Dynamically Typed

Sea ice forecasting with IceNet

IceNet is a new probabilistic, deep learning sea ice forecasting system “trained on climate simulations and observational data to forecast the next 6 months of monthly-averaged sea ice concentration maps.” It’s a U-Net model that uses 50 climate variables as input, and outputs discrete probability distributions for three different sea ice concentration classes at each grid cell. Coolest (haha) part: “IceNet runs over 2000 times faster on a laptop than SEAS5 running on a supercomputer, taking less than ten seconds on a single graphics processing unit.” Practical use cases are in planning shipping routes and in avoiding conflicts between ships and migrating walruses and whales. Pretty cool.