resolution (20240729)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_laspezia_20240729_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_laspezia_20240729_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_laspezia_20240729/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_laspezia_20240729.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_laspezia_20240729&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_laspezia_20240729
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_portofino_20240729
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_portofino_20240729.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_portofino_20240729/
| Estimated chlorophyall-a concentration at 60 m spatial resolution (20240729)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_portofino_20240729_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_portofino_20240729_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_portofino_20240729/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_portofino_20240729.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_portofino_20240729&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_portofino_20240729
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20180323
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20180323.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20180323/
| Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20180323)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20180323_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20180323_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20180323/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20180323.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20180323&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20180323
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20180323
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20180323.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20180323/
| Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20180323)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20180323_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20180323_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20180323/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20180323.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_MondrianForest_portofino_20180323&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_MondrianForest_portofino_20180323
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20180427
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20180427.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20180427/
| Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20180427)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20180427_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20180427_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20180427/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20180427.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20180427&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20180427
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20180427
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20180427.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20180427/
| Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20180427)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20180427_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20180427_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20180427/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20180427.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_MondrianForest_portofino_20180427&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_MondrianForest_portofino_20180427
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20181128
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20181128.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20181128/
| Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20181128)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20181128_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20181128_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20181128/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20181128.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20181128&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20181128
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20181128
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20181128.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20181128/
| Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20181128)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20181128_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20181128_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20181128/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20181128.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_MondrianForest_portofino_20181128&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_MondrianForest_portofino_20181128
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20190221
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20190221.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20190221/
| Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20190221)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20190221_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20190221_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20190221/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20190221.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20190221&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20190221
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20190221
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20190221.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20190221/
| Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20190221)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20190221_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20190221_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20190221/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20190221.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_MondrianForest_portofino_20190221&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_MondrianForest_portofino_20190221
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20190417
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20190417.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20190417/
| Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20190417)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20190417_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20190417_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20190417/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20190417.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20190417&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20190417
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20190417
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20190417.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20190417/
| Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20190417)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20190417_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20190417_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20190417/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20190417.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_MondrianForest_portofino_20190417&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_MondrianForest_portofino_20190417
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20190726
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20190726.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20190726/
| Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20190726)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20190726_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20190726_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20190726/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20190726.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20190726&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20190726
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20190726
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20190726.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20190726/
| Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20190726)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20190726_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20190726_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20190726/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20190726.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_MondrianForest_portofino_20190726&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_MondrianForest_portofino_20190726
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20200206
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20200206.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20200206/
| Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20200206)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20200206_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20200206_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20200206/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20200206.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20200206&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20200206
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20200206
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20200206.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20200206/
| Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20200206)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20200206_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20200206_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20200206/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20200206.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_MondrianForest_portofino_20200206&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_MondrianForest_portofino_20200206
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20200710
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20200710.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20200710/
| Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20200710)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20200710_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20200710_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20200710/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20200710.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20200710&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20200710
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20200710
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20200710.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20200710/
| Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20200710)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20200710_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20200710_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20200710/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20200710.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_MondrianForest_portofino_20200710&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_MondrianForest_portofino_20200710
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20220307
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20220307.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20220307/
| Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20220307)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20220307_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20220307_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20220307/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20220307.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20220307&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20220307
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20220307
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20220307.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20220307/
| Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20220307)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20220307_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20220307_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20220307/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20220307.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_MondrianForest_portofino_20220307&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_MondrianForest_portofino_20220307
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20220411
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20220411.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20220411/
| Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20220411)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20220411_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20220411_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20220411/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20220411.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20220411&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20220411
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20220411
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20220411.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20220411/
| Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20220411)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20220411_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20220411_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20220411/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20220411.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_MondrianForest_portofino_20220411&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_MondrianForest_portofino_20220411
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20220511
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20220511.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20220511/
| Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20220511)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20220511_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20220511_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20220511/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20220511.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20220511&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20220511
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20220511
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20220511.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20220511/
| Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20220511)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20220511_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20220511_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20220511/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20220511.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_MondrianForest_portofino_20220511&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_MondrianForest_portofino_20220511
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20220824
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20220824.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20220824/
| Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20220824)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20220824_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20220824_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20220824/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20220824.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20220824&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20220824
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20220824
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20220824.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20220824/
| Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20220824)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20220824_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20220824_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20220824/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20220824.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_MondrianForest_portofino_20220824&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_MondrianForest_portofino_20220824
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20221028
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20221028.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20221028/
| Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20221028)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20221028_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20221028_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20221028/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20221028.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20221028&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20221028
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20221028
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20221028.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20221028/
| Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20221028)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20221028_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20221028_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20221028/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20221028.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_MondrianForest_portofino_20221028&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_MondrianForest_portofino_20221028
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20230506
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20230506.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20230506/
| Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20230506)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20230506_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20230506_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20230506/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20230506.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20230506&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20230506
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20230506
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20230506.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20230506/
| Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20230506)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20230506_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20230506_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20230506/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20230506.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_MondrianForest_portofino_20230506&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_MondrianForest_portofino_20230506
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20230526
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20230526.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20230526/
| Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20230526)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20230526_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20230526_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20230526/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20230526.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20230526&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20230526
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20230526
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20230526.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20230526/
| Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20230526)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20230526_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20230526_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20230526/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20230526.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_MondrianForest_portofino_20230526&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_MondrianForest_portofino_20230526
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20231207
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20231207.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20231207/
| Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20231207)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20231207_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20231207_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20231207/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20231207.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20231207&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20231207
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20231207
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20231207.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20231207/
| Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20231207)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20231207_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20231207_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20231207/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20231207.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_MondrianForest_portofino_20231207&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_MondrianForest_portofino_20231207
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20240510
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20240510.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20240510/
| Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20240510)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20240510_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20240510_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20240510/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20240510.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20240510&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20240510
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20240510
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20240510.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20240510/
| Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20240510)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20240510_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20240510_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20240510/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20240510.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_MondrianForest_portofino_20240510&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_MondrianForest_portofino_20240510
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20240604
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20240604.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20240604/
| Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20240604)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20240604_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20240604_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20240604/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20240604.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20240604&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20240604
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20240604
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20240604.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20240604/
| Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20240604)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20240604_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20240604_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20240604/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20240604.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_MondrianForest_portofino_20240604&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_MondrianForest_portofino_20240604
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20240719
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20240719.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20240719/
| Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20240719)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20240719_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20240719_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20240719/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20240719.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20240719&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20240719
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20240719
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20240719.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20240719/
| Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20240719)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20240719_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20240719_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20240719/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_MondrianForest_portofino_20240719.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_MondrianForest_portofino_20240719&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_MondrianForest_portofino_20240719
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20240729
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20240729.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20240729/
| Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20240729)
| The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_chl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20240729_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20240729_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20240729/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20240729.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20240729&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_chlorophyll_final_output_MondrianForest_laspezia_20240729
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20220307T093204
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20220307T093204.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_20220307T093204/
| Estimated sea surface temperature at 1 km spatial resolution (20220307T093204Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_20220307T093204_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_20220307T093204_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_20220307T093204/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_20220307T093204.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_20220307T093204&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_20220307T093204
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20220411T092437
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20220411T092437.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_20220411T092437/
| Estimated sea surface temperature at 1 km spatial resolution (20220411T092437Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_20220411T092437_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_20220411T092437_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_20220411T092437/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_20220411T092437.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_20220411T092437&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_20220411T092437
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20220428T094504
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20220428T094504.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_20220428T094504/
| Estimated sea surface temperature at 1 km spatial resolution (20220428T094504Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_20220428T094504_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_20220428T094504_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_20220428T094504/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_20220428T094504.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_20220428T094504&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_20220428T094504
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20220510T101316
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20220510T101316.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_20220510T101316/
| Estimated sea surface temperature at 1 km spatial resolution (20220510T101316Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_20220510T101316_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_20220510T101316_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_20220510T101316/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_20220510T101316.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_20220510T101316&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_20220510T101316
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20220511T094705
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20220511T094705.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_20220511T094705/
| Estimated sea surface temperature at 1 km spatial resolution (20220511T094705Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_20220511T094705_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_20220511T094705_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_20220511T094705/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_20220511T094705.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_20220511T094705&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_20220511T094705
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20220701T092437
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20220701T092437.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_20220701T092437/
| Estimated sea surface temperature at 1 km spatial resolution (20220701T092437Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_20220701T092437_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_20220701T092437_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_20220701T092437/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_20220701T092437.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_20220701T092437&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_20220701T092437
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20220716T093548
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20220716T093548.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_20220716T093548/
| Estimated sea surface temperature at 1 km spatial resolution (20220716T093548Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_20220716T093548_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_20220716T093548_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_20220716T093548/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_20220716T093548.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_20220716T093548&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_20220716T093548
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20220719T091905
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20220719T091905.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_20220719T091905/
| Estimated sea surface temperature at 1 km spatial resolution (20220719T091905Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_20220719T091905_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_20220719T091905_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_20220719T091905/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_20220719T091905.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_20220719T091905&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_20220719T091905
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20220719T095813
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20220719T095813.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_20220719T095813/
| Estimated sea surface temperature at 1 km spatial resolution (20220719T095813Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_20220719T095813_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_20220719T095813_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_20220719T095813/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_20220719T095813.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_20220719T095813&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_20220719T095813
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20220824T092429
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20220824T092429.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_20220824T092429/
| Estimated sea surface temperature at 1 km spatial resolution (20220824T092429Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_20220824T092429_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_20220824T092429_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_20220824T092429/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_20220824T092429.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_20220824T092429&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_20220824T092429
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20220913T090551
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20220913T090551.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_20220913T090551/
| Estimated sea surface temperature at 1 km spatial resolution (20220913T090551Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_20220913T090551_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_20220913T090551_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_20220913T090551/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_20220913T090551.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_20220913T090551&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_20220913T090551
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20221005T093545
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20221005T093545.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_20221005T093545/
| Estimated sea surface temperature at 1 km spatial resolution (20221005T093545Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_20221005T093545_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_20221005T093545_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_20221005T093545/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_20221005T093545.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_20221005T093545&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_20221005T093545
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20221007T094510
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20221007T094510.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_20221007T094510/
| Estimated sea surface temperature at 1 km spatial resolution (20221007T094510Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_20221007T094510_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_20221007T094510_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_20221007T094510/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_20221007T094510.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_20221007T094510&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_20221007T094510
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20221028T093930
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20221028T093930.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_20221028T093930/
| Estimated sea surface temperature at 1 km spatial resolution (20221028T093930Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_20221028T093930_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_20221028T093930_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_20221028T093930/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_20221028T093930.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_20221028T093930&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_20221028T093930
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20221111T101653
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20221111T101653.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_20221111T101653/
| Estimated sea surface temperature at 1 km spatial resolution (20221111T101653Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_20221111T101653_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_20221111T101653_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_20221111T101653/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_20221111T101653.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_20221111T101653&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_20221111T101653
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20230213T093929
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20230213T093929.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_20230213T093929/
| Estimated sea surface temperature at 1 km spatial resolution (20230213T093929Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_20230213T093929_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_20230213T093929_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_20230213T093929/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_20230213T093929.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_20230213T093929&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_20230213T093929
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20230304T094657
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20230304T094657.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_20230304T094657/
| Estimated sea surface temperature at 1 km spatial resolution (20230304T094657Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_20230304T094657_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_20230304T094657_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_20230304T094657/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_20230304T094657.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_20230304T094657&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_20230304T094657
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20230417T090555
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20230417T090555.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_20230417T090555/
| Estimated sea surface temperature at 1 km spatial resolution (20230417T090555Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_20230417T090555_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_20230417T090555_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_20230417T090555/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_20230417T090555.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_20230417T090555&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_20230417T090555
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20230505T093934
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20230505T093934.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_20230505T093934/
| Estimated sea surface temperature at 1 km spatial resolution (20230505T093934Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_20230505T093934_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_20230505T093934_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_20230505T093934/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_20230505T093934.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_20230505T093934&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_20230505T093934
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20230523T101313
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20230523T101313.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_20230523T101313/
| Estimated sea surface temperature at 1 km spatial resolution (20230523T101313Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_20230523T101313_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_20230523T101313_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_20230523T101313/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_20230523T101313.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_20230523T101313&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_20230523T101313
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20230524T094702
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20230524T094702.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_20230524T094702/
| Estimated sea surface temperature at 1 km spatial resolution (20230524T094702Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_20230524T094702_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_20230524T094702_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_20230524T094702/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_20230524T094702.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_20230524T094702&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_20230524T094702
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20230626T095250
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20230626T095250.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_20230626T095250/
| Estimated sea surface temperature at 1 km spatial resolution (20230626T095250Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_20230626T095250_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_20230626T095250_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_20230626T095250/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_20230626T095250.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_20230626T095250&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_20230626T095250
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20230711T100406
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20230711T100406.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_20230711T100406/
| Estimated sea surface temperature at 1 km spatial resolution (20230711T100406Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_20230711T100406_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_20230711T100406_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_20230711T100406/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_20230711T100406.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_20230711T100406&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_20230711T100406
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20230823T094909
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20230823T094909.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_20230823T094909/
| Estimated sea surface temperature at 1 km spatial resolution (20230823T094909Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_20230823T094909_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_20230823T094909_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_20230823T094909/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_20230823T094909.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_20230823T094909&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_20230823T094909
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20230927T094136
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20230927T094136.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_20230927T094136/
| Estimated sea surface temperature at 1 km spatial resolution (20230927T094136Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_20230927T094136_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_20230927T094136_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_20230927T094136/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_20230927T094136.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_20230927T094136&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_20230927T094136
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20231009T093025
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20231009T093025.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_20231009T093025/
| Estimated sea surface temperature at 1 km spatial resolution (20231009T093025Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_20231009T093025_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_20231009T093025_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_20231009T093025/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_20231009T093025.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_20231009T093025&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_20231009T093025
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20231009T100922
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20231009T100922.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_20231009T100922/
| Estimated sea surface temperature at 1 km spatial resolution (20231009T100922Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_20231009T100922_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_20231009T100922_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_20231009T100922/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_20231009T100922.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_20231009T100922&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_20231009T100922
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20231207T093924
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20231207T093924.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_20231207T093924/
| Estimated sea surface temperature at 1 km spatial resolution (20231207T093924Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_20231207T093924_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_20231207T093924_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_20231207T093924/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_20231207T093924.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_20231207T093924&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_20231207T093924
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20240221T100924
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20240221T100924.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_20240221T100924/
| Estimated sea surface temperature at 1 km spatial resolution (20240221T100924Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_20240221T100924_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_20240221T100924_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_20240221T100924/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_20240221T100924.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_20240221T100924&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_20240221T100924
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20240307T094141
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20240307T094141.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_20240307T094141/
| Estimated sea surface temperature at 1 km spatial resolution (20240307T094141Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_20240307T094141_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_20240307T094141_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_20240307T094141/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_20240307T094141.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_20240307T094141&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_20240307T094141
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20240424T093547
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20240424T093547.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_20240424T093547/
| Estimated sea surface temperature at 1 km spatial resolution (20240424T093547Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_20240424T093547_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_20240424T093547_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_20240424T093547/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_20240424T093547.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_20240424T093547&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_20240424T093547
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20240527T102041
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20240527T102041.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_20240527T102041/
| Estimated sea surface temperature at 1 km spatial resolution (20240527T102041Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_20240527T102041_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_20240527T102041_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_20240527T102041/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_20240527T102041.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_20240527T102041&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_20240527T102041
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20240607T095649
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20240607T095649.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_20240607T095649/
| Estimated sea surface temperature at 1 km spatial resolution (20240607T095649Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_20240607T095649_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_20240607T095649_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_20240607T095649/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_20240607T095649.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_20240607T095649&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_20240607T095649
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20240618T101146
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20240618T101146.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_20240618T101146/
| Estimated sea surface temperature at 1 km spatial resolution (20240618T101146Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_20240618T101146_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_20240618T101146_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_20240618T101146/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_20240618T101146.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_20240618T101146&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_20240618T101146
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20240719T090552
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20240719T090552.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_20240719T090552/
| Estimated sea surface temperature at 1 km spatial resolution (20240719T090552Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_20240719T090552_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_20240719T090552_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_20240719T090552/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_20240719T090552.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_20240719T090552&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_20240719T090552
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20240719T100805
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20240719T100805.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_20240719T100805/
| Estimated sea surface temperature at 1 km spatial resolution (20240719T100805Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_20240719T100805_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_20240719T100805_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_20240719T100805/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_20240719T100805.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_20240719T100805&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_20240719T100805
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20240729T090814
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20240729T090814.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_20240729T090814/
| Estimated sea surface temperature at 1 km spatial resolution (20240729T090814Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_20240729T090814_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_20240729T090814_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_20240729T090814/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_20240729T090814.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_20240729T090814&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_20240729T090814
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20240729T094659
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_20240729T094659.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_20240729T094659/
| Estimated sea surface temperature at 1 km spatial resolution (20240729T094659Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_20240729T094659_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_20240729T094659_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_20240729T094659/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_20240729T094659.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_20240729T094659&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_20240729T094659
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220307T093204
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220307T093204.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220307T093204/
| Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20220307T093204Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220307T093204_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220307T093204_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220307T093204/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220307T093204.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_MondrianForest_20220307T093204&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_MondrianForest_20220307T093204
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220411T092437
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220411T092437.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220411T092437/
| Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20220411T092437Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220411T092437_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220411T092437_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220411T092437/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220411T092437.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_MondrianForest_20220411T092437&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_MondrianForest_20220411T092437
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220428T094504
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220428T094504.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220428T094504/
| Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20220428T094504Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220428T094504_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220428T094504_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220428T094504/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220428T094504.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_MondrianForest_20220428T094504&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_MondrianForest_20220428T094504
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220510T101316
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220510T101316.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220510T101316/
| Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20220510T101316Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220510T101316_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220510T101316_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220510T101316/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220510T101316.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_MondrianForest_20220510T101316&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_MondrianForest_20220510T101316
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220511T094705
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220511T094705.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220511T094705/
| Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20220511T094705Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220511T094705_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220511T094705_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220511T094705/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220511T094705.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_MondrianForest_20220511T094705&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_MondrianForest_20220511T094705
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220701T092437
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220701T092437.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220701T092437/
| Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20220701T092437Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220701T092437_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220701T092437_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220701T092437/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220701T092437.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_MondrianForest_20220701T092437&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_MondrianForest_20220701T092437
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220716T093548
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220716T093548.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220716T093548/
| Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20220716T093548Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220716T093548_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220716T093548_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220716T093548/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220716T093548.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_MondrianForest_20220716T093548&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_MondrianForest_20220716T093548
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220719T095813
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220719T095813.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220719T095813/
| Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20220719T095813Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220719T095813_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220719T095813_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220719T095813/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220719T095813.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_MondrianForest_20220719T095813&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_MondrianForest_20220719T095813
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220824T092429
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220824T092429.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220824T092429/
| Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20220824T092429Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220824T092429_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220824T092429_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220824T092429/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220824T092429.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_MondrianForest_20220824T092429&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_MondrianForest_20220824T092429
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220913T090551
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220913T090551.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220913T090551/
| Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20220913T090551Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220913T090551_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220913T090551_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220913T090551/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_MondrianForest_20220913T090551.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_MondrianForest_20220913T090551&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_MondrianForest_20220913T090551
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20221005T093545
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20221005T093545.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_MondrianForest_20221005T093545/
| Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20221005T093545Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20221005T093545_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20221005T093545_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_MondrianForest_20221005T093545/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_MondrianForest_20221005T093545.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_MondrianForest_20221005T093545&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_MondrianForest_20221005T093545
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20221007T094510
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20221007T094510.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_MondrianForest_20221007T094510/
| Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20221007T094510Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20221007T094510_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20221007T094510_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_MondrianForest_20221007T094510/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_MondrianForest_20221007T094510.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_MondrianForest_20221007T094510&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_MondrianForest_20221007T094510
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20221028T093930
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20221028T093930.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_MondrianForest_20221028T093930/
| Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20221028T093930Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20221028T093930_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20221028T093930_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_MondrianForest_20221028T093930/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_MondrianForest_20221028T093930.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_MondrianForest_20221028T093930&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_MondrianForest_20221028T093930
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20221111T101653
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20221111T101653.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_MondrianForest_20221111T101653/
| Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20221111T101653Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20221111T101653_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20221111T101653_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_MondrianForest_20221111T101653/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_MondrianForest_20221111T101653.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_MondrianForest_20221111T101653&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_MondrianForest_20221111T101653
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230213T093929
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230213T093929.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230213T093929/
| Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20230213T093929Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230213T093929_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230213T093929_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230213T093929/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230213T093929.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_MondrianForest_20230213T093929&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_MondrianForest_20230213T093929
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230304T094657
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230304T094657.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230304T094657/
| Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20230304T094657Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230304T094657_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230304T094657_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230304T094657/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230304T094657.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_MondrianForest_20230304T094657&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_MondrianForest_20230304T094657
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230417T090555
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230417T090555.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230417T090555/
| Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20230417T090555Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230417T090555_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230417T090555_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230417T090555/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230417T090555.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_MondrianForest_20230417T090555&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_MondrianForest_20230417T090555
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230505T093934
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230505T093934.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230505T093934/
| Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20230505T093934Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230505T093934_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230505T093934_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230505T093934/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230505T093934.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_MondrianForest_20230505T093934&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_MondrianForest_20230505T093934
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230523T101313
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230523T101313.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230523T101313/
| Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20230523T101313Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230523T101313_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230523T101313_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230523T101313/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230523T101313.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_MondrianForest_20230523T101313&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_MondrianForest_20230523T101313
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230524T094702
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230524T094702.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230524T094702/
| Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20230524T094702Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230524T094702_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230524T094702_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230524T094702/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230524T094702.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_MondrianForest_20230524T094702&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_MondrianForest_20230524T094702
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230626T095250
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230626T095250.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230626T095250/
| Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20230626T095250Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230626T095250_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230626T095250_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230626T095250/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230626T095250.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_MondrianForest_20230626T095250&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_MondrianForest_20230626T095250
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230711T100406
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230711T100406.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230711T100406/
| Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20230711T100406Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230711T100406_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230711T100406_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230711T100406/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230711T100406.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_MondrianForest_20230711T100406&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_MondrianForest_20230711T100406
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230823T094909
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230823T094909.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230823T094909/
| Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20230823T094909Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230823T094909_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230823T094909_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230823T094909/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230823T094909.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_MondrianForest_20230823T094909&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_MondrianForest_20230823T094909
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230927T094136
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230927T094136.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230927T094136/
| Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20230927T094136Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230927T094136_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230927T094136_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230927T094136/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_MondrianForest_20230927T094136.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_MondrianForest_20230927T094136&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_MondrianForest_20230927T094136
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20231009T093025
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20231009T093025.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_MondrianForest_20231009T093025/
| Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20231009T093025Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20231009T093025_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20231009T093025_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_MondrianForest_20231009T093025/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_MondrianForest_20231009T093025.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_MondrianForest_20231009T093025&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_MondrianForest_20231009T093025
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20231009T100922
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20231009T100922.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_MondrianForest_20231009T100922/
| Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20231009T100922Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20231009T100922_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20231009T100922_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_MondrianForest_20231009T100922/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_MondrianForest_20231009T100922.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_MondrianForest_20231009T100922&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_MondrianForest_20231009T100922
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20231207T093924
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20231207T093924.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_MondrianForest_20231207T093924/
| Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20231207T093924Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20231207T093924_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20231207T093924_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_MondrianForest_20231207T093924/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_MondrianForest_20231207T093924.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_MondrianForest_20231207T093924&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_MondrianForest_20231207T093924
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240221T100924
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240221T100924.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240221T100924/
| Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20240221T100924Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240221T100924_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240221T100924_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240221T100924/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240221T100924.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_MondrianForest_20240221T100924&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_MondrianForest_20240221T100924
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240307T094141
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240307T094141.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240307T094141/
| Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20240307T094141Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240307T094141_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240307T094141_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240307T094141/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240307T094141.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_MondrianForest_20240307T094141&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_MondrianForest_20240307T094141
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240424T093547
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240424T093547.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240424T093547/
| Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20240424T093547Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240424T093547_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240424T093547_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240424T093547/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240424T093547.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_MondrianForest_20240424T093547&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_MondrianForest_20240424T093547
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240527T102041
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240527T102041.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240527T102041/
| Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20240527T102041Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240527T102041_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240527T102041_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240527T102041/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240527T102041.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_MondrianForest_20240527T102041&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_MondrianForest_20240527T102041
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240607T095649
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240607T095649.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240607T095649/
| Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20240607T095649Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240607T095649_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240607T095649_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240607T095649/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240607T095649.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_MondrianForest_20240607T095649&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_MondrianForest_20240607T095649
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240618T101146
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240618T101146.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240618T101146/
| Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20240618T101146Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240618T101146_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240618T101146_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240618T101146/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240618T101146.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_MondrianForest_20240618T101146&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_MondrianForest_20240618T101146
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240719T090552
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240719T090552.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240719T090552/
| Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20240719T090552Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240719T090552_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240719T090552_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240719T090552/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240719T090552.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_MondrianForest_20240719T090552&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_MondrianForest_20240719T090552
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240719T100805
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240719T100805.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240719T100805/
| Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20240719T100805Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240719T100805_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240719T100805_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240719T100805/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240719T100805.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_MondrianForest_20240719T100805&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_MondrianForest_20240719T100805
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240729T090814
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240729T090814.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240729T090814/
| Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20240729T090814Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240729T090814_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240729T090814_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240729T090814/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240729T090814.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_MondrianForest_20240729T090814&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_MondrianForest_20240729T090814
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240729T094659
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240729T094659.graph
|
| https://erddap.s4raise.it/erddap/files/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240729T094659/
| Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20240729T094659Z)
| The dataset consists of estimated sea surface temperature (SST) obtained as the output of a machine learning model. Thermal infrared data from the Sentinel-3 mission of the Copernicus programme of the European Union, together with in situ sea-truth temperature measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude]):\nestimated_sst\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240729T094659_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240729T094659_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240729T094659/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-diten_sea_surface_temperature_final_output_MondrianForest_20240729T094659.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-diten_sea_surface_temperature_final_output_MondrianForest_20240729T094659&showErrors=false&email=
| UNIGE-DITEN
| unige-diten_sea_surface_temperature_final_output_MondrianForest_20240729T094659
|
| https://erddap.s4raise.it/erddap/griddap/noaa_forecast_gfs_3h_02
|
|
| https://erddap.s4raise.it/erddap/griddap/noaa_forecast_gfs_3h_02.graph
| https://erddap.s4raise.it/erddap/wms/noaa_forecast_gfs_3h_02/request
| https://erddap.s4raise.it/erddap/files/noaa_forecast_gfs_3h_02/
| Global Forecast System (GFS) model (02)
| Global Forecast System (GFS) model\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][latitude][longitude]):\nDownward_Long_Wave_Radp_Flux_surface_Mixed_intervals_Average (Downward Long-Wave Rad. Flux (Mixed_intervals Average) @ Ground or water surface, W.m-2)\nUpward_Long_Wave_Radp_Flux_surface_Mixed_intervals_Average (Upward Long-Wave Rad. Flux (Mixed_intervals Average) @ Ground or water surface, W.m-2)\nUpward_Short_Wave_Radiation_Flux_surface_Mixed_intervals_Average (Upward Short-Wave Radiation Flux (Mixed_intervals Average) @ Ground or water surface, W.m-2)\nDownward_Short_Wave_Radiation_Flux_surface_Mixed_intervals_Average (Downward Short-Wave Radiation Flux (Mixed_intervals Average) @ Ground or water surface, W.m-2)\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/noaa_forecast_gfs_3h_02_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/noaa_forecast_gfs_3h_02_iso19115.xml
| https://erddap.s4raise.it/erddap/info/noaa_forecast_gfs_3h_02/index.htmlTable
| https://www.noaa.gov/
| https://erddap.s4raise.it/erddap/rss/noaa_forecast_gfs_3h_02.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=noaa_forecast_gfs_3h_02&showErrors=false&email=
| NOAA
| noaa_forecast_gfs_3h_02
|
| https://erddap.s4raise.it/erddap/griddap/noaa_forecast_gfs_3h_03
|
|
| https://erddap.s4raise.it/erddap/griddap/noaa_forecast_gfs_3h_03.graph
| https://erddap.s4raise.it/erddap/wms/noaa_forecast_gfs_3h_03/request
| https://erddap.s4raise.it/erddap/files/noaa_forecast_gfs_3h_03/
| Global Forecast System (GFS) model (03)
| Global Forecast System (GFS) model\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][latitude][longitude]):\nGeopotential_height_surface (Geopotential height @ Ground or water surface, gpm)\nPressure_reduced_to_MSL_msl (Pressure reduced to MSL @ Mean sea level, Pa)\nPressure_surface (Pressure @ Ground or water surface, Pa)\nTemperature_surface (Temperature @ Ground or water surface, K)\nWater_equivalent_of_accumulated_snow_depth_surface (Water equivalent of accumulated snow depth @ Ground or water surface, kg.m-2)\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/noaa_forecast_gfs_3h_03_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/noaa_forecast_gfs_3h_03_iso19115.xml
| https://erddap.s4raise.it/erddap/info/noaa_forecast_gfs_3h_03/index.htmlTable
| https://www.noaa.gov/
| https://erddap.s4raise.it/erddap/rss/noaa_forecast_gfs_3h_03.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=noaa_forecast_gfs_3h_03&showErrors=false&email=
| NOAA
| noaa_forecast_gfs_3h_03
|
| https://erddap.s4raise.it/erddap/griddap/noaa_forecast_gfs_3h_04
|
|
| https://erddap.s4raise.it/erddap/griddap/noaa_forecast_gfs_3h_04.graph
| https://erddap.s4raise.it/erddap/wms/noaa_forecast_gfs_3h_04/request
| https://erddap.s4raise.it/erddap/files/noaa_forecast_gfs_3h_04/
| Global Forecast System (GFS) model (04)
| Global Forecast System (GFS) model\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][altitude][latitude][longitude]):\nTemperature_height_above_ground (Temperature @ Specified height level above ground, K)\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/noaa_forecast_gfs_3h_04_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/noaa_forecast_gfs_3h_04_iso19115.xml
| https://erddap.s4raise.it/erddap/info/noaa_forecast_gfs_3h_04/index.htmlTable
| https://www.noaa.gov/
| https://erddap.s4raise.it/erddap/rss/noaa_forecast_gfs_3h_04.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=noaa_forecast_gfs_3h_04&showErrors=false&email=
| NOAA
| noaa_forecast_gfs_3h_04
|
| https://erddap.s4raise.it/erddap/griddap/noaa_forecast_gfs_3h_05
|
|
| https://erddap.s4raise.it/erddap/griddap/noaa_forecast_gfs_3h_05.graph
|
| https://erddap.s4raise.it/erddap/files/noaa_forecast_gfs_3h_05/
| Global Forecast System (GFS) model (05)
| Global Forecast System (GFS) model\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][altitude][latitude][longitude]):\nu_component_of_wind_height_above_ground (u-component of wind @ Specified height level above ground, m/s)\nv_component_of_wind_height_above_ground (v-component of wind @ Specified height level above ground, m/s)\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/noaa_forecast_gfs_3h_05_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/noaa_forecast_gfs_3h_05_iso19115.xml
| https://erddap.s4raise.it/erddap/info/noaa_forecast_gfs_3h_05/index.htmlTable
| https://www.noaa.gov/
| https://erddap.s4raise.it/erddap/rss/noaa_forecast_gfs_3h_05.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=noaa_forecast_gfs_3h_05&showErrors=false&email=
| NOAA
| noaa_forecast_gfs_3h_05
|
| https://erddap.s4raise.it/erddap/griddap/noaa_forecast_gfs_3h_06
|
|
| https://erddap.s4raise.it/erddap/griddap/noaa_forecast_gfs_3h_06.graph
| https://erddap.s4raise.it/erddap/wms/noaa_forecast_gfs_3h_06/request
| https://erddap.s4raise.it/erddap/files/noaa_forecast_gfs_3h_06/
| Global Forecast System (GFS) model (06)
| Global Forecast System (GFS) model\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][altitude][latitude][longitude]):\nRelative_humidity_height_above_ground (Relative humidity @ Specified height level above ground, percent)\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/noaa_forecast_gfs_3h_06_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/noaa_forecast_gfs_3h_06_iso19115.xml
| https://erddap.s4raise.it/erddap/info/noaa_forecast_gfs_3h_06/index.htmlTable
| https://www.noaa.gov/
| https://erddap.s4raise.it/erddap/rss/noaa_forecast_gfs_3h_06.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=noaa_forecast_gfs_3h_06&showErrors=false&email=
| NOAA
| noaa_forecast_gfs_3h_06
|
|
| https://erddap.s4raise.it/erddap/tabledap/noaa_wind_animation_10km.subset
| https://erddap.s4raise.it/erddap/tabledap/noaa_wind_animation_10km
| https://erddap.s4raise.it/erddap/tabledap/noaa_wind_animation_10km.graph
|
| https://erddap.s4raise.it/erddap/files/noaa_wind_animation_10km/
| Global Forecast System (GFS) model - Animation of wind
| Global Forecast System (GFS) model - Animation of wind\n\ncdm_data_type = Grid\nVARIABLES:\nurl\ntime (seconds since 1970-01-01T00:00:00Z)\nname (File Name)\nlastModified (Last Modified, seconds since 1970-01-01T00:00:00Z)\nsize (bytes)\nfileType (File Type)\n
|
|
| https://erddap.s4raise.it/erddap/info/noaa_wind_animation_10km/index.htmlTable
| ???
| https://erddap.s4raise.it/erddap/rss/noaa_wind_animation_10km.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=noaa_wind_animation_10km&showErrors=false&email=
| NOAA
| noaa_wind_animation_10km
|
| https://erddap.s4raise.it/erddap/griddap/noaa_forecast_gfs_humidity
|
|
| https://erddap.s4raise.it/erddap/griddap/noaa_forecast_gfs_humidity.graph
|
| https://erddap.s4raise.it/erddap/files/noaa_forecast_gfs_humidity/
| Global Forecast System (GFS) model - Relative humidity at ground level
| Global Forecast System (GFS) model - Relative humidity at ground level\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][latitude][longitude]):\nRelative_humidity_height_above_ground\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/noaa_forecast_gfs_humidity_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/noaa_forecast_gfs_humidity_iso19115.xml
| https://erddap.s4raise.it/erddap/info/noaa_forecast_gfs_humidity/index.htmlTable
| http://188.166.63.249/thredds/dodsC/SINDBAD-GFS-1HR/SINDBAD-GFS-Relative_humidity_height_above_ground.nc.html
| https://erddap.s4raise.it/erddap/rss/noaa_forecast_gfs_humidity.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=noaa_forecast_gfs_humidity&showErrors=false&email=
| NOAA
| noaa_forecast_gfs_humidity
|
| https://erddap.s4raise.it/erddap/griddap/noaa_forecast_gfs_temperature_height_above_ground
|
|
| https://erddap.s4raise.it/erddap/griddap/noaa_forecast_gfs_temperature_height_above_ground.graph
|
| https://erddap.s4raise.it/erddap/files/noaa_forecast_gfs_temperature_height_above_ground/
| Global Forecast System (GFS) model - Temperature heght above ground
| Global Forecast System (GFS) model - Temperature heght above ground\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][latitude][longitude]):\nTemperature_height_above_ground\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/noaa_forecast_gfs_temperature_height_above_ground_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/noaa_forecast_gfs_temperature_height_above_ground_iso19115.xml
| https://erddap.s4raise.it/erddap/info/noaa_forecast_gfs_temperature_height_above_ground/index.htmlTable
| http://188.166.63.249/thredds/dodsC/SINDBAD-GFS-1HR/SINDBAD-GFS-Temperature_height_above_ground.nc.html
| https://erddap.s4raise.it/erddap/rss/noaa_forecast_gfs_temperature_height_above_ground.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=noaa_forecast_gfs_temperature_height_above_ground&showErrors=false&email=
| NOAA
| noaa_forecast_gfs_temperature_height_above_ground
|
| https://erddap.s4raise.it/erddap/griddap/noaa_forecast_gfs_temperature_isobaric
|
|
| https://erddap.s4raise.it/erddap/griddap/noaa_forecast_gfs_temperature_isobaric.graph
| https://erddap.s4raise.it/erddap/wms/noaa_forecast_gfs_temperature_isobaric/request
| https://erddap.s4raise.it/erddap/files/noaa_forecast_gfs_temperature_isobaric/
| Global Forecast System (GFS) model - Temperature isobaric
| Global Forecast System (GFS) model - Temperature isobaric\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][isobaric][latitude][longitude]):\nTemperature_isobaric (K)\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/noaa_forecast_gfs_temperature_isobaric_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/noaa_forecast_gfs_temperature_isobaric_iso19115.xml
| https://erddap.s4raise.it/erddap/info/noaa_forecast_gfs_temperature_isobaric/index.htmlTable
| http://188.166.63.249/thredds/dodsC/SINDBAD-GFS-1HR/SINDBAD-GFS-Temperature_isobaric.nc.html
| https://erddap.s4raise.it/erddap/rss/noaa_forecast_gfs_temperature_isobaric.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=noaa_forecast_gfs_temperature_isobaric&showErrors=false&email=
| NOAA
| noaa_forecast_gfs_temperature_isobaric
|
| https://erddap.s4raise.it/erddap/griddap/noaa_forecast_gfs_wind
|
|
| https://erddap.s4raise.it/erddap/griddap/noaa_forecast_gfs_wind.graph
|
| https://erddap.s4raise.it/erddap/files/noaa_forecast_gfs_wind/
| Global Forecast System (GFS) model - Wind
| Global Forecast System (GFS) model - Wind\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][latitude][longitude]):\neastward_component_of_wind_height_above_ground (m/s)\nnorthward_component_of_wind_height_above_ground (m/s)\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/noaa_forecast_gfs_wind_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/noaa_forecast_gfs_wind_iso19115.xml
| https://erddap.s4raise.it/erddap/info/noaa_forecast_gfs_wind/index.htmlTable
| http://188.166.63.249/thredds/dodsC/SINDBAD-GFS-1HR/SINDBAD-GFS-wind_height_above_ground.nc.html
| https://erddap.s4raise.it/erddap/rss/noaa_forecast_gfs_wind.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=noaa_forecast_gfs_wind&showErrors=false&email=
| NOAA
| noaa_forecast_gfs_wind
|
| https://erddap.s4raise.it/erddap/griddap/cnr-ismar_HFRADAR_TIRLIG_Totals
|
|
| https://erddap.s4raise.it/erddap/griddap/cnr-ismar_HFRADAR_TIRLIG_Totals.graph
| https://erddap.s4raise.it/erddap/wms/cnr-ismar_HFRADAR_TIRLIG_Totals/request
|
| HF RADAR TOTAL, TirLig (HFRADAR TIRLIG Totals), 2019-present
| High Frequency (HF) RADAR TOTAL - TirLig. National Research Council - Institute of Marine Science - S.S. Lerici; National Research Council - Institute of Marine Science; S.S. Lerici data from https://erddap.emodnet-physics.eu/erddap/griddap/HFRADAR_TIRLIG_Totals.das .\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][depth][latitude][longitude]):\nEWCT (West-east current component, m s-1)\nNSCT (South-north current component, m s-1)\nEWCS (Standard deviation of surface eastward sea water velocity, m s-1)\nNSCS (Standard deviation of surface northward sea water velocity, m s-1)\nCCOV (Covariance of surface sea water velocity, m2 s-2)\nGDOP (Geometrical dilution of precision, 1)\nPOSITION_QC (Position quality flag, 1)\nQCflag (Overall quality flag, 1)\nVART_QC (Variance threshold quality flag, 1)\nGDOP_QC (GDOP threshold quality flag, 1)\nDDNS_QC (Data density threshold quality flag, 1)\nCSPD_QC (Velocity threshold quality flag, 1)\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/cnr-ismar_HFRADAR_TIRLIG_Totals_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/cnr-ismar_HFRADAR_TIRLIG_Totals_iso19115.xml
| https://erddap.s4raise.it/erddap/info/cnr-ismar_HFRADAR_TIRLIG_Totals/index.htmlTable
| https://www.hfrnode.eu/
| https://erddap.s4raise.it/erddap/rss/cnr-ismar_HFRADAR_TIRLIG_Totals.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=cnr-ismar_HFRADAR_TIRLIG_Totals&showErrors=false&email=
| National Research Council - Institute of Marine Science - S.S. Lerici; National Research Council - Institute of Marine Science; S.S. Lerici
| cnr-ismar_HFRADAR_TIRLIG_Totals
|
| https://erddap.s4raise.it/erddap/griddap/cmems_mod_med_phy-cur_anfc_4_2km_3D_PT1H_m
|
|
| https://erddap.s4raise.it/erddap/griddap/cmems_mod_med_phy-cur_anfc_4_2km_3D_PT1H_m.graph
| https://erddap.s4raise.it/erddap/wms/cmems_mod_med_phy-cur_anfc_4_2km_3D_PT1H_m/request
| https://erddap.s4raise.it/erddap/files/cmems_mod_med_phy-cur_anfc_4_2km_3D_PT1H_m/
| Horizontal Velocity (3D), Hourly Mean
| Horizontal Velocity (3D) - Hourly Mean. Please check in CMEMS catalogue the INFO section for product MEDSEA_ANALYSISFORECAST_PHY_006_013 - http://marine.copernicus.eu\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][depth][latitude][longitude]):\nuo (eastward ocean current velocity, m s-1)\nvo (northward ocean current velocity, m s-1)\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/cmems_mod_med_phy-cur_anfc_4_2km_3D_PT1H_m_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/cmems_mod_med_phy-cur_anfc_4_2km_3D_PT1H_m_iso19115.xml
| https://erddap.s4raise.it/erddap/info/cmems_mod_med_phy-cur_anfc_4_2km_3D_PT1H_m/index.htmlTable
| https://www.ec.gc.ca/scitech/default.asp?lang=En&n=61B33C26-1#cmc
| https://erddap.s4raise.it/erddap/rss/cmems_mod_med_phy-cur_anfc_4_2km_3D_PT1H_m.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=cmems_mod_med_phy-cur_anfc_4_2km_3D_PT1H_m&showErrors=false&email=
| Centro Euro-Mediterraneo sui Cambiamenti Climatici - CMCC, Italy
| cmems_mod_med_phy-cur_anfc_4_2km_3D_PT1H_m
|
|
| https://erddap.s4raise.it/erddap/tabledap/acronet.subset
| https://erddap.s4raise.it/erddap/tabledap/acronet
| https://erddap.s4raise.it/erddap/tabledap/acronet.graph
|
|
| I-Change Acronet Data
| I-Change Acronet Data. CIMAFOUNDATION data from a local source.\n\ncdm_data_type = Point\nVARIABLES:\ntime (Valid Time GMT, seconds since 1970-01-01T00:00:00Z)\nSTATION_ID\nSTATION_NAME\nlatitude (degrees_north)\nlongitude (degrees_east)\nRAINGAUGE (mm)\nTEMP (Temperature, degree_C)\nHUMIDITY (relative_humidity, percent)\nWSPEED (wind_speed)\nPRESS (air_pressure)\nWSPEED_GUST (wind_speed_of_gust)\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/acronet_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/acronet_iso19115.xml
| https://erddap.s4raise.it/erddap/info/acronet/index.htmlTable
| https://www.cimafoundation.org/progetto/i-change/
| https://erddap.s4raise.it/erddap/rss/acronet.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=acronet&showErrors=false&email=
| CIMAFOUNDATION
| acronet
|
|
|
| https://erddap.s4raise.it/erddap/tabledap/MEDA2_CTD_ALL_RAW
| https://erddap.s4raise.it/erddap/tabledap/MEDA2_CTD_ALL_RAW.graph
|
|
| In situ CTD instrument at 3m depth - eLTER-Italy site Portofino Promontory - Italy (LTER_EU_IT_015) - all raw data
| The dataset represents raw data automatically collected and trasmitted in real-time by in situ CTD instrument at 3m depth, installed on the Meda2 buoy located in the eLTER-Italy site Portofino Promontory - Italy (LTER_EU_IT_015).\n\ncdm_data_type = Other\nVARIABLES:\ndate\nhour\ntime (seconds since 1970-01-01T00:00:00Z)\ntime_cet (seconds since 1970-01-01T00:00:00Z)\nPress\nTemp (Temperature)\nCond\nSal (Sal.)\nsensor_id\n
|
|
| https://erddap.s4raise.it/erddap/info/MEDA2_CTD_ALL_RAW/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/MEDA2_CTD_ALL_RAW.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=MEDA2_CTD_ALL_RAW&showErrors=false&email=
| UNIGE-DISTAV
| MEDA2_CTD_ALL_RAW
|
|
|
| https://erddap.s4raise.it/erddap/tabledap/MEDA2_CTD_LAST_RAW
| https://erddap.s4raise.it/erddap/tabledap/MEDA2_CTD_LAST_RAW.graph
|
|
| In situ CTD instrument at 3m depth - LTER-Italy site Portofino Promontory - Italy (LTER_EU_IT_015) - last row data
| The dataset represents last raw data automatically collected and trasmitted in real-time by in situ CTD instrument at 3m depth, installed on the Meda2 buoy located in the LTER-Italy site Portofino Promontory - Italy (LTER_EU_IT_015).\n\ncdm_data_type = Other\nVARIABLES:\ndate\nhour\ntime (seconds since 1970-01-01T00:00:00Z)\ntime_cet (seconds since 1970-01-01T00:00:00Z)\nPress\nTemp (Temperature)\nCond\nSal (Sal.)\nsensor_id\n
|
|
| https://erddap.s4raise.it/erddap/info/MEDA2_CTD_LAST_RAW/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/MEDA2_CTD_LAST_RAW.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=MEDA2_CTD_LAST_RAW&showErrors=false&email=
| UNIGE-DISTAV
| MEDA2_CTD_LAST_RAW
|
|
| https://erddap.s4raise.it/erddap/tabledap/MEDA2_CURR.subset
| https://erddap.s4raise.it/erddap/tabledap/MEDA2_CURR
| https://erddap.s4raise.it/erddap/tabledap/MEDA2_CURR.graph
|
| https://erddap.s4raise.it/erddap/files/MEDA2_CURR/
| In situ current in the water column - Nortek AWAC 1Mhz ADCP positioned at a depth of 10m - LTER-Italy site Portofino Promontory - Italy (LTER_EU_IT_015)
| The dataset represents data automatically collected and trasmitted in real-time by in situ ADCP instrument at 10m depth, installed on the Meda2 buoy located in the LTER-Italy site Portofino Promontory - Italy (LTER_EU_IT_015).\n\ncdm_data_type = Other\nVARIABLES:\nPLATFORMCODE\nString_ID\ntime (seconds since 1970-01-01T00:00:00Z)\ntime_cet (seconds since 1970-01-01T00:00:00Z)\nlatitude (degrees_north)\nlongitude (degrees_east)\ndepth (m)\nDate (seconds since 1970-01-01T00:00:00Z)\nTime\nCell_number\nEWCT (West-east current component, m/s)\nNSCT (South-north current component, m/s)\nUVCT (Upward current velocity, m/s)\nSpeed (m/s)\nDirection (degrees)\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/MEDA2_CURR_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/MEDA2_CURR_iso19115.xml
| https://erddap.s4raise.it/erddap/info/MEDA2_CURR/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/MEDA2_CURR.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=MEDA2_CURR&showErrors=false&email=
| UNIGE-DISTAV
| MEDA2_CURR
|
|
|
| https://erddap.s4raise.it/erddap/tabledap/MEDA2_WEATHER_STATION
| https://erddap.s4raise.it/erddap/tabledap/MEDA2_WEATHER_STATION.graph
|
| https://erddap.s4raise.it/erddap/files/MEDA2_WEATHER_STATION/
| In situ Theodor Friedrichs meteorological station at 7m amsl - LTER-Italy site Portofino Promontory - Italy (LTER_EU_IT_015)
| The dataset represents data automatically collected and trasmitted in real-time by in situ meteorological station at 7m amsl, installed on the Meda2 buoy located in the LTER-Italy site Portofino Promontory - Italy (LTER_EU_IT_015)\n\ncdm_data_type = Other\nVARIABLES:\nPLATFORMCODE\nString_ID\ntime (seconds since 1970-01-01T00:00:00Z)\ntime_cet (seconds since 1970-01-01T00:00:00Z)\nlatitude (degrees_north)\nlongitude (degrees_east)\nDate (seconds since 1970-01-01T00:00:00Z)\nTime\nAIR_PRES (Aire Pressure)\nWSPD (Wind Speed, m/s)\nWDIR (Direction relative to true north from which the wind is blowing, degrees_north)\nAIR_TEMP (Air temperature, degrees_Celsius)\nHumidity (Relative humidity)\nPSAL (Salinity, psu)\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/MEDA2_WEATHER_STATION_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/MEDA2_WEATHER_STATION_iso19115.xml
| https://erddap.s4raise.it/erddap/info/MEDA2_WEATHER_STATION/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/MEDA2_WEATHER_STATION.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=MEDA2_WEATHER_STATION&showErrors=false&email=
| UNIGE-DISTAV
| MEDA2_WEATHER_STATION
|
|
|
| https://erddap.s4raise.it/erddap/tabledap/MEDA2_WAVE_SINGLEPOINT
| https://erddap.s4raise.it/erddap/tabledap/MEDA2_WAVE_SINGLEPOINT.graph
|
| https://erddap.s4raise.it/erddap/files/MEDA2_WAVE_SINGLEPOINT/
| In situ wave - Keller High Accuracy OEM Pressure Transmitter positioned at a depth of 10m - LTER-Italy site Portofino Promontory - Italy (LTER_EU_IT_015)
| The dataset represents data automatically collected and trasmitted in real-time by in situ pressure transmitter positioned at a depth of 10m, installed on the Meda2 buoy located in the LTER-Italy site Portofino Promontory - Italy (LTER_EU_IT_015).\n\ncdm_data_type = Other\nVARIABLES:\nPLATFORMCODE\nString_ID\ntime (seconds since 1970-01-01T00:00:00Z)\ntime_cet (seconds since 1970-01-01T00:00:00Z)\nlatitude (degrees_north)\nlongitude (degrees_east)\nDate (seconds since 1970-01-01T00:00:00Z)\nTime\nspectrum_type\nprocessing_method\nVGHS (HM0 significant wave height, m)\nH3 (H3 Mean 1/3 Height, m)\nH10 (H3 Mean 1/10 Height, m)\nHmax (Maximum Height, m)\nTm02 (Mean Period, s)\nTp (Peak Period, s)\nTz (Mean Zero-crossing Period, s)\nDirTp (Peak Direction, degrees_north)\nSprTp (Directional Spread, degrees_north)\nMdir (Mean Direction, degrees_north)\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/MEDA2_WAVE_SINGLEPOINT_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/MEDA2_WAVE_SINGLEPOINT_iso19115.xml
| https://erddap.s4raise.it/erddap/info/MEDA2_WAVE_SINGLEPOINT/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/MEDA2_WAVE_SINGLEPOINT.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=MEDA2_WAVE_SINGLEPOINT&showErrors=false&email=
| UNIGE-DISTAV
| MEDA2_WAVE_SINGLEPOINT
|
|
|
| https://erddap.s4raise.it/erddap/tabledap/MEDA2_WAVE
| https://erddap.s4raise.it/erddap/tabledap/MEDA2_WAVE.graph
|
| https://erddap.s4raise.it/erddap/files/MEDA2_WAVE/
| In situ wave - Nortek AWAC 1Mhz ADCP positioned at a depth of 10m - LTER-Italy site Portofino Promontory - Italy (LTER_EU_IT_015)
| The dataset represents data automatically collected and trasmitted in real-time by in situ ADCP instrument at 10m depth, installed on the Meda2 buoy located in the LTER-Italy site Portofino Promontory - Italy (LTER_EU_IT_015).\n\ncdm_data_type = Other\nVARIABLES:\nPLATFORMCODE\nString_ID\ntime (seconds since 1970-01-01T00:00:00Z)\ntime_cet (seconds since 1970-01-01T00:00:00Z)\nlatitude (degrees_north)\nlongitude (degrees_east)\nDate (seconds since 1970-01-01T00:00:00Z)\nTime\nspectrum_type\nprocessing_method\nVGHS (HM0 significant wave height, m)\nH3 (H3 Mean 1/3 Height, m)\nH10 (H3 Mean 1/10 Height, m)\nHmax (Maximum Height, m)\nTm02 (Mean Period, s)\nTp (Peak Period, s)\nTz (Mean Zero-crossing Period, s)\nDirTp (Peak Direction, degrees_north)\nSprTp (Directional Spread, degrees_north)\nMdir (Mean Direction, degrees_north)\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/MEDA2_WAVE_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/MEDA2_WAVE_iso19115.xml
| https://erddap.s4raise.it/erddap/info/MEDA2_WAVE/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/MEDA2_WAVE.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=MEDA2_WAVE&showErrors=false&email=
| UNIGE-DISTAV
| MEDA2_WAVE
|
|
| https://erddap.s4raise.it/erddap/tabledap/XBT_ENEA_DATA.subset
| https://erddap.s4raise.it/erddap/tabledap/XBT_ENEA_DATA
| https://erddap.s4raise.it/erddap/tabledap/XBT_ENEA_DATA.graph
|
| https://erddap.s4raise.it/erddap/files/XBT_ENEA_DATA/
| INGVXBT - Collection of sea temperature (TEMP) Profiles - IN SITU MultiPointProfilesObservation
| INGVXBT - Collection of sea temperature (TEMP) Profiles - IN SITU MultiPointProfilesObservation\n\ncdm_data_type = Profile\nVARIABLES:\nPLATFORMCODE (EMODnet Platform Code)\nSOURCE\nSENSOR (Platform Sensor)\ntime (Valid Time GMT, seconds since 1970-01-01T00:00:00Z)\nTIME_QC (TIME quality flag, 1)\ndepth (m)\nDEPTH_QC (DEPTH quality flag, 1)\nlatitude (degrees_north)\nlongitude (degrees_east)\nPOSITION_QC (POSITION quality flag, 1)\nTEMP (water temperature, degree_Celsius)\nTEMP_QC (TEMP quality flag, 1)\nTEMP_DM (TEMP method of data processing)\nurl_metadata (Metadata Link)\nqc_entity\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/XBT_ENEA_DATA_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/XBT_ENEA_DATA_iso19115.xml
| https://erddap.s4raise.it/erddap/info/XBT_ENEA_DATA/index.htmlTable
| http://www.emodnet-physics.eu
| https://erddap.s4raise.it/erddap/rss/XBT_ENEA_DATA.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=XBT_ENEA_DATA&showErrors=false&email=
| INGVXBT
| XBT_ENEA_DATA
|
|
| https://erddap.s4raise.it/erddap/tabledap/XBT_ENEA_METADATA.subset
| https://erddap.s4raise.it/erddap/tabledap/XBT_ENEA_METADATA
| https://erddap.s4raise.it/erddap/tabledap/XBT_ENEA_METADATA.graph
|
| https://erddap.s4raise.it/erddap/files/XBT_ENEA_METADATA/
| INGVXBT - Collection of sea temperature (TEMP) Profiles - METADATA
| INGVXBT - Collection of sea temperature (TEMP) Profiles - METADATA\n\ncdm_data_type = Other\nVARIABLES:\nPLATFORMCODE (EMODNET Platform Code)\ncall_name (Platform Call Name)\nlatitude (degrees_north)\nlongitude (degrees_east)\ndataFeatureType\nfirstDateObservation (First Date Observation, seconds since 1970-01-01T00:00:00Z)\nlastDateObservation (Last Date Observation, seconds since 1970-01-01T00:00:00Z)\nparameters_group_longname (Parameters Info Parameter Groups)\nparameters_group_P33 (Parameters Info P33)\nparameters (Parameters Info Parameters)\nparameters_P01 (Parameters Info P01)\nWMO\ndata_DOI\nbest_practices_DOI\ndata_owner_longname (Data Owner Name)\ndata_owner_country_code\ndata_owner_country_longname (Data Owner Country Name)\ndata_owner_EDMO (Data Owner EDMO Code)\ndata_assembly_center_longname (Data Assembly Center)\nplatform_type_longname (Platform Type)\nplatform_type_SDNL06\nplatformpage_link\nintegrator_id\nIntegrationDate (Integration Date, seconds since 1970-01-01T00:00:00Z)\ningestion\nofficial_repository\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/XBT_ENEA_METADATA_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/XBT_ENEA_METADATA_iso19115.xml
| https://erddap.s4raise.it/erddap/info/XBT_ENEA_METADATA/index.htmlTable
| http://www.emodnet-physics.eu
| https://erddap.s4raise.it/erddap/rss/XBT_ENEA_METADATA.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=XBT_ENEA_METADATA&showErrors=false&email=
| INGVXBT
| XBT_ENEA_METADATA
|
|
|
| https://erddap.s4raise.it/erddap/tabledap/cnr-ismar_dispersion_model_portofino_2022
| https://erddap.s4raise.it/erddap/tabledap/cnr-ismar_dispersion_model_portofino_2022.graph
|
| https://erddap.s4raise.it/erddap/files/cnr-ismar_dispersion_model_portofino_2022/
| Lagrangian dispersal around the Portofino promontory Italy for the month of July 2022
| During July 2022, a Lagrangian dispersal experiment was carried out around the Portofino Promontory (Italy) to simulate the summer dynamics of gelatinous organism blooms. Virtual particles were released in the coastal circulation field to represent the transport and spreading of these organisms under typical seasonal current conditions. The resulting trajectories highlight how coastal currents can rapidly redistribute biological material along the Portofino coastline, illustrating the potential spatial extent and coastal impact of gelatinous blooms during summer.\n\ncdm_data_type = Point\nVARIABLES:\ntime (seconds since 1970-01-01T00:00:00Z)\nlatitude (degrees_north)\nlongitude (degrees_east)\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/cnr-ismar_dispersion_model_portofino_2022_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/cnr-ismar_dispersion_model_portofino_2022_iso19115.xml
| https://erddap.s4raise.it/erddap/info/cnr-ismar_dispersion_model_portofino_2022/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/cnr-ismar_dispersion_model_portofino_2022.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=cnr-ismar_dispersion_model_portofino_2022&showErrors=false&email=
| CNR-ISMAR
| cnr-ismar_dispersion_model_portofino_2022
|
|
|
| https://erddap.s4raise.it/erddap/tabledap/MEDA2_CTD_TEST
| https://erddap.s4raise.it/erddap/tabledap/MEDA2_CTD_TEST.graph
|
| https://erddap.s4raise.it/erddap/files/MEDA2_CTD_TEST/
| LTER_EU_IT_015_MEDA2 - CTD
| LTER_EU_IT_015_MEDA2 - CTD\n\ncdm_data_type = Other\nVARIABLES:\nPLATFORMCODE\nString_ID\ntime (seconds since 1970-01-01T00:00:00Z)\ntime_cet (seconds since 1970-01-01T00:00:00Z)\nlatitude (degrees_north)\nlongitude (degrees_east)\ndate\nhour\nPress\nTemp (Temperature)\nCond\nSal (Sal.)\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/MEDA2_CTD_TEST_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/MEDA2_CTD_TEST_iso19115.xml
| https://erddap.s4raise.it/erddap/info/MEDA2_CTD_TEST/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/MEDA2_CTD_TEST.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=MEDA2_CTD_TEST&showErrors=false&email=
| UNIGE-DISTAV
| MEDA2_CTD_TEST
|
| https://erddap.s4raise.it/erddap/griddap/unige-distav_camogli_runup_scirocco
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-distav_camogli_runup_scirocco.graph
| https://erddap.s4raise.it/erddap/wms/unige-distav_camogli_runup_scirocco/request
| https://erddap.s4raise.it/erddap/files/unige-distav_camogli_runup_scirocco/
| Maximum wave run-up considering SE storms
| The dataset was generated using the XBeach model, a numerical tool developed to simulate the impacts of extreme events on coastal areas and their associated dynamics. In this case, the model was applied to 20 historical SE storm scenarios, also considering the storm surge (wave set-up), to estimate the wave run-up on the Camogli coast. The wave conditions were derived from the hindcast dataset produced by the MeteOcean research group at the University of Genoa (https://meteocean.science/#research), while the digital elevation model was constructed using a combination of field survey data and elevation data provided by Regione Liguria (https://geoportal.regione.liguria.it/). This approach makes it possible to define offshore wave conditions that may pose potential hazards to coastal infrastructure and human safety.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][latitude][longitude]):\nzs (water level, m)\nzb (bed level, m)\nue (Eulerian velocity in cell centre, x-component, m/s)\nve (Eulerian velocity in cell centre, y-component, m/s)\nH (Hrms wave height based on instantaneous wave energy, m)\nE (wave energy, Nm/m2)\nL1 (wave length (used in dispersion relation), m)\nQb (fraction breaking waves)\nsedero (cum. sedimentation/erosion, m)\nthetamean (mean wave angle, rad)\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-distav_camogli_runup_scirocco_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-distav_camogli_runup_scirocco_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-distav_camogli_runup_scirocco/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-distav_camogli_runup_scirocco.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-distav_camogli_runup_scirocco&showErrors=false&email=
| UNIGE-DISTAV
| unige-distav_camogli_runup_scirocco
|
| https://erddap.s4raise.it/erddap/griddap/unige-distav_camogli_runup_libeccio
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-distav_camogli_runup_libeccio.graph
| https://erddap.s4raise.it/erddap/wms/unige-distav_camogli_runup_libeccio/request
| https://erddap.s4raise.it/erddap/files/unige-distav_camogli_runup_libeccio/
| Maximum wave run-up considering SW storms
| The dataset was generated using the XBeach model, a numerical tool developed to simulate the impacts of extreme events on coastal areas and their associated dynamics. In this case, the model was applied to 20 historical SW storm scenarios, also considering the storm surge (wave set-up), to estimate the wave run-up on the Camogli coast. The wave conditions were derived from the hindcast dataset produced by the MeteOcean research group at the University of Genoa (https://meteocean.science/#research), while the digital elevation model was constructed using a combination of field survey data and elevation data provided by Regione Liguria (https://geoportal.regione.liguria.it/). This approach makes it possible to define offshore wave conditions that may pose potential hazards to coastal infrastructure and human safety.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][latitude][longitude]):\nzs (water level, m)\nzb (bed level, m)\nue (Eulerian velocity in cell centre, x-component, m/s)\nve (Eulerian velocity in cell centre, y-component, m/s)\nH (Hrms wave height based on instantaneous wave energy, m)\nE (wave energy, Nm/m2)\nL1 (wave length (used in dispersion relation), m)\nQb (fraction breaking waves)\nsedero (cum. sedimentation/erosion, m)\nthetamean (mean wave angle, rad)\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-distav_camogli_runup_libeccio_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-distav_camogli_runup_libeccio_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-distav_camogli_runup_libeccio/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-distav_camogli_runup_libeccio.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-distav_camogli_runup_libeccio&showErrors=false&email=
| UNIGE-DISTAV
| unige-distav_camogli_runup_libeccio
|
|
|
| https://erddap.s4raise.it/erddap/tabledap/cnr-ismar_guard1_0002
| https://erddap.s4raise.it/erddap/tabledap/cnr-ismar_guard1_0002.graph
|
| https://erddap.s4raise.it/erddap/files/cnr-ismar_guard1_0002/
| Meda 2 Image Dataset - Portofino - Station 0002
| The dataset consits of underwater images showing fish and jellyfish activities in the marine protected area of Portofino (GE). The images are acquired by the autonomous and intelligent imaging device GUARD-1, installed on a Meda buoy in front of Punta Faro, Portofino. The GUARD-1 is deployed at 3m depth and consists of an underwater camera equipped with a lighiting system that allow the image acquisition 24h per day. The device is also equipped with an onboard AI-based image analysis tool capable to recognize the fish and the jellyfish specimens in order to automatically extract abundance time series and to select images with relevant content. The GUARD-1 device is connected to a 4G modem positioned on the Meda buoy, inside a watertight case, that transmit the acquired images together with information extracted by the AI-based tool. Both the image acquisition frequency and the data transmission frequency are programmable by the user and can be managed through a remote connection or automatically by the AI-based tool.\n\ncdm_data_type = Other\nVARIABLES:\ntime (seconds since 1970-01-01T00:00:00Z)\nname\nlatitude (degrees_north)\nlongitude (degrees_east)\nurl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/cnr-ismar_guard1_0002_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/cnr-ismar_guard1_0002_iso19115.xml
| https://erddap.s4raise.it/erddap/info/cnr-ismar_guard1_0002/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/cnr-ismar_guard1_0002.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=cnr-ismar_guard1_0002&showErrors=false&email=
| CNR-ISMAR
| cnr-ismar_guard1_0002
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_ww3
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-dicca_forecast_ww3.graph
| https://erddap.s4raise.it/erddap/wms/unige-dicca_forecast_ww3/request
|
| Mediterranean Wave and Wind Forecast
| Five days hourly forecast of wind and ocean waves generation and propagation in the Mediterranean basin. Resolution from 25km on open ocean to 300 m close to the shoreline\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [latitude][longitude][time]):\nhs (significant height of wind and swell waves, m)\nfp (wave peak frequency, s-1)\ndir (wave mean direction, degree)\ndp (peak direction, degree)\ntm (mean period, s)\nuwnd (Eastward Wind, m s-1)\nvwnd (Northward Wind, m s-1)\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-dicca_forecast_ww3_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-dicca_forecast_ww3_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-dicca_forecast_ww3/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-dicca_forecast_ww3.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-dicca_forecast_ww3&showErrors=false&email=
| UNIGE-DICCA
| unige-dicca_forecast_ww3
|
|
| https://erddap.s4raise.it/erddap/tabledap/meteotracker_navebus.subset
| https://erddap.s4raise.it/erddap/tabledap/meteotracker_navebus
| https://erddap.s4raise.it/erddap/tabledap/meteotracker_navebus.graph
|
|
| Meteorological data collected by Genoa boat-bus activities using the MeteoTracker device
| Collection of meteorological data from the MeteoTracker device. MeteoTracker is a low cost tool for participatory science. RAISE developed the data ingestion, data processing and workflow automation for offering added value datasets.\n\ncdm_data_type = Point\nVARIABLES:\ntime (seconds since 1970-01-01T00:00:00Z)\nlatitude (degrees_north)\nlongitude (degrees_east)\naltitude (m)\nspeed\ntemperature\nhumidity\npressure\ndew_point\nsolar_radiation_index\nhumidex\ntag\npotential_temperature\nD\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/meteotracker_navebus_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/meteotracker_navebus_iso19115.xml
| https://erddap.s4raise.it/erddap/info/meteotracker_navebus/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/meteotracker_navebus.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=meteotracker_navebus&showErrors=false&email=
| ETT
| meteotracker_navebus
|
|
| https://erddap.s4raise.it/erddap/tabledap/meteotracker_bus.subset
| https://erddap.s4raise.it/erddap/tabledap/meteotracker_bus
| https://erddap.s4raise.it/erddap/tabledap/meteotracker_bus.graph
|
|
| Meteorological data collected by Genoa bus activities using the MeteoTracker device
| Collection of meteorological data from the MeteoTracker device. MeteoTracker is a low cost tool for participatory science. RAISE developed the data ingestion, data processing and workflow automation for offering added value datasets.\n\ncdm_data_type = Point\nVARIABLES:\ntime (seconds since 1970-01-01T00:00:00Z)\nlatitude (degrees_north)\nlongitude (degrees_east)\naltitude (m)\nspeed\ncarbon_dioxide\npressure\nhumidity\ntemperature\ndew_point\nhumidex\npotential_temperature\ntag\nD\nvertical_temperature_gradient\nsolar_radiation_index\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/meteotracker_bus_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/meteotracker_bus_iso19115.xml
| https://erddap.s4raise.it/erddap/info/meteotracker_bus/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/meteotracker_bus.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=meteotracker_bus&showErrors=false&email=
| ETT
| meteotracker_bus
|
|
| https://erddap.s4raise.it/erddap/tabledap/meteotracker_becis.subset
| https://erddap.s4raise.it/erddap/tabledap/meteotracker_becis
| https://erddap.s4raise.it/erddap/tabledap/meteotracker_becis.graph
|
|
| Meteorological data collected during Citizen Science activities using the MeteoTracker device
| Collection of meteorological data from the MeteoTracker device. MeteoTracker is a low cost tool for participatory science. RAISE developed the data ingestion, data processing and workflow automation for offering added value datasets.\n\ncdm_data_type = Point\nVARIABLES:\ntime (seconds since 1970-01-01T00:00:00Z)\nlatitude (degrees_north)\nlongitude (degrees_east)\naltitude (m)\nspeed\ntemperature\nhumidity\npressure\ndew_point\nsolar_radiation_index\nhumidex\ntag\nvertical_temperature_gradient\npotential_temperature\nD\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/meteotracker_becis_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/meteotracker_becis_iso19115.xml
| https://erddap.s4raise.it/erddap/info/meteotracker_becis/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/meteotracker_becis.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=meteotracker_becis&showErrors=false&email=
| ETT
| meteotracker_becis
|
|
| https://erddap.s4raise.it/erddap/tabledap/swan_hellenic_meteocean_V23.subset
| https://erddap.s4raise.it/erddap/tabledap/swan_hellenic_meteocean_V23
| https://erddap.s4raise.it/erddap/tabledap/swan_hellenic_meteocean_V23.graph
|
|
| Meteorological data collected during Citizen Science V23 campaign aboard the Swan Hellenic SH Vega ship
| Meteorological data from the SH Vega ship - Cruising4Oceans project. Cruising4Oceans is a a Swan Hellenic project supporting scientific research for ocean health in 2025. RAISE developed the data ingestion and processing workflow and its automation.\n\ncdm_data_type = Point\nVARIABLES:\nDateTimeLocal (Date/time (local), seconds since 1970-01-01T00:00:00Z)\nTimezone\ntime (seconds since 1970-01-01T00:00:00Z)\nlatitude (degrees_north)\nlongitude (degrees_east)\nReport\nVoyage_state\nLocation\nVoyage_name\nSpeed_OG (kn)\nSpeed_TW (kn)\nTrue_wind_speed (Wind Speed, Bft)\nWind_direction_absolute (Wind From Direction)\nWave_height (Sea Surface Wave Significant Height)\nAir_temperature (degree_C)\nDraft_midship (m)\nTrim (m)\nDistance_OG (nm)\nDistance_TW (nm)\nSpeed_OG_QC\nSpeed_TW_QC\nTrue_wind_speed_QC\nWind_direction_absolute_QC (Wind Direction (absolute) QC)\nWave_height_QC\nAir_temperature_QC\n... (4 more variables)\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/swan_hellenic_meteocean_V23_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/swan_hellenic_meteocean_V23_iso19115.xml
| https://erddap.s4raise.it/erddap/info/swan_hellenic_meteocean_V23/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/swan_hellenic_meteocean_V23.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=swan_hellenic_meteocean_V23&showErrors=false&email=
| ETT, Swan Hellenic
| swan_hellenic_meteocean_V23
|
|
|
| https://erddap.s4raise.it/erddap/tabledap/swan_hellenic_meteocean_V24
| https://erddap.s4raise.it/erddap/tabledap/swan_hellenic_meteocean_V24.graph
|
|
| Meteorological data collected during Citizen Science V24 campaign aboard the Swan Hellenic SH Vega ship
| Meteorological data from the SH Vega ship - Cruising4Oceans project. Cruising4Oceans is a a Swan Hellenic project supporting scientific research for ocean health in 2025. RAISE developed the data ingestion and processing workflow and its automation.\n\ncdm_data_type = Point\nVARIABLES:\nDateTimeLocal (Date/time (local), seconds since 1970-01-01T00:00:00Z)\nTimezone\ntime (seconds since 1970-01-01T00:00:00Z)\nlatitude (degrees_north)\nlongitude (degrees_east)\nReport\nVoyage_state\nLocation\nVoyage_name\nShip_Speed (kn)\nTrue_wind_speed (Wind Speed, Bft)\nWind_direction_absolute (Wind From Direction)\nWave_height (Sea Surface Wave Significant Height)\nAir_temperature (degree_C)\nAtmosferic_pressure (Air Pressure, hPa)\nShip_Speed_kn_QC (Ship Speed [kn] QC)\nTrue_wind_speed_QC\nWind_direction_absolute_QC (Wind Direction (absolute) QC)\nWave_height_QC\nAir_temperature_QC\nDraft_midship_QC\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/swan_hellenic_meteocean_V24_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/swan_hellenic_meteocean_V24_iso19115.xml
| https://erddap.s4raise.it/erddap/info/swan_hellenic_meteocean_V24/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/swan_hellenic_meteocean_V24.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=swan_hellenic_meteocean_V24&showErrors=false&email=
| ETT, Swan Hellenic
| swan_hellenic_meteocean_V24
|
|
|
| https://erddap.s4raise.it/erddap/tabledap/swan_hellenic_meteocean_V25
| https://erddap.s4raise.it/erddap/tabledap/swan_hellenic_meteocean_V25.graph
|
|
| Meteorological data collected during Citizen Science V25 campaign aboard the Swan Hellenic SH Vega ship
| Meteorological data from the SH Vega ship - Cruising4Oceans project. Cruising4Oceans is a a Swan Hellenic project supporting scientific research for ocean health in 2025. RAISE developed the data ingestion and processing workflow and its automation.\n\ncdm_data_type = Point\nVARIABLES:\nDateTimeLocal (Date/time (local), seconds since 1970-01-01T00:00:00Z)\nTimezone\ntime (seconds since 1970-01-01T00:00:00Z)\nlatitude (degrees_north)\nlongitude (degrees_east)\nReport\nVoyage_state\nLocation\nVoyage_name\nSpeed_OG (kn)\nShip_Speed (kn)\nTrue_wind_speed (Wind Speed, Bft)\nWind_direction_absolute (Wind From Direction)\nWind_direction_true (degrees)\nWave_height (Sea Surface Wave Significant Height)\nAir_temperature (degree_C)\nAtmosferic_pressure (hPa)\nSpeed_OG_QC\nShip_Speed_QC\nTrue_wind_speed_QC\nWind_direction_absolute_QC (Wind Direction (absolute) QC)\nWind_direction_true_QC\nWave_height_QC\nAir_temperature_QC\nAtmosferic_pressure_QC\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/swan_hellenic_meteocean_V25_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/swan_hellenic_meteocean_V25_iso19115.xml
| https://erddap.s4raise.it/erddap/info/swan_hellenic_meteocean_V25/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/swan_hellenic_meteocean_V25.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=swan_hellenic_meteocean_V25&showErrors=false&email=
| ETT, Swan Hellenic
| swan_hellenic_meteocean_V25
|
|
|
| https://erddap.s4raise.it/erddap/tabledap/swan_hellenic_meteocean_V26
| https://erddap.s4raise.it/erddap/tabledap/swan_hellenic_meteocean_V26.graph
|
|
| Meteorological data collected during Citizen Science V26 campaign aboard the Swan Hellenic SH Vega ship
| Meteorological data from the SH Vega ship - Cruising4Oceans project. Cruising4Oceans is a a Swan Hellenic project supporting scientific research for ocean health in 2025. RAISE developed the data ingestion and processing workflow and its automation.\n\ncdm_data_type = Point\nVARIABLES:\nDateTimeLocal (Date/time (local), seconds since 1970-01-01T00:00:00Z)\nTimezone\ntime (seconds since 1970-01-01T00:00:00Z)\nlatitude (degrees_north)\nlongitude (degrees_east)\nReport\nVoyage_state\nLocation\nVoyage_name\nSpeed_OG (kn)\nShip_Speed (kn)\nTrue_wind_speed (Wind Speed, Bft)\nWind_direction_absolute (Wind From Direction)\nWind_direction_true (degrees)\nWave_height (Sea Surface Wave Significant Height)\nAir_temperature (degree_C)\nAtmosferic_pressure (hPa)\nSpeed_OG_QC\nShip_Speed_QC\nTrue_wind_speed_QC\nWind_direction_absolute_QC (Wind Direction (absolute) QC)\nWind_direction_true_QC\nWave_height_QC\nAir_temperature_QC\nAtmosferic_pressure_QC\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/swan_hellenic_meteocean_V26_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/swan_hellenic_meteocean_V26_iso19115.xml
| https://erddap.s4raise.it/erddap/info/swan_hellenic_meteocean_V26/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/swan_hellenic_meteocean_V26.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=swan_hellenic_meteocean_V26&showErrors=false&email=
| ETT, Swan Hellenic
| swan_hellenic_meteocean_V26
|
|
|
| https://erddap.s4raise.it/erddap/tabledap/swan_hellenic_meteocean_V27
| https://erddap.s4raise.it/erddap/tabledap/swan_hellenic_meteocean_V27.graph
|
|
| Meteorological data collected during Citizen Science V27 campaign aboard the Swan Hellenic SH Vega ship
| Meteorological data from the SH Vega ship - Cruising4Oceans project. Cruising4Oceans is a a Swan Hellenic project supporting scientific research for ocean health in 2025. RAISE developed the data ingestion and processing workflow and its automation.\n\ncdm_data_type = Point\nVARIABLES:\nDateTimeLocal (Date/time (local), seconds since 1970-01-01T00:00:00Z)\nTimezone\ntime (seconds since 1970-01-01T00:00:00Z)\nlatitude (degrees_north)\nlongitude (degrees_east)\nReport\nVoyage_state\nLocation\nVoyage_name\nSpeed_OG (kn)\nShip_Speed (kn)\nTrue_wind_speed (Wind Speed, Bft)\nWind_direction_absolute (Wind From Direction)\nWind_direction_true (degrees)\nWave_height (Sea Surface Wave Significant Height)\nAir_temperature (degree_C)\nAtmosferic_pressure (hPa)\nSpeed_OG_QC\nShip_Speed_QC\nTrue_wind_speed_QC\nWind_direction_absolute_QC (Wind Direction (absolute) QC)\nWind_direction_true_QC\nWave_height_QC\nAir_temperature_QC\nAtmosferic_pressure_QC\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/swan_hellenic_meteocean_V27_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/swan_hellenic_meteocean_V27_iso19115.xml
| https://erddap.s4raise.it/erddap/info/swan_hellenic_meteocean_V27/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/swan_hellenic_meteocean_V27.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=swan_hellenic_meteocean_V27&showErrors=false&email=
| ETT, Swan Hellenic
| swan_hellenic_meteocean_V27
|
|
|
| https://erddap.s4raise.it/erddap/tabledap/swan_hellenic_meteocean_V28
| https://erddap.s4raise.it/erddap/tabledap/swan_hellenic_meteocean_V28.graph
|
|
| Meteorological data collected during Citizen Science V28 campaign aboard the Swan Hellenic SH Vega ship
| Meteorological data from the SH Vega ship - Cruising4Oceans project. Cruising4Oceans is a a Swan Hellenic project supporting scientific research for ocean health in 2025. RAISE developed the data ingestion and processing workflow and its automation.\n\ncdm_data_type = Point\nVARIABLES:\nDateTimeLocal (Date/time (local), seconds since 1970-01-01T00:00:00Z)\nTimezone\ntime (seconds since 1970-01-01T00:00:00Z)\nlatitude (degrees_north)\nlongitude (degrees_east)\nReport\nVoyage_state\nLocation\nVoyage_name\nSpeed_OG (kn)\nShip_Speed (kn)\nTrue_wind_speed (Wind Speed, Bft)\nWind_direction_absolute (Wind From Direction)\nWind_direction_true (degrees)\nWave_height (Sea Surface Wave Significant Height)\nAir_temperature (degree_C)\nAtmosferic_pressure (hPa)\nSpeed_OG_QC\nShip_Speed_QC\nTrue_wind_speed_QC\nWind_direction_absolute_QC (Wind Direction (absolute) QC)\nWind_direction_true_QC\nWave_height_QC\nAir_temperature_QC\nAtmosferic_pressure_QC\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/swan_hellenic_meteocean_V28_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/swan_hellenic_meteocean_V28_iso19115.xml
| https://erddap.s4raise.it/erddap/info/swan_hellenic_meteocean_V28/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/swan_hellenic_meteocean_V28.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=swan_hellenic_meteocean_V28&showErrors=false&email=
| ETT, Swan Hellenic
| swan_hellenic_meteocean_V28
|
|
|
| https://erddap.s4raise.it/erddap/tabledap/swan_hellenic_meteocean_V29
| https://erddap.s4raise.it/erddap/tabledap/swan_hellenic_meteocean_V29.graph
|
|
| Meteorological data collected during Citizen Science V29 campaign aboard the Swan Hellenic SH Vega ship
| Meteorological data from the SH Vega ship - Cruising4Oceans project. Cruising4Oceans is a a Swan Hellenic project supporting scientific research for ocean health in 2025. RAISE developed the data ingestion and processing workflow and its automation.\n\ncdm_data_type = Point\nVARIABLES:\nDateTimeLocal (Date/time (local), seconds since 1970-01-01T00:00:00Z)\nTimezone\ntime (seconds since 1970-01-01T00:00:00Z)\nlatitude (degrees_north)\nlongitude (degrees_east)\nReport\nVoyage_state\nLocation\nVoyage_name\nSpeed_OG (kn)\nShip_Speed (kn)\nTrue_wind_speed (Wind Speed, Bft)\nWind_direction_absolute (Wind From Direction)\nWind_direction_true (degrees)\nWave_height (Sea Surface Wave Significant Height)\nAir_temperature (degree_C)\nAtmosferic_pressure (hPa)\nSpeed_OG_QC\nShip_Speed_QC\nTrue_wind_speed_QC\nWind_direction_absolute_QC (Wind Direction (absolute) QC)\nWind_direction_true_QC\nWave_height_QC\nAir_temperature_QC\nAtmosferic_pressure_QC\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/swan_hellenic_meteocean_V29_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/swan_hellenic_meteocean_V29_iso19115.xml
| https://erddap.s4raise.it/erddap/info/swan_hellenic_meteocean_V29/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/swan_hellenic_meteocean_V29.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=swan_hellenic_meteocean_V29&showErrors=false&email=
| ETT, Swan Hellenic
| swan_hellenic_meteocean_V29
|
|
|
| https://erddap.s4raise.it/erddap/tabledap/swan_hellenic_meteocean_V30
| https://erddap.s4raise.it/erddap/tabledap/swan_hellenic_meteocean_V30.graph
|
|
| Meteorological data collected during Citizen Science V30 campaign aboard the Swan Hellenic SH Vega ship
| Meteorological data from the SH Vega ship - Cruising4Oceans project. Cruising4Oceans is a a Swan Hellenic project supporting scientific research for ocean health in 2025. RAISE developed the data ingestion and processing workflow and its automation.\n\ncdm_data_type = Point\nVARIABLES:\nDateTimeLocal (Date/time (local), seconds since 1970-01-01T00:00:00Z)\nTimezone\ntime (seconds since 1970-01-01T00:00:00Z)\nlatitude (degrees_north)\nlongitude (degrees_east)\nReport\nVoyage_state\nLocation\nVoyage_name\nSpeed_OG (kn)\nShip_Speed (kn)\nTrue_wind_speed (Wind Speed, Bft)\nWind_direction_absolute (Wind From Direction)\nWind_direction_true (degrees)\nWave_height (Sea Surface Wave Significant Height)\nAir_temperature (degree_C)\nAtmosferic_pressure (hPa)\nSpeed_OG_QC\nShip_Speed_QC\nTrue_wind_speed_QC\nWind_direction_absolute_QC (Wind Direction (absolute) QC)\nWind_direction_true_QC\nWave_height_QC\nAir_temperature_QC\nAtmosferic_pressure_QC\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/swan_hellenic_meteocean_V30_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/swan_hellenic_meteocean_V30_iso19115.xml
| https://erddap.s4raise.it/erddap/info/swan_hellenic_meteocean_V30/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/swan_hellenic_meteocean_V30.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=swan_hellenic_meteocean_V30&showErrors=false&email=
| ETT, Swan Hellenic
| swan_hellenic_meteocean_V30
|
|
|
| https://erddap.s4raise.it/erddap/tabledap/swan_hellenic_meteocean_V31
| https://erddap.s4raise.it/erddap/tabledap/swan_hellenic_meteocean_V31.graph
|
|
| Meteorological data collected during Citizen Science V31 campaign aboard the Swan Hellenic SH Vega ship
| Meteorological data from the SH Vega ship - Cruising4Oceans project. Cruising4Oceans is a a Swan Hellenic project supporting scientific research for ocean health in 2025. RAISE developed the data ingestion and processing workflow and its automation.\n\ncdm_data_type = Point\nVARIABLES:\nDateTimeLocal (Date/time (local), seconds since 1970-01-01T00:00:00Z)\nTimezone\ntime (seconds since 1970-01-01T00:00:00Z)\nlatitude (degrees_north)\nlongitude (degrees_east)\nReport\nVoyage_state\nLocation\nVoyage_name\nSpeed_OG (kn)\nShip_Speed (kn)\nTrue_wind_speed (Wind Speed, Bft)\nWind_direction_absolute (Wind From Direction)\nWind_direction_true (degrees)\nWave_height (Sea Surface Wave Significant Height)\nAir_temperature (degree_C)\nAtmosferic_pressure (hPa)\nSpeed_OG_QC\nShip_Speed_QC\nTrue_wind_speed_QC\nWind_direction_absolute_QC (Wind Direction (absolute) QC)\nWind_direction_true_QC\nWave_height_QC\nAir_temperature_QC\nAtmosferic_pressure_QC\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/swan_hellenic_meteocean_V31_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/swan_hellenic_meteocean_V31_iso19115.xml
| https://erddap.s4raise.it/erddap/info/swan_hellenic_meteocean_V31/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/swan_hellenic_meteocean_V31.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=swan_hellenic_meteocean_V31&showErrors=false&email=
| ETT, Swan Hellenic
| swan_hellenic_meteocean_V31
|
|
|
| https://erddap.s4raise.it/erddap/tabledap/swan_hellenic_meteocean_V32
| https://erddap.s4raise.it/erddap/tabledap/swan_hellenic_meteocean_V32.graph
|
|
| Meteorological data collected during Citizen Science V32 campaign aboard the Swan Hellenic SH Vega ship
| Meteorological data from the SH Vega ship - Cruising4Oceans project. Cruising4Oceans is a a Swan Hellenic project supporting scientific research for ocean health in 2025. RAISE developed the data ingestion and processing workflow and its automation.\n\ncdm_data_type = Point\nVARIABLES:\nDateTimeLocal (Date/time (local), seconds since 1970-01-01T00:00:00Z)\nTimezone\ntime (seconds since 1970-01-01T00:00:00Z)\nlatitude (degrees_north)\nlongitude (degrees_east)\nReport\nVoyage_state\nLocation\nVoyage_name\nSpeed_OG (kn)\nShip_Speed (kn)\nTrue_wind_speed (Wind Speed, Bft)\nWind_direction_absolute (Wind From Direction)\nWind_direction_true (degrees)\nWave_height (Sea Surface Wave Significant Height)\nAir_temperature (degree_C)\nAtmosferic_pressure (hPa)\nSpeed_OG_QC\nShip_Speed_QC\nTrue_wind_speed_QC\nWind_direction_absolute_QC (Wind Direction (absolute) QC)\nWind_direction_true_QC\nWave_height_QC\nAir_temperature_QC\nAtmosferic_pressure_QC\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/swan_hellenic_meteocean_V32_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/swan_hellenic_meteocean_V32_iso19115.xml
| https://erddap.s4raise.it/erddap/info/swan_hellenic_meteocean_V32/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/swan_hellenic_meteocean_V32.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=swan_hellenic_meteocean_V32&showErrors=false&email=
| ETT, Swan Hellenic
| swan_hellenic_meteocean_V32
|
|
|
| https://erddap.s4raise.it/erddap/tabledap/swan_hellenic_meteocean_V33
| https://erddap.s4raise.it/erddap/tabledap/swan_hellenic_meteocean_V33.graph
|
|
| Meteorological data collected during Citizen Science V33 campaign aboard the Swan Hellenic SH Vega ship
| Meteorological data from the SH Vega ship - Cruising4Oceans project. Cruising4Oceans is a a Swan Hellenic project supporting scientific research for ocean health in 2025. RAISE developed the data ingestion and processing workflow and its automation.\n\ncdm_data_type = Point\nVARIABLES:\nDateTimeLocal (Date/time (local), seconds since 1970-01-01T00:00:00Z)\nTimezone\ntime (seconds since 1970-01-01T00:00:00Z)\nlatitude (degrees_north)\nlongitude (degrees_east)\nReport\nVoyage_state\nLocation\nVoyage_name\nSpeed_OG (kn)\nShip_Speed (kn)\nTrue_wind_speed (Wind Speed, Bft)\nWind_direction_absolute (Wind From Direction)\nWind_direction_true (degrees)\nWave_height (Sea Surface Wave Significant Height)\nAir_temperature (degree_C)\nAtmosferic_pressure (hPa)\nSpeed_OG_QC\nShip_Speed_QC\nTrue_wind_speed_QC\nWind_direction_absolute_QC (Wind Direction (absolute) QC)\nWind_direction_true_QC\nWave_height_QC\nAir_temperature_QC\nAtmosferic_pressure_QC\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/swan_hellenic_meteocean_V33_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/swan_hellenic_meteocean_V33_iso19115.xml
| https://erddap.s4raise.it/erddap/info/swan_hellenic_meteocean_V33/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/swan_hellenic_meteocean_V33.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=swan_hellenic_meteocean_V33&showErrors=false&email=
| ETT, Swan Hellenic
| swan_hellenic_meteocean_V33
|
|
|
| https://erddap.s4raise.it/erddap/tabledap/swan_hellenic_meteocean_V34
| https://erddap.s4raise.it/erddap/tabledap/swan_hellenic_meteocean_V34.graph
|
|
| Meteorological data collected during Citizen Science V34 campaign aboard the Swan Hellenic SH Vega ship
| Meteorological data from the SH Vega ship - Cruising4Oceans project. Cruising4Oceans is a a Swan Hellenic project supporting scientific research for ocean health in 2025. RAISE developed the data ingestion and processing workflow and its automation.\n\ncdm_data_type = Point\nVARIABLES:\nDateTimeLocal (Date/time (local), seconds since 1970-01-01T00:00:00Z)\nTimezone\ntime (seconds since 1970-01-01T00:00:00Z)\nlatitude (degrees_north)\nlongitude (degrees_east)\nReport\nVoyage_state\nLocation\nVoyage_name\nSpeed_OG (kn)\nShip_Speed (kn)\nTrue_wind_speed (Wind Speed, Bft)\nWind_direction_absolute (Wind From Direction)\nWind_direction_true (degrees)\nWave_height (Sea Surface Wave Significant Height)\nAir_temperature (degree_C)\nAtmosferic_pressure (hPa)\nSpeed_OG_QC\nShip_Speed_QC\nTrue_wind_speed_QC\nWind_direction_absolute_QC (Wind Direction (absolute) QC)\nWind_direction_true_QC\nWave_height_QC\nAir_temperature_QC\nAtmosferic_pressure_QC\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/swan_hellenic_meteocean_V34_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/swan_hellenic_meteocean_V34_iso19115.xml
| https://erddap.s4raise.it/erddap/info/swan_hellenic_meteocean_V34/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/swan_hellenic_meteocean_V34.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=swan_hellenic_meteocean_V34&showErrors=false&email=
| ETT, Swan Hellenic
| swan_hellenic_meteocean_V34
|
|
|
| https://erddap.s4raise.it/erddap/tabledap/swan_hellenic_meteocean_V35
| https://erddap.s4raise.it/erddap/tabledap/swan_hellenic_meteocean_V35.graph
|
|
| Meteorological data collected during Citizen Science V35 campaign aboard the Swan Hellenic SH Vega ship
| Meteorological data from the SH Vega ship - Cruising4Oceans project. Cruising4Oceans is a a Swan Hellenic project supporting scientific research for ocean health in 2025. RAISE developed the data ingestion and processing workflow and its automation.\n\ncdm_data_type = Point\nVARIABLES:\nDateTimeLocal (Date/time (local), seconds since 1970-01-01T00:00:00Z)\nTimezone\ntime (seconds since 1970-01-01T00:00:00Z)\nlatitude (degrees_north)\nlongitude (degrees_east)\nReport\nVoyage_state\nLocation\nVoyage_name\nSpeed_OG (kn)\nShip_Speed (kn)\nTrue_wind_speed (Wind Speed, Bft)\nWind_direction_absolute (Wind From Direction)\nWind_direction_true (degrees)\nWave_height (Sea Surface Wave Significant Height)\nAir_temperature (degree_C)\nAtmosferic_pressure (hPa)\nSpeed_OG_QC\nShip_Speed_QC\nTrue_wind_speed_QC\nWind_direction_absolute_QC (Wind Direction (absolute) QC)\nWind_direction_true_QC\nWave_height_QC\nAir_temperature_QC\nAtmosferic_pressure_QC\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/swan_hellenic_meteocean_V35_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/swan_hellenic_meteocean_V35_iso19115.xml
| https://erddap.s4raise.it/erddap/info/swan_hellenic_meteocean_V35/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/swan_hellenic_meteocean_V35.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=swan_hellenic_meteocean_V35&showErrors=false&email=
| ETT, Swan Hellenic
| swan_hellenic_meteocean_V35
|
|
|
| https://erddap.s4raise.it/erddap/tabledap/swan_hellenic_meteocean_V36
| https://erddap.s4raise.it/erddap/tabledap/swan_hellenic_meteocean_V36.graph
|
|
| Meteorological data collected during Citizen Science V36 campaign aboard the Swan Hellenic SH Vega ship
| Meteorological data from the SH Vega ship - Cruising4Oceans project. Cruising4Oceans is a a Swan Hellenic project supporting scientific research for ocean health in 2025. RAISE developed the data ingestion and processing workflow and its automation.\n\ncdm_data_type = Point\nVARIABLES:\nDateTimeLocal (Date/time (local), seconds since 1970-01-01T00:00:00Z)\nTimezone\ntime (seconds since 1970-01-01T00:00:00Z)\nlatitude (degrees_north)\nlongitude (degrees_east)\nReport\nVoyage_state\nLocation\nVoyage_name\nSpeed_OG (kn)\nShip_Speed (kn)\nTrue_wind_speed (Wind Speed, Bft)\nWind_direction_absolute (Wind From Direction)\nWind_direction_true (degrees)\nWave_height (Sea Surface Wave Significant Height)\nAir_temperature (degree_C)\nAtmosferic_pressure (hPa)\nSpeed_OG_QC\nShip_Speed_QC\nTrue_wind_speed_QC\nWind_direction_absolute_QC (Wind Direction (absolute) QC)\nWind_direction_true_QC\nWave_height_QC\nAir_temperature_QC\nAtmosferic_pressure_QC\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/swan_hellenic_meteocean_V36_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/swan_hellenic_meteocean_V36_iso19115.xml
| https://erddap.s4raise.it/erddap/info/swan_hellenic_meteocean_V36/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/swan_hellenic_meteocean_V36.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=swan_hellenic_meteocean_V36&showErrors=false&email=
| ETT, Swan Hellenic
| swan_hellenic_meteocean_V36
|
|
|
| https://erddap.s4raise.it/erddap/tabledap/swan_hellenic_meteocean_V56
| https://erddap.s4raise.it/erddap/tabledap/swan_hellenic_meteocean_V56.graph
|
|
| Meteorological data collected during Citizen Science V56 campaign aboard the Swan Hellenic SH Vega ship
| Meteorological data from the SH Vega ship - Cruising4Oceans project. Cruising4Oceans is a a Swan Hellenic project supporting scientific research for ocean health in 2025. RAISE developed the data ingestion and processing workflow and its automation.\n\ncdm_data_type = Point\nVARIABLES:\nDateTimeLocal (Date/time (local), seconds since 1970-01-01T00:00:00Z)\nTimezone\ntime (seconds since 1970-01-01T00:00:00Z)\nlatitude (degrees_north)\nlongitude (degrees_east)\nReport\nVoyage_state\nLocation\nVoyage_name\nShip_Speed (kn)\nTrue_wind_speed (Wind Speed, Bft)\nWind_direction_absolute (Wind From Direction)\nWave_height (Sea Surface Wave Significant Height)\nAir_temperature (degree_C)\nAtmosferic_pressure (Air Pressure, hPa)\nShip_Speed_kn_QC (Ship Speed [kn] QC)\nTrue_wind_speed_QC\nWind_direction_absolute_QC (Wind Direction (absolute) QC)\nWave_height_QC\nAir_temperature_QC\nDraft_midship_QC\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/swan_hellenic_meteocean_V56_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/swan_hellenic_meteocean_V56_iso19115.xml
| https://erddap.s4raise.it/erddap/info/swan_hellenic_meteocean_V56/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/swan_hellenic_meteocean_V56.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=swan_hellenic_meteocean_V56&showErrors=false&email=
| ETT, Swan Hellenic
| swan_hellenic_meteocean_V56
|
|
|
| https://erddap.s4raise.it/erddap/tabledap/swan_hellenic_meteocean_V57
| https://erddap.s4raise.it/erddap/tabledap/swan_hellenic_meteocean_V57.graph
|
|
| Meteorological data collected during Citizen Science V57 campaign aboard the Swan Hellenic SH Vega ship
| Meteorological data from the SH Vega ship - Cruising4Oceans project. Cruising4Oceans is a a Swan Hellenic project supporting scientific research for ocean health in 2025. RAISE developed the data ingestion and processing workflow and its automation.\n\ncdm_data_type = Point\nVARIABLES:\nDateTimeLocal (Date/time (local), seconds since 1970-01-01T00:00:00Z)\nTimezone\ntime (seconds since 1970-01-01T00:00:00Z)\nlatitude (degrees_north)\nlongitude (degrees_east)\nReport\nVoyage_state\nLocation\nVoyage_name\nShip_Speed (kn)\nTrue_wind_speed (Wind Speed, Bft)\nWind_direction_absolute (Wind From Direction)\nWave_height (Sea Surface Wave Significant Height)\nAir_temperature (degree_C)\nAtmosferic_pressure (Air Pressure, hPa)\nShip_Speed_kn_QC (Ship Speed [kn] QC)\nTrue_wind_speed_QC\nWind_direction_absolute_QC (Wind Direction (absolute) QC)\nWave_height_QC\nAir_temperature_QC\nDraft_midship_QC\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/swan_hellenic_meteocean_V57_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/swan_hellenic_meteocean_V57_iso19115.xml
| https://erddap.s4raise.it/erddap/info/swan_hellenic_meteocean_V57/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/swan_hellenic_meteocean_V57.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=swan_hellenic_meteocean_V57&showErrors=false&email=
| ETT, Swan Hellenic
| swan_hellenic_meteocean_V57
|
|
| https://erddap.s4raise.it/erddap/tabledap/meteotracker.subset
| https://erddap.s4raise.it/erddap/tabledap/meteotracker
| https://erddap.s4raise.it/erddap/tabledap/meteotracker.graph
|
|
| Meteorological data collected using the MeteoTracker device
| Collection of meteorological data from the MeteoTracker device. MeteoTracker is a low cost tool for participatory science. RAISE developed the data ingestion, data processing and workflow automation for offering added value datasets.\n\ncdm_data_type = Point\nVARIABLES:\ntime (seconds since 1970-01-01T00:00:00Z)\nlatitude (degrees_north)\nlongitude (degrees_east)\naltitude (m)\nspeed\ntemperature\nhumidity\npressure\ndew_point\nsolar_radiation_index\nhumidex\ntag\npotential_temperature\nD\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/meteotracker_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/meteotracker_iso19115.xml
| https://erddap.s4raise.it/erddap/info/meteotracker/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/meteotracker.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=meteotracker&showErrors=false&email=
| ETT
| meteotracker
|
|
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_sensors_weather_ECOWITT001.subset
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_sensors_weather_ECOWITT001
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_sensors_weather_ECOWITT001.graph
|
| https://erddap.s4raise.it/erddap/files/ingv-lasomma_sensors_weather_ECOWITT001/
| Meteorological observations by Ecowitt Weather Station - ECOWITT001
| The Ecowitt Weather Station dataset provides real-time and high-frequency meteorological observations collected by a consumer-grade wireless weather station. The environmental variables monitored by the Ecowitt weather station and accessible through the LA SOMMA portal are: temperature, wind direction, wind gust, wind speed, atmospheric pressure, rainfall intensity, and relative humidity .Data are transmitted at regular intervals through the Ecowitt API. Measurements are delivered as minute-level or multi-minute time series, depending on the configured reporting interval.\n\ncdm_data_type = Other\nVARIABLES:\next_id\ntime (seconds since 1970-01-01T00:00:00Z)\ncum\nrain_intensity\nair_temperature\nrelative_humidity\nair_pressure\nwind_speed\nwind_from_direction\nwind_gust (Wind Speed Of Gust)\nwind_gust_from_direction\nbattery\n
|
|
| https://erddap.s4raise.it/erddap/info/ingv-lasomma_sensors_weather_ECOWITT001/index.htmlTable
| https://indra.artys.it/INGVRAISE/index.html
| https://erddap.s4raise.it/erddap/rss/ingv-lasomma_sensors_weather_ECOWITT001.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=ingv-lasomma_sensors_weather_ECOWITT001&showErrors=false&email=
| INGV, AGI srl
| ingv-lasomma_sensors_weather_ECOWITT001
|
|
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_sensors_weather_ECOWITT002.subset
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_sensors_weather_ECOWITT002
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_sensors_weather_ECOWITT002.graph
|
| https://erddap.s4raise.it/erddap/files/ingv-lasomma_sensors_weather_ECOWITT002/
| Meteorological observations by Ecowitt Weather Station - ECOWITT002
| The Ecowitt Weather Station dataset provides real-time and high-frequency meteorological observations collected by a consumer-grade wireless weather station. The environmental variables monitored by the Ecowitt weather station and accessible through the LA SOMMA portal are: temperature, wind direction, wind gust, wind speed, atmospheric pressure, rainfall intensity, and relative humidity .Data are transmitted at regular intervals through the Ecowitt API. Measurements are delivered as minute-level or multi-minute time series, depending on the configured reporting interval.\n\ncdm_data_type = Other\nVARIABLES:\next_id\ntime (seconds since 1970-01-01T00:00:00Z)\ncum\nrain_intensity\nair_temperature\nrelative_humidity\nair_pressure\nwind_speed\nwind_from_direction\nwind_gust (Wind Speed Of Gust)\nwind_gust_from_direction\nbattery\n
|
|
| https://erddap.s4raise.it/erddap/info/ingv-lasomma_sensors_weather_ECOWITT002/index.htmlTable
| https://indra.artys.it/INGVRAISE/index.html
| https://erddap.s4raise.it/erddap/rss/ingv-lasomma_sensors_weather_ECOWITT002.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=ingv-lasomma_sensors_weather_ECOWITT002&showErrors=false&email=
| INGV, AGI srl
| ingv-lasomma_sensors_weather_ECOWITT002
|
|
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_sensors_weather_ECOWITT003.subset
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_sensors_weather_ECOWITT003
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_sensors_weather_ECOWITT003.graph
|
| https://erddap.s4raise.it/erddap/files/ingv-lasomma_sensors_weather_ECOWITT003/
| Meteorological observations by Ecowitt Weather Station - ECOWITT003
| The Ecowitt Weather Station dataset provides real-time and high-frequency meteorological observations collected by a consumer-grade wireless weather station. The environmental variables monitored by the Ecowitt weather station and accessible through the LA SOMMA portal are: temperature, wind direction, wind gust, wind speed, atmospheric pressure, rainfall intensity, and relative humidity .Data are transmitted at regular intervals through the Ecowitt API. Measurements are delivered as minute-level or multi-minute time series, depending on the configured reporting interval.\n\ncdm_data_type = Other\nVARIABLES:\next_id\ntime (seconds since 1970-01-01T00:00:00Z)\ncum\nrain_intensity\nair_temperature\nrelative_humidity\nair_pressure\nwind_speed\nwind_from_direction\nwind_gust (Wind Speed Of Gust)\nwind_gust_from_direction\nbattery\n
|
|
| https://erddap.s4raise.it/erddap/info/ingv-lasomma_sensors_weather_ECOWITT003/index.htmlTable
| https://indra.artys.it/INGVRAISE/index.html
| https://erddap.s4raise.it/erddap/rss/ingv-lasomma_sensors_weather_ECOWITT003.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=ingv-lasomma_sensors_weather_ECOWITT003&showErrors=false&email=
| INGV, AGI srl
| ingv-lasomma_sensors_weather_ECOWITT003
|
|
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_sensors_weather_ECOWITT004.subset
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_sensors_weather_ECOWITT004
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_sensors_weather_ECOWITT004.graph
|
| https://erddap.s4raise.it/erddap/files/ingv-lasomma_sensors_weather_ECOWITT004/
| Meteorological observations by Ecowitt Weather Station - ECOWITT004
| The Ecowitt Weather Station dataset provides real-time and high-frequency meteorological observations collected by a consumer-grade wireless weather station. The environmental variables monitored by the Ecowitt weather station and accessible through the LA SOMMA portal are: temperature, wind direction, wind gust, wind speed, atmospheric pressure, rainfall intensity, and relative humidity .Data are transmitted at regular intervals through the Ecowitt API. Measurements are delivered as minute-level or multi-minute time series, depending on the configured reporting interval.\n\ncdm_data_type = Other\nVARIABLES:\next_id\ntime (seconds since 1970-01-01T00:00:00Z)\ncum\nrain_intensity\nair_temperature\nrelative_humidity\nair_pressure\nwind_speed\nwind_from_direction\nwind_gust (Wind Speed Of Gust)\nwind_gust_from_direction\nbattery\n
|
|
| https://erddap.s4raise.it/erddap/info/ingv-lasomma_sensors_weather_ECOWITT004/index.htmlTable
| https://indra.artys.it/INGVRAISE/index.html
| https://erddap.s4raise.it/erddap/rss/ingv-lasomma_sensors_weather_ECOWITT004.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=ingv-lasomma_sensors_weather_ECOWITT004&showErrors=false&email=
| INGV, AGI srl
| ingv-lasomma_sensors_weather_ECOWITT004
|
|
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_sensors_data_sea_quality_stat_genova.subset
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_sensors_data_sea_quality_stat_genova
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_sensors_data_sea_quality_stat_genova.graph
|
| https://erddap.s4raise.it/erddap/files/ingv-lasomma_sensors_data_sea_quality_stat_genova/
| Multiparametric measurements in a fixed position in the Genova port
| An IoT - blue box system installed on a 'boat of opportunity' measured temperature, salinity, pH, redox, chlorophyll, oxygen, phycoerythrin, turbidity and fDOM at a depth of 0.5 meters initially at at 5-minute intervals and then at 20 minutes intervals\n\ncdm_data_type = Other\nVARIABLES:\next_id\ncruise\nstation\ntype\ntime (seconds since 1970-01-01T00:00:00Z)\ntemp (Temperature)\npsal\nalky\ncpwc\nphyc\ntsed\nwbrx\ncmfl\ndeph\ndoxy_mg_l\ndoxy_perc\nqv_odv_sample\ntemp_qf\npsal_qf\nalky_qf\ncpwc_qf\nphyc_qf\ntsed_qf\nwbrx_qf\ncmfl_qf\n... (4 more variables)\n
|
|
| https://erddap.s4raise.it/erddap/info/ingv-lasomma_sensors_data_sea_quality_stat_genova/index.htmlTable
| https://indra.artys.it/INGVRAISE/index.html
| https://erddap.s4raise.it/erddap/rss/ingv-lasomma_sensors_data_sea_quality_stat_genova.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=ingv-lasomma_sensors_data_sea_quality_stat_genova&showErrors=false&email=
| INGV, OceanHis SrL
| ingv-lasomma_sensors_data_sea_quality_stat_genova
|
|
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_sensors_data_sea_quality_move_cinqueterre_massa.subset
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_sensors_data_sea_quality_move_cinqueterre_massa
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_sensors_data_sea_quality_move_cinqueterre_massa.graph
|
| https://erddap.s4raise.it/erddap/files/ingv-lasomma_sensors_data_sea_quality_move_cinqueterre_massa/
| Multiparametric mission along the eastern ligurian coast from Cinque Terre to Marina di Massa
| An IoT - blue box system installed on a 'boat of opportunity' measured temperature, salinity, pH, redox, chlorophyll, oxygen, phycoerythrin and turbidity at a depth of 0.5 meters at 5-minute intervals on the continental shelf off Cinque Terre and La Spezia\n\ncdm_data_type = Other\nVARIABLES:\next_id\ncruise\nstation\ntype\ntime (seconds since 1970-01-01T00:00:00Z)\nlatitude (degrees_north)\nlongitude (degrees_east)\ntemp (Temperature)\npsal\nalky\ncpwc\nphyc\ntsed\nwbrx\ncmfl\ndeph\ndoxy_mg_l\ndoxy_perc\nqv_odv_sample\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/ingv-lasomma_sensors_data_sea_quality_move_cinqueterre_massa_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/ingv-lasomma_sensors_data_sea_quality_move_cinqueterre_massa_iso19115.xml
| https://erddap.s4raise.it/erddap/info/ingv-lasomma_sensors_data_sea_quality_move_cinqueterre_massa/index.htmlTable
| https://indra.artys.it/INGVRAISE/index.html
| https://erddap.s4raise.it/erddap/rss/ingv-lasomma_sensors_data_sea_quality_move_cinqueterre_massa.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=ingv-lasomma_sensors_data_sea_quality_move_cinqueterre_massa&showErrors=false&email=
| INGV, OceanHis SrL
| ingv-lasomma_sensors_data_sea_quality_move_cinqueterre_massa
|
|
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_sensors_data_sea_quality_move_genova_portofino.subset
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_sensors_data_sea_quality_move_genova_portofino
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_sensors_data_sea_quality_move_genova_portofino.graph
|
| https://erddap.s4raise.it/erddap/files/ingv-lasomma_sensors_data_sea_quality_move_genova_portofino/
| Multiparametric mission along the eastern ligurian coast from Genova to Portofino
| An IoT - blue box system installed on a 'boat of opportunity' measured temperature, salinity, pH, redox, chlorophyll, oxygen, phycoerythrin and turbidity at a depth of 0.5 meters at 5-minute intervals on the continental shelf off Genova and in open sea from Genova to Portofino\n\ncdm_data_type = Other\nVARIABLES:\next_id\ncruise\nstation\ntype\ntime (seconds since 1970-01-01T00:00:00Z)\nlatitude (degrees_north)\nlongitude (degrees_east)\ntemp (Temperature)\npsal\nalky\ncpwc\nphyc\ntsed\nwbrx\ncmfl\ndeph\ndoxy_mg_l\ndoxy_perc\nqv_odv_sample\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/ingv-lasomma_sensors_data_sea_quality_move_genova_portofino_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/ingv-lasomma_sensors_data_sea_quality_move_genova_portofino_iso19115.xml
| https://erddap.s4raise.it/erddap/info/ingv-lasomma_sensors_data_sea_quality_move_genova_portofino/index.htmlTable
| https://indra.artys.it/INGVRAISE/index.html
| https://erddap.s4raise.it/erddap/rss/ingv-lasomma_sensors_data_sea_quality_move_genova_portofino.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=ingv-lasomma_sensors_data_sea_quality_move_genova_portofino&showErrors=false&email=
| INGV, OceanHis SrL
| ingv-lasomma_sensors_data_sea_quality_move_genova_portofino
|
|
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_sensors_data_sea_quality_move_ischia.subset
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_sensors_data_sea_quality_move_ischia
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_sensors_data_sea_quality_move_ischia.graph
|
| https://erddap.s4raise.it/erddap/files/ingv-lasomma_sensors_data_sea_quality_move_ischia/
| Multiparametric mission around Ischia island
| An IoT - blue box system installed on a 'boat of opportunity' measured temperature, salinity, pH, redox, chlorophyll, oxygen, phycoerythrin and turbidity at a depth of 0.5 meters at 5-minute intervals on the continental shelf off Ischia island\n\ncdm_data_type = Other\nVARIABLES:\next_id\ncruise\nstation\ntype\ntime (seconds since 1970-01-01T00:00:00Z)\nlatitude (degrees_north)\nlongitude (degrees_east)\ntemp (Temperature)\npsal\nalky\ncpwc\nphyc\ntsed\nwbrx\ncmfl\ndeph\ndoxy_mg_l\ndoxy_perc\nqv_odv_sample\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/ingv-lasomma_sensors_data_sea_quality_move_ischia_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/ingv-lasomma_sensors_data_sea_quality_move_ischia_iso19115.xml
| https://erddap.s4raise.it/erddap/info/ingv-lasomma_sensors_data_sea_quality_move_ischia/index.htmlTable
| https://indra.artys.it/INGVRAISE/index.html
| https://erddap.s4raise.it/erddap/rss/ingv-lasomma_sensors_data_sea_quality_move_ischia.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=ingv-lasomma_sensors_data_sea_quality_move_ischia&showErrors=false&email=
| INGV, OceanHis SrL
| ingv-lasomma_sensors_data_sea_quality_move_ischia
|
|
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_sensors_data_sea_quality_move_genova.subset
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_sensors_data_sea_quality_move_genova
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_sensors_data_sea_quality_move_genova.graph
|
| https://erddap.s4raise.it/erddap/files/ingv-lasomma_sensors_data_sea_quality_move_genova/
| Multiparametric mission off Genova April 2025
| An IoT - blue box system installed on a 'boat of opportunity' measured temperature, salinity, pH, redox, chlorophyll, oxygen, phycoerythrin and turbidity at a depth of 0.5 meters at 5-minute intervals on the coastal zone off Genova\n\ncdm_data_type = Other\nVARIABLES:\next_id\ncruise\nstation\ntype\ntime (seconds since 1970-01-01T00:00:00Z)\nlatitude (degrees_north)\nlongitude (degrees_east)\ntemp (Temperature)\npsal\nalky\ncpwc\nphyc\ntsed\nwbrx\ncmfl\ndeph\ndoxy_mg_l\ndoxy_perc\nqv_odv_sample\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/ingv-lasomma_sensors_data_sea_quality_move_genova_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/ingv-lasomma_sensors_data_sea_quality_move_genova_iso19115.xml
| https://erddap.s4raise.it/erddap/info/ingv-lasomma_sensors_data_sea_quality_move_genova/index.htmlTable
| https://indra.artys.it/INGVRAISE/index.html
| https://erddap.s4raise.it/erddap/rss/ingv-lasomma_sensors_data_sea_quality_move_genova.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=ingv-lasomma_sensors_data_sea_quality_move_genova&showErrors=false&email=
| INGV, OceanHis SrL
| ingv-lasomma_sensors_data_sea_quality_move_genova
|
|
|
| https://erddap.s4raise.it/erddap/tabledap/cnr-ismar_guard1_0001
| https://erddap.s4raise.it/erddap/tabledap/cnr-ismar_guard1_0001.graph
|
| https://erddap.s4raise.it/erddap/files/cnr-ismar_guard1_0001/
| Mussel farm image dataset - Gulf of La Spezia - Station 0001
| The dataset consits of underwater images showing fish activity within the mussel farm in the Gulf of La Spezia. The images are acquired by the autonomous and intelligent imaging device GUARD-1, installed on a surface buoy of the mussel farm in the gulf of La Spezia. The GUARD-1 is deployed at 3m depth and consists of an underwater camera equipped with a lighiting system that allow the image acquisition 24h per day. The device is also equipped with an onboard AI-based image analysis tool capable to recognize the fish specimens in order to automatically extract abundance time series and to select images with relevant content. The GUARD-1 device is connected to a 4G modem positioned on the surface buoy, inside a watertight case, that transmit the acquired images together with information extracted by the AI-based tool. Both the image acquisition frequency and the data transmission frequency are programmable by the user and can be managed through a remote connection or automatically by the AI-based tool.\n\ncdm_data_type = Other\nVARIABLES:\ntime (seconds since 1970-01-01T00:00:00Z)\nname\nlatitude (degrees_north)\nlongitude (degrees_east)\nurl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/cnr-ismar_guard1_0001_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/cnr-ismar_guard1_0001_iso19115.xml
| https://erddap.s4raise.it/erddap/info/cnr-ismar_guard1_0001/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/cnr-ismar_guard1_0001.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=cnr-ismar_guard1_0001&showErrors=false&email=
| CNR-ISMAR
| cnr-ismar_guard1_0001
|
|
|
| https://erddap.s4raise.it/erddap/tabledap/cnr-ismar_guard1_0001_0022
| https://erddap.s4raise.it/erddap/tabledap/cnr-ismar_guard1_0001_0022.graph
|
| https://erddap.s4raise.it/erddap/files/cnr-ismar_guard1_0001_0022/
| Mussel farm image dataset - Gulf of La Spezia - Station 0001 and 0022
| The dataset consits of underwater images showing fish activity within the mussel farm in the Gulf of La Spezia. The images are acquired by the autonomous and intelligent imaging device GUARD-1, installed on a surface buoy of the mussel farm in the gulf of La Spezia. The GUARD-1 is deployed at 3m depth and consists of an underwater camera equipped with a lighiting system that allow the image acquisition 24h per day. The device is also equipped with an onboard AI-based image analysis tool capable to recognize the fish specimens in order to automatically extract abundance time series and to select images with relevant content. The GUARD-1 device is connected to a 4G modem positioned on the surface buoy, inside a watertight case, that transmit the acquired images together with information extracted by the AI-based tool. Both the image acquisition frequency and the data transmission frequency are programmable by the user and can be managed through a remote connection or automatically by the AI-based tool.\n\ncdm_data_type = Other\nVARIABLES:\ntime (seconds since 1970-01-01T00:00:00Z)\nname\nlatitude (degrees_north)\nlongitude (degrees_east)\nurl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/cnr-ismar_guard1_0001_0022_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/cnr-ismar_guard1_0001_0022_iso19115.xml
| https://erddap.s4raise.it/erddap/info/cnr-ismar_guard1_0001_0022/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/cnr-ismar_guard1_0001_0022.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=cnr-ismar_guard1_0001_0022&showErrors=false&email=
| CNR-ISMAR
| cnr-ismar_guard1_0001_0022
|
|
|
| https://erddap.s4raise.it/erddap/tabledap/cnr-ismar_guard1_0022
| https://erddap.s4raise.it/erddap/tabledap/cnr-ismar_guard1_0022.graph
|
| https://erddap.s4raise.it/erddap/files/cnr-ismar_guard1_0022/
| Mussel farm image dataset - Gulf of La Spezia - Station 0022
| The dataset consits of underwater images showing fish activity within the mussel farm in the Gulf of La Spezia. The images are acquired by the autonomous and intelligent imaging device GUARD-1, installed on a surface buoy of the mussel farm in the gulf of La Spezia. The GUARD-1 is deployed at 3m depth and consists of an underwater camera equipped with a lighiting system that allow the image acquisition 24h per day. The device is also equipped with an onboard AI-based image analysis tool capable to recognize the fish specimens in order to automatically extract abundance time series and to select images with relevant content. The GUARD-1 device is connected to a 4G modem positioned on the surface buoy, inside a watertight case, that transmit the acquired images together with information extracted by the AI-based tool. Both the image acquisition frequency and the data transmission frequency are programmable by the user and can be managed through a remote connection or automatically by the AI-based tool.\n\ncdm_data_type = Other\nVARIABLES:\ntime (seconds since 1970-01-01T00:00:00Z)\nname\nlatitude (degrees_north)\nlongitude (degrees_east)\nurl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/cnr-ismar_guard1_0022_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/cnr-ismar_guard1_0022_iso19115.xml
| https://erddap.s4raise.it/erddap/info/cnr-ismar_guard1_0022/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/cnr-ismar_guard1_0022.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=cnr-ismar_guard1_0022&showErrors=false&email=
| CNR-ISMAR
| cnr-ismar_guard1_0022
|
|
|
| https://erddap.s4raise.it/erddap/tabledap/cnr-ismar_guard1_0102
| https://erddap.s4raise.it/erddap/tabledap/cnr-ismar_guard1_0102.graph
|
| https://erddap.s4raise.it/erddap/files/cnr-ismar_guard1_0102/
| Mussel farm image dataset - Gulf of La Spezia - Station 0102
| The dataset consits of underwater images showing fish activity within the mussel farm in the Gulf of La Spezia. The images are acquired by the autonomous and intelligent imaging device GUARD-1, installed on a fixed boat used as a field laboratory by the mussel farm operators, in the gulf of La Spezia. The GUARD-1 is deployed at 3m depth and consists of an underwater camera equipped with a lighiting system that allow the image acquisition 24h per day. The GUARD-1 device is connected to a 4G modem positioned on the fixed boat that transmit the acquired images. Both the image acquisition frequency and the data transmission frequency are programmable by the user and can be managed through a remote connection.\n\ncdm_data_type = Other\nVARIABLES:\ntime (seconds since 1970-01-01T00:00:00Z)\nname\nlatitude (degrees_north)\nlongitude (degrees_east)\nurl\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/cnr-ismar_guard1_0102_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/cnr-ismar_guard1_0102_iso19115.xml
| https://erddap.s4raise.it/erddap/info/cnr-ismar_guard1_0102/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/cnr-ismar_guard1_0102.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=cnr-ismar_guard1_0102&showErrors=false&email=
| CNR-ISMAR
| cnr-ismar_guard1_0102
|
|
|
| https://erddap.s4raise.it/erddap/tabledap/unige-dicca_dispersion_forecast_simple_mover
| https://erddap.s4raise.it/erddap/tabledap/unige-dicca_dispersion_forecast_simple_mover.graph
|
| https://erddap.s4raise.it/erddap/files/unige-dicca_dispersion_forecast_simple_mover/
| Particle dispersion model
| The particle dispersion model used is Pygnome. The forcings used for the forecast are the persistence of the sea current velocity, in its u and v components, and the average wind speed and direction calculated over the previous six hours. The model generates a forecast output with a six-hour time horizon. Every hour, new current and wind data are acquired, thus updating the forecast hour by hour for the next six hours.\n\ncdm_data_type = Point\nVARIABLES:\ntime (time since the beginning of the simulation, seconds since 1970-01-01T00:00:00Z)\nlatitude (latitude of the particle, degrees_north)\nlongitude (longitude of the particle, degrees_east)\nparticle_count (number of particles in a given timestep, 1)\nspill_num (spill to which the particle belongs)\nsurface_concentration (surface concentration of oil, g m-2)\ndepth (particle depth below sea surface, m)\nid (particle ID)\nage (age of particle from time of release, minutes)\nstatus_codes (particle status code)\ndensity (emulsion density at end of timestep, kg/m^3)\nviscosity (emulsion viscosity at end of timestep, m^2/sec)\nmass (mass of particle, kilograms)\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-dicca_dispersion_forecast_simple_mover_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-dicca_dispersion_forecast_simple_mover_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-dicca_dispersion_forecast_simple_mover/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-dicca_dispersion_forecast_simple_mover.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-dicca_dispersion_forecast_simple_mover&showErrors=false&email=
| UNIGE-DICCA
| unige-dicca_dispersion_forecast_simple_mover
|
| https://erddap.s4raise.it/erddap/griddap/cmems_mod_med_bgc-pft_anfc_4_2km_P1D_m
|
|
| https://erddap.s4raise.it/erddap/griddap/cmems_mod_med_bgc-pft_anfc_4_2km_P1D_m.graph
| https://erddap.s4raise.it/erddap/wms/cmems_mod_med_bgc-pft_anfc_4_2km_P1D_m/request
| https://erddap.s4raise.it/erddap/files/cmems_mod_med_bgc-pft_anfc_4_2km_P1D_m/
| Phytoplankton Carbon Biomass, Zooplankton Carbon Biomass, Chlorophyll and PFTs (3D), Daily Mean
| Phytoplankton Carbon Biomass, Zooplankton Carbon Biomass, Chlorophyll and PFTs (3D) - Daily Mean. Please check in CMEMS catalogue the INFO section for product MEDSEA_ANALYSISFORECAST_BGC_006_014 - http://marine.copernicus.eu/\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][depth][latitude][longitude]):\nchl (Chlorophyll, mg m-3)\ndiatoC (Diatoms Carbon Biomass, MMol' 'M-3)\ndiatoChla (Diatoms Chlorophyll concentration, mg m-3)\ndinoC (Dinoflagellates Carbon Biomass, MMol' 'M-3)\ndinoChla (Dinoflagellates Chlorophyll concentration, mg m-3)\nnanoC (Nanophytoplankton Carbon Biomass, MMol' 'M-3)\nnanoChla (Nanophytoplankton Chlorophyll concentration, mg m-3)\nphyc (Phytoplankton Carbon Biomass, MMol' 'M-3)\npicoC (Picophytoplankton Carbon Biomass, MMol' 'M-3)\npicoChla (Picophytoplankton Chlorophyll concentration, mg m-3)\nzooc (Zooplankton Carbon Biomass, MMol' 'M-3)\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/cmems_mod_med_bgc-pft_anfc_4_2km_P1D_m_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/cmems_mod_med_bgc-pft_anfc_4_2km_P1D_m_iso19115.xml
| https://erddap.s4raise.it/erddap/info/cmems_mod_med_bgc-pft_anfc_4_2km_P1D_m/index.htmlTable
| ???
| https://erddap.s4raise.it/erddap/rss/cmems_mod_med_bgc-pft_anfc_4_2km_P1D_m.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=cmems_mod_med_bgc-pft_anfc_4_2km_P1D_m&showErrors=false&email=
| OGS, Trieste - Italy
| cmems_mod_med_bgc-pft_anfc_4_2km_P1D_m
|
|
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_srs_rainfall_bonassola.subset
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_srs_rainfall_bonassola
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_srs_rainfall_bonassola.graph
|
| https://erddap.s4raise.it/erddap/files/ingv-lasomma_srs_rainfall_bonassola/
| Real-time rainfall intensity and cumulate precipitation maps by SRS - Smart Rainfall System - Bonassola
| The SRS (Smart Rainfall System) dataset originates from a dense network of microwave sensors designed for satellite down-links and developed by the University of Genoa together with Artys and Darts Engineering (Genoa, Italy). By analysing the attenuation of satellite signals received by standard parabolic antennas, the system retrieves real-time estimates of rainfall intensity along each observation link and produces high-resolution precipitation maps over the monitored area. The resulting dataset provides continuous time series of rainfall intensity and cumulative precipitation (15 minutes, 1-2-8-12, 24 hours), enabling detailed spatio-temporal characterization of rainfall dynamics.\n\ncdm_data_type = Grid\nVARIABLES:\ntime (seconds since 1970-01-01T00:00:00Z)\nsrs_id\nsrs_sat_id\nrain_level\n
|
|
| https://erddap.s4raise.it/erddap/info/ingv-lasomma_srs_rainfall_bonassola/index.htmlTable
| https://indra.artys.it/INGVRAISE/index.html
| https://erddap.s4raise.it/erddap/rss/ingv-lasomma_srs_rainfall_bonassola.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=ingv-lasomma_srs_rainfall_bonassola&showErrors=false&email=
| INGV, Darts Engineering Srl
| ingv-lasomma_srs_rainfall_bonassola
|
|
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_srs_rainfall_genova.subset
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_srs_rainfall_genova
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_srs_rainfall_genova.graph
|
| https://erddap.s4raise.it/erddap/files/ingv-lasomma_srs_rainfall_genova/
| Real-time rainfall intensity and cumulate precipitation maps by SRS - Smart Rainfall System - Genova
| The SRS (Smart Rainfall System) dataset originates from a dense network of microwave sensors designed for satellite down-links and developed by the University of Genoa together with Artys and Darts Engineering (Genoa, Italy). By analysing the attenuation of satellite signals received by standard parabolic antennas, the system retrieves real-time estimates of rainfall intensity along each observation link and produces high-resolution precipitation maps over the monitored area. The resulting dataset provides continuous time series of rainfall intensity and cumulative precipitation (15 minutes, 1-2-8-12, 24 hours), enabling detailed spatio-temporal characterization of rainfall dynamics.\n\ncdm_data_type = Grid\nVARIABLES:\ntime (seconds since 1970-01-01T00:00:00Z)\nsrs_id\nsrs_sat_id\nrain_level\n
|
|
| https://erddap.s4raise.it/erddap/info/ingv-lasomma_srs_rainfall_genova/index.htmlTable
| https://indra.artys.it/INGVRAISE/index.html
| https://erddap.s4raise.it/erddap/rss/ingv-lasomma_srs_rainfall_genova.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=ingv-lasomma_srs_rainfall_genova&showErrors=false&email=
| INGV, Darts Engineering Srl
| ingv-lasomma_srs_rainfall_genova
|
|
|
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_srs_rainfall_laspezia
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_srs_rainfall_laspezia.graph
|
| https://erddap.s4raise.it/erddap/files/ingv-lasomma_srs_rainfall_laspezia/
| Real-time rainfall intensity and cumulate precipitation maps by SRS - Smart Rainfall System - La Spezia
| The SRS (Smart Rainfall System) dataset originates from a dense network of microwave sensors designed for satellite down-links and developed by the University of Genoa together with Artys and Darts Engineering (Genoa, Italy). By analysing the attenuation of satellite signals received by standard parabolic antennas, the system retrieves real-time estimates of rainfall intensity along each observation link and produces high-resolution precipitation maps over the monitored area. The resulting dataset provides continuous time series of rainfall intensity and cumulative precipitation (15 minutes, 1-2-8-12, 24 hours), enabling detailed spatio-temporal characterization of rainfall dynamics.\n\ncdm_data_type = Grid\nVARIABLES:\ntime (seconds since 1970-01-01T00:00:00Z)\nsrs_id\nsrs_sat_id\nrain_level\n
|
|
| https://erddap.s4raise.it/erddap/info/ingv-lasomma_srs_rainfall_laspezia/index.htmlTable
| https://indra.artys.it/INGVRAISE/index.html
| https://erddap.s4raise.it/erddap/rss/ingv-lasomma_srs_rainfall_laspezia.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=ingv-lasomma_srs_rainfall_laspezia&showErrors=false&email=
| INGV, Darts Engineering Srl
| ingv-lasomma_srs_rainfall_laspezia
|
|
|
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_srs_rainfall_livorno
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_srs_rainfall_livorno.graph
|
| https://erddap.s4raise.it/erddap/files/ingv-lasomma_srs_rainfall_livorno/
| Real-time rainfall intensity and cumulate precipitation maps by SRS - Smart Rainfall System - Livorno
| The SRS (Smart Rainfall System) dataset originates from a dense network of microwave sensors designed for satellite down-links and developed by the University of Genoa together with Artys and Darts Engineering (Genoa, Italy). By analysing the attenuation of satellite signals received by standard parabolic antennas, the system retrieves real-time estimates of rainfall intensity along each observation link and produces high-resolution precipitation maps over the monitored area. The resulting dataset provides continuous time series of rainfall intensity and cumulative precipitation (15 minutes, 1-2-8-12, 24 hours), enabling detailed spatio-temporal characterization of rainfall dynamics.\n\ncdm_data_type = Grid\nVARIABLES:\ntime (seconds since 1970-01-01T00:00:00Z)\nsrs_id\nsrs_sat_id\nrain_level\n
|
|
| https://erddap.s4raise.it/erddap/info/ingv-lasomma_srs_rainfall_livorno/index.htmlTable
| https://indra.artys.it/INGVRAISE/index.html
| https://erddap.s4raise.it/erddap/rss/ingv-lasomma_srs_rainfall_livorno.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=ingv-lasomma_srs_rainfall_livorno&showErrors=false&email=
| INGV, Darts Engineering Srl
| ingv-lasomma_srs_rainfall_livorno
|
|
|
| https://erddap.s4raise.it/erddap/tabledap/unige-distav_riomaggiore_buoy_temp_curr_data
| https://erddap.s4raise.it/erddap/tabledap/unige-distav_riomaggiore_buoy_temp_curr_data.graph
|
|
| Riomaggiore in situ buoy sea water temperature and sub surface current data
| The dataset represents data automatically collected and trasmitted in real-time by in situ buoy located in Riomaggiore\n\ncdm_data_type = Other\nVARIABLES:\ntime (Timestamp, seconds since 1970-01-01T00:00:00Z)\nlatitude (degrees_north)\nlongitude (degrees_east)\nsw_temperature_3m (SW Temp 3m, degree_C)\nsw_temperature_6_5m (SW Temp 6.5m, degree_C)\nspeed_mean (Speed, cm/s)\nspeed_std (cm/s)\ndirection_mean (Direction, degrees_north)\ndirection_std (degrees_north)\ntilt\ntilt_std\nread_count\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-distav_riomaggiore_buoy_temp_curr_data_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-distav_riomaggiore_buoy_temp_curr_data_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-distav_riomaggiore_buoy_temp_curr_data/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-distav_riomaggiore_buoy_temp_curr_data.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-distav_riomaggiore_buoy_temp_curr_data&showErrors=false&email=
| UNIGE-DISTAV
| unige-distav_riomaggiore_buoy_temp_curr_data
|
|
|
| https://erddap.s4raise.it/erddap/tabledap/unige-distav_riomaggiore_buoy_wave_wind_data
| https://erddap.s4raise.it/erddap/tabledap/unige-distav_riomaggiore_buoy_wave_wind_data.graph
|
|
| Riomaggiore in situ buoy wave and wind data
| The dataset represents data automatically collected and trasmitted in real-time by in situ buoy located in Riomaggiore\n\ncdm_data_type = Other\nVARIABLES:\ntime (Timestamp, seconds since 1970-01-01T00:00:00Z)\nlatitude (degrees_north)\nlongitude (degrees_east)\nsignificantWaveHeight (Significant Wave Height, m)\npeakPeriod (Wave Peak Period, s)\nmeanPeriod (Wave Mean Period, s)\npeakDirection (Wave Peak Direction, degrees)\nmeanDirection (Wave Mean Direction, degrees)\npeakDirectionalSpread (Wave Peak Directional Spread, degrees)\nmeanDirectionalSpread (Wave Mean Directional Spread, degrees)\nwind_direction (degrees_north)\nwind_speed (m/s)\nair_pressure (Barometric Pressure, hPa)\nsurfaceTemp (Surface Temp, degree_C)\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-distav_riomaggiore_buoy_wave_wind_data_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-distav_riomaggiore_buoy_wave_wind_data_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-distav_riomaggiore_buoy_wave_wind_data/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-distav_riomaggiore_buoy_wave_wind_data.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-distav_riomaggiore_buoy_wave_wind_data&showErrors=false&email=
| UNIGE-DISTAV
| unige-distav_riomaggiore_buoy_wave_wind_data
|
| https://erddap.s4raise.it/erddap/griddap/cmems_mod_med_phy-sal_anfc_4_2km_2D_PT1H_m
|
|
| https://erddap.s4raise.it/erddap/griddap/cmems_mod_med_phy-sal_anfc_4_2km_2D_PT1H_m.graph
| https://erddap.s4raise.it/erddap/wms/cmems_mod_med_phy-sal_anfc_4_2km_2D_PT1H_m/request
| https://erddap.s4raise.it/erddap/files/cmems_mod_med_phy-sal_anfc_4_2km_2D_PT1H_m/
| Sea Surface Salinity (2D), Hourly Mean
| Sea Surface Salinity (2D) - Hourly Mean. Please check in CMEMS catalogue the INFO section for product MEDSEA_ANALYSISFORECAST_PHY_006_013 - http://marine.copernicus.eu\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][latitude][longitude]):\nso (salinity, PSU)\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/cmems_mod_med_phy-sal_anfc_4_2km_2D_PT1H_m_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/cmems_mod_med_phy-sal_anfc_4_2km_2D_PT1H_m_iso19115.xml
| https://erddap.s4raise.it/erddap/info/cmems_mod_med_phy-sal_anfc_4_2km_2D_PT1H_m/index.htmlTable
| https://www.ec.gc.ca/scitech/default.asp?lang=En&n=61B33C26-1#cmc
| https://erddap.s4raise.it/erddap/rss/cmems_mod_med_phy-sal_anfc_4_2km_2D_PT1H_m.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=cmems_mod_med_phy-sal_anfc_4_2km_2D_PT1H_m&showErrors=false&email=
| Centro Euro-Mediterraneo sui Cambiamenti Climatici - CMCC, Italy
| cmems_mod_med_phy-sal_anfc_4_2km_2D_PT1H_m
|
|
| https://erddap.s4raise.it/erddap/tabledap/brizo.subset
| https://erddap.s4raise.it/erddap/tabledap/brizo
| https://erddap.s4raise.it/erddap/tabledap/brizo.graph
|
|
| Sea Surface Temperature data collected during Citizen Science activities using the Brizo device
| Brizo is a smart temperature logger to support marine environmental research. The system offers data in near real time.\n\ncdm_data_type = Point\nVARIABLES:\nplatformcode\nmission\ntime (seconds since 1970-01-01T00:00:00Z)\nlatitude (degrees_north)\nlongitude (degrees_east)\ntemperature\ntimestamp (seconds since 1970-01-01T00:00:00Z)\nauthor\ncommand\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/brizo_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/brizo_iso19115.xml
| https://erddap.s4raise.it/erddap/info/brizo/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/brizo.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=brizo&showErrors=false&email=
| ETT
| brizo
|
| https://erddap.s4raise.it/erddap/griddap/cmems_mod_med_phy-tem_anfc_4_2km_3D_PT1H_m
|
|
| https://erddap.s4raise.it/erddap/griddap/cmems_mod_med_phy-tem_anfc_4_2km_3D_PT1H_m.graph
| https://erddap.s4raise.it/erddap/wms/cmems_mod_med_phy-tem_anfc_4_2km_3D_PT1H_m/request
| https://erddap.s4raise.it/erddap/files/cmems_mod_med_phy-tem_anfc_4_2km_3D_PT1H_m/
| Sea Temperature (3D), Hourly Mean
| Sea Temperature (3D) - Hourly Mean. Please check in CMEMS catalogue the INFO section for product MEDSEA_ANALYSISFORECAST_PHY_006_013 - http://marine.copernicus.eu\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][depth][latitude][longitude]):\nthetao (sea temperature, degree_C)\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/cmems_mod_med_phy-tem_anfc_4_2km_3D_PT1H_m_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/cmems_mod_med_phy-tem_anfc_4_2km_3D_PT1H_m_iso19115.xml
| https://erddap.s4raise.it/erddap/info/cmems_mod_med_phy-tem_anfc_4_2km_3D_PT1H_m/index.htmlTable
| https://www.ec.gc.ca/scitech/default.asp?lang=En&n=61B33C26-1#cmc
| https://erddap.s4raise.it/erddap/rss/cmems_mod_med_phy-tem_anfc_4_2km_3D_PT1H_m.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=cmems_mod_med_phy-tem_anfc_4_2km_3D_PT1H_m&showErrors=false&email=
| Centro Euro-Mediterraneo sui Cambiamenti Climatici - CMCC, Italy
| cmems_mod_med_phy-tem_anfc_4_2km_3D_PT1H_m
|
|
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_sensors_wave_meter_andora.subset
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_sensors_wave_meter_andora
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_sensors_wave_meter_andora.graph
|
| https://erddap.s4raise.it/erddap/files/ingv-lasomma_sensors_wave_meter_andora/
| Sea waves parameters - Andora
| Sea-waves parameters -Hs (Significant wave height),Tm (Mean Period). And Tp (Peak Period) retrieved from inland micro-seismic measurements - Andora\n\ncdm_data_type = Other\nVARIABLES:\next_id\ntime (seconds since 1970-01-01T00:00:00Z)\nhs\ntm\ntp\nvalid\n
|
|
| https://erddap.s4raise.it/erddap/info/ingv-lasomma_sensors_wave_meter_andora/index.htmlTable
| https://indra.artys.it/INGVRAISE/index.html
| https://erddap.s4raise.it/erddap/rss/ingv-lasomma_sensors_wave_meter_andora.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=ingv-lasomma_sensors_wave_meter_andora&showErrors=false&email=
| INGV, AGI srl
| ingv-lasomma_sensors_wave_meter_andora
|
|
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_sensors_wave_meter_bonassola.subset
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_sensors_wave_meter_bonassola
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_sensors_wave_meter_bonassola.graph
|
| https://erddap.s4raise.it/erddap/files/ingv-lasomma_sensors_wave_meter_bonassola/
| Sea waves parameters - Bonassola
| Sea-waves parameters -Hs (Significant wave height),Tm (Mean Period). And Tp (Peak Period) retrieved from inland micro-seismic measurements - Bonassola\n\ncdm_data_type = Other\nVARIABLES:\next_id\ntime (seconds since 1970-01-01T00:00:00Z)\nhs\ntm\ntp\nvalid\n
|
|
| https://erddap.s4raise.it/erddap/info/ingv-lasomma_sensors_wave_meter_bonassola/index.htmlTable
| https://indra.artys.it/INGVRAISE/index.html
| https://erddap.s4raise.it/erddap/rss/ingv-lasomma_sensors_wave_meter_bonassola.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=ingv-lasomma_sensors_wave_meter_bonassola&showErrors=false&email=
| INGV, AGI srl
| ingv-lasomma_sensors_wave_meter_bonassola
|
|
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_sensors_wave_meter_genovacnr.subset
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_sensors_wave_meter_genovacnr
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_sensors_wave_meter_genovacnr.graph
|
| https://erddap.s4raise.it/erddap/files/ingv-lasomma_sensors_wave_meter_genovacnr/
| Sea waves parameters - Genova CNR
| Sea-waves parameters -Hs (Significant wave height),Tm (Mean Period). And Tp (Peak Period) retrieved from inland micro-seismic measurements - Genova CNR\n\ncdm_data_type = Other\nVARIABLES:\next_id\ntime (seconds since 1970-01-01T00:00:00Z)\nhs\ntm\ntp\nvalid\n
|
|
| https://erddap.s4raise.it/erddap/info/ingv-lasomma_sensors_wave_meter_genovacnr/index.htmlTable
| https://indra.artys.it/INGVRAISE/index.html
| https://erddap.s4raise.it/erddap/rss/ingv-lasomma_sensors_wave_meter_genovacnr.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=ingv-lasomma_sensors_wave_meter_genovacnr&showErrors=false&email=
| INGV, AGI srl
| ingv-lasomma_sensors_wave_meter_genovacnr
|
|
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_sensors_wave_meter_livornocnr.subset
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_sensors_wave_meter_livornocnr
| https://erddap.s4raise.it/erddap/tabledap/ingv-lasomma_sensors_wave_meter_livornocnr.graph
|
| https://erddap.s4raise.it/erddap/files/ingv-lasomma_sensors_wave_meter_livornocnr/
| Sea waves parameters - Livorno
| Sea-waves parameters -Hs (Significant wave height),Tm (Mean Period). And Tp (Peak Period) retrieved from inland micro-seismic measurements - Livorno\n\ncdm_data_type = Other\nVARIABLES:\next_id\ntime (seconds since 1970-01-01T00:00:00Z)\nhs\ntm\ntp\nvalid\n
|
|
| https://erddap.s4raise.it/erddap/info/ingv-lasomma_sensors_wave_meter_livornocnr/index.htmlTable
| https://indra.artys.it/INGVRAISE/index.html
| https://erddap.s4raise.it/erddap/rss/ingv-lasomma_sensors_wave_meter_livornocnr.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=ingv-lasomma_sensors_wave_meter_livornocnr&showErrors=false&email=
| INGV, AGI srl
| ingv-lasomma_sensors_wave_meter_livornocnr
|
|
|
| https://erddap.s4raise.it/erddap/tabledap/omirl_stazioni_mare
| https://erddap.s4raise.it/erddap/tabledap/omirl_stazioni_mare.graph
|
| https://erddap.s4raise.it/erddap/files/omirl_stazioni_mare/
| Stazioni OMIRL (Osservatorio Meteo Idrologico della Regione Liguria), Osservazioni georiferite di parametri meteo-marini ed idrologici in tempo reale sulla Liguria, Stazioni a mare
| Stazioni OMIRL (Osservatorio Meteo Idrologico della Regione Liguria) - Osservazioni georiferite di parametri meteo-marini ed idrologici in tempo reale sulla Liguria - Stazioni a mare\n\ncdm_data_type = Point\nVARIABLES:\nname\nshortCode (Short Code)\ntime (Valid Time GMT, seconds since 1970-01-01T00:00:00Z)\nlatitude (degrees_north)\nlongitude (degrees_east)\nVHZA (Average zero crossing wave height (Hzm), m)\nAIR_TEMP (Air temperature, degree_C)\nWSPD (Horizontal wind speed, m/s)\nGSPD (Gust wind speed, m/s)\nWDIR (Wind from direction relative true north, degrees)\nWSPD_2d (Horizontal wind speed, m/s)\nGSPD_2d (Gust wind speed, m/s)\nWDIR_2d (Wind from direction relative true north, degrees)\nATMP (Atmospheric pressure at sea level, hPa)\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/omirl_stazioni_mare_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/omirl_stazioni_mare_iso19115.xml
| https://erddap.s4raise.it/erddap/info/omirl_stazioni_mare/index.htmlTable
| https://omirl.regione.liguria.it/
| https://erddap.s4raise.it/erddap/rss/omirl_stazioni_mare.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=omirl_stazioni_mare&showErrors=false&email=
| OMIRL
| omirl_stazioni_mare
|
|
|
| https://erddap.s4raise.it/erddap/tabledap/omirl_stazioni_terra
| https://erddap.s4raise.it/erddap/tabledap/omirl_stazioni_terra.graph
|
| https://erddap.s4raise.it/erddap/files/omirl_stazioni_terra/
| Stazioni OMIRL (Osservatorio Meteo Idrologico della Regione Liguria), Osservazioni georiferite di parametri meteo-marini ed idrologici in tempo reale sulla Liguria, Stazioni a terra
| Stazioni OMIRL (Osservatorio Meteo Idrologico della Regione Liguria) - Osservazioni georiferite di parametri meteo-marini ed idrologici in tempo reale sulla Liguria - Stazioni a terra\n\ncdm_data_type = Point\nVARIABLES:\nname\nshortCode (Short Code)\ntime (Valid Time GMT, seconds since 1970-01-01T00:00:00Z)\nlatitude (degrees_north)\nlongitude (degrees_east)\nRAIN_1h\nCUMUL_1h\nRAIN_5m\nCUMUL_5m\nRAIN_7d\nCUMUL_7d\nRAIN_1d\nCUMUL_1d\nAIR_TEMP (air_temperature, degree_C)\nTMIN (air_temperature, degree_C)\nTMAX (air_temperature, degree_C)\nRLEV (Water surface height above a specific datum, m)\nATMP (air_pressure)\nTENS (Battery voltage, V)\nmunicipality\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/omirl_stazioni_terra_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/omirl_stazioni_terra_iso19115.xml
| https://erddap.s4raise.it/erddap/info/omirl_stazioni_terra/index.htmlTable
| https://omirl.regione.liguria.it/
| https://erddap.s4raise.it/erddap/rss/omirl_stazioni_terra.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=omirl_stazioni_terra&showErrors=false&email=
| OMIRL
| omirl_stazioni_terra
|
|
| https://erddap.s4raise.it/erddap/tabledap/swan_hellenic_openctd.subset
| https://erddap.s4raise.it/erddap/tabledap/swan_hellenic_openctd
| https://erddap.s4raise.it/erddap/tabledap/swan_hellenic_openctd.graph
|
|
| Temperature and conductivity data collected during Citizen Science campaigns aboard the Swan Hellenic SH Vega using OpenCTD
| Dataset of Temperature and Salinity data in the water column collected by OpenCTD during Swan Hellenic expeditions. OpenCTD is a oceanographic instrument designed for budget-restricted scientists, educators, and researchers working in nearshore coastal ecosystems.\n\ncdm_data_type = Point\nVARIABLES:\nADATAA01 (seconds since 1970-01-01T00:00:00Z)\nHour\ntime (seconds since 1970-01-01T00:00:00Z)\ndepth (m)\nTEMP (Temperature)\nPSLTZZ01\nPRESPR01\nTEMPPR01\nTEMPPR02\nTEMPPR03\nCNDCZZ01\nTEMPPR01_QC\nTEMPPR02_QC\nTEMPPR03_QC\nCNDCZZ01_QC\nlatitude (degrees_north)\nlongitude (degrees_east)\nnotes\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/swan_hellenic_openctd_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/swan_hellenic_openctd_iso19115.xml
| https://erddap.s4raise.it/erddap/info/swan_hellenic_openctd/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/swan_hellenic_openctd.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=swan_hellenic_openctd&showErrors=false&email=
| ETT, Swan Hellenic
| swan_hellenic_openctd
|
|
| https://erddap.s4raise.it/erddap/tabledap/envlogger_0443_0B00_FC09_09.subset
| https://erddap.s4raise.it/erddap/tabledap/envlogger_0443_0B00_FC09_09
| https://erddap.s4raise.it/erddap/tabledap/envlogger_0443_0B00_FC09_09.graph
|
|
| Temperature data collected during Citizen Science activities using EnvLogger sensor (0443 0B00 FC09 09)
| EnvLogger is a miniaturised temperature logger to support marine environmental research. Data is downloaded by a mobile app and RAISE developed the data processing pipeline and its automation\n\ncdm_data_type = Point\nVARIABLES:\ntime (seconds since 1970-01-01T00:00:00Z)\ntemp (Temperature, degree_Celsius)\nserial_number\nsensor_id\nlatitude (degrees_north)\nlongitude (degrees_east)\naccuracy (GPS Position Accuracy, meter)\ndepth (Sensor depth, m)\nsampling_resolution\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/envlogger_0443_0B00_FC09_09_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/envlogger_0443_0B00_FC09_09_iso19115.xml
| https://erddap.s4raise.it/erddap/info/envlogger_0443_0B00_FC09_09/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/envlogger_0443_0B00_FC09_09.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=envlogger_0443_0B00_FC09_09&showErrors=false&email=
| ETT
| envlogger_0443_0B00_FC09_09
|
|
| https://erddap.s4raise.it/erddap/tabledap/envlogger_0457_9200_461D_0D.subset
| https://erddap.s4raise.it/erddap/tabledap/envlogger_0457_9200_461D_0D
| https://erddap.s4raise.it/erddap/tabledap/envlogger_0457_9200_461D_0D.graph
|
|
| Temperature data collected during Citizen Science activities using EnvLogger sensor (0457 9200 461D 0D)
| EnvLogger is a miniaturised temperature logger to support marine environmental research. Data is downloaded by a mobile app and RAISE developed the data processing pipeline and its automation\n\ncdm_data_type = Point\nVARIABLES:\ntime (seconds since 1970-01-01T00:00:00Z)\ntemp (Temperature, degree_Celsius)\nserial_number\nsensor_id\nlatitude (degrees_north)\nlongitude (degrees_east)\naccuracy (GPS Position Accuracy, meter)\ndepth (Sensor depth, m)\nsampling_resolution\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/envlogger_0457_9200_461D_0D_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/envlogger_0457_9200_461D_0D_iso19115.xml
| https://erddap.s4raise.it/erddap/info/envlogger_0457_9200_461D_0D/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/envlogger_0457_9200_461D_0D.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=envlogger_0457_9200_461D_0D&showErrors=false&email=
| ETT
| envlogger_0457_9200_461D_0D
|
|
| https://erddap.s4raise.it/erddap/tabledap/envlogger_0475_3A00_FF2D_05.subset
| https://erddap.s4raise.it/erddap/tabledap/envlogger_0475_3A00_FF2D_05
| https://erddap.s4raise.it/erddap/tabledap/envlogger_0475_3A00_FF2D_05.graph
|
|
| Temperature data collected during Citizen Science activities using EnvLogger sensor (0475 3A00 FF2D 05)
| EnvLogger is a miniaturised temperature logger to support marine environmental research. Data is downloaded by a mobile app and RAISE developed the data processing pipeline and its automation\n\ncdm_data_type = Point\nVARIABLES:\ntime (seconds since 1970-01-01T00:00:00Z)\ntemp (Temperature, degree_Celsius)\nserial_number\nsensor_id\nlatitude (degrees_north)\nlongitude (degrees_east)\naccuracy (GPS Position Accuracy, meter)\ndepth (Sensor depth, m)\nsampling_resolution\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/envlogger_0475_3A00_FF2D_05_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/envlogger_0475_3A00_FF2D_05_iso19115.xml
| https://erddap.s4raise.it/erddap/info/envlogger_0475_3A00_FF2D_05/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/envlogger_0475_3A00_FF2D_05.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=envlogger_0475_3A00_FF2D_05&showErrors=false&email=
| ETT
| envlogger_0475_3A00_FF2D_05
|
|
| https://erddap.s4raise.it/erddap/tabledap/envlogger_047E_2300_6F1E_0A.subset
| https://erddap.s4raise.it/erddap/tabledap/envlogger_047E_2300_6F1E_0A
| https://erddap.s4raise.it/erddap/tabledap/envlogger_047E_2300_6F1E_0A.graph
|
|
| Temperature data collected during Citizen Science activities using EnvLogger sensor (047E 2300 6F1E 0A)
| EnvLogger is a miniaturised temperature logger to support marine environmental research. Data is downloaded by a mobile app and RAISE developed the data processing pipeline and its automation\n\ncdm_data_type = Point\nVARIABLES:\ntime (seconds since 1970-01-01T00:00:00Z)\ntemp (Temperature, degree_Celsius)\nserial_number\nsensor_id\nlatitude (degrees_north)\nlongitude (degrees_east)\naccuracy (GPS Position Accuracy, meter)\ndepth (Sensor depth, m)\nsampling_resolution\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/envlogger_047E_2300_6F1E_0A_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/envlogger_047E_2300_6F1E_0A_iso19115.xml
| https://erddap.s4raise.it/erddap/info/envlogger_047E_2300_6F1E_0A/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/envlogger_047E_2300_6F1E_0A.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=envlogger_047E_2300_6F1E_0A&showErrors=false&email=
| ETT
| envlogger_047E_2300_6F1E_0A
|
|
| https://erddap.s4raise.it/erddap/tabledap/envlogger_0484_0C00_220F_07.subset
| https://erddap.s4raise.it/erddap/tabledap/envlogger_0484_0C00_220F_07
| https://erddap.s4raise.it/erddap/tabledap/envlogger_0484_0C00_220F_07.graph
|
|
| Temperature data collected during Citizen Science activities using EnvLogger sensor (0484 0C00 220F 07)
| EnvLogger is a miniaturised temperature logger to support marine environmental research. Data is downloaded by a mobile app and RAISE developed the data processing pipeline and its automation\n\ncdm_data_type = Point\nVARIABLES:\ntime (seconds since 1970-01-01T00:00:00Z)\ntemp (Temperature, degree_Celsius)\nserial_number\nsensor_id\nlatitude (degrees_north)\nlongitude (degrees_east)\naccuracy (GPS Position Accuracy, meter)\ndepth (Sensor depth, m)\nsampling_resolution\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/envlogger_0484_0C00_220F_07_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/envlogger_0484_0C00_220F_07_iso19115.xml
| https://erddap.s4raise.it/erddap/info/envlogger_0484_0C00_220F_07/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/envlogger_0484_0C00_220F_07.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=envlogger_0484_0C00_220F_07&showErrors=false&email=
| ETT
| envlogger_0484_0C00_220F_07
|
|
| https://erddap.s4raise.it/erddap/tabledap/smartbay_ctd-calibrated_enea.subset
| https://erddap.s4raise.it/erddap/tabledap/smartbay_ctd-calibrated_enea
| https://erddap.s4raise.it/erddap/tabledap/smartbay_ctd-calibrated_enea.graph
|
| https://erddap.s4raise.it/erddap/files/smartbay_ctd-calibrated_enea/
| The \"Smart Bay Santa Teresa Underwater Observatory\" - CDT calibrated data
| In July 2024 a preliminary real-time monitoring and transmission system based on wireless underwater networking (IoUT) has been implemented in the harbour of La Spezia, aiming to create an early warning system for temperature increase and to monitor oxygen and pH level. Currently the Smart Bay Santa Teresa Underwater Observatory is equipped with a system of transmission nodes (EMBRC-UP) connected to advanced probes (RAISE), distributed in 12 stations throughout the Gulf. Physical-chemical data (temperature, dissolved oxygen, pH, conductivity, current, turbidity, chlorophyll) are acquired with a frequency of 1 data per hour and transmitted in real time, validated with analytical approaches and weekly and monthly measurement campaigns conducted by ENEA. Biogeochemical are analytically measured (total alkalinity, pH) and derived (pCO2, saturation state, dissolved inorganic carbon) weekly and monthly, together with high precision data profiles (measured by means of a CTD probe).\n\ncdm_data_type = Point\nVARIABLES:\ntime (Date Time(UTC), seconds since 1970-01-01T00:00:00Z)\nDepth (m)\nPressure (db)\nTemperature (degrees_C)\nConductivity (Sea Water Electrical Conductivity, mS/cm)\nOxygen_mg_l (Oxygen, mg/l)\nChlorophyll (Concentration Of Chlorophyll In Sea Water, ug/l)\nTurbidity\npH_NBS\nSalinity (Sea Water Practical Salinity, PSU)\nDensity_Kg_m3 (Density, Kg/m^3)\nOxygen_ml_l (Oxygen, ml/l)\nOxygen_umol_l (Oxygen, umol/l)\nOxygen_percentage (Oxygen, %)\nDensity_Kg_m3_1000 (Density, Kg/m^3-1000))\nSound_Velocity (m/s)\npH_T\n... (7 more variables)\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/smartbay_ctd-calibrated_enea_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/smartbay_ctd-calibrated_enea_iso19115.xml
| https://erddap.s4raise.it/erddap/info/smartbay_ctd-calibrated_enea/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/smartbay_ctd-calibrated_enea.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=smartbay_ctd-calibrated_enea&showErrors=false&email=
| ENEA
| smartbay_ctd-calibrated_enea
|
|
| https://erddap.s4raise.it/erddap/tabledap/smartbay_current_enea.subset
| https://erddap.s4raise.it/erddap/tabledap/smartbay_current_enea
| https://erddap.s4raise.it/erddap/tabledap/smartbay_current_enea.graph
|
| https://erddap.s4raise.it/erddap/files/smartbay_current_enea/
| The \"Smart Bay Santa Teresa Underwater Observatory\" - Current data
| In July 2024 a preliminary real-time monitoring and transmission system based on wireless underwater networking (IoUT) has been implemented in the harbour of La Spezia, aiming to create an early warning system for temperature increase and to monitor oxygen and pH level. Currently the Smart Bay Santa Teresa Underwater Observatory is equipped with a system of transmission nodes (EMBRC-UP) connected to advanced probes (RAISE), distributed in 12 stations throughout the Gulf. Physical-chemical data (temperature, dissolved oxygen, pH, conductivity, current, turbidity, chlorophyll) are acquired with a frequency of 1 data per hour and transmitted in real time, validated with analytical approaches and weekly and monthly measurement campaigns conducted by ENEA. Biogeochemical are analytically measured (total alkalinity, pH) and derived (pCO2, saturation state, dissolved inorganic carbon) weekly and monthly, together with high precision data profiles (measured by means of a CTD probe).\n\ncdm_data_type = Point\nVARIABLES:\ntime (Date Time(UTC), seconds since 1970-01-01T00:00:00Z)\nlatitude (degrees_north)\nlongitude (degrees_east)\nTemperature (Temperature(degrees C), degrees C)\nDirection (degrees)\nVelocity (m/s)\nHeading (degrees)\nNorth_Velocity_m_s (North Velocity, m/s)\nEast_Velocity_m_s (East Velocity, m/s)\nEcho_Amplitude_dB (d B)\nEcho_Amplitude_mW (Echo Amplitude, mW)\nStation\nName\nProfondita\nBottom_depth\nDeclinazione_Magnetica\nProbe_S_N\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/smartbay_current_enea_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/smartbay_current_enea_iso19115.xml
| https://erddap.s4raise.it/erddap/info/smartbay_current_enea/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/smartbay_current_enea.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=smartbay_current_enea&showErrors=false&email=
| ENEA
| smartbay_current_enea
|
|
| https://erddap.s4raise.it/erddap/tabledap/smartbay_co2_enea.subset
| https://erddap.s4raise.it/erddap/tabledap/smartbay_co2_enea
| https://erddap.s4raise.it/erddap/tabledap/smartbay_co2_enea.graph
|
| https://erddap.s4raise.it/erddap/files/smartbay_co2_enea/
| The \"Smart Bay Santa Teresa Underwater Observatory\"- Carbon dioxide data
| In July 2024 a preliminary real-time monitoring and transmission system based on wireless underwater networking (IoUT) has been implemented in the harbour of La Spezia, aiming to create an early warning system for temperature increase and to monitor oxygen and pH level. Currently the Smart Bay Santa Teresa Underwater Observatory is equipped with a system of transmission nodes (EMBRC-UP) connected to advanced probes (RAISE), distributed in 12 stations throughout the Gulf. Physical-chemical data (temperature, dissolved oxygen, pH, conductivity, current, turbidity, chlorophyll) are acquired with a frequency of 1 data per hour and transmitted in real time, validated with analytical approaches and weekly and monthly measurement campaigns conducted by ENEA. Biogeochemical are analytically measured (total alkalinity, pH) and derived (pCO2, saturation state, dissolved inorganic carbon) weekly and monthly, together with high precision data profiles (measured by means of a CTD probe).\n\ncdm_data_type = Point\nVARIABLES:\ntime (Date Time(UTC), seconds since 1970-01-01T00:00:00Z)\nlatitude (degrees_north)\nlongitude (degrees_east)\nCO2 (ppm)\nInternal_Temperature_IRGA (degrees_C)\nRelative_Humidity\nInternal_Temperature_Sensore_Humidity (degrees_C)\nCell_Pressure (h Pa)\nBattery_Voltage (V)\npCO2_mbar (pCO2, mbar)\npCO2_Pa (pCO2, Pa)\npCO2_uatm (pCO2, uatm)\nStation\nName\nProfondita\nBottom_depth\nProbe_S_N\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/smartbay_co2_enea_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/smartbay_co2_enea_iso19115.xml
| https://erddap.s4raise.it/erddap/info/smartbay_co2_enea/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/smartbay_co2_enea.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=smartbay_co2_enea&showErrors=false&email=
| ENEA
| smartbay_co2_enea
|
| https://erddap.s4raise.it/erddap/griddap/unige-distav_voltri_water_level_scirocco
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-distav_voltri_water_level_scirocco.graph
| https://erddap.s4raise.it/erddap/wms/unige-distav_voltri_water_level_scirocco/request
| https://erddap.s4raise.it/erddap/files/unige-distav_voltri_water_level_scirocco/
| Water level considering SE storms
| The dataset was generated using the XBeach model, a numerical tool developed to simulate the impacts of extreme events on coastal areas and their associated dynamics. In this case, the model was applied to 20 historical SE storm scenarios to estimate water level under extreme conditions. The wave conditions were derived from the hindcast dataset produced by the MeteOcean research group at the University of Genoa (https://meteocean.science/#research), while the digital elevation model was constructed using a combination of field survey data and elevation data provided by Regione Liguria (https://geoportal.regione.liguria.it/). This approach makes it possible to define offshore wave conditions that may pose potential hazards to coastal infrastructure and human safety.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][latitude][longitude]):\nzs (water level, m)\nzb (bed level, m)\nue (Eulerian velocity in cell centre, x-component, m/s)\nve (Eulerian velocity in cell centre, y-component, m/s)\nH (Hrms wave height based on instantaneous wave energy, m)\nE (wave energy, Nm/m2)\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-distav_voltri_water_level_scirocco_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-distav_voltri_water_level_scirocco_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-distav_voltri_water_level_scirocco/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-distav_voltri_water_level_scirocco.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-distav_voltri_water_level_scirocco&showErrors=false&email=
| UNIGE-DISTAV
| unige-distav_voltri_water_level_scirocco
|
| https://erddap.s4raise.it/erddap/griddap/unige-distav_voltri_water_level_scirocco_setup
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-distav_voltri_water_level_scirocco_setup.graph
| https://erddap.s4raise.it/erddap/wms/unige-distav_voltri_water_level_scirocco_setup/request
| https://erddap.s4raise.it/erddap/files/unige-distav_voltri_water_level_scirocco_setup/
| Water level considering SE storms and storm surge
| The dataset was generated using the XBeach model, a numerical tool developed to simulate the impacts of extreme events on coastal areas and their associated dynamics. In this case, the model was applied to 20 historical SE storm scenarios, also considering the storm surge (wave set-up) to estimate water level under extreme conditions. The wave conditions were derived from the hindcast dataset produced by the MeteOcean research group at the University of Genoa (https://meteocean.science/#research), while the digital elevation model was constructed using a combination of field survey data and elevation data provided by Regione Liguria (https://geoportal.regione.liguria.it/). This approach makes it possible to define offshore wave conditions that may pose potential hazards to coastal infrastructure and human safety.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][latitude][longitude]):\nzs (water level, m)\nzb (bed level, m)\nue (Eulerian velocity in cell centre, x-component, m/s)\nve (Eulerian velocity in cell centre, y-component, m/s)\nH (Hrms wave height based on instantaneous wave energy, m)\nE (wave energy, Nm/m2)\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-distav_voltri_water_level_scirocco_setup_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-distav_voltri_water_level_scirocco_setup_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-distav_voltri_water_level_scirocco_setup/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-distav_voltri_water_level_scirocco_setup.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-distav_voltri_water_level_scirocco_setup&showErrors=false&email=
| UNIGE-DISTAV
| unige-distav_voltri_water_level_scirocco_setup
|
| https://erddap.s4raise.it/erddap/griddap/unige-distav_voltri_water_level_libeccio
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-distav_voltri_water_level_libeccio.graph
| https://erddap.s4raise.it/erddap/wms/unige-distav_voltri_water_level_libeccio/request
| https://erddap.s4raise.it/erddap/files/unige-distav_voltri_water_level_libeccio/
| Water level considering SW storms
| The dataset was generated using the XBeach model, a numerical tool developed to simulate the impacts of extreme events on coastal areas and their associated dynamics. In this case, the model was applied to 20 historical SW storm scenarios to estimate water level under extreme conditions. The wave conditions were derived from the hindcast dataset produced by the MeteOcean research group at the University of Genoa (https://meteocean.science/#research), while the digital elevation model was constructed using a combination of field survey data and elevation data provided by Regione Liguria (https://geoportal.regione.liguria.it/). This approach makes it possible to define offshore wave conditions that may pose potential hazards to coastal infrastructure and human safety.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][latitude][longitude]):\nzs (water level, m)\nzb (bed level, m)\nue (Eulerian velocity in cell centre, x-component, m/s)\nve (Eulerian velocity in cell centre, y-component, m/s)\nH (Hrms wave height based on instantaneous wave energy, m)\nE (wave energy, Nm/m2)\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-distav_voltri_water_level_libeccio_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-distav_voltri_water_level_libeccio_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-distav_voltri_water_level_libeccio/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-distav_voltri_water_level_libeccio.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-distav_voltri_water_level_libeccio&showErrors=false&email=
| UNIGE-DISTAV
| unige-distav_voltri_water_level_libeccio
|
| https://erddap.s4raise.it/erddap/griddap/unige-distav_voltri_water_level_libeccio_setup
|
|
| https://erddap.s4raise.it/erddap/griddap/unige-distav_voltri_water_level_libeccio_setup.graph
| https://erddap.s4raise.it/erddap/wms/unige-distav_voltri_water_level_libeccio_setup/request
| https://erddap.s4raise.it/erddap/files/unige-distav_voltri_water_level_libeccio_setup/
| Water level considering SW storms and storm surge
| The dataset was generated using the XBeach model, a numerical tool developed to simulate the impacts of extreme events on coastal areas and their associated dynamics. In this case, the model was applied to 20 historical SW storm scenarios, also considering the storm surge (wave set-up) to estimate water level under extreme conditions. The wave conditions were derived from the hindcast dataset produced by the MeteOcean research group at the University of Genoa (https://meteocean.science/#research), while the digital elevation model was constructed using a combination of field survey data and elevation data provided by Regione Liguria (https://geoportal.regione.liguria.it/). This approach makes it possible to define offshore wave conditions that may pose potential hazards to coastal infrastructure and human safety.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][latitude][longitude]):\nzs (water level, m)\nzb (bed level, m)\nue (Eulerian velocity in cell centre, x-component, m/s)\nve (Eulerian velocity in cell centre, y-component, m/s)\nH (Hrms wave height based on instantaneous wave energy, m)\nE (wave energy, Nm/m2)\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-distav_voltri_water_level_libeccio_setup_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-distav_voltri_water_level_libeccio_setup_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-distav_voltri_water_level_libeccio_setup/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-distav_voltri_water_level_libeccio_setup.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-distav_voltri_water_level_libeccio_setup&showErrors=false&email=
| UNIGE-DISTAV
| unige-distav_voltri_water_level_libeccio_setup
|
| https://erddap.s4raise.it/erddap/griddap/cmems_mod_med_wav_anfc_4_2km_PT1H_i
|
|
| https://erddap.s4raise.it/erddap/griddap/cmems_mod_med_wav_anfc_4_2km_PT1H_i.graph
| https://erddap.s4raise.it/erddap/wms/cmems_mod_med_wav_anfc_4_2km_PT1H_i/request
| https://erddap.s4raise.it/erddap/files/cmems_mod_med_wav_anfc_4_2km_PT1H_i/
| Wave fields (2D), Hourly Instantaneous
| Wave fields (2D) - Hourly Instantaneous. Please check in CMEMS catalogue the INFO section for product MEDSEA_ANALYSISFORECAST_WAV_006_017 - http://marine.copernicus.eu\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][latitude][longitude]):\nVCMX (Maximum crest trough wave height (Hc,max), m)\nVHM0 (Spectral significant wave height (Hm0), m)\nVHM0_SW1 (Spectral significant primary swell wave height, m)\nVHM0_SW2 (Spectral significant secondary swell wave height, m)\nVHM0_WW (Spectral significant wind wave height, m)\nVMDR (Mean wave direction from (Mdir), degree)\nVMDR_SW1 (Mean primary swell wave direction from, degree)\nVMDR_SW2 (Mean secondary swell wave direction from, degree)\nVMDR_WW (Mean wind wave direction from, degree)\nVMXL (Height of the highest crest, m)\nVPED (Wave principal direction at spectral peak, degree)\nVSDX (Stokes drift U, m/s)\nVSDY (Stokes drift V, m/s)\nVTM01_SW1 (Spectral moments (0,1) primary swell wave period, s)\nVTM01_SW2 (Spectral moments (0,1) secondary swell wave period, s)\nVTM01_WW (Spectral moments (0,1) wind wave period, s)\nVTM02 (Spectral moments (0,2) wave period (Tm02), s)\nVTM10 (Spectral moments (-1,0) wave period (Tm-10), s)\nVTPK (Wave period at spectral peak / peak period (Tp), s)\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/cmems_mod_med_wav_anfc_4_2km_PT1H_i_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/cmems_mod_med_wav_anfc_4_2km_PT1H_i_iso19115.xml
| https://erddap.s4raise.it/erddap/info/cmems_mod_med_wav_anfc_4_2km_PT1H_i/index.htmlTable
| ???
| https://erddap.s4raise.it/erddap/rss/cmems_mod_med_wav_anfc_4_2km_PT1H_i.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=cmems_mod_med_wav_anfc_4_2km_PT1H_i&showErrors=false&email=
| HCMR -Athens,Greece
| cmems_mod_med_wav_anfc_4_2km_PT1H_i
|
|
| https://erddap.s4raise.it/erddap/tabledap/unige-dicca_forecast_ww3_point_camogli.subset
| https://erddap.s4raise.it/erddap/tabledap/unige-dicca_forecast_ww3_point_camogli
| https://erddap.s4raise.it/erddap/tabledap/unige-dicca_forecast_ww3_point_camogli.graph
|
| https://erddap.s4raise.it/erddap/files/unige-dicca_forecast_ww3_point_camogli/
| Wave Forecast for Coastal Erosion Applications - east point
| Five days hourly forecast of ocean waves in the proximity of the Ligurian coastline for coastal risk applications- east point\n\ncdm_data_type = Grid\nVARIABLES:\ntime (seconds since 1970-01-01T00:00:00Z)\nlatitude (degrees_north)\nlongitude (degrees_east)\ndir (Wave mean direction, degree)\ndp (Peak direction, degree)\nfp (Wave peak frequency, s-1)\nhs (Significant height of wind and swell waves, m)\ntm (Mean period, s)\nforecast_date\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-dicca_forecast_ww3_point_camogli_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-dicca_forecast_ww3_point_camogli_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-dicca_forecast_ww3_point_camogli/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-dicca_forecast_ww3_point_camogli.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-dicca_forecast_ww3_point_camogli&showErrors=false&email=
| UNIGE-DICCA
| unige-dicca_forecast_ww3_point_camogli
|
|
| https://erddap.s4raise.it/erddap/tabledap/unige-dicca_forecast_ww3_point_ovest.subset
| https://erddap.s4raise.it/erddap/tabledap/unige-dicca_forecast_ww3_point_ovest
| https://erddap.s4raise.it/erddap/tabledap/unige-dicca_forecast_ww3_point_ovest.graph
|
| https://erddap.s4raise.it/erddap/files/unige-dicca_forecast_ww3_point_ovest/
| Wave Forecast for Coastal Erosion Applications - ovest point
| Five days hourly forecast of ocean waves in the proximity of the Ligurian coastline for coastal risk applications- ovest point\n\ncdm_data_type = Grid\nVARIABLES:\ntime (seconds since 1970-01-01T00:00:00Z)\nlatitude (degrees_north)\nlongitude (degrees_east)\ndir (Wave mean direction, degree)\ndp (Peak direction, degree)\nfp (Wave peak frequency, s-1)\nhs (Significant height of wind and swell waves, m)\ntm (Mean period, s)\nforecast_date\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/unige-dicca_forecast_ww3_point_ovest_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/unige-dicca_forecast_ww3_point_ovest_iso19115.xml
| https://erddap.s4raise.it/erddap/info/unige-dicca_forecast_ww3_point_ovest/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/unige-dicca_forecast_ww3_point_ovest.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=unige-dicca_forecast_ww3_point_ovest&showErrors=false&email=
| UNIGE-DICCA
| unige-dicca_forecast_ww3_point_ovest
|
| https://erddap.s4raise.it/erddap/griddap/cima_forecast_1_5km_01
|
|
| https://erddap.s4raise.it/erddap/griddap/cima_forecast_1_5km_01.graph
| https://erddap.s4raise.it/erddap/wms/cima_forecast_1_5km_01/request
|
| WRF (Weather Research and Forecasting Model) 1.5 km (01)
| WRF-1.5km OL: Open loop configuration (without data assimilation) with 3 two-way nested domains respectively having spatial resolution 13.5, 4.5 and 1.5 km with 50 vertical levels. The analysis and boundary data (hourly frequency) data are obtained from the Global Forecasting System (GFS) model at 0.25 degrees of resolution. One run per day (00 UTC) is made with the GFS data with a forecast time horizon of 48 hours to have 2 full days of forecasting (hourly time resolution). This forecast is performed on computing resources at CINECA (about 1600 cores) and is delivered to within 7:00 UTC.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][latitude][longitude]):\nQ2 (kg kg-1)\nT2 (K)\nTH2 (K)\nPSFC (Pa)\nU10 (Eastward Wind Component, m s-1)\nV10 (Northward Wind Component, m s-1)\nLPI (m^2 s-2)\nACSNOW (kg m-2)\nRAINC (mm)\nRAINNC (mm)\nSNOWNC (mm)\nGRAUPELNC (mm)\nHAILNC (mm)\nSWDOWN (W m-2)\nSWDOWNC (W m-2)\nPBLH (m)\nHFX (W m-2)\nQFX (kg m-2 s-1)\nLH (W m-2)\nWSPD10MAX (WSPD10 MAX, m s-1)\nW_UP_MAX (m s-1)\n... (10 more variables)\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/cima_forecast_1_5km_01_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/cima_forecast_1_5km_01_iso19115.xml
| https://erddap.s4raise.it/erddap/info/cima_forecast_1_5km_01/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/cima_forecast_1_5km_01.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=cima_forecast_1_5km_01&showErrors=false&email=
| CIMA
| cima_forecast_1_5km_01
|
| https://erddap.s4raise.it/erddap/griddap/cima_forecast_1_5km_02
|
|
| https://erddap.s4raise.it/erddap/griddap/cima_forecast_1_5km_02.graph
| https://erddap.s4raise.it/erddap/wms/cima_forecast_1_5km_02/request
|
| WRF (Weather Research and Forecasting Model) 1.5 km (02)
| WRF-1.5km OL: Open loop configuration (without data assimilation) with 3 two-way nested domains respectively having spatial resolution 13.5, 4.5 and 1.5 km with 50 vertical levels. The analysis and boundary data (hourly frequency) data are obtained from the Global Forecasting System (GFS) model at 0.25 degrees of resolution. One run per day (00 UTC) is made with the GFS data with a forecast time horizon of 48 hours to have 2 full days of forecasting (hourly time resolution). This forecast is performed on computing resources at CINECA (about 1600 cores) and is delivered to within 7:00 UTC.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][lev][latitude][longitude]):\nU_PL (m s-1)\nV_PL (m s-1)\nT_PL (K)\nRH_PL (Relative Humidity, percent)\nGHT_PL (m)\nS_PL (m s-1)\nTD_PL (K)\nQ_PL (kg/kg)\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/cima_forecast_1_5km_02_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/cima_forecast_1_5km_02_iso19115.xml
| https://erddap.s4raise.it/erddap/info/cima_forecast_1_5km_02/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/cima_forecast_1_5km_02.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=cima_forecast_1_5km_02&showErrors=false&email=
| CIMA
| cima_forecast_1_5km_02
|
| https://erddap.s4raise.it/erddap/griddap/cima_forecast_2_5km_01
|
|
| https://erddap.s4raise.it/erddap/griddap/cima_forecast_2_5km_01.graph
| https://erddap.s4raise.it/erddap/wms/cima_forecast_2_5km_01/request
|
| WRF (Weather Research and Forecasting Model) 2.5 km including 3DVAR assimilation (radar data) (01)
| Configuration with 3DVAR variational assimilation with 3 two-way nested domains respectively with spatial resolution 22.5, 7.5 and 2.5 km with 50 vertical levels. The analysis data and boundary conditions (with tri-hourly frequency) are obtained from the GFS model at 0.25 degrees of resolution. This forecast is performed on computing resources at CIMA and is delivered within 3:30 UTC. The assimilation scheme is performed as it follows: WRF-2.5 km is initialized with the GFS model of the 18UTC, whose analysis is integrated, by means of 3DVAR, by CAPPI radar remote sensing data of the Italian Civil Protection Department (ICPD). The WRF model is thus executed for 3 hours until 21UTC, when a second 3DVAR assimilation cycle is applied. Finally, the WRF model is executed until 00UTC when the final assimilation cycle is performed. The simulation is then carried out for a further 48 hours starting from 00UTC in order to have 2 complete days of forecasting.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][latitude][longitude]):\nQ2 (kg kg-1)\nT2 (K)\nTH2 (K)\nPSFC (Pa)\nU10 (Eastward Wind Component, m s-1)\nV10 (Northward Wind Component, m s-1)\nLPI (m^2 s-2)\nACSNOW (kg m-2)\nRAINC (mm)\nRAINNC (mm)\nSNOWNC (mm)\nGRAUPELNC (mm)\nHAILNC (mm)\nSWDOWN (W m-2)\nSWDOWNC (W m-2)\nPBLH (m)\nHFX (W m-2)\nQFX (kg m-2 s-1)\n... (11 more variables)\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/cima_forecast_2_5km_01_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/cima_forecast_2_5km_01_iso19115.xml
| https://erddap.s4raise.it/erddap/info/cima_forecast_2_5km_01/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/cima_forecast_2_5km_01.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=cima_forecast_2_5km_01&showErrors=false&email=
| CIMA
| cima_forecast_2_5km_01
|
| https://erddap.s4raise.it/erddap/griddap/cima_forecast_2_5km_02
|
|
| https://erddap.s4raise.it/erddap/griddap/cima_forecast_2_5km_02.graph
| https://erddap.s4raise.it/erddap/wms/cima_forecast_2_5km_02/request
|
| WRF (Weather Research and Forecasting Model) 2.5 km including 3DVAR assimilation (radar data) (02)
| Configuration with 3DVAR variational assimilation with 3 two-way nested domains respectively with spatial resolution 22.5, 7.5 and 2.5 km with 50 vertical levels. The analysis data and boundary conditions (with tri-hourly frequency) are obtained from the GFS model at 0.25 degrees of resolution. This forecast is performed on computing resources at CIMA and is delivered within 3:30 UTC. The assimilation scheme is performed as it follows: WRF-2.5 km is initialized with the GFS model of the 18UTC, whose analysis is integrated, by means of 3DVAR, by CAPPI radar remote sensing data of the Italian Civil Protection Department (ICPD). The WRF model is thus executed for 3 hours until 21UTC, when a second 3DVAR assimilation cycle is applied. Finally, the WRF model is executed until 00UTC when the final assimilation cycle is performed. The simulation is then carried out for a further 48 hours starting from 00UTC in order to have 2 complete days of forecasting.\n\ncdm_data_type = Grid\nVARIABLES (all of which use the dimensions [time][lev][latitude][longitude]):\nU_PL (m s-1)\nV_PL (m s-1)\nT_PL (K)\nRH_PL (Relative Humidity, percent)\nGHT_PL (m)\nS_PL (m s-1)\nTD_PL (K)\nQ_PL (kg/kg)\n
| https://erddap.s4raise.it/erddap/metadata/fgdc/xml/cima_forecast_2_5km_02_fgdc.xml
| https://erddap.s4raise.it/erddap/metadata/iso19115/xml/cima_forecast_2_5km_02_iso19115.xml
| https://erddap.s4raise.it/erddap/info/cima_forecast_2_5km_02/index.htmlTable
| https://www.raiseliguria.it/spoke-3/
| https://erddap.s4raise.it/erddap/rss/cima_forecast_2_5km_02.rss
| https://erddap.s4raise.it/erddap/subscriptions/add.html?datasetID=cima_forecast_2_5km_02&showErrors=false&email=
| CIMA
| cima_forecast_2_5km_02
|
