Physical Knowledge Enhanced Deep Neural Network for Sea Surface Temperature Prediction
This work addresses the challenge of ill-defined ocean dynamics in oceanography by supplementing numerical models with data-driven physical knowledge, though it appears incremental as it builds on existing GAN and encoder techniques.
The paper tackles the problem of improving Sea Surface Temperature (SST) prediction by transferring physical knowledge from historical observational data to numerical models, resulting in a method that considerably enhances prediction performance compared to state-of-the-art baselines.
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.