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.

翻译：传统上，数值模型通过表示物理方程来模拟海洋动力学，被广泛应用于海洋学研究。然而，许多与海洋动力学相关的因素似乎难以明确定义。我们认为，从观测数据中迁移物理知识能够进一步提高数值模型在预测海面温度（SST）时的精度。近年来，地球观测技术的进步带来了数据的巨大增长。因此，探索如何利用日益增长的历史观测数据来改进和补充数值模型至关重要。为此，我们提出了一种海面温度预测方法，该方法将物理知识从历史观测数据迁移到数值模型中。具体而言，我们结合使用编码器和生成对抗网络（GAN）从观测数据中捕获物理知识，然后将数值模型数据输入预训练模型以生成物理增强数据，进而用于海面温度预测。实验结果表明，与多种最先进的基线方法相比，所提出的方法显著提升了海面温度预测性能。