Convection initiation over the Central United States as viewed from the GOES-17 satellite. Clouds at various stages of development are seen in white , with lightning indicated by the blue shading. The prediction challenge is to determine which clouds will develop into thunderstorms in the next hour, researchers said. Credit: National Atmospheric and Oceanic Administration and the Cooperative Institute for Research in the Atmosphere.
Greybush leads NSF-funded project using AI to better understand weather patterns
Posted on September 17, 2024Editor’s Note: A version of this story was originally published on Penn State News.
UNIVERSITY PARK, Pa. — Steven Greybush, Penn State Institute for Computational and Data Sciences (ICDS) co-hire and associate professor of meteorology, is leading a three-year project to better understand and predict extreme weather events on Earth. Greybush, co-lead investigator, David John Gagne at the National Center for Atmospheric Research (NCAR) and their teams received a $973,396 grant from the U.S. National Science Foundation (NSF). This was one of 20 awards made by NSF to support artificial intelligence (AI) advancements in geosciences.
The research teams focus is on using deep learning, a type of AI that is essentially good at extracting spatial patterns from image data, to predict which cloud systems will eventually develop into thunderstorms. This is a process known as convection initiation. They will also investigate why their approach predicts what it does and how much the researchers should trust the prediction, which is a technique called uncertainty quantification.
The goal of the research is to develop AI that can more accurately predict thunderstorm formation and explain how thunderstorms occur. This research has the potential to provide insights on how to better incorporate AI methods into other research projects conducted by Greybush including winter weather forecasting and Mars atmospheric predictions.
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