Deep Learning Postprocessing to Enhance Subseasonal Soil Moisture Forecasts Across Europe

Flash droughts are a unique natural hazard characterized by their sudden onset and rapid intensification. Accurate and reliable forecasts on subseasonal-to-seasonal (S2S) timescales are crucial for effective preparation and mitigation of the impacts of these events. To enhance the accuracy of soil moisture predictions—a key factor in identifying flash droughts—we propose a hybrid modeling approach that integrates state-of-the-art dynamical forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) with deep learning techniques (DL).

We use a set of DL models of different complexity for post-processing soil moisture forecasts to not only improve S2S forecasts by correcting systematic errors inherent in numerical weather prediction models, but also to enhance the spatial resolution of the forecasts.  This downscaling process is crucial as it addresses a common limitation in S2S forecasts, the coarse spatial resolution that can overlook some variations in soil moisture at a higher spatial scale. By using deterministic inputs, such as the mean and spread from the ensemble forecasting system, we further assess forecast uncertainty through dropout neural networks via Monte Carlo (MC) sampling. This technique allows us to generate probabilistic forecasts by applying MC dropout during the testing phase, thereby generating probabilistic forecasts. Our results show that the DL models outperform the S2S forecasts and lead to skillful S2S forecasts. This advanced modeling framework aims to deliver skillful soil moisture S2S forecasts, ultimately contributing to more effective strategies for managing and mitigating the effects of flash drought events.