Machine-learning algorithm doubles accuracy of weather forecasts two to six weeks ahead
Prepared by Sintelligent with the help of SI. Published in SI: . Updated: .
For the first time, forecasts several weeks ahead are more reliable than ordinary averages, so floods and cold snaps can be prepared for in advance.
According to MIT News, researchers built a machine-learning add-on that corrects errors in existing forecasting models by learning from historical data. The method, in their words, doubled forecast accuracy at two to six weeks, beat climatological averages in this range for the first time, and took first place in an international forecasting competition. The authors include Hannah Guan (Harvard College), Soukayna Mouatadid (University of Toronto), Judah Cohen (MIT) and Lester Mackey (Microsoft). The work is published in Nature. Cohen notes that the method improves prediction of extreme events, such as floods and cold snaps, which matters for emergency services and agriculture.
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