{"id":33996,"date":"2026-07-01T12:30:00","date_gmt":"2026-07-01T17:30:00","guid":{"rendered":"https:\/\/www.ecoticias.com\/en\/?p=33996"},"modified":"2026-07-01T06:14:48","modified_gmt":"2026-07-01T11:14:48","slug":"nasa-is-developing-and-testing-an-artificial-intelligence-system-that-identifies-93-of-the-signs-of-a-flash-flood-and-could-provide-crucial-time-to-respond","status":"publish","type":"post","link":"https:\/\/www.ecoticias.com\/en\/nasa-is-developing-and-testing-an-artificial-intelligence-system-that-identifies-93-of-the-signs-of-a-flash-flood-and-could-provide-crucial-time-to-respond\/33996\/","title":{"rendered":"NASA is developing and testing an artificial intelligence system that identifies 93% of the signs of a flash flood and could provide crucial time to respond"},"content":{"rendered":"\n

When rain starts falling hard, the difference between a close call and a disaster can come down to minutes. NASA is now testing a machine learning system<\/a> designed to help meteorologists spot the warning signs of flash floods faster, especially when storms are changing quickly and the data is mounting up.<\/p>\n\n\n\n

The tool is called the Transient Artifact and Continuous Learning System, or TACLS. Built through a collaboration between NASA\u2019s Jet Propulsion Laboratory, the University of California, San Diego, and NOAA\u2019s National Weather Service, it uses satellite-network data and machine learning to flag unusual jumps in atmospheric moisture that can point to dangerous flooding ahead.<\/p>\n\n\n\n

A more responsive look at flood danger<\/h2>\n\n\n\n

Flash floods are frightening because they can develop with little warning. A dry street can turn into a rushing channel before people have time to move cars, leave low-lying roads, or check on neighbors.<\/p>\n\n\n\n

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A vast storm system curls over the ocean, showing the kind of fast-changing weather patterns NASA\u2019s AI flood-warning system is designed to help forecasters track.<\/figcaption><\/figure>\n\n\n\n

TACLS is meant to give forecasters another set of eyes. NASA says the system can operate in near real time and produce forecasts in as little as 15 minutes, a short window that can matter a great deal when water starts moving fast.<\/p>\n\n\n\n

\u201cThat\u2019s really what we wanted to do, to give meteorologists a tool to help decision making for flash flood warnings<\/a>,\u201d said Yehuda Bock, a distinguished researcher at UCSD\u2019s Scripps Institution of Oceanography and principal investigator for TACLS. The key point is not that AI replaces the forecaster. It helps point the forecaster toward something urgent.<\/p>\n\n\n\n

How TACLS reads the sky<\/h2>\n\n\n\n

So, how does a flood-warning system begin with satellites that many people associate with navigation? TACLS analyzes data from the Global Navigation Satellite System<\/a>, the broad family of satellite networks used for positioning and timing around the world.<\/p>\n\n\n\n

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Read More: Scientists at MIT have created a robot with an \u201celephant\u2019s memory\u201d that promises to tell you where you left your keys last night\u2014and it does so in a matter of seconds<\/a><\/h3>\n<\/div>\n<\/div><\/div>\n<\/div>\n\n\n\n

As those satellite signals pass through the lower atmosphere, water vapor<\/a> slows them down slightly. That delay can be studied to estimate how much moisture is sitting above a specific place on Earth, which is useful because sudden moisture increases can be a clue that heavy rain is building.<\/p>\n\n\n\n

In practical terms, TACLS watches for unusual moisture patterns. The machine learning model was trained with more than 30 years of GNSS data, then built to separate false distortions in the data from real, short-lived weather events that deserve human attention.<\/p>\n\n\n\n

What the testing showed<\/h2>\n\n\n\n

NASA says TACLS was tested on simulations using severe weather events from 2017 through 2023. Those cases included atmospheric rivers, monsoonal convection, and remnants of tropical cyclones, all weather setups that can bring intense rain.<\/p>\n\n\n\n

In that testing, the system correctly identified 93% of issued flash-flood warnings. That does not mean every future flood will be predicted perfectly, but it does suggest the tool can recognize many of the same danger signals that lead human forecasters to issue warnings.<\/p>\n\n\n\n

That number matters because meteorologists often work under pressure, scanning large volumes of information while storms evolve. Anyone who has watched a weather alert pop up during a commute knows the stakes are not abstract. Traffic jams, flooded underpasses, and fast-moving runoff can turn ordinary routines into emergencies.<\/p>\n\n\n\n

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