Luján, Schatz, Butler, Welch Introduce Legislation Promoting Use Of AI To Predict, Respond To Extreme Weather

Bill Would Direct Key Federal Agencies to Use AI Tools to Improve Weather Forecasting, Crack Down on Illegal Deforestation, Optimize Electrical Grids

WASHINGTON – U.S. Senators Ben Ray Luján (D-N.M.), Brian Schatz (D-Hawai‘i), Laphonza Butler (D-Calif.), and Peter Welch (D-Vt.) this week introduced the Transformational AI to Modernize the Economy (TAME) against Extreme Weather Act, legislation aimed at promoting the adoption and implementation of artificial intelligence to better predict and respond to extreme weather.

Each year, extreme weather threatens human life and economic prosperity. Between 2013 and 2022, the federal government reported 5,291 deaths and nearly $256 billion in property and crop damage as a result of disasters. The toll of extreme weather has intensified in recent years. In 2023 alone, the U.S. experienced 28 distinct extreme weather events, resulting in at least 492 deaths and each costing at least a billion dollars.

“As extreme weather impacts communities in New Mexico and across the country, we must leverage every tool in our disposal to improve weather forecasting,” said Senator Luján. “I’m proud to join my colleagues in introducing this legislation that improves federal capabilities to use artificial intelligence to better predict emerging weather patterns, ensuring our communities are well-informed and prepared.”

“By using extraordinarily powerful AI tools to confront extreme weather, we can save lives and communities,” said Senator Schatz. “This bill would require federal agencies to adopt AI tools in ways that improve weather forecasts, increase grid resilience, and improve environmental review, while also maximizing federal resources and strengthening public-private partnerships in advanced computing.”

“As climate change threatens to upend the homes and livelihoods of thousands of Californians, we must ensure our communities are equipped to take smarter action to combat extreme weather and climate impacts at every turn,” said Senator Butler. “The TAME Against Extreme Weather Act would take steps in assisting federal agencies with cutting-edge, AI tech to meet the unprecedented challenges of climate change head on.”

“Extreme weather is only getting more severe and more frequent. We need to use every tool at our disposal—including artificial intelligence—to save lives and livelihoods. By requiring federal agencies to use AI in proactive ways, such as boosting grid resiliency and improving weather forecasts, this bill will allow us to better predict and respond to extreme weather events and mitigate their impacts,” said Senator Welch.

Specifically, the TAME Extreme Weather Act directs three relevant federal agencies with a particular nexus to extreme weather to use AI tools:

  • National Oceanic and Atmospheric Administration: Improve weather forecasting, wildfire detection, and modeling of how and where wildfires will spread. 
  • Department of Agriculture: Crack down on deforestation and illegal wood products that increase foreign nations’ vulnerability to extreme weather events.
  • Department of Energy: Optimize electrical grids and transmission against disruption from extreme weather events and develop technology for comprehensive and efficient environmental review.

The TAME Extreme Weather Act encourages academic and private sector collaboration to acquire data and explore new public-private partnerships that advance research and development in these technical and capital-intensive fields. The bill requires the development and curation of publicly available environmental datasets, allowing scientists, businesses, and the public to benefit from government technological advances. These datasets will help ensure the strength and quality of the data used to train AI systems and their positive applications. Additionally, the bill requires agencies to develop and disseminate best practices to minimize the environmental impacts of AI.



Filter & Sort Results

Date Range
Date Range
Sort Results