Artificial Intelligence for Earth Science Poster Session

Wednesday, 15 January 2025: 3:00 PM-4:30 PM
Hall C (New Orleans Ernest N. Morial Convention Center)
Host: 24th Conference on Artificial Intelligence for Environmental Science
Submitters:
Kyle A. Hilburn; Christina E. Kumler, CIRA, Boulder, CO and Tiago Quintino, ECMWF, Forecast Department, Reading
Cochairs:
Kara D. Lamb, Earth and Environmental engineering, Eath and Env Dep, New York City, NY and Lena Heuscher

This session on AI for Earth Sciences explores the wide-ranging applications of AI across diverse Earth Science domains that are not covered under other more specific sessions. A broad spectrum of disciplines, such as  atmospheric science, geophysics, oceanography and climatology, are within topic. The session seeks contributions that demonstrate how AI can enhance our ability to monitor, model, and manage Earth's systems, thus improving our understanding of natural processes and informing decision-making.

Papers:
724
Enhancing Automated Frontal Boundary Detection with Geostationary Satellite Imagery
Andrew Douglas Justin, NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES), Norman, OK

725
Estimating Statewide Atmospheric Visibility From Camera Images
Maximus X. Sasser, Univ. of Oklahoma, Norman, OK; and M. Wilson Reyes, V. P. Kadiyala, A. Kurbanovas, K. J. Sulia, C. D. Thorncroft, and A. H. Fagg

Handout (2.5 MB)

727
Deep Learning-Based Prediction Model for Greenhouse Gas Emission in Gulf-Coast Communities
MASHUQ ISLAM, Xavier University of Louisiana, New Orleans, LA; and M. Gaseller, F. Landry, T. L. Al Khawli, A. Sunda-Meya, and S. ISLAM

729
Machine Learning-Based Estimation of Peak Wind Gusts Utilizing a Global Reanalysis Dataset
JAYA SINGH, ATMOSPHERIC SCIENCE RESEARCH CENTER, UALBANY, Albany, NY; and S. BASU

730
A Machine Learning Approach to Smoothing Tornado Climatology Maps
Kaleb Raven Clover, Central Michigan University, Mount Pleasant, MI; and S. Das and J. T. Allen

732
Sensor Location-Invariant Multi-Regime Methane Leak Localization and Quantification with Knowledge-Guided Deep Learning
Charlie Becker, NCAR, Boulder, CO; and D. J. Gagne II, T. Martin, D. Munoz-Esparza, J. Sauer, and B. Travis

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner