2m_temperature
, Celsius degrees) - ssrd: Surface solar radiation (surface_solar_radiation_downwards
, Watt per square meter) - ssrdc: Surface solar radiation clear-sky (surface_solar_radiation_downward_clear_sky
, Watt per square meter) - ro: Runoff (runoff
, millimeters) There are also a set of derived variables: - ws10: Wind speed at 10 meters (derived by 10m_u_component_of_wind
and 10m_v_component_of_wind
, meters per second) - ws100: Wind speed at 100 meters (derived by 100m_u_component_of_wind
and 100m_v_component_of_wind
, meters per second) - CS: Clear-Sky index (the ratio between the solar radiation and the solar radiation clear-sky) - HDD/CDD: Heating/Cooling Degree days (derived by 2-meter temperature the EUROSTAT definition. For each variable we have 367 440 hourly samples (from 01-01-1980 00:00:00 to 31-12-2021 23:00:00) for 34/115/309 regions (NUTS 0/1/2). The data is provided in two formats: - NetCDF version 4 (all the variables hourly and CDD/HDD daily). NOTE: the variables are stored as int16
type using a scale_factor
to minimise the size of the files. - Comma Separated Value ("single index" format for all the variables and the time frequencies and "stacked" only for daily and monthly) All the CSV files are stored in a zipped file for each variable. ## Methodology The time-series have been generated using the following workflow: 1. The NetCDF files are downloaded from the Copernicus Data Store from the ERA5 hourly data on single levels from 1979 to present dataset 2. The data is read in R with the climate4r packages and aggregated using the function /get_ts_from_shp
from panas. All the variables are aggregated at the NUTS boundaries using the average except for the runoff, which consists of the sum of all the grid points within the regional/national borders. 3. The derived variables (wind speed, CDD/HDD, clear-sky) are computed and all the CSV files are generated using R 4. The NetCDF are created using xarray
in Python 3.8. ## Example notebooks In the folder notebooks
on the associated Github repository there are two Jupyter notebooks which shows how to deal effectively with the NetCDF data in xarray
and how to visualise them in several ways by using matplotlib or the enlopy package. There are currently two notebooks: - exploring-ERA-NUTS: it shows how to open the NetCDF files (with Dask), how to manipulate and visualise them. - ERA-NUTS-explore-with-widget: explorer interactively the datasets with jupyter and ipywidgets. The notebook exploring-ERA-NUTS
is also available rendered as HTML. ## Additional files In the folder additional files
on the associated Github repository there is a map showing the spatial resolution of the ERA5 reanalysis and a CSV file specifying the number of grid points with respect to each NUTS0/1/2 region. ## License This dataset is released under CC-BY-4.0 license. Not seeing a result you expected?
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2m_temperature
, Celsius degrees) - ssrd: Surface solar radiation (surface_solar_radiation_downwards
, Watt per square meter) - ssrdc: Surface solar radiation clear-sky (surface_solar_radiation_downward_clear_sky
, Watt per square meter) - ro: Runoff (runoff
, millimeters) There are also a set of derived variables: - ws10: Wind speed at 10 meters (derived by 10m_u_component_of_wind
and 10m_v_component_of_wind
, meters per second) - ws100: Wind speed at 100 meters (derived by 100m_u_component_of_wind
and 100m_v_component_of_wind
, meters per second) - CS: Clear-Sky index (the ratio between the solar radiation and the solar radiation clear-sky) - HDD/CDD: Heating/Cooling Degree days (derived by 2-meter temperature the EUROSTAT definition. For each variable we have 367 440 hourly samples (from 01-01-1980 00:00:00 to 31-12-2021 23:00:00) for 34/115/309 regions (NUTS 0/1/2). The data is provided in two formats: - NetCDF version 4 (all the variables hourly and CDD/HDD daily). NOTE: the variables are stored as int16
type using a scale_factor
to minimise the size of the files. - Comma Separated Value ("single index" format for all the variables and the time frequencies and "stacked" only for daily and monthly) All the CSV files are stored in a zipped file for each variable. ## Methodology The time-series have been generated using the following workflow: 1. The NetCDF files are downloaded from the Copernicus Data Store from the ERA5 hourly data on single levels from 1979 to present dataset 2. The data is read in R with the climate4r packages and aggregated using the function /get_ts_from_shp
from panas. All the variables are aggregated at the NUTS boundaries using the average except for the runoff, which consists of the sum of all the grid points within the regional/national borders. 3. The derived variables (wind speed, CDD/HDD, clear-sky) are computed and all the CSV files are generated using R 4. The NetCDF are created using xarray
in Python 3.8. ## Example notebooks In the folder notebooks
on the associated Github repository there are two Jupyter notebooks which shows how to deal effectively with the NetCDF data in xarray
and how to visualise them in several ways by using matplotlib or the enlopy package. There are currently two notebooks: - exploring-ERA-NUTS: it shows how to open the NetCDF files (with Dask), how to manipulate and visualise them. - ERA-NUTS-explore-with-widget: explorer interactively the datasets with jupyter and ipywidgets. The notebook exploring-ERA-NUTS
is also available rendered as HTML. ## Additional files In the folder additional files
on the associated Github repository there is a map showing the spatial resolution of the ERA5 reanalysis and a CSV file specifying the number of grid points with respect to each NUTS0/1/2 region. ## License This dataset is released under CC-BY-4.0 license.