FNP: Fourier Neural Processes for Arbitrary-Resolution Data Assimilation
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arXiv
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| Format: | Preprint |
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2024
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| author | Chen, Kun Chen, Tao Ye, Peng Chen, Hao Chen, Kang Han, Tao Ouyang, Wanli Bai, Lei |
| author_facet | Chen, Kun Chen, Tao Ye, Peng Chen, Hao Chen, Kang Han, Tao Ouyang, Wanli Bai, Lei |
| contents | Data assimilation is a vital component in modern global medium-range weather forecasting systems to obtain the best estimation of the atmospheric state by combining the short-term forecast and observations. Recently, AI-based data assimilation approaches have attracted increasing attention for their significant advantages over traditional techniques in terms of computational consumption. However, existing AI-based data assimilation methods can only handle observations with a specific resolution, lacking the compatibility and generalization ability to assimilate observations with other resolutions. Considering that complex real-world observations often have different resolutions, we propose the \textit{\textbf{Fourier Neural Processes}} (FNP) for \textit{arbitrary-resolution data assimilation} in this paper. Leveraging the efficiency of the designed modules and flexible structure of neural processes, FNP achieves state-of-the-art results in assimilating observations with varying resolutions, and also exhibits increasing advantages over the counterparts as the resolution and the amount of observations increase. Moreover, our FNP trained on a fixed resolution can directly handle the assimilation of observations with out-of-distribution resolutions and the observational information reconstruction task without additional fine-tuning, demonstrating its excellent generalization ability across data resolutions as well as across tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_01645 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FNP: Fourier Neural Processes for Arbitrary-Resolution Data Assimilation Chen, Kun Chen, Tao Ye, Peng Chen, Hao Chen, Kang Han, Tao Ouyang, Wanli Bai, Lei Machine Learning Artificial Intelligence Data assimilation is a vital component in modern global medium-range weather forecasting systems to obtain the best estimation of the atmospheric state by combining the short-term forecast and observations. Recently, AI-based data assimilation approaches have attracted increasing attention for their significant advantages over traditional techniques in terms of computational consumption. However, existing AI-based data assimilation methods can only handle observations with a specific resolution, lacking the compatibility and generalization ability to assimilate observations with other resolutions. Considering that complex real-world observations often have different resolutions, we propose the \textit{\textbf{Fourier Neural Processes}} (FNP) for \textit{arbitrary-resolution data assimilation} in this paper. Leveraging the efficiency of the designed modules and flexible structure of neural processes, FNP achieves state-of-the-art results in assimilating observations with varying resolutions, and also exhibits increasing advantages over the counterparts as the resolution and the amount of observations increase. Moreover, our FNP trained on a fixed resolution can directly handle the assimilation of observations with out-of-distribution resolutions and the observational information reconstruction task without additional fine-tuning, demonstrating its excellent generalization ability across data resolutions as well as across tasks. |
| title | FNP: Fourier Neural Processes for Arbitrary-Resolution Data Assimilation |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2406.01645 |