Surrogate Modelling for Sea Ice Concentration using Lightweight Neural Ensemble
This work addresses sea ice prediction for ship routing and environmental monitoring, offering a more efficient alternative to resource-intensive physical models.
The authors tackled sea ice concentration forecasting by developing LANE-SI, a lightweight neural ensemble method, achieving a 20% improvement over the physics-based SEAS5 system for the Kara Sea.
The modeling and forecasting of sea ice conditions in the Arctic region are important tasks for ship routing, offshore oil production, and environmental monitoring. We propose the adaptive surrogate modeling approach named LANE-SI (Lightweight Automated Neural Ensembling for Sea Ice) that uses ensemble of relatively simple deep learning models with different 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 forecast 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.