MambaDS: Near-Surface Meteorological Field Downscaling with Topography Constrained Selective State Space Modeling
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866914917829836800 |
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| author | Liu, Zili Chen, Hao Bai, Lei Li, Wenyuan Ouyang, Wanli Zou, Zhengxia Shi, Zhenwei |
| author_facet | Liu, Zili Chen, Hao Bai, Lei Li, Wenyuan Ouyang, Wanli Zou, Zhengxia Shi, Zhenwei |
| contents | In an era of frequent extreme weather and global warming, obtaining precise, fine-grained near-surface weather forecasts is increasingly essential for human activities. Downscaling (DS), a crucial task in meteorological forecasting, enables the reconstruction of high-resolution meteorological states for target regions from global-scale forecast results. Previous downscaling methods, inspired by CNN and Transformer-based super-resolution models, lacked tailored designs for meteorology and encountered structural limitations. Notably, they failed to efficiently integrate topography, a crucial prior in the downscaling process. In this paper, we address these limitations by pioneering the selective state space model into the meteorological field downscaling and propose a novel model called MambaDS. This model enhances the utilization of multivariable correlations and topography information, unique challenges in the downscaling process while retaining the advantages of Mamba in long-range dependency modeling and linear computational complexity. Through extensive experiments in both China mainland and the continental United States (CONUS), we validated that our proposed MambaDS achieves state-of-the-art results in three different types of meteorological field downscaling settings. We will release the code subsequently. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2408_10854 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MambaDS: Near-Surface Meteorological Field Downscaling with Topography Constrained Selective State Space Modeling Liu, Zili Chen, Hao Bai, Lei Li, Wenyuan Ouyang, Wanli Zou, Zhengxia Shi, Zhenwei Atmospheric and Oceanic Physics Artificial Intelligence Computer Vision and Pattern Recognition In an era of frequent extreme weather and global warming, obtaining precise, fine-grained near-surface weather forecasts is increasingly essential for human activities. Downscaling (DS), a crucial task in meteorological forecasting, enables the reconstruction of high-resolution meteorological states for target regions from global-scale forecast results. Previous downscaling methods, inspired by CNN and Transformer-based super-resolution models, lacked tailored designs for meteorology and encountered structural limitations. Notably, they failed to efficiently integrate topography, a crucial prior in the downscaling process. In this paper, we address these limitations by pioneering the selective state space model into the meteorological field downscaling and propose a novel model called MambaDS. This model enhances the utilization of multivariable correlations and topography information, unique challenges in the downscaling process while retaining the advantages of Mamba in long-range dependency modeling and linear computational complexity. Through extensive experiments in both China mainland and the continental United States (CONUS), we validated that our proposed MambaDS achieves state-of-the-art results in three different types of meteorological field downscaling settings. We will release the code subsequently. |
| title | MambaDS: Near-Surface Meteorological Field Downscaling with Topography Constrained Selective State Space Modeling |
| topic | Atmospheric and Oceanic Physics Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.10854 |