Diffusion Model-based Probabilistic Downscaling for 180-year East Asian Climate Reconstruction
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917630608146432 |
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| author | Ling, Fenghua Lu, Zeyu Luo, Jing-Jia Bai, Lei Behera, Swadhin K. Jin, Dachao Pan, Baoxiang Jiang, Huidong Yamagata, Toshio |
| author_facet | Ling, Fenghua Lu, Zeyu Luo, Jing-Jia Bai, Lei Behera, Swadhin K. Jin, Dachao Pan, Baoxiang Jiang, Huidong Yamagata, Toshio |
| contents | As our planet is entering into the "global boiling" era, understanding regional climate change becomes imperative. Effective downscaling methods that provide localized insights are crucial for this target. Traditional approaches, including computationally-demanding regional dynamical models or statistical downscaling frameworks, are often susceptible to the influence of downscaling uncertainty. Here, we address these limitations by introducing a diffusion probabilistic downscaling model (DPDM) into the meteorological field. This model can efficiently transform data from 1° to 0.1° resolution. Compared with deterministic downscaling schemes, it not only has more accurate local details, but also can generate a large number of ensemble members based on probability distribution sampling to evaluate the uncertainty of downscaling. Additionally, we apply the model to generate a 180-year dataset of monthly surface variables in East Asia, offering a more detailed perspective for understanding local scale climate change over the past centuries. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_06646 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Diffusion Model-based Probabilistic Downscaling for 180-year East Asian Climate Reconstruction Ling, Fenghua Lu, Zeyu Luo, Jing-Jia Bai, Lei Behera, Swadhin K. Jin, Dachao Pan, Baoxiang Jiang, Huidong Yamagata, Toshio Atmospheric and Oceanic Physics Machine Learning Geophysics As our planet is entering into the "global boiling" era, understanding regional climate change becomes imperative. Effective downscaling methods that provide localized insights are crucial for this target. Traditional approaches, including computationally-demanding regional dynamical models or statistical downscaling frameworks, are often susceptible to the influence of downscaling uncertainty. Here, we address these limitations by introducing a diffusion probabilistic downscaling model (DPDM) into the meteorological field. This model can efficiently transform data from 1° to 0.1° resolution. Compared with deterministic downscaling schemes, it not only has more accurate local details, but also can generate a large number of ensemble members based on probability distribution sampling to evaluate the uncertainty of downscaling. Additionally, we apply the model to generate a 180-year dataset of monthly surface variables in East Asia, offering a more detailed perspective for understanding local scale climate change over the past centuries. |
| title | Diffusion Model-based Probabilistic Downscaling for 180-year East Asian Climate Reconstruction |
| topic | Atmospheric and Oceanic Physics Machine Learning Geophysics |
| url | https://arxiv.org/abs/2402.06646 |