Artificial Intelligence for Satellite Observations 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:
Maria J. Molina and Kara D. Lamb, Earth and Environmental engineering, Eath and Env Dep, New York City, NY

Satellites are extremely valuable for their ability to provide global observations of the Earth System. Artificial intelligence is a powerful tool for processing and analyzing imagery. This session seeks applications of artificial intelligence for utilizing satellite observations. Example topics include: image segmentation, image-to-image translation, quality control, resolution enhancement, data fusion, and other topics that use satellite observations as an input or target variable in artificial intelligence applications.

Papers:
734
Applying Machine Learning Techniques to Retrieve and Improve ATMS-Based Temperature, Water Vapor, and Precipitation Estimates
Shuyan Liu, CIRA, Fort Collins, CO; and C. Grassotti and Q. Liu

735
Objectively Identifying Transverse Cirrus Bands in Tropical Cyclones using a Convolutional Neural Network
John Mark Mayhall, MSFC, Huntsville, AL; and P. Duran, A. T. White, and R. A. Wade
Manuscript (566.1 kB)

Handout (735.9 kB)

736
AI-Based Retrieval and Prediction of Weather Imagery Using Geostationary Satellite Data
Byeongyeon Kim, National Institute of Meteorological Science, Seogwipo-si, Jeju-do, South korea; and A. Cho, H. Lee, C. Han, S. Kim, G. Lee, M. Choe, H. M. Park, and K. Shin

737
A Machine Learning Approach to Resolving the Internal Structure of Clouds
Simona Renee Seastrand, SAIC, Offutt AFB, NE; and B. Abeln, A. Torres, and A. Pham

738
Expanding a data fusion approach for 4-D cloud nowcasting
David Ryglicki, MyRadar, Orlando, FL; and A. J. Wimmers and S. Garimella

739
A Neural Network Approach To Retrieve Atmospheric Temperatures From Observations Of Constellation Of Smallsats.
Dingdong Li, City College of New York, New York, NY; and M. Goldberg

740
Attention-Based Deep Fusion CNN for Geostationary Satellite Rainfall Estimates over Taiwan

741
Scales of Tornado Damage Detectability using Machine Learning
Elizabeth Tirone, Cooperative Institute for Severe and High-Impact Weather Research and Operations (CIWRO), Norman, OK; and M. Coniglio, R. W. Moore III, L. Sims, and D. Candela

742
A Deep Learning Approach to Estimate CERES Broadband Flux at 1km Resolution with MODIS Radiances
Mitchell Dodson, University of Alabama in Huntsville, Huntsville, AL; and S. W. Freeman and S. Christopher

743
Estimating Tropospheric Methane from Cross-track Infrared Sounder (CrIS) Spectra using a Machine Learning Method
Likun Wang, University of Maryland, College Park, MD; and L. Zhou and S. Kalluri

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