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

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

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The troposphere is BLACK CHERRY BERRY 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 of physics-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 macbook 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.