Research
TACLS uses GNSS and ML to predict flash floods
The Verge AIhigh confidence
Why it matters
- →Repurposes existing GNSS satellite infrastructure for weather monitoring without new hardware costs.
- →Uses LSTM networks to analyze atmospheric moisture trends before precipitation reaches the ground.
- →Addresses the critical gap in flash flood warning lead times caused by sparse rain gauge coverage.
The National Weather Service is rolling out TACLS (Transient Artifact and Continuous Learning System), a new tool designed to improve flash flood warnings using satellite data and machine learning. Developed by UCSD, NASA, and the NWS, the system analyzes delays in Global Navigation Satellite System (GNSS) signals caused by atmospheric moisture to estimate precipitable water levels in real time. A long short-term memory model processes this data to identify areas at risk of transitioning from rain to dangerous flash floods faster than traditional gauge networks allow. Initially piloted in California, the technology aims to provide forecasters with earlier, more accurate alerts as it expands to all 122 NWS offices.
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