Environmental News Network - Using Machine Learning to Improve Subseasonal Climate Forecasting

Details: By Massachusetts Institute of Technology; 13.Mar

Judah Cohen, director of seasonal forecasting at AER (Atmospheric and Environmental Research) and visiting scientist in MIT's Department of Civil and Environmental Engineering, and Ernest Fraenkel, professor of biological engineering at MIT, have won first place in three out of four temperature forecasting categories in the Sub-Seasonal Climate Forecast Rodeo competition, hosted by the National Oceanic and Atmospheric Administration and sponsored by the U.S. Bureau of Reclamation.

Mohammad Munshed

Judah Cohen, director of seasonal forecasting at AER (Atmospheric and Environmental Research) and visiting scientist in MIT's Department of Civil and Environmental Engineering, and Ernest Fraenkel, professor of biological engineering at MIT, have won first place in three out of four temperature forecasting categories in the Sub-Seasonal Climate Forecast Rodeo competition, hosted by the National Oceanic and Atmospheric Administration and sponsored by the U.S. Bureau of Reclamation.

The MIT researchers, who were joined by Stanford University PhD students Jessica Hwang and Paulo Orenstein and Microsoft researcher Lester Mackey, beat the operational long-range forecasting model used by the U.S. government.

To be eligible for the competition, the teams were required to submit their climate predictions every two weeks between April 17, 2017 and April 18, 2018. The goal was to create a model that the western United States would be able to rely on weeks in advance to help manage water resources and prepare for wildfires and drought.

Image by WikimediaImages from Pixabay

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Using Machine Learning to Improve Subseasonal Climate Forecasting