ML postprocessing doubles 2–6 week weather forecast accuracy, paper finds
Researchers including scientists from MIT developed a machine-learning postprocessing model that learns and corrects errors in existing forecasting systems; when applied to a gold-standard model it doubled forecasting accuracy in the 2–6 week (subseasonal) range, placed first in an international forecasting competition, and for the first time beat climatological averages, the team reports in Nature. Coauthors include Judah Cohen, Lester Mackey, Microsoft researchers, ECMWF contributors, and others.