<ul><li>Issuing timely severe weather warnings helps mitigate potentially disastrous consequences.</li><li>Neural Weather Models (NWMs) offer a computationally inexpensive and fast approach for forecasting atmospheric environments.</li><li>The study applied statistical and deep learning post-processing methods to forecast wind gusts using NWMs.</li><li>Results confirmed the added value of NWMs for extreme wind forecasting and designing more responsive early-warning systems.</li></ul>

Improving Predictions of Convective Storm Wind Gusts through Statistical Post-Processing of Neural Weather Models

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