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NASA Uses Machine Learning to Enhance Flash Flood Warnings
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NASA Uses Machine Learning to Enhance Flash Flood Warnings

NASA News · Jun 16, 2026, 3:23 PM · Also reported by 1 other source

Why this matters: new research or scientific developments with potential real-world impact.

The Transient Artifact and Continuous Learning System (TACLS) leverages data from continuously operating satellite networks coupled with machine learning models to help meteorologists at the National Weather Service forecast flash floods more efficiently. This new software is the result of a collaboration between NASA’s Jet Propulsion Laboratory, the University of California, San Diego (UCSD), and the National Oceanic and Atmospheric Administration (NOAA) National Weather Service (NWS). Show downloads TACLS test prediction run TACLS test prediction run (Original)MP4 Close To view this video please enable JavaScript, and consider upgrading to a web browser that supports HTML5 video A visual analysis from a TACLS test prediction run using data from flash floods the week of Christmas, 2025. The image shows flash flood warning (FFW) probabilities generated by TACLS (in shades of red) and overlaid on areas that received flash flood warnings from the National Weather Service (in blue). Credit: UCSD Scripps Institution of Oceanography Created with support from NASA’s Earth Science Technology Office (ESTO), TACLS leverages machine learning to automatically locate evidence (unusual increases in atmospheric moisture) of impending flash flooding that meteorologists may otherwise miss as they analyze large amounts of data. TACLS flags that evidence, indicates where flash flooding could likely occur, and displays that information via a user-friendly visualization for human analysts to interpret. Those analysts can then decide whether to issue a flash flood warning or weather advisory. This novel framework for tracking extreme weather events and predicting imminent flash floods operates in near real-time, producing forecasts in as little as fifteen minutes. “That’s really what we wanted to do, to give meteorologists a tool to help decision making for flash flood warnings,” said Yehuda Bock, Distinguished Researcher at the UCSD Scripps Institution of Oceanography and principal inv

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