MIT AI forecasts extreme weather without historical data

MIT engineers have built an AI tool that forecasts extreme weather without training on historical disaster data.

Kai Chang, a mechanical engineering graduate student, and Professor Themis Sapsis developed the tool. It produces maps of events that have not appeared in a region’s historical record but remain statistically-possible. Each map also carries estimates of the event’s likely duration and intensity, alongside a separate estimate of the area it might affect.

Forecasting extreme weather events without historical precedent

Sapsis holds the William I. Koch Professorship in Mechanical and Ocean Engineering at MIT. Both researchers are affiliated with the MIT Center for Computational Science and Engineering, and Sapsis also holds an appointment with the MIT Institute for Data, Systems, and Society. The pair describe the method, named Extreme Event Aware or η-learning, in a paper published in Nature Communications on 20 August.

Existing risk models work differently. Insurers, city planners, and grid operato...