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.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][y][x]):
Convective_precipitation_surface_Mixed_intervals_Accumulation (Convective precipitation (Mixed_intervals Accumulation) @ Ground or water surface, kg.m-2)
Evaporation_surface_Mixed_intervals_Accumulation (Evaporation (Mixed_intervals Accumulation) @ Ground or water surface, kg.m-2)
Total_precipitation_surface_Mixed_intervals_Accumulation (Total precipitation (Mixed_intervals Accumulation) @ Ground or water surface, kg.m-2)


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.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][y][x]):
Convective_available_potential_energy_surface (Convective available potential energy @ Ground or water surface, J/kg)
Convective_inhibition_surface (Convective inhibition @ Ground or water surface, J/kg)
Downward_long_wave_radiation_flux_surface (Downward long-wave radiation flux @ Ground or water surface, W.m-2)
Downward_short_wave_radiation_flux_surface (Downward short-wave radiation flux @ Ground or water surface, W.m-2)
Frictional_velocity_surface (Frictional velocity @ Ground or water surface, m/s)
Geopotential_height_adiabatic_condensation_lifted (Geopotential height @ Level of adiabatic condensation lifted from the surface, gpm)
Geopotential_height_cloud_base (Geopotential height @ Cloud base level, gpm)
Geopotential_height_cloud_ceiling (Geopotential height @ Cloud ceiling, gpm)
Geopotential_height_cloud_tops (Geopotential height @ Level of cloud tops, gpm)
Geopotential_height_highest_tropospheric_freezing (Geopotential height @ Highest tropospheric freezing level, gpm)
Geopotential_height_surface (Geopotential height @ Ground or water surface, gpm)
Geopotential_height_zeroDegC_isotherm (Geopotential height @ Level of 0 °C isotherm, gpm)
Land_cover_0_sea_1_land_surface (Land cover (0 = sea, 1 = land) @ Ground or water surface)
Latent_heat_net_flux_surface (Latent heat net flux @ Ground or water surface, W.m-2)
MSLP_Eta_model_reduction_msl (MSLP (Eta model reduction) @ Mean sea level, Pa)
Planetary_boundary_layer_height_surface (Planetary boundary layer height @ Ground or water surface, m)
Precipitable_water_surface_layer (Precipitable water @ Ground or water surface layer, kg.m-2)
Precipitation_rate_surface (Precipitation rate @ Ground or water surface, kg.m-2.s-1)
Pressure_cloud_base (Pressure @ Cloud base level, Pa)
Pressure_reduced_to_MSL_msl (Pressure reduced to MSL @ Mean sea level, Pa)
Pressure_surface (Pressure @ Ground or water surface, Pa)
Sensible_heat_net_flux_surface (Sensible heat net flux @ Ground or water surface, W.m-2)
Snow_depth_surface (Snow depth @ Ground or water surface, m)
Snow_free_albedo_surface (Snow free albedo @ Ground or water surface, percent)
Temperature_surface (Temperature @ Ground or water surface, K)
Total_cloud_cover_surface_layer (Total cloud cover @ Ground or water surface layer, percent)
... (5 more variables)


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.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][altitude][y][x]):
u_component_of_wind_height_above_ground (u-component of wind @ Specified height level above ground, m/s)
v_component_of_wind_height_above_ground (v-component of wind @ Specified height level above ground, m/s)


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.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][altitude][y][x]):
Dewpoint_temperature_height_above_ground (Dewpoint temperature @ Specified height level above ground, K)
Relative_humidity_height_above_ground (Relative humidity @ Specified height level above ground, percent)
Specific_humidity_height_above_ground (Specific humidity @ Specified height level above ground, kg/kg)


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.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][altitude][y][x]):
Temperature_height_above_ground (Temperature @ Specified height level above ground, K)


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.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][hybrid][y][x]):
Geopotential_height_hybrid (Geopotential height @ Hybrid level, gpm)
u_component_of_wind_hybrid (u-component of wind @ Hybrid level, m/s)
v_component_of_wind_hybrid (v-component of wind @ Hybrid level, m/s)


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.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][isobaric][y][x]):
Geopotential_height_isobaric (Geopotential height @ Isobaric surface, gpm)
Relative_humidity_isobaric (Relative humidity @ Isobaric surface, percent)
Temperature_isobaric (Temperature @ Isobaric surface, K)
Vertical_velocity_pressure_isobaric (Vertical velocity (pressure) @ Isobaric surface, Pa/s)
u_component_of_wind_isobaric (u-component of wind @ Isobaric surface, m/s)
v_component_of_wind_isobaric (v-component of wind @ Isobaric surface, m/s)


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.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][isobaric1][y][x]):
Absolute_vorticity_isobaric (Absolute vorticity @ Isobaric surface, 1/s)


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.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][pressure_difference_layer][y][x]):
u_component_of_wind_pressure_difference_layer (u-component of wind @ Level at specified pressure difference from ground to level layer, m/s)
v_component_of_wind_pressure_difference_layer (v-component of wind @ Level at specified pressure difference from ground to level layer, m/s)


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.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][altitude][y][x]):
Snow_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)


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.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][y][x]):
Total_precipitation_surface_3_Hour_Accumulation (Total precipitation (3 Hours Accumulation) @ Ground or water surface, kg.m-2)
Total_precipitation_surface_Mixed_intervals_Accumulation (Total precipitation (Mixed_intervals Accumulation) @ Ground or water surface, kg.m-2)


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.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][y][x]):
Convective_precipitation_surface_Mixed_intervals_Accumulation (Convective precipitation (Mixed_intervals Accumulation) @ Ground or water surface, kg.m-2)
Evaporation_surface_Mixed_intervals_Accumulation (Evaporation (Mixed_intervals Accumulation) @ Ground or water surface, kg.m-2)
Total_precipitation_surface_Mixed_intervals_Accumulation (Total precipitation (Mixed_intervals Accumulation) @ Ground or water surface, kg.m-2)


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.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][y][x]):
Convective_available_potential_energy_surface (Convective available potential energy @ Ground or water surface, J/kg)
Convective_inhibition_surface (Convective inhibition @ Ground or water surface, J/kg)
Downward_long_wave_radiation_flux_surface (Downward long-wave radiation flux @ Ground or water surface, W.m-2)
Downward_short_wave_radiation_flux_surface (Downward short-wave radiation flux @ Ground or water surface, W.m-2)
Frictional_velocity_surface (Frictional velocity @ Ground or water surface, m/s)
Geopotential_height_adiabatic_condensation_lifted (Geopotential height @ Level of adiabatic condensation lifted from the surface, gpm)
Geopotential_height_cloud_base (Geopotential height @ Cloud base level, gpm)
Geopotential_height_cloud_ceiling (Geopotential height @ Cloud ceiling, gpm)
Geopotential_height_cloud_tops (Geopotential height @ Level of cloud tops, gpm)
Geopotential_height_highest_tropospheric_freezing (Geopotential height @ Highest tropospheric freezing level, gpm)
Geopotential_height_surface (Geopotential height @ Ground or water surface, gpm)
Geopotential_height_zeroDegC_isotherm (Geopotential height @ Level of 0 °C isotherm, gpm)
Land_cover_0_sea_1_land_surface (Land cover (0 = sea, 1 = land) @ Ground or water surface)
Latent_heat_net_flux_surface (Latent heat net flux @ Ground or water surface, W.m-2)
MSLP_Eta_model_reduction_msl (MSLP (Eta model reduction) @ Mean sea level, Pa)
Planetary_boundary_layer_height_surface (Planetary boundary layer height @ Ground or water surface, m)
Precipitable_water_surface_layer (Precipitable water @ Ground or water surface layer, kg.m-2)
Precipitation_rate_surface (Precipitation rate @ Ground or water surface, kg.m-2.s-1)
Pressure_cloud_base (Pressure @ Cloud base level, Pa)
Pressure_reduced_to_MSL_msl (Pressure reduced to MSL @ Mean sea level, Pa)
Pressure_surface (Pressure @ Ground or water surface, Pa)
Sensible_heat_net_flux_surface (Sensible heat net flux @ Ground or water surface, W.m-2)
Snow_depth_surface (Snow depth @ Ground or water surface, m)
Snow_free_albedo_surface (Snow free albedo @ Ground or water surface, percent)
Temperature_surface (Temperature @ Ground or water surface, K)
Total_cloud_cover_surface_layer (Total cloud cover @ Ground or water surface layer, percent)
... (5 more variables)


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.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][altitude][y][x]):
u_component_of_wind_height_above_ground (u-component of wind @ Specified height level above ground, m/s)
v_component_of_wind_height_above_ground (v-component of wind @ Specified height level above ground, m/s)


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.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][altitude][y][x]):
Dewpoint_temperature_height_above_ground (Dewpoint temperature @ Specified height level above ground, K)
Relative_humidity_height_above_ground (Relative humidity @ Specified height level above ground, percent)
Specific_humidity_height_above_ground (Specific humidity @ Specified height level above ground, kg/kg)


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.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][altitude][y][x]):
Temperature_height_above_ground (Temperature @ Specified height level above ground, K)


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.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][hybrid][y][x]):
Geopotential_height_hybrid (Geopotential height @ Hybrid level, gpm)
u_component_of_wind_hybrid (u-component of wind @ Hybrid level, m/s)
v_component_of_wind_hybrid (v-component of wind @ Hybrid level, m/s)


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.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][isobaric][y][x]):
Geopotential_height_isobaric (Geopotential height @ Isobaric surface, gpm)
Relative_humidity_isobaric (Relative humidity @ Isobaric surface, percent)
Temperature_isobaric (Temperature @ Isobaric surface, K)
Vertical_velocity_pressure_isobaric (Vertical velocity (pressure) @ Isobaric surface, Pa/s)
u_component_of_wind_isobaric (u-component of wind @ Isobaric surface, m/s)
v_component_of_wind_isobaric (v-component of wind @ Isobaric surface, m/s)


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.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][isobaric1][y][x]):
Absolute_vorticity_isobaric (Absolute vorticity @ Isobaric surface, 1/s)


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.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][pressure_difference_layer][y][x]):
u_component_of_wind_pressure_difference_layer (u-component of wind @ Level at specified pressure difference from ground to level layer, m/s)
v_component_of_wind_pressure_difference_layer (v-component of wind @ Level at specified pressure difference from ground to level layer, m/s)


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.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][altitude][y][x]):
Snow_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)


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.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][y][x]):
Total_precipitation_surface_3_Hour_Accumulation (Total precipitation (3 Hours Accumulation) @ Ground or water surface, kg.m-2)
Total_precipitation_surface_Mixed_intervals_Accumulation (Total precipitation (Mixed_intervals Accumulation) @ Ground or water surface, kg.m-2)


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.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][y][x]):
Convective_precipitation_surface_Mixed_intervals_Accumulation (Convective precipitation (Mixed_intervals Accumulation) @ Ground or water surface, kg.m-2)
Evaporation_surface_Mixed_intervals_Accumulation (Evaporation (Mixed_intervals Accumulation) @ Ground or water surface, kg.m-2)
Total_precipitation_surface_Mixed_intervals_Accumulation (Total precipitation (Mixed_intervals Accumulation) @ Ground or water surface, kg.m-2)


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.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][altitude][y][x]):
Snow_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)


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.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][y][x]):
Convective_available_potential_energy_surface (Convective available potential energy @ Ground or water surface, J/kg)
Convective_inhibition_surface (Convective inhibition @ Ground or water surface, J/kg)
Downward_long_wave_radiation_flux_surface (Downward long-wave radiation flux @ Ground or water surface, W.m-2)
Downward_short_wave_radiation_flux_surface (Downward short-wave radiation flux @ Ground or water surface, W.m-2)
Frictional_velocity_surface (Frictional velocity @ Ground or water surface, m/s)
Geopotential_height_adiabatic_condensation_lifted (Geopotential height @ Level of adiabatic condensation lifted from the surface, gpm)
Geopotential_height_cloud_base (Geopotential height @ Cloud base level, gpm)
Geopotential_height_cloud_ceiling (Geopotential height @ Cloud ceiling, gpm)
Geopotential_height_cloud_tops (Geopotential height @ Level of cloud tops, gpm)
Geopotential_height_highest_tropospheric_freezing (Geopotential height @ Highest tropospheric freezing level, gpm)
Geopotential_height_surface (Geopotential height @ Ground or water surface, gpm)
Geopotential_height_zeroDegC_isotherm (Geopotential height @ Level of 0 °C isotherm, gpm)
Land_cover_0_sea_1_land_surface (Land cover (0 = sea, 1 = land) @ Ground or water surface)
Latent_heat_net_flux_surface (Latent heat net flux @ Ground or water surface, W.m-2)
MSLP_Eta_model_reduction_msl (MSLP (Eta model reduction) @ Mean sea level, Pa)
Planetary_boundary_layer_height_surface (Planetary boundary layer height @ Ground or water surface, m)
Precipitable_water_surface_layer (Precipitable water @ Ground or water surface layer, kg.m-2)
Precipitation_rate_surface (Precipitation rate @ Ground or water surface, kg.m-2.s-1)
Pressure_cloud_base (Pressure @ Cloud base level, Pa)
Pressure_reduced_to_MSL_msl (Pressure reduced to MSL @ Mean sea level, Pa)
Pressure_surface (Pressure @ Ground or water surface, Pa)
Sensible_heat_net_flux_surface (Sensible heat net flux @ Ground or water surface, W.m-2)
Snow_depth_surface (Snow depth @ Ground or water surface, m)
Snow_free_albedo_surface (Snow free albedo @ Ground or water surface, percent)
Temperature_surface (Temperature @ Ground or water surface, K)
Total_cloud_cover_surface_layer (Total cloud cover @ Ground or water surface layer, percent)
... (5 more variables)


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.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][altitude][y][x]):
u_component_of_wind_height_above_ground (u-component of wind @ Specified height level above ground, m/s)
v_component_of_wind_height_above_ground (v-component of wind @ Specified height level above ground, m/s)


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.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][altitude][y][x]):
Dewpoint_temperature_height_above_ground (Dewpoint temperature @ Specified height level above ground, K)
Relative_humidity_height_above_ground (Relative humidity @ Specified height level above ground, percent)
Specific_humidity_height_above_ground (Specific humidity @ Specified height level above ground, kg/kg)


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.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][altitude][y][x]):
Temperature_height_above_ground (Temperature @ Specified height level above ground, K)


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.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][hybrid][y][x]):
Geopotential_height_hybrid (Geopotential height @ Hybrid level, gpm)
u_component_of_wind_hybrid (u-component of wind @ Hybrid level, m/s)
v_component_of_wind_hybrid (v-component of wind @ Hybrid level, m/s)


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.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][isobaric][y][x]):
Geopotential_height_isobaric (Geopotential height @ Isobaric surface, gpm)
Relative_humidity_isobaric (Relative humidity @ Isobaric surface, percent)
Temperature_isobaric (Temperature @ Isobaric surface, K)
Vertical_velocity_pressure_isobaric (Vertical velocity (pressure) @ Isobaric surface, Pa/s)
u_component_of_wind_isobaric (u-component of wind @ Isobaric surface, m/s)
v_component_of_wind_isobaric (v-component of wind @ Isobaric surface, m/s)


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.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][isobaric1][y][x]):
Absolute_vorticity_isobaric (Absolute vorticity @ Isobaric surface, 1/s)


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.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][pressure_difference_layer][y][x]):
u_component_of_wind_pressure_difference_layer (u-component of wind @ Level at specified pressure difference from ground to level layer, m/s)
v_component_of_wind_pressure_difference_layer (v-component of wind @ Level at specified pressure difference from ground to level layer, m/s)


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.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][y][x]):
Total_precipitation_surface_3_Hour_Accumulation (Total precipitation (3 Hours Accumulation) @ Ground or water surface, kg.m-2)
Total_precipitation_surface_Mixed_intervals_Accumulation (Total precipitation (Mixed_intervals Accumulation) @ Ground or water surface, kg.m-2)


CMEMS HR-OC Mediterranean Sea transparency (spm, tur) and geophysical (chl) daily observations mosaic

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
CHL (Chlorophyll-a concentration derived from MSI L2R using HR-OC L2W processor, mg m-3)

Brockmann Consult GmbH, RBINS, VITO for CMEMS, Mercator Ocean


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.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [latitude][longitude]):
estimated_chl


Estimated chlorophyall-a concentration at 60 m spatial resolution (20180427)


Estimated chlorophyall-a concentration at 60 m spatial resolution (20181128)


Estimated chlorophyall-a concentration at 60 m spatial resolution (20190221)


Estimated chlorophyall-a concentration at 60 m spatial resolution (20190417)


Estimated chlorophyall-a concentration at 60 m spatial resolution (20190726)


Estimated chlorophyall-a concentration at 60 m spatial resolution (20200206)


Estimated chlorophyall-a concentration at 60 m spatial resolution (20200710)


Estimated chlorophyall-a concentration at 60 m spatial resolution (20220307)


Estimated chlorophyall-a concentration at 60 m spatial resolution (20220411)


Estimated chlorophyall-a concentration at 60 m spatial resolution (20220511)


Estimated chlorophyall-a concentration at 60 m spatial resolution (20220824)


Estimated chlorophyall-a concentration at 60 m spatial resolution (20221028)


Estimated chlorophyall-a concentration at 60 m spatial resolution (20230506)


Estimated chlorophyall-a concentration at 60 m spatial resolution (20230526)


Estimated chlorophyall-a concentration at 60 m spatial resolution (20231207)


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.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [latitude][longitude]):
estimated_chl


Estimated chlorophyall-a concentration at 60 m spatial resolution (20240604)


Estimated chlorophyall-a concentration at 60 m spatial resolution (20240719)


Estimated chlorophyall-a concentration at 60 m spatial resolution (20240729)


Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20180323)


Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20180427)


Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20181128)


Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20190221)


Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20190417)


Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20190726)


Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20200206)


Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20200710)


Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20220307)


Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20220411)


Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20220511)


Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20220824)


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.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [latitude][longitude]):
estimated_chl


Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20230506)


Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20230526)


Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20231207)


Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20240510)


Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20240604)


Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20240719)


Estimated chlorophyll-a concentration at 60 m spatial resolution - Mondrian forest (20240729)


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

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [latitude][longitude]):
estimated_sst


Estimated sea surface temperature at 1 km spatial resolution (20220411T092437Z)


Estimated sea surface temperature at 1 km spatial resolution (20220428T094504Z)


Estimated sea surface temperature at 1 km spatial resolution (20220510T101316Z)


Estimated sea surface temperature at 1 km spatial resolution (20220511T094705Z)


Estimated sea surface temperature at 1 km spatial resolution (20220701T092437Z)


Estimated sea surface temperature at 1 km spatial resolution (20220716T093548Z)


Estimated sea surface temperature at 1 km spatial resolution (20220719T091905Z)


Estimated sea surface temperature at 1 km spatial resolution (20220719T095813Z)


Estimated sea surface temperature at 1 km spatial resolution (20220824T092429Z)


Estimated sea surface temperature at 1 km spatial resolution (20220913T090551Z)


Estimated sea surface temperature at 1 km spatial resolution (20221005T093545Z)


Estimated sea surface temperature at 1 km spatial resolution (20221007T094510Z)


Estimated sea surface temperature at 1 km spatial resolution (20221028T093930Z)


Estimated sea surface temperature at 1 km spatial resolution (20221111T101653Z)


Estimated sea surface temperature at 1 km spatial resolution (20230213T093929Z)


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

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [latitude][longitude]):
estimated_sst


Estimated sea surface temperature at 1 km spatial resolution (20230417T090555Z)


Estimated sea surface temperature at 1 km spatial resolution (20230505T093934Z)


Estimated sea surface temperature at 1 km spatial resolution (20230523T101313Z)


Estimated sea surface temperature at 1 km spatial resolution (20230524T094702Z)


Estimated sea surface temperature at 1 km spatial resolution (20230626T095250Z)


Estimated sea surface temperature at 1 km spatial resolution (20230711T100406Z)


Estimated sea surface temperature at 1 km spatial resolution (20230823T094909Z)


Estimated sea surface temperature at 1 km spatial resolution (20230927T094136Z)


Estimated sea surface temperature at 1 km spatial resolution (20231009T093025Z)


Estimated sea surface temperature at 1 km spatial resolution (20231009T100922Z)


Estimated sea surface temperature at 1 km spatial resolution (20231207T093924Z)


Estimated sea surface temperature at 1 km spatial resolution (20240221T100924Z)


Estimated sea surface temperature at 1 km spatial resolution (20240307T094141Z)


Estimated sea surface temperature at 1 km spatial resolution (20240424T093547Z)


Estimated sea surface temperature at 1 km spatial resolution (20240527T102041Z)


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

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [latitude][longitude]):
estimated_sst


Estimated sea surface temperature at 1 km spatial resolution (20240618T101146Z)


Estimated sea surface temperature at 1 km spatial resolution (20240719T090552Z)


Estimated sea surface temperature at 1 km spatial resolution (20240719T100805Z)


Estimated sea surface temperature at 1 km spatial resolution (20240729T090814Z)


Estimated sea surface temperature at 1 km spatial resolution (20240729T094659Z)


Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20220307T093204Z)


Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20220411T092437Z)


Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20220428T094504Z)


Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20220510T101316Z)


Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20220511T094705Z)


Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20220701T092437Z)


Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20220716T093548Z)


Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20220719T095813Z)


Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20220824T092429Z)


Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20220913T090551Z)


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

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [latitude][longitude]):
estimated_sst


Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20221007T094510Z)


Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20221028T093930Z)


Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20221111T101653Z)


Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20230213T093929Z)


Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20230304T094657Z)


Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20230417T090555Z)


Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20230505T093934Z)


Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20230523T101313Z)


Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20230524T094702Z)


Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20230626T095250Z)


Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20230711T100406Z)


Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20230823T094909Z)


Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20230927T094136Z)


Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20231009T093025Z)


Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20231009T100922Z)


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

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [latitude][longitude]):
estimated_sst


Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20240221T100924Z)


Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20240307T094141Z)


Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20240424T093547Z)


Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20240527T102041Z)


Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20240607T095649Z)


Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20240618T101146Z)


Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20240719T090552Z)


Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20240719T100805Z)


Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20240729T090814Z)


Estimated sea surface temperature at 1 km spatial resolution - Mondrian forest (20240729T094659Z)


Global Forecast System (GFS) model

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
Downward_Long_Wave_Radp_Flux_surface_Mixed_intervals_Average (Downward Long-Wave Rad. Flux (Mixed_intervals Average) @ Ground or water surface, W.m-2)
Upward_Long_Wave_Radp_Flux_surface_Mixed_intervals_Average (Upward Long-Wave Rad. Flux (Mixed_intervals Average) @ Ground or water surface, W.m-2)
Upward_Short_Wave_Radiation_Flux_surface_Mixed_intervals_Average (Upward Short-Wave Radiation Flux (Mixed_intervals Average) @ Ground or water surface, W.m-2)
Downward_Short_Wave_Radiation_Flux_surface_Mixed_intervals_Average (Downward Short-Wave Radiation Flux (Mixed_intervals Average) @ Ground or water surface, W.m-2)


Global Forecast System (GFS) model

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
Geopotential_height_surface (Geopotential height @ Ground or water surface, gpm)
Pressure_reduced_to_MSL_msl (Pressure reduced to MSL @ Mean sea level, Pa)
Pressure_surface (Pressure @ Ground or water surface, Pa)
Temperature_surface (Temperature @ Ground or water surface, K)
Water_equivalent_of_accumulated_snow_depth_surface (Water equivalent of accumulated snow depth @ Ground or water surface, kg.m-2)


Global Forecast System (GFS) model

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][altitude][latitude][longitude]):
Temperature_height_above_ground (Temperature @ Specified height level above ground, K)


Global Forecast System (GFS) model

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][altitude][latitude][longitude]):
u_component_of_wind_height_above_ground (u-component of wind @ Specified height level above ground, m/s)
v_component_of_wind_height_above_ground (v-component of wind @ Specified height level above ground, m/s)


Global Forecast System (GFS) model

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][altitude][latitude][longitude]):
Relative_humidity_height_above_ground (Relative humidity @ Specified height level above ground, percent)


Global Forecast System (GFS) model - Relative humidity at ground level

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
Relative_humidity_height_above_ground


Global Forecast System (GFS) model - Temperature  heght above ground

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
Temperature_height_above_ground


Global Forecast System (GFS) model - Temperature isobaric

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][isobaric][latitude][longitude]):
Temperature_isobaric (K)


Global Forecast System (GFS) model - Wind

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
eastward_component_of_wind_height_above_ground (m/s)
northward_component_of_wind_height_above_ground (m/s)


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 .

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][depth][latitude][longitude]):
EWCT (West-east current component, m s-1)
NSCT (South-north current component, m s-1)
EWCS (Standard deviation of surface eastward sea water velocity, m s-1)
NSCS (Standard deviation of surface northward sea water velocity, m s-1)
CCOV (Covariance of surface sea water velocity, m2 s-2)
GDOP (Geometrical dilution of precision, 1)
POSITION_QC (Position quality flag, 1)
QCflag (Overall quality flag, 1)
VART_QC (Variance threshold quality flag, 1)
GDOP_QC (GDOP threshold quality flag, 1)
DDNS_QC (Data density threshold quality flag, 1)
CSPD_QC (Velocity threshold quality flag, 1)


Horizontal Velocity (3D) - Hourly Mean. Please check in CMEMS catalogue the INFO section for product MEDSEA_ANALYSISFORECAST_PHY_006_013 - http://marine.copernicus.eu

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][depth][latitude][longitude]):
uo (eastward ocean current velocity, m s-1)
vo (northward ocean current velocity, m s-1)

Centro Euro-Mediterraneo sui Cambiamenti Climatici - CMCC, Italy


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.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
zs (water level, m)
zb (bed level, m)
ue (Eulerian velocity in cell centre, x-component, m/s)
ve (Eulerian velocity in cell centre, y-component, m/s)
H (Hrms wave height based on instantaneous wave energy, m)
E (wave energy, Nm/m2)
L1 (wave length (used in dispersion relation), m)
Qb (fraction breaking waves)
sedero (cum. sedimentation/erosion, m)
thetamean (mean wave angle, rad)


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.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
zs (water level, m)
zb (bed level, m)
ue (Eulerian velocity in cell centre, x-component, m/s)
ve (Eulerian velocity in cell centre, y-component, m/s)
H (Hrms wave height based on instantaneous wave energy, m)
E (wave energy, Nm/m2)
L1 (wave length (used in dispersion relation), m)
Qb (fraction breaking waves)
sedero (cum. sedimentation/erosion, m)
thetamean (mean wave angle, rad)


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

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [latitude][longitude][time]):
hs (significant height of wind and swell waves, m)
fp (wave peak frequency, s-1)
dir (wave mean direction, degree)
dp (peak direction, degree)
tm (mean period, s)
uwnd (Eastward Wind, m s-1)
vwnd (Northward Wind, m s-1)


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/

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][depth][latitude][longitude]):
chl (Chlorophyll, mg m-3)
diatoC (Diatoms Carbon Biomass, MMol' 'M-3)
diatoChla (Diatoms Chlorophyll concentration, mg m-3)
dinoC (Dinoflagellates Carbon Biomass, MMol' 'M-3)
dinoChla (Dinoflagellates Chlorophyll concentration, mg m-3)
nanoC (Nanophytoplankton Carbon Biomass, MMol' 'M-3)
nanoChla (Nanophytoplankton Chlorophyll concentration, mg m-3)
phyc (Phytoplankton Carbon Biomass, MMol' 'M-3)
picoC (Picophytoplankton Carbon Biomass, MMol' 'M-3)
picoChla (Picophytoplankton Chlorophyll concentration, mg m-3)
zooc (Zooplankton Carbon Biomass, MMol' 'M-3)


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

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
so (salinity, PSU)


Sea Temperature (3D) - Hourly Mean. Please check in CMEMS catalogue the INFO section for product MEDSEA_ANALYSISFORECAST_PHY_006_013 - http://marine.copernicus.eu

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][depth][latitude][longitude]):
thetao (sea temperature, degree_C)


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.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
zs (water level, m)
zb (bed level, m)
ue (Eulerian velocity in cell centre, x-component, m/s)
ve (Eulerian velocity in cell centre, y-component, m/s)
H (Hrms wave height based on instantaneous wave energy, m)
E (wave energy, Nm/m2)


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.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
zs (water level, m)
zb (bed level, m)
ue (Eulerian velocity in cell centre, x-component, m/s)
ve (Eulerian velocity in cell centre, y-component, m/s)
H (Hrms wave height based on instantaneous wave energy, m)
E (wave energy, Nm/m2)


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.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
zs (water level, m)
zb (bed level, m)
ue (Eulerian velocity in cell centre, x-component, m/s)
ve (Eulerian velocity in cell centre, y-component, m/s)
H (Hrms wave height based on instantaneous wave energy, m)
E (wave energy, Nm/m2)


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.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
zs (water level, m)
zb (bed level, m)
ue (Eulerian velocity in cell centre, x-component, m/s)
ve (Eulerian velocity in cell centre, y-component, m/s)
H (Hrms wave height based on instantaneous wave energy, m)
E (wave energy, Nm/m2)


Wave fields (2D) - Hourly Instantaneous. Please check in CMEMS catalogue the INFO section for product MEDSEA_ANALYSISFORECAST_WAV_006_017 - http://marine.copernicus.eu

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
VCMX (Maximum crest trough wave height (Hc,max), m)
VHM0 (Spectral significant wave height (Hm0), m)
VHM0_SW1 (Spectral significant primary swell wave height, m)
VHM0_SW2 (Spectral significant secondary swell wave height, m)
VHM0_WW (Spectral significant wind wave height, m)
VMDR (Mean wave direction from (Mdir), degree)
VMDR_SW1 (Mean primary swell wave direction from, degree)
VMDR_SW2 (Mean secondary swell wave direction from, degree)
VMDR_WW (Mean wind wave direction from, degree)
VMXL (Height of the highest crest, m)
VPED (Wave principal direction at spectral peak, degree)
VSDX (Stokes drift U, m/s)
VSDY (Stokes drift V, m/s)
VTM01_SW1 (Spectral moments (0,1) primary swell wave period, s)
VTM01_SW2 (Spectral moments (0,1) secondary swell wave period, s)
VTM01_WW (Spectral moments (0,1) wind wave period, s)
VTM02 (Spectral moments (0,2) wave period (Tm02), s)
VTM10 (Spectral moments (-1,0) wave period (Tm-10), s)
VTPK (Wave period at spectral peak / peak period (Tp), s)


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.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
Q2 (kg kg-1)
T2 (K)
TH2 (K)
PSFC (Pa)
U10 (Eastward Wind Component, m s-1)
V10 (Northward Wind Component, m s-1)
LPI (m^2 s-2)
ACSNOW (kg m-2)
RAINC (mm)
RAINNC (mm)
SNOWNC (mm)
GRAUPELNC (mm)
HAILNC (mm)
SWDOWN (W m-2)
SWDOWNC (W m-2)
PBLH (m)
HFX (W m-2)
QFX (kg m-2 s-1)
LH (W m-2)
WSPD10MAX (WSPD10 MAX, m s-1)
W_UP_MAX (m s-1)
... (10 more variables)


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.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][lev][latitude][longitude]):
U_PL (m s-1)
V_PL (m s-1)
T_PL (K)
RH_PL (Relative Humidity, percent)
GHT_PL (m)
S_PL (m s-1)
TD_PL (K)
Q_PL (kg/kg)


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.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
Q2 (kg kg-1)
T2 (K)
TH2 (K)
PSFC (Pa)
U10 (Eastward Wind Component, m s-1)
V10 (Northward Wind Component, m s-1)
LPI (m^2 s-2)
ACSNOW (kg m-2)
RAINC (mm)
RAINNC (mm)
SNOWNC (mm)
GRAUPELNC (mm)
HAILNC (mm)
SWDOWN (W m-2)
SWDOWNC (W m-2)
PBLH (m)
HFX (W m-2)
QFX (kg m-2 s-1)
... (11 more variables)


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.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][lev][latitude][longitude]):
U_PL (m s-1)
V_PL (m s-1)
T_PL (K)
RH_PL (Relative Humidity, percent)
GHT_PL (m)
S_PL (m s-1)
TD_PL (K)
Q_PL (kg/kg)