Downscaling wind speed based on coupled environmental factors and machine learning

This is the authors' abstract. We don't add key points for this paper.

Wind speed changes impact society and have important implications for climate change studies. Thus, high‐resolution and high‐quality wind speed datasets are necessary for environmental monitoring and ecosystem research. However, there is no complete set of high spatial and temporal resolution wind speed datasets for China. Additionally, it is extremely challenging to produce wind speed data at high spatial and temporal resolution for large‐scale regions with diverse climate types and complex topographies, such as China. In this study, we used multisource remote sensing images, obtained data on various environmental factors through the Google Earth Engine and Evapotranspiration (ET) Watch Cloud platforms, and combined machine learning algorithms to downscale the ERA5 reanalysis wind speed data, and finally obtained the daily wind speed datasets with 1 km spatial resolution for China in 2015. To verify the accuracy of the model and data products, we selected several metrics to evaluate in conjunction with the actual site observed data. The results show that the multifactor combination model of artificial neural network combining land surface temperature, sunshine durations and roughness factors outperforms a single‐factor combination model, and the results were in good agreement with the original data ( R 2 of 0.95 and RMSE of 0.40 m·s −1 ). The final wind speed data products were also in good agreement with the observed meteorological data ( R 2 range of 0.86–0.95 and RMSE range of 0.33–0.44 m·s −1 ); moreover, the accuracy and precision were greatly improved over the original data. This study provided a dataset that has potential applications in future climate change and ecosystem studies.