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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 […]
\nThe post MIT AI forecasts extreme weather without historical data appeared first on AI News.
", "content_html": "MIT engineers have built an AI tool that forecasts extreme weather without training on historical disaster data.
\n\n\n\nKai 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.
\n\n\n\nSapsis 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.
\n\n\n\nExisting 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.
\n\n\n\nChang 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.
\n\n\n\nSapsis 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.”
\n\n\n\nThe 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.
\n\n\n\nLearning 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.
\n\n\n\nThe 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.
\n\n\n\nThe 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.
\n\n\n\nThe 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.
\n\n\n\nThe 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.
\n\n\n\nA 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.
\n\n\n\nThe 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.
\n\n\n\nApplying 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.
\n\n\n\nSapsis notes that global infrastructure has been optimised for efficiency, leaving little slack in the systems it supports.
\n\n\n\n“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.”
\n\n\n\nSee also: Samsung health AI models analyse wearable biosignal data
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\nThe post MIT AI forecasts extreme weather without historical data appeared first on AI News.
", "content_text": "MIT\nengineers have built an AI tool that forecasts extreme weather without training on historical disaster data.\nKai 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.\nForecasting extreme weather events without historical precedent\nSapsis 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\na paper\npublished in Nature Communications on 20 August.\nExisting 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.\nChang 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.\nSapsis 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.”\nCombining point statistics with spatial detail\nThe 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.\nLearning 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.\nThe 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.\nThe 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.\nThe 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.\nTesting infrastructure against worst-case maps\nThe 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.\nA 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.\nThe 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.\nLimits of the demonstration so far\nApplying 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.\nSapsis notes that global infrastructure has been optimised for efficiency, leaving little slack in the systems it supports.\n“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.”\nSee also:\nSamsung health AI models analyse wearable biosignal data\nWant to learn more about AI and big data from industry leaders?\nCheck out\nAI & Big Data Expo\ntaking place in Amsterdam, California, and London. The comprehensive event is part of\nTechEx\nand is co-located with other leading technology events including the\nCyber Security & Cloud Expo\n. Click\nhere\nfor more information.\nAI News is powered by\nTechForge Media\n. Explore other upcoming enterprise technology events and webinars\nhere\n.\nThe post\nMIT AI forecasts extreme weather without historical data\nappeared first on\nAI News\n.", "image": { "url": "https://virtues.guru/tech-assets/95_Case_Library_and_Geyer_Center_for_Information_Technology_at_Colgate_University.jpg", "file": "95_Case_Library_and_Geyer_Center_for_Information_Technology_at_Colgate_University.jpg", "title": "File:Case Library and Geyer Center for Information Technology at Colgate University.jpg" }, "language": "en", "translations": { "pt": { "title": "MIT AI prevê eventos climáticos extremos sem dados históricos", "content_text": "Engenheiros do MIT desenvolveram uma ferramenta de IA que prevê eventos climáticos extremos sem depender de dados históricos de desastres. A ferramenta, chamada \"Extreme Event Aware\" ou η-learning, produz mapas de eventos estatisticamente possíveis que não apareceram no histórico de uma região. Ela pode gerar mapas de cenários de eventos climáticos extremos, como uma tempestade que ocorre uma vez a cada século, para ajudar os planejadores urbanos a se prepararem para eventos extremos plausíveis. A ferramenta foi testada com dados de precipitação em todo o continente dos EUA e pode produzir mapas de tempestades com níveis de chuva que não têm correspondência nos registros observacionais.", "summary_en": "MIT engineers have developed an AI tool that forecasts extreme weather without relying on historical disaster data. The tool, named Extreme Event Aware or η-learning, produces maps of statistically-possible events that have not appeared in a region's historical record. It can generate maps of extreme weather scenarios, such as a once-in-a-century storm, to help city planners prepare for plausible extreme events. The tool has been tested on precipitation data across the continental US and can produce maps of storms with rainfall levels that have no match in the observational record.", "translated_at": "2026-08-26T06:23:45.776294+00:00", "model": "translategemma:4b", "summary_model": "llama3.1:8b", "source": "json_summary" } } }