Algorithm helps forecast frequency of extreme weather

By combining machine learning with dynamical systems theory, a team at MIT have now developed a method to "correct" the predictions from coarse climate models. The team’s approach “nudges” a climate model’s simulations into more realistic patterns over large scales. (Learn more: https://news.mit.edu/2024/mit-derived-algorithm-helps-forecast-frequency-extreme-weather-0326)

This animation shows the evolution of storms around the northern hemisphere, as a result of a high-resolution storm model, combined with the MIT team’s corrected global climate model. The simulation improves the modeling of extreme values for wind, temperature, and humidity, which typically have significant errors in coarse scale models.

Courtesy of Ruby Leung and Shixuan Zhang, PNNL

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