Articles | Volume 25, issue 1
https://doi.org/10.5194/nhess-25-41-2025
https://doi.org/10.5194/nhess-25-41-2025
Brief communication
 | 
03 Jan 2025
Brief communication |  | 03 Jan 2025

Brief communication: Training of AI-based nowcasting models for rainfall early warning should take into account user requirements

Georgy Ayzel and Maik Heistermann

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Cited articles

Agrawal, S., Barrington, L., Bromberg, C., Burge, J., Gazen, C., and Hickey, J.: Machine Learning for Precipitation Nowcasting from Radar Images, arXiv [preprint], https://doi.org/10.48550/arXiv.1912.12132, 2019. a
Ayzel, G.: The RainNet2024 family of models for precipitation nowcasting, Zenodo [code], https://doi.org/10.5281/zenodo.12547127, 2024. a
Ayzel, G., Scheffer, T., and Heistermann, M.: RainNet v1.0: a convolutional neural network for radar-based precipitation nowcasting, Geosci. Model Dev., 13, 2631–2644, https://doi.org/10.5194/gmd-13-2631-2020, 2020. a, b, c, d, e, f
DWD: Warnkriterien für Starkregen, https://www.dwd.de/DE/wetter/warnungen_aktuell/kriterien/warnkriterien.html?nn=508722#doc453962bodyText3 (last access: 2 January 2025), 2024. a
Franch, G., Nerini, D., Pendesini, M., Coviello, L., Jurman, G., and Furlanello, C.: Precipitation Nowcasting with Orographic Enhanced Stacked Generalization: Improving Deep Learning Predictions on Extreme Events, Atmosphere, 11, 267, https://doi.org/10.3390/atmos11030267, 2020. a
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Short summary
Forecasting rainfall over the next hour is an essential feature of early warning systems. Deep learning (DL) has emerged as a powerful alternative to conventional nowcasting technologies, but it still struggles to adequately predict impact-relevant heavy rainfall. We think that DL could do much better if the training tasks were defined more specifically and that such specification presents an opportunity to better align the output of nowcasting models with actual user requirements.
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