ERA5 Climate Gridded Data

Date modified: March 2022

The European Centre for Medium Range Weather Forecasts (ECMWF) global climate reanalysis product, ERA5, is a comprehensive reanalysis, with data avaliable from 1979 (soon to be backed dated to January 1950) to near real time. This dataset provides a numerical assessment of the modern climate. The ERA5 dataset provides all essential atmospheric meteorological parameters such as air temperature, pressure and wind at different altitudes, along with surface parameters like rainfall, and sea parameters like sea-surface temperature and wave height. ERA5 is the fifth generation ECMWF atmospheric reanalyses of the global climate, and the first reanalysis produced as an operational service.

The ERA5 dataset is produced using a similar process as a regular numerical weather forecast. It uses a data assimilation and forecast loop, taking into account most of the available meteorological observations and analysing them with a state of the art numerical model, producing a continuous, spatially consistent and homogeneous dataset. The ERA5 dataset produced contains one high resolution realisation at 31km horizontal resolution (referred to as « reanalysis » or « HRES ») and a reduced resolution ten member ensemble (referred to as « ensemble » or « EDA »).

The ERA5 dataset is external to the Digital Earth Africa Open Data Cube. Digital Earth Africa provides the load_era5() and load_era5_netcdf() wrapper functions to enable users to access the data from the AWS Public Dataset Program in the Sandbox environment. The ERA5 Climate Gridded Data notebook demonstrates how to access and load the ERA5 data.

For more information on the ERA5, see the ERA5 data documentation from ECMWF.

A Jupyter Notebook which demonstrates loading and using ERA5 data in the Sandbox is also available.

Specifications 

Table 1: ERA5 product specifications.

Specifications
Product name	ECMWF ERA5 Reanalysis
Cell size - X (km) \(\dagger\)	31km (~0.28°) for the ERA5 HRES	63km (0.5625°) for the ERA5 EDA
Cell size - Y (km) \(\dagger\)	-31km (~-0.28°) for the ERA5 HRES	-63km (0.5625°) for the ERA5 EDA
Temporal Extent	1979 -
Update frequency	Monthly
Data Format	NetCDF and Zarr

\(\dagger\) Wave data are produced and archived at a reduced latitude/longitude grid with a resolution of 0.36° (HRES) and 1.0° (EDA).

Measurements 

15 surface or single level variables are available in Zarr format and are accessible using the load_era5() wrapper function to stream the data.

Table 2: Measurements available in Zarr format.

Catagory	Variable	Name for query
Precipitation	Total precipitation	precipitation_amount_1hour_Accumulation
Wind	10 metre U wind component	eastward_wind_at_10_metres
	10 metre V wind component	northward_wind_at_10_metres
	100 metre U wind component	eastward_wind_at_100_metres
	100 metre V wind component	northward_wind_at_100_metres
Temperature	2 metre temperature	air_temperature_at_2_metres
	2 metres maximum temperature since previous post-processing	air_temperature_at_2_metres_1hour_Maximum
	2 metres minimum temperature since previous post-processing	air_temperature_at_2_metres_1hour_Minimum
	2 metre dew point temperature	dew_point_temperature_at_2_metres
	Sea surface temperature	sea_surface_temperature
Air Pressure	Mean sea level pressure	air_pressure_at_mean_sea_level
	Surface pressure	surface_air_pressure
Snow	Snow density	snow_density
	Snow depth	lwe_thickness_of_surface_snow_amount
Solar radiation	Surface solar radiation downwards	integral_wrt_time_of_surface_direct_downwelling_shortwave_flux_in_air_1hour_Accumulation

The following sea surface variables are only available in netcdf format, and can be downloaded using the load_era5_netcdf() function.

Table 3: Measurements available in NetCDF format.

Catagory	Variable	Name for query
Wave	Mean wave period	sea_surface_wave_mean_period
	Mean direction of waves	sea_surface_wave_from_direction
	Significant height of combined wind waves and swell	significant_height_of_wind_and_swell_waves

Processing 

The ERA5 product is produced from the best available observation data from satellites and in-situ stations using 4D-Var data assimilation and model forecasts in CY41R2 of the ECMWF Integrated Forecast System (IFS). For more details on how the reanalysis product is produced see the ERA5 data documentation.

Media and example images 

Figure 1: Maximum air temperature at 2 metres height for January 2021 for Africa.

ERA5 Air Temperature

Credit: Contains ERA5 air temperature data generated using Copernicus Climate Change Service information 2021.

References 

Jon Olauson, ERA5: The new champion of wind power modelling?, Renewable Energy, Volume 126, 2018, Pages 322-331, ISSN 0960-1481, https://doi.org/10.1016/j.renene.2018.03.056. (https://www.sciencedirect.com/science/article/pii/S0960148118303677)

ERA5: data documentation

ERA5 Data on S3 via AWS Public Dataset Program

ECMWF Reanalysis v5 (ERA5) website

License 

The ERA5 product is provided under a free of charge, worldwide, non-exclusive, royalty free and perpetual license. See https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf for additional information.

Acknowledgements 

ERA5 is produced by the Copernicus Climate Change Service (C3S) at the European Centre for Medium-Range Weather Forecasts (ECMWF).

Data access 

Amazon Web Services S3 

The ERA5 data is available on AWS S3 via the AWS Public Dataset Program.

Table 4: AWS data access details

AWS S3 details
Bucket ARN	`arn:aws:s3:::era5-pds`
Region	`us-east-1`
Product name	`ecmwf-era5`

The ERA5 data is divided into distinct NetCDF granules organized by year, month, and variable name.

The data is structured as follows:

/{year}/{month}/main.nc
               /data/{var1}.nc
                    /{var2}.nc
                    /{....}.nc
                    /{varN}.nc

where year is expressed as four digits (e.g. YYYY) and month as two digits (e.g. MM). Individual data variables (var1 through varN) use names corresponding to NetCDF CF standard names convention plus any applicable additional info, such as vertical coordinate.

A sample path for air temperature would take the following form:

/2008/01/data/air_temperature_at_2_metres.nc

The ERA5 Climate Gridded Data notebook demonstrates how to access the ERA5 data in the Sandbox environment using the load_era5() and load_era5_netcdf() wrapper functions.

Technical information 

Information on the known issues with the ERA5 and the accuracy and uncertainity of the dataset is provided in the ERA5 data documentation.