FNP: Fourier Neural Processes for Arbitrary-Resolution Data Assimilation

• URL: http://arxiv.org/abs/2406.01645v1
• Date: Mon, 3 Jun 2024 12:24:24 GMT
• Title: FNP: Fourier Neural Processes for Arbitrary-Resolution Data Assimilation
• Authors: Kun Chen, Tao Chen, Peng Ye, Hao Chen, Kang Chen, Tao Han, Wanli Ouyang, Lei Bai,
• Abstract summary: We propose textittextbfFourier Neural Processes (FNP) for textitarbitrary-resolution data assimilation in this paper. 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.
• Score: 58.149902193341816
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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