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- W3193710367 abstract "Precipitation nowcasting refers to the prediction of rainfall with high spatiotemporal resolutions in a timely and accurate manner for the next 6 hours. The skillful and high-quality rainfall forecasts meet various operational needs in support of rainstorm monitoring, alerting or warning systems that are invaluable to weather services, and disaster risk reduction of high-impact weather or rainstorms for protecting people's lives. Conventional approaches that rely on expert knowledge are not easy to generalize and require considerable computational cost. Recently, deep learning (DL)-based methods for precipitation nowcasting have shown promise in overcoming these problems. In this chapter, we introduce current progress of DL-based methods for precipitation nowcasting. Firstly, we mathematically formulate precipitation nowcasting as a spatiotemporal sequence forecasting problem and introduce several general learning strategies. After that, we provide a comprehensive review of existing DL-based models and introduce a systematic benchmark for performance evaluation. Finally, future research directions on development of DL in precipitation nowcasting and meteorological forecasting applications are discussed." @default.
- W3193710367 created "2021-08-30" @default.
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- W3193710367 date "2021-08-20" @default.
- W3193710367 modified "2023-10-18" @default.
- W3193710367 title "Deep Learning and the Weather Forecasting Problem: Precipitation Nowcasting" @default.
- W3193710367 doi "https://doi.org/10.1002/9781119646181.ch15" @default.
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