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Open AccessArticle

Precipitation Nowcasting with Orographic Enhanced Stacked Generalization: Improving Deep Learning Predictions on Extreme Events

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Predictive Models for Biomedicine and Environment, Fondazione Bruno Kessler, 38123 Trento, Italy
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Department of Information Engineering and Computer Science (DISI), University of Trento, 38123 Trento, Italy
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Federal Office of Meteorology and Climatology, MeteoSwiss, 6605 Locarno, Switzerland
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Meteotrentino, 38122 Trento, Italy
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HK3 Lab, 20129 Milano, Italy
*
Author to whom correspondence should be addressed.
Joint last author.
Atmosphere 2020, 11(3), 267; https://doi.org/10.3390/atmos11030267
Received: 4 February 2020 / Revised: 5 March 2020 / Accepted: 5 March 2020 / Published: 7 March 2020
One of the most crucial applications of radar-based precipitation nowcasting systems is the short-term forecast of extreme rainfall events such as flash floods and severe thunderstorms. While deep learning nowcasting models have recently shown to provide better overall skill than traditional echo extrapolation models, they suffer from conditional bias, sometimes reporting lower skill on extreme rain rates compared to Lagrangian persistence, due to excessive prediction smoothing. This work presents a novel method to improve deep learning prediction skills in particular for extreme rainfall regimes. The solution is based on model stacking, where a convolutional neural network is trained to combine an ensemble of deep learning models 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 proposed architecture was applied on the recently released TAASRAD19 radar dataset: the initial ensemble was built by training four models with the same TrajGRU architecture over different rainfall thresholds on the first six years of the dataset, while the following three years of data were used for the stacked model. The stacked model can reach the same skill of Lagrangian persistence on extreme rain rates while retaining superior performance on lower rain regimes. View Full-Text
Keywords: rainfall; nowcasting; deep learning; stacked generalization; convolutional recurrent neural networks; data augmentation; conditional bias; ensemble forecasting rainfall; nowcasting; deep learning; stacked generalization; convolutional recurrent neural networks; data augmentation; conditional bias; ensemble forecasting
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Franch, G.; Nerini, D.; Pendesini, M.; Coviello, L.; Jurman, G.; Furlanello, C. Precipitation Nowcasting with Orographic Enhanced Stacked Generalization: Improving Deep Learning Predictions on Extreme Events. Atmosphere 2020, 11, 267.

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