SSRF-Net: A Stagewise Scheduled Rainfall Forecasting Network With an Asymmetric Architecture

Deterministic deep learning models for precipitation nowcasting often face several limitations, including cumulative error in long-sequence predictions, over-smoothing, and a reduced ability to capture rare, high-impact rainfall due to data imbalance. To address these challenges, we propose the stag...

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Vydané v:	IEEE transactions on geoscience and remote sensing Ročník 63; s. 1 - 18
Hlavní autori:	Luo, Wenjie, Li, Chaorong, Ling, Xudong, Deng, Chuanhu, Wang, Zhuo
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Asymmetric encoder–decoder (E–D) Asymmetry Coders Compression Data models Decoding Deep learning error accumulation Floating point arithmetic Forecasting iterative prediction Meteorological radar Nowcasting Precipitation precipitation nowcasting Predictions Predictive models Rain Rainfall Reliability Smoothing Spatiotemporal phenomena Training
ISSN:	0196-2892, 1558-0644
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