Predicting tropical cyclones (TCs) accurately is crucial for disaster mitigation and public safety. Although the forecasting accuracy of TC tracks has improved substantially in recent decades, progress in the forecasting of TC intensity remains limited. In recent years, deep learning methods have shown great potential in TC intensity prediction; however, they still face challenges, including limited interpretability, cumbersome feature engineering, and unreliable real-time operational forecasts.
Interpretable deep learning network significantly improves tropical cyclone intensity forecast accuracy
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