Physics-Guided Generative Adversarial Networks for Sea Subsurface Temperature Prediction. (arXiv:2111.03064v1 [cs.LG])

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 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 paper, we propose a novel
framework based on generative adversarial network (GAN) combined with 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 numerical model. Then, observation data are used to
calibrate the GAN-based model parameters to obtain 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.