Learning to Infer Weather States Using Partial Observations
Abstract Accurate state estimation of the high‐dimensional, chaotic Earth's atmosphere marks a Sisyphean task, yet is indispensable for initiating weather forecasts and gauging climate variability. While much effort is devoted to assimilating observations and forecasts to infer weather states,...
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-03-01
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| Series: | Journal of Geophysical Research: Machine Learning and Computation |
| Online Access: | https://doi.org/10.1029/2024JH000260 |
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| _version_ | 1850063183871475712 |
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| author | Jie Chao Baoxiang Pan Quanliang Chen Shangshang Yang Jingnan Wang Congyi Nai Yue Zheng Xichen Li Huiling Yuan Xi Chen Bo Lu Ziniu Xiao |
| author_facet | Jie Chao Baoxiang Pan Quanliang Chen Shangshang Yang Jingnan Wang Congyi Nai Yue Zheng Xichen Li Huiling Yuan Xi Chen Bo Lu Ziniu Xiao |
| author_sort | Jie Chao |
| collection | DOAJ |
| description | Abstract Accurate state estimation of the high‐dimensional, chaotic Earth's atmosphere marks a Sisyphean task, yet is indispensable for initiating weather forecasts and gauging climate variability. While much effort is devoted to assimilating observations and forecasts to infer weather states, the inherent low‐dimensional statistical structure in atmospheric circulation, shaped by geophysical laws and geographic boundaries, is underutilized as an informative prior for state inference, or as reference for assessing representative of existing observations and planning new ones. We realize these potential by learning climatological distribution from climate reanalysis/simulation, using a deep generative model. For a case study of estimating 2 m temperature spatial patterns, the learned distribution faithfully reproduces climatology statistics. A combination of the learned climatological prior with few station observations yields strong posterior of spatial pattern estimates, which are spatially coherent, faithful and adaptive to observational constraints, and uncertainty‐aware. This allows us to evaluate each observation's value in reducing pattern estimation uncertainty, and guide optimal observation network design by pinpointing the most informative sites. Our study showcases how generative models can extract and utilize information produced in the chaotic evolution of climate system. |
| format | Article |
| id | doaj-art-7ec668e479e949529ddade6bf02b9c2c |
| institution | DOAJ |
| issn | 2993-5210 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Geophysical Research: Machine Learning and Computation |
| spelling | doaj-art-7ec668e479e949529ddade6bf02b9c2c2025-08-20T02:49:43ZengWileyJournal of Geophysical Research: Machine Learning and Computation2993-52102025-03-0121n/an/a10.1029/2024JH000260Learning to Infer Weather States Using Partial ObservationsJie Chao0Baoxiang Pan1Quanliang Chen2Shangshang Yang3Jingnan Wang4Congyi Nai5Yue Zheng6Xichen Li7Huiling Yuan8Xi Chen9Bo Lu10Ziniu Xiao11National Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric Physics Chinese Academy of Science Beijing ChinaNational Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric Physics Chinese Academy of Science Beijing ChinaSchool of Atmospheric Sciences Chengdu University of Information Technology Chengdu ChinaNational Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric Physics Chinese Academy of Science Beijing ChinaNational Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric Physics Chinese Academy of Science Beijing ChinaNational Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric Physics Chinese Academy of Science Beijing ChinaClustertech LTD Hong Kong ChinaNational Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric Physics Chinese Academy of Science Beijing ChinaKey Laboratory of Mesoscale Severe Weather Ministry of Education, and School of Atmospheric Sciences Nanjing University Nanjing ChinaNational Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric Physics Chinese Academy of Science Beijing ChinaNational Climate Center China Meteorological Administration Beijing ChinaNational Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric Physics Chinese Academy of Science Beijing ChinaAbstract Accurate state estimation of the high‐dimensional, chaotic Earth's atmosphere marks a Sisyphean task, yet is indispensable for initiating weather forecasts and gauging climate variability. While much effort is devoted to assimilating observations and forecasts to infer weather states, the inherent low‐dimensional statistical structure in atmospheric circulation, shaped by geophysical laws and geographic boundaries, is underutilized as an informative prior for state inference, or as reference for assessing representative of existing observations and planning new ones. We realize these potential by learning climatological distribution from climate reanalysis/simulation, using a deep generative model. For a case study of estimating 2 m temperature spatial patterns, the learned distribution faithfully reproduces climatology statistics. A combination of the learned climatological prior with few station observations yields strong posterior of spatial pattern estimates, which are spatially coherent, faithful and adaptive to observational constraints, and uncertainty‐aware. This allows us to evaluate each observation's value in reducing pattern estimation uncertainty, and guide optimal observation network design by pinpointing the most informative sites. Our study showcases how generative models can extract and utilize information produced in the chaotic evolution of climate system.https://doi.org/10.1029/2024JH000260 |
| spellingShingle | Jie Chao Baoxiang Pan Quanliang Chen Shangshang Yang Jingnan Wang Congyi Nai Yue Zheng Xichen Li Huiling Yuan Xi Chen Bo Lu Ziniu Xiao Learning to Infer Weather States Using Partial Observations Journal of Geophysical Research: Machine Learning and Computation |
| title | Learning to Infer Weather States Using Partial Observations |
| title_full | Learning to Infer Weather States Using Partial Observations |
| title_fullStr | Learning to Infer Weather States Using Partial Observations |
| title_full_unstemmed | Learning to Infer Weather States Using Partial Observations |
| title_short | Learning to Infer Weather States Using Partial Observations |
| title_sort | learning to infer weather states using partial observations |
| url | https://doi.org/10.1029/2024JH000260 |
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