Surrogate Modelling for Sea Ice Concentration using Lightweight Neural
  Ensemble
Julia Borisova Nikolay O. Nikitin
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.