Precipitation Downscaling with Spatiotemporal Video Diffusion
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866911925533671424 |
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| author | Srivastava, Prakhar Yang, Ruihan Kerrigan, Gavin Dresdner, Gideon McGibbon, Jeremy Bretherton, Christopher Mandt, Stephan |
| author_facet | Srivastava, Prakhar Yang, Ruihan Kerrigan, Gavin Dresdner, Gideon McGibbon, Jeremy Bretherton, Christopher Mandt, Stephan |
| contents | In climate science and meteorology, high-resolution local precipitation (rain and snowfall) predictions are limited by the computational costs of simulation-based methods. Statistical downscaling, or super-resolution, is a common workaround where a low-resolution prediction is improved using statistical approaches. Unlike traditional computer vision tasks, weather and climate applications require capturing the accurate conditional distribution of high-resolution given low-resolution patterns to assure reliable ensemble averages and unbiased estimates of extreme events, such as heavy rain. This work extends recent video diffusion models to precipitation super-resolution, employing a deterministic downscaler followed by a temporally-conditioned diffusion model to capture noise characteristics and high-frequency patterns. We test our approach on FV3GFS output, an established large-scale global atmosphere model, and compare it against six state-of-the-art baselines. Our analysis, capturing CRPS, MSE, precipitation distributions, and qualitative aspects using California and the Himalayas as examples, establishes our method as a new standard for data-driven precipitation downscaling. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_06071 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Precipitation Downscaling with Spatiotemporal Video Diffusion Srivastava, Prakhar Yang, Ruihan Kerrigan, Gavin Dresdner, Gideon McGibbon, Jeremy Bretherton, Christopher Mandt, Stephan Computer Vision and Pattern Recognition Machine Learning Atmospheric and Oceanic Physics In climate science and meteorology, high-resolution local precipitation (rain and snowfall) predictions are limited by the computational costs of simulation-based methods. Statistical downscaling, or super-resolution, is a common workaround where a low-resolution prediction is improved using statistical approaches. Unlike traditional computer vision tasks, weather and climate applications require capturing the accurate conditional distribution of high-resolution given low-resolution patterns to assure reliable ensemble averages and unbiased estimates of extreme events, such as heavy rain. This work extends recent video diffusion models to precipitation super-resolution, employing a deterministic downscaler followed by a temporally-conditioned diffusion model to capture noise characteristics and high-frequency patterns. We test our approach on FV3GFS output, an established large-scale global atmosphere model, and compare it against six state-of-the-art baselines. Our analysis, capturing CRPS, MSE, precipitation distributions, and qualitative aspects using California and the Himalayas as examples, establishes our method as a new standard for data-driven precipitation downscaling. |
| title | Precipitation Downscaling with Spatiotemporal Video Diffusion |
| topic | Computer Vision and Pattern Recognition Machine Learning Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2312.06071 |