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3.2 3.2

Variable definitions

The AgERA5 includes 22 agronomic relevant variables. See Table 3-1.

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3.3 3.3

Input data used

Logically the ERA5 data set is the main input data set. See https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 ERA5 provides hourly estimates of a large number of atmospheric, land and oceanic climate variables. The data cover the earth on a 30 km grid and resolve the atmosphere using 137 levels from the surface up to a height of 80 km. ERA5 includes information about uncertainties for all variables at reduced spatial and temporal resolutions.
Concerning the archive the years 1979 to present were available during the project. The remaining ERA5 dataset from 1950 to 1978 was expected to be available for use by summer 2019. Note that two versions of ERA5 are available through the CDS:

• interpolated to a 0.25° grid
• original ERA5 model level data (reanalysis-era5-complete) The latter version was used in this project.

 In addition, publication of quality-assured monthly updates of ERA5 were about to start during the project. These monthly additions then come available within 3 months of real time. Finally, preliminary daily updates of the dataset will be made available to users within 7 days of real time. This was expected to start later in 2019.
ERA5 has a wide list of variables. See the following link: https://software.ecmwf.int/wiki/display/CKB/ERA5+data+ ERA5: data documentation, especially the tables:

• 2: surface, instantaneous (averages)
• 3: surface, accumulations
• 4: surface, minimum/maximum

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Data of the HRES model were needed as a training data set to derive the bias correction. HRES data is not part of the C3S catalogue and was accessed through the contract (C3S422Lot1WEnR).

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Table 5-1: MAE (HRES-ERA5corrected) and MAE improvement of different bias corrected variables. The MAE improvements indicate the added value through the bias correction. All metrics were calculated for different regions and for subsets of grid points meeting certain conditions. E.g. “Land & above 800m” only uses grid points being located on land and above 800m. “Coasts & Lakes” subsets all grid points with a land fraction between 10% and 90%.

The MAE indicates the error of the corrected data (HRES-ERA5corrected), while the MAE improvement compares the error of the corrected versus the not corrected ERA5 data. All metrics were aggregated for different regions and certain subsets of grid points. Overall, the temperature, humidity and wind speed variables benefit most from the correction. The MAE is reduced by 30% to 60% in the majority of cases. Grid points being located in mountainous areas or along coasts and lakes are improved most. This is not surprising as these are the areas where the largest systematic differences between ERA5 and HRES can be expected. But not only the relative improvements are quite large, also the absolute MAE values after the correction are small. The MAE for the 24h mean of the 2m temperatures (2t_davg) for example is for all continents below 0.72K, and for 4 of 6 continents even below 0.51K.
For the solar radiation flux (ssrd_dsumdiff) the MAE improvement is solid and ranges between 2% and 14%, depending on the region and subset. The results of element "24h mean cloud cover" (tcc_davg) are mixed. For most grid points the correction doesn't add any value. The MAE improvement of the majority of all grid points (land and below 800m) is between -2% and +4%, and therefore near zero. Only for grid points above 800m we can observe a small but clear improvement (2% - 8%).
The following conclusions were drawn from the evaluation study:

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https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5

https://software.ecmwf.int/wiki/display/CKB/ERA5+data+documentation

http://marswiki.jrc.ec.europa.eu/agri4castwiki/index.php/Meteorological_data_from_ECMWF_mo dels.

https://confluence.ecmwf.int/display/CKB/ERA5%3A+What+is+the+spatial+reference

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