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Short name | Long name | Unit | Aggregation | AGROVOC URI |
Cloud_Cover_Mean | Total cloud cover (00-00LT) | (0 - 1) | Mean | |
Dew_Point_Temperature_2m_Mean | 2 meter dewpoint temperature (00-00LT) | K | Mean | |
Preciptation_Flux | Total precipitation (00-00LT) | mm d-1 | Sum | |
Preciptation_Rain_Duration_Fraction | Precipitation type duration - rain (00-00LT) | - | Count | |
Preciptation_Solid_Duration_Fraction | Precipitation type duration - solid fraction (no hail) composed of: precipitation types freezing rain (3), snow (5), wet snow (6), mixture of rain and snow (7) and ice pellets (8) (00-00LT) | - | Count | |
Relative_Humidity_2m_06h | Relative humidity at 06LT | % | - | |
Relative_Humidity_2m_09h | Relative humidity at 09LT | % | - | |
Relative_Humidity_2m_12h | Relative humidity at 12LT | % | - | |
Relative_Humidity_2m_15h | Relative humidity at 15LT | % | - | |
Relative_Humidity_2m_18h | Relative humidity at 18LT | % | - | |
Snow_Thickness_LWE_Mean | Snow liquid water equivalent (00-00LT) | cm of liquid water equivalent | Mean | |
Snow_Thickness_Mean | Snow depth (00-00LT) | cm snow | Mean | |
Solar_Radiation_Flux | Surface solar radiation downwards (00-00LT) | J m-2d-1 | Sum | |
Temperature_Air_2m_Max_24h | Maximum air temperature at 2 meter (00-00LT) | K | Maximum | |
Temperature_Air_2m_Max_Day_Time | Maximum air temperature at 2 meter (06-18LT) | K | Maximum | |
Temperature_Air_2m_Mean_24h | 2 meter air temperature (00-00LT) | K | Mean | |
Temperature_Air_2m_Mean_Day_Tim e |
| K | Mean | |
Temperature_Air_2m_Mean_Night_Ti me |
| K | Mean | |
Temperature_Air_2m_Min_24h | Minimum air temperature at 2 meter (00-00LT) | K | Minimum | |
Temperature_Air_2m_Min_Night_Time | Minimum air temperature at 2 meter (18-06LT) | K | Minimum | |
Vapour_Pressure_Mean | Vapour pressure (00-00LT) | hPa | Mean | |
Wind_Speed_10m_Mean | 10 meter wind component (00-00LT) | m s-1 | Mean |
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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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Table 3-2: Essential variables used for the AgERA5 productAnchor table3_2 table3_2
Variable name | Unit | Short | Reference | Group |
Snow density | kg m-3 | rsn | table 2 | INST1 |
Snow depth | m of water | sd | table 2 | INST1 |
10 metre U wind component | m s-1 | u10 | table 2 | INST1 |
10 metre V wind component | m s-1 | v10 | table 2 | INST1 |
Total cloud cover | (0 - 1) | tcc | table 2 | INST1 |
2 metre temperature | K | 2t | table 2 | INST1 |
2 metre dewpoint temperature | K | 2d | table 2 | INST1 |
Surface solar radiation downwards | J m-2 | ssrd | table 3 | ACCMNMX |
Total precipitation | m | tp | table 3 | ACCMNMX |
Precipitation type | code table | ptype | table 2 | INST2 |
Maximum temperature at 2 metres since | K | mx2t | table 5 | ACCMNMX |
Minimum temperature at 2 metres since | K | mn2t | table 5 | ACCMNMX |
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%.
Land | Land & below 800m | Land & above 800m | Coasts & Lakes | ||||||
Variable | Region | MAE | MAE Impr | MAE | MAE Impr | MAE | MAE Impr | MAE | MAE Impr |
2t_davg [K] | Africa | 0.44 | 40% | 0.42 | 36% | 0.47 | 48% | 0.36 | 50% |
2t_davg | Asia | 0.72 | 36% | 0.67 | 27% | 0.86 | 48% | 0.66 | 32% |
2t_davg | Australia | 0.43 | 42% | 0.43 | 35% | 0.37 | 83% | 0.30 | 49% |
2t_davg | Europe | 0.51 | 36% | 0.47 | 30% | 0.75 | 55% | 0.45 | 38% |
2t_davg | N-America | 0.71 | 31% | 0.67 | 25% | 0.85 | 41% | 0.68 | 28% |
2t_davg | S-America | 0.45 | 50% | 0.42 | 41% | 0.61 | 65% | 0.38 | 48% |
2d_davg [K] | Africa | 0.76 | 38% | 0.77 | 38% | 0.76 | 39% | 0.55 | 46% |
2d_davg | Asia | 0.90 | 29% | 0.81 | 25% | 1.09 | 35% | 0.73 | 28% |
2d_davg | Australia | 0.57 | 34% | 0.57 | 28% | 0.43 | 78% | 0.36 | 43% |
2d_davg | Europe | 0.58 | 28% | 0.55 | 22% | 0.81 | 46% | 0.54 | 27% |
2d_davg | N-America | 0.80 | 23% | 0.73 | 18% | 0.97 | 32% | 0.70 | 21% |
2d_davg | S-America | 0.54 | 42% | 0.44 | 37% | 0.99 | 50% | 0.41 | 40% |
ff_davg [m/s] | Africa | 0.27 | 25% | 0.26 | 22% | 0.28 | 32% | 0.33 | 47% |
ff_davg | Asia | 0.29 | 28% | 0.27 | 24% | 0.34 | 35% | 0.36 | 35% |
ff_davg | Australia | 0.24 | 31% | 0.25 | 30% | 0.22 | 41% | 0.31 | 53% |
ff_davg | Europe | 0.25 | 31% | 0.24 | 31% | 0.32 | 33% | 0.33 | 48% |
ff_davg | N-America | 0.29 | 28% | 0.28 | 26% | 0.33 | 31% | 0.33 | 34% |
ff_davg | S-America | 0.23 | 30% | 0.22 | 26% | 0.27 | 42% | 0.32 | 51% |
tcc_davg [0-1] | Africa | 0.08 | 3% | 0.08 | 2% | 0.08 | 4% | 0.08 | 5% |
tcc_davg | Asia | 0.07 | 0% | 0.07 | -2% | 0.08 | 4% | 0.08 | -2% |
tcc_davg | Australia | 0.06 | -1% | 0.06 | -1% | 0.06 | 5% | 0.07 | 2% |
tcc_davg | Europe | 0.07 | -1% | 0.07 | -1% | 0.07 | 2% | 0.07 | -1% |
tcc_davg | N-America | 0.08 | 0% | 0.08 | -1% | 0.07 | 2% | 0.08 | -1% |
tcc_davg | S-America | 0.07 | 4% | 0.07 | 3% | 0.07 | 8% | 0.07 | 5% |
ssrd_dsumdiff [J/m2d] | Africa | 1055575 | 7% | 1030480 | 7% | 1118699 | 8% | 1151300 | 13% |
ssrd_dsumdiff | Asia | 872717 | 4% | 836249 | 3% | 958997 | 7% | 899084 | 5% |
ssrd_dsumdiff | Australia | 1205911 | 6% | 1177253 | 6% | 1772895 | 14% | 1497494 | 12% |
ssrd_dsumdiff | Europe | 832226 | 2% | 815116 | 2% | 951428 | 5% | 782759 | 4% |
ssrd_dsumdiff | N-America | 899054 | 4% | 902781 | 3% | 888809 | 6% | 916596 | 4% |
ssrd_dsumdiff | S-America | 1427243 | 9% | 1448626 | 9% | 1328043 | 13% | 1316248 | 11% |
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.
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