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 operators typically want to know what a once-in-a-century storm might look like for a specific location. Current simulations usually depend on datasets that already contain extreme events, learning the conditions that produced them before projecting similar patterns forward.

Chang argues this current approach creates a limit on what such models can show. “These methods assume there are very disastrous events that we have seen in the dataset, and they build a method to either estimate the risk of those events, or they try to predict exactly the events that have happened,” he says.

Sapsis frames the same limitation through Hurricane Katrina. “An event like Hurricane Katrina is something that happens every 30 to 40 years,” he adds. “What will be the Katrina that happens every 100 years? How bad will it be? That’s exactly what we’re trying to quantify, to help planners prepare for plausible extreme scenarios.”

Combining point statistics with spatial detail

The algorithm works from two types of data. Point statistics capture how often a given intensity level, such as the maximum rainfall recorded across a map, occurs within a dataset. Spatial maps show how an event’s impact varies across a region.

Learning the statistical relationship between the two lets the algorithm build spatial patterns for events beyond anything in its training data, without needing prior examples of those exact extremes.

The researchers tested the approach on precipitation across the continental US. They started with 25 years of hourly rainfall data, pooled into daily maps, and computed point statistics describing how often the maximum rainfall on a map reached a given level across that full record.

The training window for the spatial model was narrow. They trained that part of the algorithm using paired low-resolution and high-resolution maps drawn from only the first six months of the 25-year record, a period that contained few or no examples of the heaviest rainfall levels.

The algorithm learned how patterns in the low-resolution maps corresponded to detail in the high-resolution versions, then applied the point statistics from the full record to constrain how extreme the generated patterns could become.

Testing infrastructure against worst-case maps

The highest rainfall ever recorded in New York City measures 200 millimetres. The method can generate plausible maps of a storm that produces 300 millimetres instead, a level with no match in the observational record.

A user can prompt the trained algorithm to show what a once-in-a-century storm might look like for a named city. The output takes the form of maps showing statistically-plausible storms at that frequency. Each map carries its own size and area of coverage, and rainfall intensity varies across the set as well. According to Chang, the algorithm can generate large volumes of these scenarios at once.

The generated maps could help a city test its seawall against a storm surge beyond anything recorded. The same maps could show whether the power grid would hold during a longer heatwave, or whether firefighting resources could contain a wildfire larger than any on file.

Limits of the demonstration so far

Applying the method to a new hazard requires relevant point statistics and spatial data for that specific hazard, according to Chang and Sapsis. The pair point to possible extensions once that data is available, such as visualising severe floods and wildfires with no equivalent in the historical record.

Sapsis notes that global infrastructure has been optimised for efficiency, leaving little slack in the systems it supports.

“A single extreme event propagates through supply chains, energy markets, and food systems in weeks,” he explains. “Being able to put a probability on an event that hasn’t happened yet is now a question of national and economic resilience.”

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