Lightweight Neural Ensemble Approach for Arctic Sea Ice Forecasting

Julia Borisova, Nikolay O. Nikitin

Abstract: Predictive modeling of sea ice conditions in the Arctic region is important task for environmental monitoring, climate change issue and offshore oil production. The existing physics-based and data-driven solutions for ice forecasting are usually not flexible enough to satisfy the domain-specific requirements. Therefore, we propose the lightweight adaptive modeling approach named LANE-SI (Lightweight Automated Neural Ensembling for Sea Ice). It use ensemble of simple encoder-decoder architecture deep learning models with differ-ent loss functions for forecasting of spatial distribution for sea ice concentration in the specified water area. Experimental studies confirm the quality of a long-term fore-cast based on a deep learning model fitted to the specific water area is comparable to resource-intensive physical modeling, and for some periods of the year, it is superior. We achieved a 20 % improvement against the state-of-the-art physics-based forecast system SEAS5 for the Kara Sea.