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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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing Vol. 63; pp. 1 - 18
Main Authors: Luo, Wenjie, Li, Chaorong, Ling, Xudong, Deng, Chuanhu, Wang, Zhuo
Format: Journal Article
Language:English
Published: New York IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
Online Access:Get full text
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Summary: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 stagewise scheduled rainfall forecasting network (SSRF-Net), a convolutional framework for continuous multistep rainfall prediction that achieves lower floating-point operations (FLOPs) than competitive baselines under a standardized evaluation. Our framework introduces a multistage, sliding-window prediction mechanism trained with teacher forcing and scheduled sampling to mitigate error accumulation and stabilize training. We design an asymmetric encoder-decoder (E-D) architecture featuring a differential selective encoder (DSE) for selective feature compression and an additive fusion decoder (AFD) that progressively reconstructs details and alleviates over-smoothing. We further introduce an intensity-weighted Gaussian KL divergence loss that aligns sequence-level Gaussian summaries (means and variances) of predictions and ground truth via a KL term, prioritizing heavy-rain events without assuming pixelwise Gaussianity. Extensive experiments on the KNMI and SEVIR datasets show that SSRF-Net outperforms strong baselines, particularly for moderate to severe precipitation; on KNMI, it yields up to 41.8% higher per-frame critical success index (CSI) at the 30-mm/h threshold, with consistent gains on SEVIR.
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2025.3621627