2 days 1.1 km resolution forecast over Liguria (01)

Summary:
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)

Summary:
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)

Summary:
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)

Summary:
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)

Summary:
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)

Summary:
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)

Summary:
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)

Summary:
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)

Summary:
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)

Summary:
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 - Animation of waves

Summary:
Hourly 3-dimensional atmospheric gridded data, with a temporal coverage of 48 hours. Data cover Central and Eastern Liguria - Animation of waves

cdm_data_type = Grid
VARIABLES:
url
time (seconds since 1970-01-01T00:00:00Z)
name (File Name)
lastModified (Last Modified, seconds since 1970-01-01T00:00:00Z)
size (bytes)
fileType (File Type)


2 days 1.1 km resolution forecast over Liguria - Animation of wind

Summary:
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

cdm_data_type = Grid
VARIABLES:
url
time (seconds since 1970-01-01T00:00:00Z)
name (File Name)
lastModified (Last Modified, seconds since 1970-01-01T00:00:00Z)
size (bytes)
fileType (File Type)


2 days 1.1 km resolution forecast over Liguria - Precipitation

Summary:
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)

Summary:
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)

Summary:
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)

Summary:
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)

Summary:
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)

Summary:
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)

Summary:
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)

Summary:
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)

Summary:
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)

Summary:
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)

Summary:
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 - Animation of wind

Summary:
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

cdm_data_type = Grid
VARIABLES:
url
time (seconds since 1970-01-01T00:00:00Z)
name (File Name)
lastModified (Last Modified, seconds since 1970-01-01T00:00:00Z)
size (bytes)
fileType (File Type)


2 days 3.3 km resolution forecast over Northern and Central Italy - Precipitation

Summary:
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)

Summary:
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)

Summary:
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)

Summary:
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)

Summary:
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)

Summary:
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)

Summary:
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)

Summary:
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)

Summary:
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)

Summary:
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)

Summary:
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 - Animation of wind

Summary:
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

cdm_data_type = Grid
VARIABLES:
url
time (seconds since 1970-01-01T00:00:00Z)
name (File Name)
lastModified (Last Modified, seconds since 1970-01-01T00:00:00Z)
size (bytes)
fileType (File Type)


5 days 10 km resolution forecast over Southern Europe and Mediterranean basin - Precipitation

Summary:
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)


AI-based particle dispersion model

Summary:
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.

cdm_data_type = Point
VARIABLES:
time (time since the beginning of the simulation, seconds since 1970-01-01T00:00:00Z)
latitude (latitude of the particle, degrees_north)
longitude (longitude of the particle, degrees_east)
particle_count (number of particles in a given timestep, 1)
mass (mass of particle, kilograms)
status_codes (particle status code)
age (age of particle from time of release, minutes)
density (emulsion density at end of timestep, kg/m^3)
spill_num (spill to which the particle belongs)
surface_concentration (surface concentration of oil, g m-2)
depth (particle depth below sea surface, m)
id (particle ID)
viscosity (emulsion viscosity at end of timestep, m^2/sec)


Camogli in situ buoy sea water temperature and sub surface current data

Summary:
The dataset represents data automatically collected and trasmitted in real-time by in situ buoy located in Camogli

cdm_data_type = Other
VARIABLES:
time (Timestamp, seconds since 1970-01-01T00:00:00Z)
latitude (degrees_north)
longitude (degrees_east)
sw_temperature_3m (SW Temp 3m, degree_Celsius)
sw_temperature_6_5m (SW Temp 6.5m, degree_Celsius)
speed_mean (Speed, cm/s)
speed_std (cm/s)
direction_mean (Direction, degrees_north)
direction_std (degrees_north)
tilt
tilt_std
read_count


Camogli in situ Camogli buoy wave and wind data

Summary:
The dataset represents data automatically collected and trasmitted in real-time by in situ buoy located in Camogli

cdm_data_type = Other
VARIABLES:
time (Timestamp, seconds since 1970-01-01T00:00:00Z)
latitude (degrees_north)
longitude (degrees_east)
significantWaveHeight (Significant Wave Height, m)
peakPeriod (Wave Peak Period, s)
meanPeriod (Wave Mean Period, s)
peakDirection (Wave Peak Direction, degrees)
meanDirection (Wave Mean Direction, degrees)
peakDirectionalSpread (Wave Peak Directional Spread, degrees)
meanDirectionalSpread (Wave Mean Directional Spread, degrees)
wind_direction (degrees_north)
wind_speed (m/s)
air_pressure (Barometric Pressure, hPa)
surfaceTemp (Surface Temp, degree_C)


Summary:
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


collection of algal photos collected by the @lgawarning platform - algal bloom participatory environmental monitoring system

Summary:
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

cdm_data_type = Point
VARIABLES:
time (seconds since 1970-01-01T00:00:00Z)
site_name (Position)
latitude (degrees_north)
longitude (degrees_east)
algae_type (Type)
description
sample_volume (Volume, ml)
operator_id (User)
image


Collection of plastic litter photos collected by the Custodians Earth Solution Platform (CESP)

Summary:
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.

cdm_data_type = Other
VARIABLES:
time (seconds since 1970-01-01T00:00:00Z)
latitude (degrees_north)
longitude (degrees_east)
event
event_description
description
image


Data from a local source.

Summary:
Data from a local source.

cdm_data_type = Point
VARIABLES:
EventID (Event ID)
time (seconds since 1970-01-01T00:00:00Z)
latitude (degrees_north)
longitude (degrees_east)
Depth_Km (Depth)
Author
Catalog
Contributor
ContributorID (Contributor ID)
MagType (Mag Type)
Magnitude
MagAuthor (Mag Author)
EventLocationName (Event Location Name)


Global Forecast System (GFS) model (02)

Summary:
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 (03)

Summary:
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 (04)

Summary:
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 (05)

Summary:
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 (06)

Summary:
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)


Summary:
Global Forecast System (GFS) model - Animation of wind

cdm_data_type = Grid
VARIABLES:
url
time (seconds since 1970-01-01T00:00:00Z)
name (File Name)
lastModified (Last Modified, seconds since 1970-01-01T00:00:00Z)
size (bytes)
fileType (File Type)


Summary:
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


Summary:
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


Summary:
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)


Summary:
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

Summary:
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

Summary:
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


I-Change Acronet Data

Summary:
I-Change Acronet Data. CIMAFOUNDATION data from a local source.

cdm_data_type = Point
VARIABLES:
time (Valid Time GMT, seconds since 1970-01-01T00:00:00Z)
STATION_ID
STATION_NAME
latitude (degrees_north)
longitude (degrees_east)
RAINGAUGE (mm)
TEMP (Temperature, degree_C)
HUMIDITY (relative_humidity, percent)
WSPEED (wind_speed)
PRESS (air_pressure)
WSPEED_GUST (wind_speed_of_gust)


In situ CTD instrument at 3m depth - eLTER-Italy site Portofino Promontory - Italy (LTER_EU_IT_015) - all raw data

Summary:
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).

cdm_data_type = Other
VARIABLES:
date
hour
time (seconds since 1970-01-01T00:00:00Z)
time_cet (seconds since 1970-01-01T00:00:00Z)
Press
Temp (Temperature)
Cond
Sal (Sal.)
sensor_id


In situ CTD instrument at 3m depth - LTER-Italy site Portofino Promontory - Italy (LTER_EU_IT_015) - last row data

Summary:
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).

cdm_data_type = Other
VARIABLES:
date
hour
time (seconds since 1970-01-01T00:00:00Z)
time_cet (seconds since 1970-01-01T00:00:00Z)
Press
Temp (Temperature)
Cond
Sal (Sal.)
sensor_id


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)

Summary:
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).

cdm_data_type = Other
VARIABLES:
PLATFORMCODE
String_ID
time (seconds since 1970-01-01T00:00:00Z)
time_cet (seconds since 1970-01-01T00:00:00Z)
latitude (degrees_north)
longitude (degrees_east)
depth (m)
Date (seconds since 1970-01-01T00:00:00Z)
Time
Cell_number
EWCT (West-east current component, m/s)
NSCT (South-north current component, m/s)
UVCT (Upward current velocity, m/s)
Speed (m/s)
Direction (degrees)


In situ Theodor Friedrichs meteorological station at 7m amsl - LTER-Italy site Portofino Promontory - Italy (LTER_EU_IT_015)

Summary:
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)

cdm_data_type = Other
VARIABLES:
PLATFORMCODE
String_ID
time (seconds since 1970-01-01T00:00:00Z)
time_cet (seconds since 1970-01-01T00:00:00Z)
latitude (degrees_north)
longitude (degrees_east)
Date (seconds since 1970-01-01T00:00:00Z)
Time
AIR_PRES (Aire Pressure)
WSPD (Wind Speed, m/s)
WDIR (Direction relative to true north from which the wind is blowing, degrees_north)
AIR_TEMP (Air temperature, degrees_Celsius)
Humidity (Relative humidity)
PSAL (Salinity, psu)


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)

Summary:
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).

cdm_data_type = Other
VARIABLES:
PLATFORMCODE
String_ID
time (seconds since 1970-01-01T00:00:00Z)
time_cet (seconds since 1970-01-01T00:00:00Z)
latitude (degrees_north)
longitude (degrees_east)
Date (seconds since 1970-01-01T00:00:00Z)
Time
spectrum_type
processing_method
VGHS (HM0 significant wave height, m)
H3 (H3 Mean 1/3 Height, m)
H10 (H3 Mean 1/10 Height, m)
Hmax (Maximum Height, m)
Tm02 (Mean Period, s)
Tp (Peak Period, s)
Tz (Mean Zero-crossing Period, s)
DirTp (Peak Direction, degrees_north)
SprTp (Directional Spread, degrees_north)
Mdir (Mean Direction, degrees_north)


In situ wave - Nortek AWAC 1Mhz ADCP positioned at a depth of 10m - LTER-Italy site Portofino Promontory - Italy (LTER_EU_IT_015)

Summary:
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).

cdm_data_type = Other
VARIABLES:
PLATFORMCODE
String_ID
time (seconds since 1970-01-01T00:00:00Z)
time_cet (seconds since 1970-01-01T00:00:00Z)
latitude (degrees_north)
longitude (degrees_east)
Date (seconds since 1970-01-01T00:00:00Z)
Time
spectrum_type
processing_method
VGHS (HM0 significant wave height, m)
H3 (H3 Mean 1/3 Height, m)
H10 (H3 Mean 1/10 Height, m)
Hmax (Maximum Height, m)
Tm02 (Mean Period, s)
Tp (Peak Period, s)
Tz (Mean Zero-crossing Period, s)
DirTp (Peak Direction, degrees_north)
SprTp (Directional Spread, degrees_north)
Mdir (Mean Direction, degrees_north)


Summary:
INGVXBT - Collection of sea temperature (TEMP) Profiles - IN SITU MultiPointProfilesObservation

cdm_data_type = Profile
VARIABLES:
PLATFORMCODE (EMODnet Platform Code)
SOURCE
SENSOR (Platform Sensor)
time (Valid Time GMT, seconds since 1970-01-01T00:00:00Z)
TIME_QC (TIME quality flag, 1)
depth (m)
DEPTH_QC (DEPTH quality flag, 1)
latitude (degrees_north)
longitude (degrees_east)
POSITION_QC (POSITION quality flag, 1)
TEMP (water temperature, degree_Celsius)
TEMP_QC (TEMP quality flag, 1)
TEMP_DM (TEMP method of data processing)
url_metadata (Metadata Link)
qc_entity


Summary:
INGVXBT - Collection of sea temperature (TEMP) Profiles - METADATA

cdm_data_type = Other
VARIABLES:
PLATFORMCODE (EMODNET Platform Code)
call_name (Platform Call Name)
latitude (degrees_north)
longitude (degrees_east)
dataFeatureType
firstDateObservation (First Date Observation, seconds since 1970-01-01T00:00:00Z)
lastDateObservation (Last Date Observation, seconds since 1970-01-01T00:00:00Z)
parameters_group_longname (Parameters Info Parameter Groups)
parameters_group_P33 (Parameters Info P33)
parameters (Parameters Info Parameters)
parameters_P01 (Parameters Info P01)
WMO
data_DOI
best_practices_DOI
data_owner_longname (Data Owner Name)
data_owner_country_code
data_owner_country_longname (Data Owner Country Name)
data_owner_EDMO (Data Owner EDMO Code)
data_assembly_center_longname (Data Assembly Center)
platform_type_longname (Platform Type)
platform_type_SDNL06
platformpage_link
integrator_id
IntegrationDate (Integration Date, seconds since 1970-01-01T00:00:00Z)
ingestion
official_repository


Lagrangian dispersal around the Portofino promontory Italy for the month of July 2022

Summary:
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.

cdm_data_type = Point
VARIABLES:
time (seconds since 1970-01-01T00:00:00Z)
latitude (degrees_north)
longitude (degrees_east)


LTER_EU_IT_015_MEDA2 - CTD

Summary:
LTER_EU_IT_015_MEDA2 - CTD

cdm_data_type = Other
VARIABLES:
PLATFORMCODE
String_ID
time (seconds since 1970-01-01T00:00:00Z)
time_cet (seconds since 1970-01-01T00:00:00Z)
latitude (degrees_north)
longitude (degrees_east)
date
hour
Press
Temp (Temperature)
Cond
Sal (Sal.)


Maximum wave run-up considering SE storms

Summary:
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

Summary:
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)


Meda 2 Image Dataset - Portofino - Station 0002

Summary:
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.

cdm_data_type = Other
VARIABLES:
time (seconds since 1970-01-01T00:00:00Z)
name
latitude (degrees_north)
longitude (degrees_east)
url


Mediterranean Wave and Wind Forecast

Summary:
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)


Meteorological data collected by Genoa boat-bus activities using the MeteoTracker device

Summary:
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.

cdm_data_type = Point
VARIABLES:
time (seconds since 1970-01-01T00:00:00Z)
latitude (degrees_north)
longitude (degrees_east)
altitude (m)
speed
temperature
humidity
pressure
dew_point
solar_radiation_index
humidex
tag
potential_temperature
D


Meteorological data collected by Genoa bus activities using the MeteoTracker device

Summary:
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.

cdm_data_type = Point
VARIABLES:
time (seconds since 1970-01-01T00:00:00Z)
latitude (degrees_north)
longitude (degrees_east)
altitude (m)
speed
carbon_dioxide
pressure
humidity
temperature
dew_point
humidex
potential_temperature
tag
D
vertical_temperature_gradient
solar_radiation_index


Meteorological data collected during Citizen Science activities using the MeteoTracker device

Summary:
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.

cdm_data_type = Point
VARIABLES:
time (seconds since 1970-01-01T00:00:00Z)
latitude (degrees_north)
longitude (degrees_east)
altitude (m)
speed
temperature
humidity
pressure
dew_point
solar_radiation_index
humidex
tag
vertical_temperature_gradient
potential_temperature
D


Meteorological data collected during Citizen Science V23 campaign aboard the Swan Hellenic SH Vega ship

Summary:
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.

cdm_data_type = Point
VARIABLES:
DateTimeLocal (Date/time (local), seconds since 1970-01-01T00:00:00Z)
Timezone
time (seconds since 1970-01-01T00:00:00Z)
latitude (degrees_north)
longitude (degrees_east)
Report
Voyage_state
Location
Voyage_name
Speed_OG (kn)
Speed_TW (kn)
True_wind_speed (Wind Speed, Bft)
Wind_direction_absolute (Wind From Direction)
Wave_height (Sea Surface Wave Significant Height)
Air_temperature (degree_C)
Draft_midship (m)
Trim (m)
Distance_OG (nm)
Distance_TW (nm)
Speed_OG_QC
Speed_TW_QC
True_wind_speed_QC
Wind_direction_absolute_QC (Wind Direction (absolute) QC)
Wave_height_QC
Air_temperature_QC
... (4 more variables)


Meteorological data collected during Citizen Science V24 campaign aboard the Swan Hellenic SH Vega ship

Summary:
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.

cdm_data_type = Point
VARIABLES:
DateTimeLocal (Date/time (local), seconds since 1970-01-01T00:00:00Z)
Timezone
time (seconds since 1970-01-01T00:00:00Z)
latitude (degrees_north)
longitude (degrees_east)
Report
Voyage_state
Location
Voyage_name
Ship_Speed (kn)
True_wind_speed (Wind Speed, Bft)
Wind_direction_absolute (Wind From Direction)
Wave_height (Sea Surface Wave Significant Height)
Air_temperature (degree_C)
Atmosferic_pressure (Air Pressure, hPa)
Ship_Speed_kn_QC (Ship Speed [kn] QC)
True_wind_speed_QC
Wind_direction_absolute_QC (Wind Direction (absolute) QC)
Wave_height_QC
Air_temperature_QC
Draft_midship_QC


Meteorological data collected during Citizen Science V25 campaign aboard the Swan Hellenic SH Vega ship

Summary:
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.

cdm_data_type = Point
VARIABLES:
DateTimeLocal (Date/time (local), seconds since 1970-01-01T00:00:00Z)
Timezone
time (seconds since 1970-01-01T00:00:00Z)
latitude (degrees_north)
longitude (degrees_east)
Report
Voyage_state
Location
Voyage_name
Speed_OG (kn)
Ship_Speed (kn)
True_wind_speed (Wind Speed, Bft)
Wind_direction_absolute (Wind From Direction)
Wind_direction_true (degrees)
Wave_height (Sea Surface Wave Significant Height)
Air_temperature (degree_C)
Atmosferic_pressure (hPa)
Speed_OG_QC
Ship_Speed_QC
True_wind_speed_QC
Wind_direction_absolute_QC (Wind Direction (absolute) QC)
Wind_direction_true_QC
Wave_height_QC
Air_temperature_QC
Atmosferic_pressure_QC


Meteorological data collected during Citizen Science V26 campaign aboard the Swan Hellenic SH Vega ship


Meteorological data collected during Citizen Science V27 campaign aboard the Swan Hellenic SH Vega ship


Meteorological data collected during Citizen Science V28 campaign aboard the Swan Hellenic SH Vega ship


Meteorological data collected during Citizen Science V29 campaign aboard the Swan Hellenic SH Vega ship


Meteorological data collected during Citizen Science V30 campaign aboard the Swan Hellenic SH Vega ship


Meteorological data collected during Citizen Science V31 campaign aboard the Swan Hellenic SH Vega ship


Meteorological data collected during Citizen Science V32 campaign aboard the Swan Hellenic SH Vega ship


Meteorological data collected during Citizen Science V33 campaign aboard the Swan Hellenic SH Vega ship


Meteorological data collected during Citizen Science V34 campaign aboard the Swan Hellenic SH Vega ship


Meteorological data collected during Citizen Science V35 campaign aboard the Swan Hellenic SH Vega ship


Meteorological data collected during Citizen Science V36 campaign aboard the Swan Hellenic SH Vega ship


Meteorological data collected during Citizen Science V56 campaign aboard the Swan Hellenic SH Vega ship


Meteorological data collected during Citizen Science V57 campaign aboard the Swan Hellenic SH Vega ship


Meteorological data collected using the MeteoTracker device


Meteorological observations by Ecowitt Weather Station - ECOWITT001

Summary:
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.

cdm_data_type = Other
VARIABLES:
ext_id
time (seconds since 1970-01-01T00:00:00Z)
cum
rain_intensity
air_temperature
relative_humidity
air_pressure
wind_speed
wind_from_direction
wind_gust (Wind Speed Of Gust)
wind_gust_from_direction
battery


Meteorological observations by Ecowitt Weather Station - ECOWITT002


Meteorological observations by Ecowitt Weather Station - ECOWITT003


Meteorological observations by Ecowitt Weather Station - ECOWITT004


Multiparametric measurements in a fixed position in the Genova port

Summary:
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

cdm_data_type = Other
VARIABLES:
ext_id
cruise
station
type
time (seconds since 1970-01-01T00:00:00Z)
temp (Temperature)
psal
alky
cpwc
phyc
tsed
wbrx
cmfl
deph
doxy_mg_l
doxy_perc
qv_odv_sample
temp_qf
psal_qf
alky_qf
cpwc_qf
phyc_qf
tsed_qf
wbrx_qf
cmfl_qf
... (4 more variables)


Multiparametric mission along the eastern ligurian coast from Cinque Terre to Marina di Massa

Summary:
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

cdm_data_type = Other
VARIABLES:
ext_id
cruise
station
type
time (seconds since 1970-01-01T00:00:00Z)
latitude (degrees_north)
longitude (degrees_east)
temp (Temperature)
psal
alky
cpwc
phyc
tsed
wbrx
cmfl
deph
doxy_mg_l
doxy_perc
qv_odv_sample


Multiparametric mission along the eastern ligurian coast from Genova to Portofino

Summary:
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

cdm_data_type = Other
VARIABLES:
ext_id
cruise
station
type
time (seconds since 1970-01-01T00:00:00Z)
latitude (degrees_north)
longitude (degrees_east)
temp (Temperature)
psal
alky
cpwc
phyc
tsed
wbrx
cmfl
deph
doxy_mg_l
doxy_perc
qv_odv_sample


Multiparametric mission around Ischia island

Summary:
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

cdm_data_type = Other
VARIABLES:
ext_id
cruise
station
type
time (seconds since 1970-01-01T00:00:00Z)
latitude (degrees_north)
longitude (degrees_east)
temp (Temperature)
psal
alky
cpwc
phyc
tsed
wbrx
cmfl
deph
doxy_mg_l
doxy_perc
qv_odv_sample


Multiparametric mission off Genova April 2025

Summary:
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

cdm_data_type = Other
VARIABLES:
ext_id
cruise
station
type
time (seconds since 1970-01-01T00:00:00Z)
latitude (degrees_north)
longitude (degrees_east)
temp (Temperature)
psal
alky
cpwc
phyc
tsed
wbrx
cmfl
deph
doxy_mg_l
doxy_perc
qv_odv_sample


Mussel farm image dataset - Gulf of La Spezia - Station 0001

Summary:
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.

cdm_data_type = Other
VARIABLES:
time (seconds since 1970-01-01T00:00:00Z)
name
latitude (degrees_north)
longitude (degrees_east)
url


Mussel farm image dataset - Gulf of La Spezia - Station 0001 and 0022


Mussel farm image dataset - Gulf of La Spezia - Station 0022


Mussel farm image dataset - Gulf of La Spezia - Station 0102

Summary:
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.

cdm_data_type = Other
VARIABLES:
time (seconds since 1970-01-01T00:00:00Z)
name
latitude (degrees_north)
longitude (degrees_east)
url


Particle dispersion model

Summary:
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.

cdm_data_type = Point
VARIABLES:
time (time since the beginning of the simulation, seconds since 1970-01-01T00:00:00Z)
latitude (latitude of the particle, degrees_north)
longitude (longitude of the particle, degrees_east)
particle_count (number of particles in a given timestep, 1)
spill_num (spill to which the particle belongs)
surface_concentration (surface concentration of oil, g m-2)
depth (particle depth below sea surface, m)
id (particle ID)
age (age of particle from time of release, minutes)
status_codes (particle status code)
density (emulsion density at end of timestep, kg/m^3)
viscosity (emulsion viscosity at end of timestep, m^2/sec)
mass (mass of particle, kilograms)


Phytoplankton Carbon Biomass, Zooplankton Carbon Biomass, Chlorophyll and PFTs (3D), Daily Mean

Summary:
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)


Real-time rainfall intensity and cumulate precipitation maps by SRS - Smart Rainfall System - Bonassola

Summary:
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.

cdm_data_type = Grid
VARIABLES:
time (seconds since 1970-01-01T00:00:00Z)
srs_id
srs_sat_id
rain_level


Real-time rainfall intensity and cumulate precipitation maps by SRS - Smart Rainfall System - Genova


Real-time rainfall intensity and cumulate precipitation maps by SRS - Smart Rainfall System - La Spezia


Real-time rainfall intensity and cumulate precipitation maps by SRS - Smart Rainfall System - Livorno


Riomaggiore in situ buoy sea water temperature and sub surface current data

Summary:
The dataset represents data automatically collected and trasmitted in real-time by in situ buoy located in Riomaggiore

cdm_data_type = Other
VARIABLES:
time (Timestamp, seconds since 1970-01-01T00:00:00Z)
latitude (degrees_north)
longitude (degrees_east)
sw_temperature_3m (SW Temp 3m, degree_C)
sw_temperature_6_5m (SW Temp 6.5m, degree_C)
speed_mean (Speed, cm/s)
speed_std (cm/s)
direction_mean (Direction, degrees_north)
direction_std (degrees_north)
tilt
tilt_std
read_count


Riomaggiore in situ buoy wave and wind data

Summary:
The dataset represents data automatically collected and trasmitted in real-time by in situ buoy located in Riomaggiore

cdm_data_type = Other
VARIABLES:
time (Timestamp, seconds since 1970-01-01T00:00:00Z)
latitude (degrees_north)
longitude (degrees_east)
significantWaveHeight (Significant Wave Height, m)
peakPeriod (Wave Peak Period, s)
meanPeriod (Wave Mean Period, s)
peakDirection (Wave Peak Direction, degrees)
meanDirection (Wave Mean Direction, degrees)
peakDirectionalSpread (Wave Peak Directional Spread, degrees)
meanDirectionalSpread (Wave Mean Directional Spread, degrees)
wind_direction (degrees_north)
wind_speed (m/s)
air_pressure (Barometric Pressure, hPa)
surfaceTemp (Surface Temp, degree_C)


Sea Surface Salinity (2D), Hourly Mean

Summary:
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 Surface Temperature data collected during Citizen Science activities using the Brizo device

Summary:
Brizo is a smart temperature logger to support marine environmental research. The system offers data in near real time.

cdm_data_type = Point
VARIABLES:
platformcode
mission
time (seconds since 1970-01-01T00:00:00Z)
latitude (degrees_north)
longitude (degrees_east)
temperature
timestamp (seconds since 1970-01-01T00:00:00Z)
author
command


Sea Temperature (3D), Hourly Mean

Summary:
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)


Sea waves parameters - Andora

Summary:
Sea-waves parameters -Hs (Significant wave height),Tm (Mean Period). And Tp (Peak Period) retrieved from inland micro-seismic measurements - Andora

cdm_data_type = Other
VARIABLES:
ext_id
time (seconds since 1970-01-01T00:00:00Z)
hs
tm
tp
valid


Sea waves parameters - Bonassola

Summary:
Sea-waves parameters -Hs (Significant wave height),Tm (Mean Period). And Tp (Peak Period) retrieved from inland micro-seismic measurements - Bonassola

cdm_data_type = Other
VARIABLES:
ext_id
time (seconds since 1970-01-01T00:00:00Z)
hs
tm
tp
valid


Sea waves parameters - Genova CNR

Summary:
Sea-waves parameters -Hs (Significant wave height),Tm (Mean Period). And Tp (Peak Period) retrieved from inland micro-seismic measurements - Genova CNR

cdm_data_type = Other
VARIABLES:
ext_id
time (seconds since 1970-01-01T00:00:00Z)
hs
tm
tp
valid


Sea waves parameters - Livorno

Summary:
Sea-waves parameters -Hs (Significant wave height),Tm (Mean Period). And Tp (Peak Period) retrieved from inland micro-seismic measurements - Livorno

cdm_data_type = Other
VARIABLES:
ext_id
time (seconds since 1970-01-01T00:00:00Z)
hs
tm
tp
valid


Stazioni OMIRL (Osservatorio Meteo Idrologico della Regione Liguria), Osservazioni georiferite di parametri meteo-marini ed idrologici in tempo reale sulla Liguria, Stazioni a mare

Summary:
Stazioni OMIRL (Osservatorio Meteo Idrologico della Regione Liguria) - Osservazioni georiferite di parametri meteo-marini ed idrologici in tempo reale sulla Liguria - Stazioni a mare

cdm_data_type = Point
VARIABLES:
name
shortCode (Short Code)
time (Valid Time GMT, seconds since 1970-01-01T00:00:00Z)
latitude (degrees_north)
longitude (degrees_east)
VHZA (Average zero crossing wave height (Hzm), m)
AIR_TEMP (Air temperature, degree_C)
WSPD (Horizontal wind speed, m/s)
GSPD (Gust wind speed, m/s)
WDIR (Wind from direction relative true north, degrees)
WSPD_2d (Horizontal wind speed, m/s)
GSPD_2d (Gust wind speed, m/s)
WDIR_2d (Wind from direction relative true north, degrees)
ATMP (Atmospheric pressure at sea level, hPa)


Stazioni OMIRL (Osservatorio Meteo Idrologico della Regione Liguria), Osservazioni georiferite di parametri meteo-marini ed idrologici in tempo reale sulla Liguria, Stazioni a terra

Summary:
Stazioni OMIRL (Osservatorio Meteo Idrologico della Regione Liguria) - Osservazioni georiferite di parametri meteo-marini ed idrologici in tempo reale sulla Liguria - Stazioni a terra

cdm_data_type = Point
VARIABLES:
name
shortCode (Short Code)
time (Valid Time GMT, seconds since 1970-01-01T00:00:00Z)
latitude (degrees_north)
longitude (degrees_east)
RAIN_1h
CUMUL_1h
RAIN_5m
CUMUL_5m
RAIN_7d
CUMUL_7d
RAIN_1d
CUMUL_1d
AIR_TEMP (air_temperature, degree_C)
TMIN (air_temperature, degree_C)
TMAX (air_temperature, degree_C)
RLEV (Water surface height above a specific datum, m)
ATMP (air_pressure)
TENS (Battery voltage, V)
municipality


Temperature and conductivity data collected during Citizen Science campaigns aboard the Swan Hellenic SH Vega using OpenCTD

Summary:
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.

cdm_data_type = Point
VARIABLES:
ADATAA01 (seconds since 1970-01-01T00:00:00Z)
Hour
time (seconds since 1970-01-01T00:00:00Z)
depth (m)
TEMP (Temperature)
PSLTZZ01
PRESPR01
TEMPPR01
TEMPPR02
TEMPPR03
CNDCZZ01
TEMPPR01_QC
TEMPPR02_QC
TEMPPR03_QC
CNDCZZ01_QC
latitude (degrees_north)
longitude (degrees_east)
notes


Temperature data collected during Citizen Science activities using EnvLogger sensor (0443 0B00 FC09 09)

Summary:
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

cdm_data_type = Point
VARIABLES:
time (seconds since 1970-01-01T00:00:00Z)
temp (Temperature, degree_Celsius)
serial_number
sensor_id
latitude (degrees_north)
longitude (degrees_east)
accuracy (GPS Position Accuracy, meter)
depth (Sensor depth, m)
sampling_resolution


Temperature data collected during Citizen Science activities using EnvLogger sensor (0457 9200 461D 0D)


Temperature data collected during Citizen Science activities using EnvLogger sensor (0475 3A00 FF2D 05)


Temperature data collected during Citizen Science activities using EnvLogger sensor (047E 2300 6F1E 0A)


Temperature data collected during Citizen Science activities using EnvLogger sensor (0484 0C00 220F 07)


The "Smart Bay Santa Teresa Underwater Observatory" - CDT calibrated data

Summary:
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).

cdm_data_type = Point
VARIABLES:
time (Date Time(UTC), seconds since 1970-01-01T00:00:00Z)
Depth (m)
Pressure (db)
Temperature (degrees_C)
Conductivity (Sea Water Electrical Conductivity, mS/cm)
Oxygen_mg_l (Oxygen, mg/l)
Chlorophyll (Concentration Of Chlorophyll In Sea Water, ug/l)
Turbidity
pH_NBS
Salinity (Sea Water Practical Salinity, PSU)
Density_Kg_m3 (Density, Kg/m^3)
Oxygen_ml_l (Oxygen, ml/l)
Oxygen_umol_l (Oxygen, umol/l)
Oxygen_percentage (Oxygen, %)
Density_Kg_m3_1000 (Density, Kg/m^3-1000))
Sound_Velocity (m/s)
pH_T
... (7 more variables)


The "Smart Bay Santa Teresa Underwater Observatory" - Current data

Summary:
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).

cdm_data_type = Point
VARIABLES:
time (Date Time(UTC), seconds since 1970-01-01T00:00:00Z)
latitude (degrees_north)
longitude (degrees_east)
Temperature (Temperature(degrees C), degrees C)
Direction (degrees)
Velocity (m/s)
Heading (degrees)
North_Velocity_m_s (North Velocity, m/s)
East_Velocity_m_s (East Velocity, m/s)
Echo_Amplitude_dB (d B)
Echo_Amplitude_mW (Echo Amplitude, mW)
Station
Name
Profondita
Bottom_depth
Declinazione_Magnetica
Probe_S_N


The "Smart Bay Santa Teresa Underwater Observatory"- Carbon dioxide data

Summary:
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).

cdm_data_type = Point
VARIABLES:
time (Date Time(UTC), seconds since 1970-01-01T00:00:00Z)
latitude (degrees_north)
longitude (degrees_east)
CO2 (ppm)
Internal_Temperature_IRGA (degrees_C)
Relative_Humidity
Internal_Temperature_Sensore_Humidity (degrees_C)
Cell_Pressure (h Pa)
Battery_Voltage (V)
pCO2_mbar (pCO2, mbar)
pCO2_Pa (pCO2, Pa)
pCO2_uatm (pCO2, uatm)
Station
Name
Profondita
Bottom_depth
Probe_S_N


Water level considering SE storms

Summary:
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

Summary:
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)


Water level considering SW storms

Summary:
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

Summary:
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

Summary:
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)


Wave Forecast for Coastal Erosion Applications - east point

Summary:
Five days hourly forecast of ocean waves in the proximity of the Ligurian coastline for coastal risk applications- east point

cdm_data_type = Grid
VARIABLES:
time (seconds since 1970-01-01T00:00:00Z)
latitude (degrees_north)
longitude (degrees_east)
dir (Wave mean direction, degree)
dp (Peak direction, degree)
fp (Wave peak frequency, s-1)
hs (Significant height of wind and swell waves, m)
tm (Mean period, s)
forecast_date


Wave Forecast for Coastal Erosion Applications - ovest point

Summary:
Five days hourly forecast of ocean waves in the proximity of the Ligurian coastline for coastal risk applications- ovest point

cdm_data_type = Grid
VARIABLES:
time (seconds since 1970-01-01T00:00:00Z)
latitude (degrees_north)
longitude (degrees_east)
dir (Wave mean direction, degree)
dp (Peak direction, degree)
fp (Wave peak frequency, s-1)
hs (Significant height of wind and swell waves, m)
tm (Mean period, s)
forecast_date


WRF (Weather Research and Forecasting Model)  1.5 km (01)

Summary:
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)

Summary:
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)

Summary:
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)

Summary:
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)