DuoCast: Duo-Probabilistic Diffusion for Precipitation Nowcasting
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866918336070156288 |
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| author | Wen, Penghui He, Mengwei Filippi, Patrick Zhao, Na Zhang, Feng Bishop, Thomas Francis Wang, Zhiyong Hu, Kun |
| author_facet | Wen, Penghui He, Mengwei Filippi, Patrick Zhao, Na Zhang, Feng Bishop, Thomas Francis Wang, Zhiyong Hu, Kun |
| contents | Accurate short-term precipitation forecasting is critical for weather-sensitive decision-making in agriculture, transportation, and disaster response. Existing deep learning approaches often struggle to balance global structural consistency with local detail preservation, especially under complex meteorological conditions. We propose DuoCast, a dual-diffusion framework that decomposes precipitation forecasting into low- and high-frequency components modeled in orthogonal latent subspaces. We theoretically prove that this frequency decomposition reduces prediction error compared to conventional single branch U-Net diffusion models. In DuoCast, the low-frequency model captures large-scale trends via convolutional encoders conditioned on weather front dynamics, while the high-frequency model refines fine-scale variability using a self-attention-based architecture. Experiments on four benchmark radar datasets show that DuoCast consistently outperforms state-of-the-art baselines, achieving superior accuracy in both spatial detail and temporal evolution. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_01091 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DuoCast: Duo-Probabilistic Diffusion for Precipitation Nowcasting Wen, Penghui He, Mengwei Filippi, Patrick Zhao, Na Zhang, Feng Bishop, Thomas Francis Wang, Zhiyong Hu, Kun Computer Vision and Pattern Recognition Accurate short-term precipitation forecasting is critical for weather-sensitive decision-making in agriculture, transportation, and disaster response. Existing deep learning approaches often struggle to balance global structural consistency with local detail preservation, especially under complex meteorological conditions. We propose DuoCast, a dual-diffusion framework that decomposes precipitation forecasting into low- and high-frequency components modeled in orthogonal latent subspaces. We theoretically prove that this frequency decomposition reduces prediction error compared to conventional single branch U-Net diffusion models. In DuoCast, the low-frequency model captures large-scale trends via convolutional encoders conditioned on weather front dynamics, while the high-frequency model refines fine-scale variability using a self-attention-based architecture. Experiments on four benchmark radar datasets show that DuoCast consistently outperforms state-of-the-art baselines, achieving superior accuracy in both spatial detail and temporal evolution. |
| title | DuoCast: Duo-Probabilistic Diffusion for Precipitation Nowcasting |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.01091 |