A Deep‐Learning‐Based Wind Gust Estimation Scheme in Complex Terrain

Abstract Accurate prediction of gusts is critical for risk mitigation, but the turbulent processes that generate gusts are unresolved in conventional numerical weather prediction models. Here we introduce a deep‐learning‐based wind gust estimation scheme (DL‐GUST) that uses archived diagnostics from the Weather Research and Forecasting (WRF) model within a long short‐term memory (LSTM) network as an offline, one‐way gust‐estimation framework. Evaluated over the northwestern inland region of China, DL‐GUST achieves a root‐mean‐square error (RMSE) of 1.98 m and a Pearson correlation coefficient (PCC) of 0.93 on an independent temporally separated test set. Relative to empirical, statistical, and physics‐based baseline methods, RMSE is reduced by 45%–60%, and by 50%–65% for the strongest gust events from the four test months. Additional validation over the European Alps and the southeastern coastal region of China shows useful but non‐uniform transfer skill across different surface environments and weather conditions.