Improving Predictions of Convective Storm Wind Gusts through Statistical Post-Processing of Neural Weather Models
This work addresses the need for timely severe weather warnings to mitigate disasters, offering incremental improvements in wind gust forecasting for meteorologists and early-warning systems.
The paper tackled the problem of forecasting convective storm wind gusts by applying statistical and deep learning post-processing methods to Neural Weather Models, resulting in improved predictions that outperformed direct approaches across lead times and wind gust speeds, with specific gains in probabilistic forecasting using generalized extreme-value distributions in Switzerland.
Issuing timely severe weather warnings helps mitigate potentially disastrous consequences. Recent advancements in Neural Weather Models (NWMs) offer a computationally inexpensive and fast approach for forecasting atmospheric environments on a 0.25° global grid. For thunderstorms, these environments can be empirically post-processed to predict wind gust distributions at specific locations. With the Pangu-Weather NWM, we apply a hierarchy of statistical and deep learning post-processing methods to forecast hourly wind gusts up to three days ahead. To ensure statistical robustness, we constrain our probabilistic forecasts using generalised extreme-value distributions across five regions in Switzerland. Using a convolutional neural network to post-process the predicted atmospheric environment's spatial patterns yields the best results, outperforming direct forecasting approaches across lead times and wind gust speeds. Our results confirm the added value of NWMs for extreme wind forecasting, especially for designing more responsive early-warning systems.