HB2770, the Transformational Artificial Intelligence to Modernize the Economy against Extreme Weather and Wildfires Act (the TAME Extreme Weather and Wildfires Act), directs the Under Secretary of Commerce for Oceans and Atmosphere to develop and test artificial intelligence weather models and to curate comprehensive training datasets to support a long-running archive of past weather. It also calls for assessing non-federal AI weather models, awarding contracts or expanding cooperative institutes, and promoting data-sharing through an open license framework, with safeguards for security and IP.
The bill prioritizes environmental safeguards, workforce development, and active partnerships with industry and academia to accelerate AI-enabled forecasting, warning, and information delivery.
At a Glance
What It Does
Not later than two years after enactment, the Under Secretary must develop and curate comprehensive, metadata-rich training datasets for weather forecasting and may develop a global AI weather model for testing and deployment. It directs ongoing assessments, reporting, and the expansion of cooperative institutes and contracting opportunities to advance AI applications in forecasting.
Who It Affects
NOAA forecast offices, federal research partners (DOE, NASA, NSF), private AI developers, universities, and cooperative institutes, plus emergency managers and communities at risk from extreme weather and wildfires.
Why It Matters
It creates a formal, data-centric pathway to modernize weather forecasting using AI, with open-data provisions, cross-agency collaboration, and a workforce-and-innovation mandate designed to improve readiness and resilience.
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What This Bill Actually Does
The bill assigns NOAA’s Under Secretary a structured program to advance artificial intelligence in weather forecasting. It requires the creation of large, well-documented training datasets and, optionally, the development of a global AI-based weather model that can be tested against existing numerical models.
It also directs NOAA to coordinate with other federal agencies and to pursue partnerships with universities and private firms to share data, co-fund research, and co-develop AI weather tools. Importantly, the act balances openness with safeguards: data and code may be released under an open license, but releases can be limited to protect national security, trade secrets, and intellectual property, and to comply with existing contracts.
The bill emphasizes continued support for observations, basic research, and numerical weather models even as AI-driven approaches are expanded. It also tasks NOAA with a Fire Environment Modeling Program to forecast wildland fire behavior and to warn at-risk communities, and it sets out a pathway for workforce development, interior collaboration, and better information delivery to decision-makers.
Finally, it contemplates new structures for private-public partnerships and co-investment to accelerate innovation while outlining governance around data access and IP sharing.
The Five Things You Need to Know
The bill requires the Under Secretary to develop and curate a comprehensive, metadata-rich training dataset for weather forecasting within two years.
It authorizes the creation and testing of a global AI weather model and requires annual reporting on AI activities to Congress.
Public data and code developed under the Act may be released under an open license, with exemptions for national security, IP, and restricted data.
A Fire Environment Modeling Program must be established to warn communities, predict wildfires, and monitor smoke and hazards.
The act encourages private-public partnerships and co-investment, including shared IP and resources, to accelerate research and deployment of AI weather tools.
Section-by-Section Breakdown
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Definitions
Defines artificial intelligence and AI weather models, sets terminology for datasets, data provenance, and the Under Secretary’s role. These definitions establish what counts as an AI-based forecasting tool, how data are curated, and what constitutes a usable training and observational data ecosystem. The definitional scope is designed to enable consistent development, validation, and sharing of AI-enabled forecasting capabilities across the Weather Enterprise.
Earth System Forecasting and Information Delivery
Tasked with developing and curating comprehensive training datasets and metadata to support weather, water, and space weather forecasting, the act envisions building a long-term, auditable data record. It explicitly contemplates a global AI weather model and the use of cooperative institutes and competitive contracting to expand forecasting capabilities, while emphasizing environmental safeguards and continued support for traditional observations and numerical models.
Advanced AI Applications for Weather and Information Delivery
Encourages applying AI to improve data assimilation, rapid emulation of forecast models for confidence assessments, and enhanced decision support for communities. The goal is to accelerate the translation of forecasts into actionable guidance for responders and the public, leveraging AI to shorten decision cycles and increase forecast reliability.
Technical Assistance on Use of AI Weather Models
Requires regular surveying of non-federal AI weather models to provide evaluation, best practices, and forecaster support. It also facilitates joint testing with forecasters and emergency managers to inform operational use, ensuring a pathway for non-federal tools to be vetted and integrated into practice.
Fire Environment Modeling Program
Establishes an AI-driven program to analyze observational and synthetic data on built and natural environments to warn at-risk communities, improve wildfire detection and forecasting, and forecast fire propagation and smoke. It also contemplates data acquisition and integration with weather outputs to enable comprehensive risk assessments.
Partnerships for Transformational Innovation
Calls for innovative private-public and academic partnerships to advance forecasting science, including co-investment, shared IP, and resource sharing to propel high-risk, high-return research and the transition of AI tools into operations.
Federal Government Workforce Expertise
Directs NOAA to build, recruit, and sustain a professional workforce capable of deploying AI-based forecasting tools, drawing on private-sector partnerships and training opportunities to expand capacity and expertise.
Data Access
Authorizes public release of data and code under an open license where appropriate, while permitting accommodations to protect national security, IP, trade secrets, and contractually restricted information. It preserves the NOAA mission and sets guardrails to prevent compromising security or proprietary interests.
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Explore Technology in Codify Search →Who Benefits and Who Bears the Cost
Every bill creates winners and losers. Here's who stands to gain and who bears the cost.
Who Benefits
- NOAA and the Under Secretary’s office gain clearer authority, resources, and a structured roadmap to modernize forecasting with AI, including access to curated datasets and testbeds.
- NOAA forecast offices and forecasters benefit from improved tools, training environments, and best practices that improve accuracy and decision support.
- Emergency managers and at-risk communities stand to receive faster, clearer warnings and more precise risk assessments from AI-augmented forecasts.
- Private-sector AI developers and universities gain access to data-sharing opportunities, co-investment options, and potential IP collaboration that can accelerate product development.
- Researchers in weather, climate, and data science gain standardized datasets and evaluation frameworks to test and validate AI approaches.
Who Bears the Cost
- Federal budgetary resources needed to fund dataset development, AI model testing, and cooperative institutes.
- NOAA forecast offices incur ongoing costs associated with integrating new AI tools and retraining staff.
- Private-sector and academic partners may need to contribute capital, personnel, and share IP under cooperative arrangements.
- Security, privacy, and IP compliance costs to ensure data releases do not jeopardize national security or proprietary information.
- Transitional costs as traditional models and workflows adapt to AI-enabled processes.
Key Issues
The Core Tension
The central dilemma is how to accelerate AI-enabled forecasting while preserving security, IP, and the reliability of weather predictions. Opening data and sharing tools can drive innovation and resilience, but without strong governance and transparent validation, AI models risk overreliance, misinterpretation, or unintended consequences in critical decision-making.
The bill seeks to democratize data and accelerate AI-enabled forecasting, but it must balance openness with security and IP protections. Releasing data and code under an open license can spur innovation, yet it requires careful handling of national security concerns, contractual restrictions, and protection of sensitive information.
The reliance on AI models raises questions about validation, governance, and the potential displacement of traditional forecasting roles. The Act envisions significant private-public collaboration, which brings benefits in speed and resources but also raises concerns about accountability, data quality control, and effective integration into existing operational workflows.
Implementation will hinge on funding stability, clear performance metrics, and robust testing in testbeds and real-world pilots.
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