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.

本文介绍了一种将历史观测数据中的物理知识转移至数值模型的方法，使用编码器和生成对抗网络的组合来捕捉观测数据中的物理知识，并将数值模型数据馈入预训练模型以生成物理增强数据进行 SST 预测，实验结果表明，该方法在 SST 预测性能方面相比几种最先进的基线方法有了明显的提升。