Session: Artificial Intelligence for Environmental Science I (104th AMS Annual Meeting)

Monday, 29 January 2024: 10:45 AM-12:00 PM

345/346 (The Baltimore Convention Center)

Host: 23rd Conference on Artificial Intelligence for Environmental Science

Cochairs:

Ryan Lagerquist; Rob Redmon and Philippe E. Tissot

This session is open to a wide variety of research, active work, and advancements in applications of artificial intelligence or machine learning methods and models to environmental science.

Papers:

10:45 AM

2A.1

2024 Update on the NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES)

Amy McGovern, NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography Univ. of Oklahoma (AI2ES), Norman, OK

11:00 AM

2A.2

Machine Integration and Learning for Earth Systems (MILES): Bridging Key Gaps in Machine Learning for Earth System Science

David John Gagne II, Ph.D., NCAR MILES, Boulder, CO; and J. Schreck, C. Becker, G. Gantos, T. Martin, W. Petzke, W. Chuang, W. E. Chapman, K. J. Mayer, M. J. Molina, J. T. Radford, C. D. Wirz, M. G. Cains, J. Demuth, O. V. Wilhelmi, R. E. Morss, and J. Anderson

11:15 AM

2A.3

A Benchmark Dataset for Statistical Downscaling of Meteorological Fields with Deep Neural Networks

Michael Langguth, Forschungszentrum Juelich, Juelich, Nordrhein-Westfalen, Germany; and S. Stadtler, B. Gong, and M. G. Schultz

11:30 AM

2A.4

Can AI Models Perform Dynamical Downscaling? Training Deep Generative Models to Downscale ERA5 Variables to High-Resolution COSMO-CLM Dynamical Reanalyses over Italy

Elena Tomasi, Fondazione Bruno Kessler - FBK, Trento, TN, Italy; and G. Franch and M. Cristoforetti

11:45 AM

2A.5

Uncovering the Drivers of Urban Air Pollution Using Spatial Machine Learning for Improved Analysis and Forecasting

Firas Gerges, Princeton University, Princeton, NJ; and M. Llaguno-Munitxa, M. A. Zondlo, M. Boufadel, and E. Bou-Zeid