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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 Oak Ridge National Laboratory 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.

A new Global Biomass Resource Assessment developed by ORNL scientists gathered data from 55 countries resulting in a first-of-its kind compilation of current and future sustainable biomass supply estimates around the world. 

Three flights, five thousand miles and half a dozen clearances and permissions stood between Tetiana Maltseva and the Department of Energy’s Oak Ridge National Laboratory. When she finally arrived at the lab to represent Ukraine at the 2024 Nuclear Energy Management School, her vision was clear. 

A group of high school graduates and community college students visited ORNL to meet staff and find out just what goes on at a DOE national laboratory. The Job Shadow Day was arranged by tnAchieves, a student support organization that works to increase higher educational opportunities for students across Tennessee through scholarships and mentorship. 
 

Scientists at ORNL completed a study of how well vegetation survived extreme heat events in both urban and rural communities across the country in recent years. The analysis informs pathways for climate mitigation, including ways to reduce the effect of urban heat islands.

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.

A first-ever dataset bridging molecular information about the poplar tree microbiome to ecosystem-level processes has been released by a team of DOE scientists led by ORNL. The project aims to inform research regarding how natural systems function, their vulnerability to a changing climate and ultimately how plants might be engineered for better performance as sources of bioenergy and natural carbon storage.

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.

The Department of Energy’s Oak Ridge National Laboratory and Tennessee State University have signed a memorandum of understanding to strengthen research cooperation and provide diverse undergraduate students enriching educational research opportunities at the lab.