Machine Learning Emulation of Spatial Deposition from a Multi-Physics Ensemble of Weather and Atmospheric Transport Models by Nipun Gunawardena, Giuliana Pallotta, Matthew Simpson, Donald D. Lucas

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Predicting wind-driven spatial deposition through simulated color images using deep autoencoders by M. Giselle Fernández-Godino, Donald D. Lucas, Qingkai Kong

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Sensitivity of a Bayesian source-term estimation model to spatiotemporal sensor resolution by Derek D. Jensen, Donald D. Lucas, Katherine A. Lundquist, Lee G. Glascoe

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Future loss of Arctic sea-ice cover could drive a substantial decrease in California’s rainfall by Ivana Cvijanovic, Benjamin D. Santer, Céline Bonfils, Donald D. Lucas, John C. H. Chiang, Susan Zimmerman

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Improving Seasonal Forecast Using Probabilistic Deep Learning by Baoxiang Pan, Gemma J. Anderson, André Goncalves, Donald D. Lucas, Céline J. W. Bonfils, Jiwoo Lee

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Autumn Surface Wind Trends over California during 1979–2020 by Callum F. Thompson, Charles Jones, Leila Carvalho, Anna T. Trugman, Donald D. Lucas, Daisuke Seto, Kevin Varga

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Deep convolutional autoencoders as generic feature extractors in seismological applications by Qingkai Kong, Andrea Chiang, Ana C. Aguiar, M. Giselle Fernández-Godino, Stephen C. Myers, Donald D. Lucas

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Large Eddy Simulations of Turbulent and Buoyant Flows in Urban and Complex Terrain Areas Using the Aeolus Model by Akshay A. Gowardhan, Dana L. McGuffin, Donald D. Lucas, Stephanie J. Neuscamman, Otto Alvarez, Lee G. Glascoe

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Learning to Correct Climate Projection Biases by Baoxiang Pan, Gemma J. Anderson, André Goncalves, Donald D. Lucas, Céline J. W. Bonfils, Jiwoo Lee, Yang Tian, Hsi‐Yen Ma

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