Papers › Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model
Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model
Xingjian Shi, Zhihan Gao, Leonard Lausen, Hao Wang, Dit-yan Yeung, Wai-kin Wong, Wang-chun Woo
With the goal of making high-resolution forecasts of regional rainfall, precipitation nowcasting has become an important and fundamental technology underlying various public services ranging from rainstorm warnings to flight safety. Recently, the Convolutional LSTM (ConvLSTM) model has been shown to outperform traditional optical flow based methods for precipitation nowcasting, suggesting that deep learning models have a huge potential for solving the problem. However, the convolutional recurrence structure in ConvLSTM-based models is location-invariant while natural motion and transformation (e.g., rotation) are location-variant in general. Furthermore, since deep-learning-based precipitation nowcasting is a newly emerging area, clear evaluation protocols have not yet been established. To address these problems, we propose both a new model and a benchmark for precipitation nowcasting. Specifically, we go beyond ConvLSTM and propose the Trajectory GRU (TrajGRU) model that can actively learn the location-variant structure for recurrent connections. Besides, we provide a benchmark that includes a real-world large-scale dataset from the Hong Kong Observatory, a new training loss, and a comprehensive evaluation protocol to facilitate future research and gauge the state of the art.
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Code
Syntology Ran 9 of 10 code samples harvested from 2 repositories linked to this paper; 1 has no recorded run. Of those that ran: 1 ran · violated contract; 6 ran · our draft was wrong; 2 ran · fixture could not drive it.
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Code Syntology ran Syntology
10 samples harvested; 9 ran; 0 honoured the contract we drafted; 1 has no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Video Prediction | KTH | TrajGRU | Cond | 10 | #27 of 31 | Archive leaderboard | report |
| Video Prediction | KTH | TrajGRU | PSNR | 26.97 | #27 of 31 | Archive leaderboard | report |
| Video Prediction | KTH | TrajGRU | Pred | 20 | #27 of 31 | Archive leaderboard | report |
| Video Prediction | KTH | TrajGRU | SSIM | 0.790 | #27 of 31 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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