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Abstract: Deep Learning Atmospheric Models Reliably Simulate Out-of-Sample Temperature Extremes, and Blocking Frequencies (AGU25)

Deep Learning Atmospheric Models Reliably Simulate Out-of-Sample Temperature Extremes, and Blocking Frequencies

Zilu Meng, University of Washington, Atmospheric and Climate Science, Seattle, United States, Gregory J Hakim, University of Washington, Department of Atmospheric and Climate Science, Seattle, United States, Wenchang Yang, Princeton University, Department of Geosciences, Princeton, NJ, United States and Gabriel Andres Vecchi, Princeton University, Department of Geosciences and The High Meadows Environmental Institute, Princeton, NJ, United States
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