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The ForWarn visualization tool was co-developed by ORNL with the U.S. Forest Service. The tool captures and analyzes satellite imagery to track impacts such as storms, wildfire and pests on forests across the nation.

Researchers at 91���� used the Frontier supercomputer to train the world’s largest AI model for weather prediction, paving the way for hyperlocal, ultra-accurate forecasts. This achievement earned them a finalist nomination for the prestigious Gordon Bell Prize for Climate Modeling.

ORNL researchers modeled how hurricane cloud cover would affect solar energy generation as a storm followed 10 possible trajectories over the Caribbean and Southern U.S.

Lee's paper at the August conference in Bellevue, Washington, combined weather and power outage data for three states – Texas, Michigan and Hawaii –  and used a machine learning model to predict how extreme weather such as thunderstorms, floods and tornadoes would affect local power grids and to estimate the risk for outages. The paper relied on data from the National Weather Service and the U.S. Department of Energy’s Environment for Analysis of Geo-Located Energy Information, or EAGLE-I, database.

U.S. Secretary of Energy Jennifer Granholm visited 91���� today to attend a groundbreaking ceremony for the U.S. Stable Isotope Production and Research Center. The facility is slated to receive $75 million in funding from the Inflation Reduction Act.