  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.
