NASA Uses Machine Learning to Enhance Flash Flood Warnings

The Transient Artifact and Continuous Learning System leverages data from continuously operating satellite networks coupled with machine learning models to help meteorologists at the National Weather Service forecast flash floods more. The institutional report frames the development in practical terms and ties it to the broader mission or observing effort.

The significance lies in Earth science becomes stronger when local observations can be placed inside a broader physical pattern that spans time and geography. The planet operates as a coupled system in which atmospheric, oceanic, cryospheric and solid-Earth processes interact across timescales from days to millions of years. A measurement that captures one variable at one location and one moment has limited interpretive value until it is embedded in the longer series and wider spatial coverage that allow natural variability to be separated from forced change. 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. UCSD Scripps Institution of Oceanography Created with support from NASA’s Earth Science Technology Office (ESTO), TACLS leverages machine learning to automatically locate evidence.

A cyclone makes landfall across the California coast on November 19, 2024. NASA This learning system has two main components.

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. In simulations testing, TACLS used data from diverse severe weather events, including atmospheric rivers, monsoonal convection, and tropical cyclone remnants, between 2017 and.

Water vapor in the troposphere delays signals from these satellites as they travel to Earth. This signal delay can be analyzed to calculate the amount of water vapor in the atmosphere over a particular location on Earth.

The broader interest lies in linking the observation to climatic, geophysical or environmental dynamics that extend well beyond the immediate event or location. Earth science is unusual in that its most important questions operate on timescales that no single research career can observe directly, making the archival record, whether in ice, sediment, rock or satellite data, as important as any new measurement. Results that can be embedded in that record, and that either confirm or challenge the patterns it reveals, carry disproportionate scientific weight.

The TACLS analytic back-end software suite features a machine learning model trained using more than 30 years of past GNSS data. Several past innovations developed at JPL are leveraged by TACLS to process GNSS data and present the results.

Because the account originates with NASA News Releases, it functions best as a primary institutional report that is close to the data and operations, not as independent scientific validation. Institutional communications are produced by organizations with legitimate interests in presenting their work in a favorable light, which does not make them unreliable but does make them partial. Details that complicate the narrative, including instrument limitations, unexpected failures and results below projections, tend to be minimized relative to progress messages. Technical documentation and peer-reviewed publications, where they exist, provide the complementary layer that institutional releases cannot substitute.

The next step is to place the result inside longer time series and to compare it with independent instruments and independent sites. Earth system observations gain most of their interpretive power from network density and temporal depth, not from any single measurement however precise. Model simulations that assimilate the new data will help clarify whether the observation fits comfortably within known natural variability or represents a shift that existing models do not reproduce.

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