Dataset - Downscaling ERA5 Wind Speed Data: A Machine Learning approach considering Topographic Influences

This dataset provides three products:

1. The topographic data.  

These data are provided as GeoTIFF files for Europe with 1km x 1km spatial resolution. These maps include:

• Digital Elevation Model (DEM) map: Europe_DEM.tif
• Slope map: Europe_slope.tif
• Aspect map: Europe_aspect.tif
• Topographic Position Index (TPI) with a 5 km radius map: Europe_TPI_5.tif
• Topographic Position Index (TPI) with a 75 km radius map: Europe_TPI_75.tif
• Terrain Diversity Index (TDI) map: Europe_TDI.tif

These data can be used as input maps for the preprocessing step. In addition, the two TPI maps can also be used in the regression process.

2. The resulting map of the preprocessing step. 

This map offers predictions on the quality of ERA5 data across Europe and is also provided as a GeoTIFF file with 1km x 1km spatial resolution under the name:

•  Europe_classification.tif

In this map, Class1 represents a good ERA5 quality with an RMSE of less than 1.5 m/s, Class2 represents a moderate ERA5 quality with an RMSE bigger than 1.5 m/s but less than 3 m/s, while Class 3 indicates a poor ERA5 quality with an RMSE greater than 3 m/s.

3. The downscaled wind speed time series data.

Europe has been divided into 64 equal area blocks to accommodate the large data size. Each downscaled dataset is provided as a NetCDF file, offering hourly wind speed time series for a year (8760 hours) at approximately 1km x 1km spatial resolution. Each NetCDF file has three dimensions: 'lon' representing longitude, 'lat' representing latitude, and 'time' representing the hour. The variable name for wind speed in the NetCDF file is 'WindSpeed'. The 'WindSpeed' variable is stored as an Int32 data type in the NetCDF file, with values multiplied by 10000 in order to significantly reduce the data size. To utilize this variable, please divide it by 10000.

For regions identified as Class1 and Class2, the downscaled wind speed is obtained through a simple nearest neighbour spatial interpolation of ERA5 due to the good quality of ERA5 in these regions. However, for the regions identified as Class3, the downscaled wind speed is derived using the machine learning-based regression approach described in the relevant publication. The geographic extent and the visual representation for each block are provided in 'Readme.pdf' document.

To cite this dataset, please cite our published paper in Environmental Research Letters (DOI: 10.1088/1748-9326/aceb0a)