DEEP LEARNING ON THE SPHERE FOR MULTI-MODEL ENSEMBLING OF SIGNIFICANT WAVE HEIGHT

When working with geophysical variables on a global scale, a solution for processing data on the surface of a sphere is needed. At the same time, region-specific dynamics that deviate from the general behavior across the globe also need to be accounted for. Addressing these two necessities, we propose the first Deep Learning approach for multi-model ensembling that operates directly on the sphere. Our methodology allows to progressively allocate region-specific model complexity, guided by the clustering of the model forecasting errors. We evaluate our proposed method on a multi-model ensembling application of significant wave height, where the proposed method is shown to outperform 2D CNNs with less than half the parameters needed, while producing comparable results to models with more than 10 times the number of parameters.