Deep learning prediction of two-dimensional ocean dynamics with wavelet-compressed data.

Abstract

This study addresses the challenge represented by the application of deep learning models to the prediction of ocean dynamics using datasets over a large region or with high spatial or temporal resolution In a previous study by the authors of this article, they showed that such a challenge could be met by using a divide and conquer approach. The domain was in fact split into multiple sub-regions, which were small enough to be predicted individually and in parallel with each other by a deep learning model. At each time step of the prediction process, the sub-model solutions would be merged at the boundary of each sub-region to remove discontinuities between consecutive domains in order to predict the evolution of the full domain. This approach led to the growth of non-dynamical errors that decreased the prediction skill of our model. In the study herein, we show that wavelets can be used to compress the data and reduce its dimension. Each compression level reduces by a factor of two the horizontal resolution of the dataset. We show that despite the loss of information, a level 3 compression produces an improved prediction of the ocean two-dimensional data in comparison to the divide and conquer approach. Our method is evaluated on the prediction of the sea surface height of the most energetic feature of the Gulf of Mexico, namely the Loop Current.