Uploaded September 2026 | Updated September 2026, 1 week ago
Argonne National Laboratory is a multidisciplinary science and engineering research center where scientists and engineers pioneer breakthroughs that address some of the world’s most pressing challenges. In this video, atmospheric scientist Yan Feng discusses PRECISION, an Argonne-led project under DOE’s Genesis Mission that combines artificial intelligence, physics and real-world observations to improve weather and Earth system predictions.
Accurately predicting rain, storms and extreme weather remains a major challenge in Earth system science, with important implications for energy infrastructure, water resources and public safety. Today’s large-scale models struggle to capture the tiny processes occurring inside clouds that influence when and where precipitation falls. Highly detailed simulations can represent these processes, but they are too computationally expensive to run at large scales, while existing AI approaches can become unreliable within prediction models or fail to capture important physics because of limited training data.
To address these challenges, Feng and her team, working with the National Center for Atmospheric Research and Aeolus Labs, are developing PRECISION, an AI framework that integrates physics, machine learning and observations. The system first learns from highly detailed simulations of cloud behavior and then applies that knowledge within a faster, large-scale atmospheric model. It is also designed to continually improve using real-world observations from U.S. Department of Energy monitoring instruments and satellites.
By combining detailed cloud physics with AI at much larger scales, PRECISION aims to improve predictions of storms, droughts, floods and other extreme weather across timescales ranging from weeks to decades. More accurate predictions could help energy providers, water managers and emergency planners better anticipate changing conditions and make informed decisions. Learn more about how Argonne is advancing this work under DOE’s Genesis Mission.
Find out more about Argonne’s role supporting the DOE’s Genesis Mission ►► anl.gov/genesis-mission/projects/physics-Informed-machine-learning-of-cloud-microphysics-for-high-resolution-earth-system-modeling-with-observationally-constrained-online-training
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ABOUT ARGONNE
Argonne National Laboratory seeks solutions to pressing national problems in science and technology by conducting leading-edge basic and applied research in virtually every scientific discipline. Argonne is managed by UChicago Argonne, LLC for the U.S. Department of Energy’s Office of Science.
ABOUT THE DEPARTMENT OF ENERGY OFFICE OF SCIENCE
The U.S. Department of Energy’s Office of Science is the single largest supporter of basic research in the physical sciences in the United States and is working to address some of the most pressing challenges of our time. For more information, visit the Office of Science website.
Argonne National Laboratory is a multidisciplinary science and engineering research center where scientists and engineers pioneer breakthroughs that address some of the world’s most pressing challenges. In this video, atmospheric scientist Yan Feng discusses PRECISION, an Argonne-led project under DOE’s Genesis Mission that combines artificial intelligence, physics and real-world observations to improve weather and Earth system predictions.
Accurately predicting rain, storms and extreme weather remains a major challenge in Earth system science, with important implications for energy infrastructure, water resources and public safety. Today’s large-scale models struggle to capture the tiny processes occurring inside clouds that influence when and where precipitation falls. Highly detailed simulations can represent these processes, but they are too computationally expensive to run at large scales, while existing AI approaches can become unreliable within prediction models or fail to capture important physics because of limited training data.
To address these challenges, Feng and her team, working with the National Center for Atmospheric Research and Aeolus Labs, are developing PRECISION, an AI framework that integrates physics, machine learning and observations. The system first learns from highly detailed simulations of cloud behavior and then applies that knowledge within a faster, large-scale atmospheric model. It is also designed to continually improve using real-world observations from U.S. Department of Energy monitoring instruments and satellites.
By combining detailed cloud physics with AI at much larger scales, PRECISION aims to improve predictions of storms, droughts, floods and other extreme weather across timescales ranging from weeks to decades. More accurate predictions could help energy providers, water managers and emergency planners better anticipate changing conditions and make informed decisions. Learn more about how Argonne is advancing this work under DOE’s Genesis Mission.
Find out more about Argonne’s role supporting the DOE’s Genesis Mission ►► anl.gov/genesis-mission/projects/physics-Informed-machine-learning-of-cloud-microphysics-for-high-resolution-earth-system-modeling-with-observationally-constrained-online-training
Still haven't subscribed to Argonne National Laboratory on YouTube? ►► bit.ly/2Vyzwvm
Join us on Facebook ► bit.ly/ArgonneFacebook
Follow us on X ► bit.ly/ArgonneTwitter
We’re on Instagram ► bit.ly/ArgonneInstagram
Connect with us on LinkedIn ► bit.ly/ArgonneLinkedIn
ABOUT ARGONNE
Argonne National Laboratory seeks solutions to pressing national problems in science and technology by conducting leading-edge basic and applied research in virtually every scientific discipline. Argonne is managed by UChicago Argonne, LLC for the U.S. Department of Energy’s Office of Science.
ABOUT THE DEPARTMENT OF ENERGY OFFICE OF SCIENCE
The U.S. Department of Energy’s Office of Science is the single largest supporter of basic research in the physical sciences in the United States and is working to address some of the most pressing challenges of our time. For more information, visit the Office of Science website.
