Publications
Citation: Imme Ebert-Uphoff, Lander Ver Hoef, John S. Schreck, Jason Stock, Maria J. Molina, Amy McGovern, Michael Yu, Bill Petzke, Bill, Kyle Hilburn, David M. Hall, David J. Gagne, William F. Campbell, Jacob T. Radford, Jebb Q. Stewart, and Sam Scheuerman. Measuring Sharpness of AI-Generated Meteorological Imagery. Artificial Intelligence for the Earth Systems. Early online release: 09 June 2025. https://doi.org/10.1175/AIES-D-24-0083.1
Short summary: AI-based estimates of meteorological images, e.g., for forecasting applications, often lack sharpness, but there are no well established metrics to measure sharpness of meteorological imagery. This manuscript seeks to close this gap by exploring sharp-ness metrics for meteorological imagery, analyzing their properties, and providing guidelines for their interpretation. We hope that the tools provided here will aid the development of AI algorithms that provide more realistic meteorological imagery.
Download Weekly Report (PDF)Posted on: July 11, 2025
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