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


Description:

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:
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

Denoising Raw Photon Counting Atmospheric Lidar Data Using Autoencoders
Patrick A. Selmer, SSAI, Lanham, MD; and J. E. Yorks and E. P. Nowottnick

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

Cloudstream: Long-term Cloud Detection Image Prediction Model Using Multi-Channel Satellite Images
Eunbin Cho, SIA(SI analytics), Daejeon, South korea; SI-Analytics, Daejeon, South korea; and E. Kim and Y. Choi

Deep Learning Enhancements for Global Synthetic Radar Analyses
Mark S. Veillette, MIT Lincoln Laboratory, Lexington, MA; and K. L. Yeakel, P. Khorrami, and O. Simek

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

Submission Deadline: Thursday, 31 August 2023



See more of: 23rd Conference on Artificial Intelligence for Environmental Science
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