Table of Contents
Atmosphere, Volume 11, Issue 3 (March 2020) – 92 articles
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Cover Story (view full-size image) One of the most crucial applications of radar-based precipitation nowcasting systems is the [...] Read more. One of the most crucial applications of radar-based precipitation nowcasting systems is the short-term forecast of extreme rainfall. A novel method to improve deep learning prediction skills of extreme rainfall regimes is presented based on model stacking. A convolutional neural network is trained to combine an ensemble of deep learning models along with orographic features, doubling the prediction skills with respect to the ensemble members and their average on extreme rain rates, and outperforming them on all rain regimes. The ensemble is built by training four models with the same architecture over different rainfall thresholds. This combination can reach the same skill of Lagrangian persistence on extreme rain rates while retaining a superior performance on lower rain regimes. View this paper.