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arXiv · 2107.06991

Physics-informed generative neural network: an application to troposphere temperature prediction

Zhihao Chen·Jie Gao·Weikai Wang·Zheng Yan
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Abstract

The troposphere is one of the atmospheric layers where most weather phenomena occur. Temperature variations in the troposphere, especially at 500 hPa, a typical level of the middle troposphere, are significant indicators of future weather changes. Numerical weather prediction is effective for temperature prediction, but its computational complexity hinders a timely response. This paper proposes a novel temperature prediction approach in framework ofphysics-informed deep learning. The new model, called PGnet, builds upon a generative neural network with a mask matrix. The mask is designed to distinguish the low-quality predicted regions generated by the first physical stage. The generative neural network takes the mask as prior for the second-stage refined predictions. A mask-loss and a jump pattern strategy are developed to train the generative neural network without accumulating errors during making time-series predictions. Experiments on ERA5 demonstrate that PGnet can generate more refined temperature predictions than the state-of-the-art.

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Zhihao Chen, Jie Gao, Weikai Wang, Zheng Yan. 2021-07-08. Physics-informed generative neural network: an application to troposphere temperature prediction. https://doi.org/10.1088/1748-9326/abfde9

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