Lightweight Neural Ensemble Approach for Arctic Sea Ice Forecasting

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.