AQcGAN - A Deep Conditional Generative Adversarial Network for Emulating and Extending Global Atmospheric Composition Forecasts
AQcGAN - A Deep Conditional Generative Adversarial Network for Emulating and Extending Global Atmospheric Composition Forecasts
Jennifer A. Sleeman1, Viral Shah2,3, Patricia Castellanos4, Christoph Keller5, Christopher James Ribaudo6, Adeline Hillier6, Caroline Tang1, Collin Kofroth6, Alex Chen7, Pam Wales2,8, Jules Kouatchou8,9, Ravi Govindaraju8,10, Katherine Breen8,11 and K. Emma Knowland12, (1)Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States, (2)Morgan State University, GESTAR II, Baltimore, United States, (3)NASA Goddard Space Flight Center, Global Modeling and Assimilation Office, Greenbelt, MD, United States, (4)NASA Goddard Space Flight Center, Global Modeling and Assimilation Office (Code 610.1), Greenbelt, United States, (5)Swiss Re, Washington, United States, (6)Johns Hopkins University Applied Physics Laboratory, Laurel, United States, (7)Johns Hopkins Applied Physics Laboratory, Laurel, United States, (8)NASA Goddard Space Flight Center, Global Modeling and Assimilation Office, Greenbelt, United States, (9)Science Systems and Applications, Inc., Lanham, United States, (10)Science Systems and Applications, Inc., Lanham, MD, United States, (11)Morgan State University, Baltimore, United States, (12)NASA Headquarters, Washington, DC, United States