HR-2770-119
Referred to the House Committee on Science, Space, and Technology.
Sponsored by Scott Franklin (R-FL)
What it does
This bill would direct the National Oceanic and Atmospheric Administration (NOAA) to develop and use artificial intelligence tools to improve weather forecasting, wildfire prediction, and public information delivery. It would require NOAA to build AI-ready training datasets, develop and test a global AI weather model, create a wildfire environment modeling program, and establish public-private partnerships for research and development. The bill would also require NOAA to maintain its existing traditional weather observation and numerical modeling programs alongside any new AI systems.
Who benefits
The general public, who would receive more accurate and timely weather and wildfire warnings. Residents of wildfire-prone states such as California, Oregon, and Florida who face elevated risk from extreme weather events. Emergency managers and first responders who would gain better decision-support tools. Private weather companies and academic institutions that would gain access to open-license government datasets and potential co-investment partnerships. NOAA's existing cooperative institutes that may receive expanded scope and funding. AI and data science researchers who would benefit from new federal training datasets and workforce development programs.
Who is hurt
Traditional numerical weather modeling contractors and vendors whose market position could be disrupted if AI models displace existing contracts. NOAA employees in legacy forecasting roles who may face workforce transition pressures. Taxpayers who would bear the cost of building new AI infrastructure while existing systems are maintained in parallel. Private weather data companies that currently sell data NOAA might make freely available under open licenses, potentially undercutting their business models. Communities that rely on current NOAA staffing levels at local weather forecast offices, which could face resource reallocation toward AI development.
Supporters argue
Supporters argue that AI weather models — such as those already developed by Google DeepMind and Huawei — have demonstrated forecast accuracy rivaling or exceeding traditional numerical models at a fraction of the computational cost, and that the U.S. risks falling behind international competitors without federal investment. They contend that faster, more precise wildfire and extreme weather predictions would directly save lives and reduce property losses, which cost the U.S. economy an estimated $150 billion annually according to NOAA data. They also argue the bill's explicit requirement to maintain traditional observation and modeling programs prevents over-reliance on any single forecasting approach.
Opponents argue
Opponents argue that AI weather models are trained on historical data and may perform poorly under novel climate conditions not well-represented in past records, creating a false sense of confidence in high-stakes emergency situations. They contend that the bill's open-license data sharing provisions could inadvertently expose sensitive infrastructure or national security-relevant environmental data, and that the co-investment and shared intellectual property provisions with private entities may allow corporations to profit from publicly funded research without adequate public benefit guarantees. They further argue that without specific appropriations, the bill's mandates may strain NOAA's existing budget and workforce rather than supplement them.