TAME Extreme Weather and Wildfires Act

House Votes

This bill would direct NOAA to use artificial intelligence (AI) to make weather and wildfire forecasts faster, clearer, and more useful for families, first responders, and communities. It calls for building big training datasets, possibly creating a global AI weather model, and improving how forecast information is delivered so people can better prepare for storms and other hazards . It also creates an AI-based wildfire program to warn at‑risk communities, predict and detect fires, track smoke, and forecast how fires might spread, using both real-world and synthetic data. The bill keeps support for traditional forecasting methods, too, so AI adds to—not replaces—current models and observations.

NOAA would review non‑federal AI weather models, share best practices, and help forecasters and emergency managers use both AI and traditional models in daily operations. It would study how to judge these models fairly and report on ways to improve seasonal forecasts. Data and code created under the bill could be shared for free under an open license, while protecting national security, trade secrets, and other sensitive information . The bill also encourages partnerships with universities and companies, co‑investment in high‑risk, high‑reward research, and building a skilled federal workforce to use AI for better forecasts and public safety.

Key points

• Who is affected: NOAA forecasters and weather offices; emergency managers; firefighters and at‑risk communities; researchers; universities and private companies working on weather and wildfire tools .
• What changes: Build AI-ready datasets and possibly a global AI weather model; improve how forecast info reaches the public; create an AI wildfire warning and smoke‑tracking program; share data/code openly when safe; train and grow the federal AI weather workforce; set up new partnerships and co‑funded projects .
• When: Within two years, build the core training datasets and report on AI model work; within one year, launch the wildfire program and report on using “reforecast” techniques to improve seasonal and sub‑seasonal models; ongoing help to forecast offices continues after that .