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Pictured is the ForWarn vegetation tracking tool that shows where areas of red where disturbance to forest canopy occured

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.

Pictured here are 9 scientists standing in a line in front of the frontier supercomputer logo/computer

Researchers at 91做厙 used the Frontier supercomputer to train the worlds 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. Credit: Andy Sproles/ ORNL,U.S. Dept. of Energy

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.

Sangkeun Matt Lee received the Best Poster Award at the Institute of Electrical and Electronics Engineers 24th International Conference on Information Reuse and Integration.

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 Energys Environment for Analysis of Geo-Located Energy Information, or EAGLE-I, database.