Physics-guided generative adversarial networks for sea subsurface temperature prediction

Abstract

Sea subsurface temperature, an essential component of aquatic wildlife, underwater dynamics, and heat transfer with the sea surface, is affected by global warming in climate change. Existing research is commonly based on either physics-based numerical models or data-based models. Physical modeling and machine learning are traditionally considered as two unrelated fields for the sea subsurface temperature prediction task, with very different scientific paradigms (physics-driven and data-driven). However, we believe that both methods are complementary to each other. Physical modeling methods can offer the potential for extrapolation beyond observational conditions, while data-driven methods are flexible in adapting to data and are capable of detecting unexpected patterns. The combination of both approaches is very attractive and offers potential performance improvement. In this article, we propose a novel framework based on a generative adversarial network (GAN) combined with a numerical model to predict sea subsurface temperature. First, a GAN-based model is used to learn the simplified physics between the surface temperature and the target subsurface temperature in the numerical model. Then, observation data are used to calibrate the GAN-based model parameters to obtain a better prediction. We evaluate the proposed framework by predicting daily sea subsurface temperature in the South China Sea. Extensive experiments demonstrate the effectiveness of the proposed framework compared to existing state-of-the-art methods.

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