Physics-Guided Generative Adversarial Networks for Sea Subsurface Temperature Prediction

Abstract

This work has been accepted by IEEE TNNLS for publication. 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 datadriven). However, we believe both methods are complementary to each other. Physical modeling methods can offer the potential for extrapolation beyond observational conditions, while datadriven 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.

MCSTNet: a memory-contextual spatiotemporal transfer network for prediction of SST sequences and fronts with remote sensing data

“…Wei et al (2020) employ a neural network to forecast South China Sea temperature based on the Ice Analysis (OSTIA) data. Likewise, Meng et al (2021) propose a generative adversarial network (GAN) based on physics-guided learning and apply observation data from the South China Sea to calibrate parameters, improving the prediction performance of sea subsurface temperature. In addition, Zheng et al (2020) propose a DL network with a bias correction and a DNN to predict SST data and then tropical instability waves (TIWs) based on the predicted SST data.…”