Physical Knowledge Enhanced Deep Neural Network for Sea Surface Temperature Prediction

• URL: http://arxiv.org/abs/2304.09376v1
• Date: Wed, 19 Apr 2023 02:08:54 GMT
• Title: Physical Knowledge Enhanced Deep Neural Network for Sea Surface Temperature Prediction
• Authors: Yuxin Meng, Feng Gao, Eric Rigall, Ran Dong, Junyu Dong, Qian Du
• Abstract summary: We introduce a method for Sea Surface Temperature (SST) prediction that transfers physical knowledge from historical observations to numerical models. Specifically, we use a combination of an encoder and a generative adversarial network (GAN) to capture physical knowledge from the observed data. The numerical model data is then fed into the pre-trained model to generate physics-enhanced data, which can then be used for SST prediction.
• Score: 29.989387641655625
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traditionally, numerical models have been deployed in oceanography studies to simulate ocean dynamics by representing physical equations. However, many factors pertaining to ocean dynamics seem to be ill-defined. We argue that transferring physical knowledge from observed data could further improve the accuracy of numerical models when predicting Sea Surface Temperature (SST). Recently, the advances in earth observation technologies have yielded a monumental growth of data. Consequently, it is imperative to explore ways in which to improve and supplement numerical models utilizing the ever-increasing amounts of historical observational data. To this end, we introduce a method for SST prediction that transfers physical knowledge from historical observations to numerical models. Specifically, we use a combination of an encoder and a generative adversarial network (GAN) to capture physical knowledge from the observed data. The numerical model data is then fed into the pre-trained model to generate physics-enhanced data, which can then be used for SST prediction. Experimental results demonstrate that the proposed method considerably enhances SST prediction performance when compared to several state-of-the-art baselines.

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