DiffSR: Learning Radar Reflectivity Synthesis via Diffusion Model from Satellite Observations
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909383492894720 |
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| author | He, Xuming Zhou, Zhiwang Zhang, Wenlong Zhao, Xiangyu Chen, Hao Chen, Shiqi Bai, Lei |
| author_facet | He, Xuming Zhou, Zhiwang Zhang, Wenlong Zhao, Xiangyu Chen, Hao Chen, Shiqi Bai, Lei |
| contents | Weather radar data synthesis can fill in data for areas where ground observations are missing. Existing methods often employ reconstruction-based approaches with MSE loss to reconstruct radar data from satellite observation. However, such methods lead to over-smoothing, which hinders the generation of high-frequency details or high-value observation areas associated with convective weather. To address this issue, we propose a two-stage diffusion-based method called DiffSR. We first pre-train a reconstruction model on global-scale data to obtain radar estimation and then synthesize radar reflectivity by combining radar estimation results with satellite data as conditions for the diffusion model. Extensive experiments show that our method achieves state-of-the-art (SOTA) results, demonstrating the ability to generate high-frequency details and high-value areas. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_06714 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DiffSR: Learning Radar Reflectivity Synthesis via Diffusion Model from Satellite Observations He, Xuming Zhou, Zhiwang Zhang, Wenlong Zhao, Xiangyu Chen, Hao Chen, Shiqi Bai, Lei Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Weather radar data synthesis can fill in data for areas where ground observations are missing. Existing methods often employ reconstruction-based approaches with MSE loss to reconstruct radar data from satellite observation. However, such methods lead to over-smoothing, which hinders the generation of high-frequency details or high-value observation areas associated with convective weather. To address this issue, we propose a two-stage diffusion-based method called DiffSR. We first pre-train a reconstruction model on global-scale data to obtain radar estimation and then synthesize radar reflectivity by combining radar estimation results with satellite data as conditions for the diffusion model. Extensive experiments show that our method achieves state-of-the-art (SOTA) results, demonstrating the ability to generate high-frequency details and high-value areas. |
| title | DiffSR: Learning Radar Reflectivity Synthesis via Diffusion Model from Satellite Observations |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2411.06714 |