Session 13B Applications of Machine Learning to Remote Sensing of Aerosol, Cloud, and Precipitation Properties

Thursday, 1 February 2024: 8:30 AM-10:00 AM
338 (The Baltimore Convention Center)
Host: 23rd Conference on Artificial Intelligence for Environmental Science
Cochairs:
Christina E. Kumler, CIRA, Boulder, CO and Ryan A. Sobash

This session invites submissions that have utilized AI methods for remote sensing applications. Additionally, we invite those who have used AI in atmospheric applications involving clouds and aerosols. Abstracts can include model development for retrieval, classification, and/or numerical weather prediction.

Papers:
8:30 AM
13B.1
A Generalized Deep Neural Network for Estimating Severe Hail Likelihood from Satellite Infrared Cloud Top Patterns and Microwave Radiances
Benjamin Scarino, NASA, Hampton, VA; and K. M. Bedka, S. D. Bang, K. Itterly, and D. J. Cecil

8:45 AM
13B.2
Generating Synthetic Visible Satellite Data with Machine Learning
Tim W Reid, MIT Lincoln Laboratory, Lexington, MA; and P. Khorrami, O. Simek, and M. S. Veillette

9:00 AM
13B.3
Exploring Texture Analysis to Aid Classification of Meteorological Phenomena in Satellite Imagery
Kristina Moen, Colorado State University, Fort Collins, CO; and N. J. Mitchell, Y. Lee, L. Ver Hoef, E. J. King, I. Ebert-Uphoff, K. A. Hilburn, and W. Line

9:15 AM
13B.4
Three-Dimensional Convective Updraft Cell Segmentation Using Deep Learning
Md. Rafsan Jani, Morgan State University, Baltimore, MD; and C. Padilla, M. R. Hasan, A. O. Ajala, X. Li, and M. M. Rahman

13B.5
Forecast Correction Models for Systematic Errors in Different Atmospheric Cloud Regimes.

9:45 AM
13B.6
Near-Real-Time Aerosol Retrievals Via Neural Networks: Application to OMPS Limb Profiler Measurements
Michael D. Himes, NASA Postdoctoral Program, Greenbelt, MD; and G. Taha, T. Zhu, D. Kahn, and N. A. Kramarova

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- Indicates paper has been withdrawn from meeting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