Posters IV

Thursday, 1 February 2024: 3:00 PM-4:30 PM
Hall E (The Baltimore Convention Center)
Host: 23rd Conference on Artificial Intelligence for Environmental Science

Papers:
893
Exploring Cross-Validation Techniques for ML Predictions of Rare Cold-Stunning Events
Jarett Woodall, Texas A&M Univ.-Corpus Christi, Corpus Christi, TX; and M. C. White, H. Marrero, M. Vicens-Miquel, and P. E. Tissot

894
A Performance Comparison Of Clustering Techniques For Obtaining Freshwater Extents Of Rangeland Ponds And Lakes In Africa Using Earth Observations
Soni Yatheendradas, The University of Maryland, College Park, MD; and K. Slinski

895
Decoding Climate Complexity: Novel Approaches to S2S Forecasting through Advanced AI Technology
Pratik Shukla, Univ. of Maryland Baltimore County, Baltimore, MD; and M. Halem

896
Detecting and Tracking Iceberg A-76A from VIIRS Observations with U-Net Deep Learning Model
Tiancheng Steven Shao, Montgomery Blair High School, Silver Spring, MD; and B. Zhang, S. Uprety, and J. Dong

Deep Learning Enhancements for Global Synthetic Radar Analyses

897
Can Neural Networks Learn to See Airborne Dust and Sand in Thermal Satellite Imagery?
Micah Wallace, GESTAR II, Baltimore, MD; and I. T. Carroll and A. M. Sayer

898
Development of U.S. Based 20-Year Daily, 1-Km-Resolution, Multi-Species (PM2.5, O3, NO2, Dust) Surface Air Pollution Estimates By Machine Learning Data Fusion: Incorporating 1-Km Emission and 12-Km CMAQ
Beiming Tang, NOAA, fairfax, VA; and D. Tong, Y. Li, S. Ma, C. Chang, B. H. Baek, Y. Tang, and B. Baker

899
Improving the Sharpness of Deep Learning Generated Weather Predictions
Michael Yu, AI2ES & University of Oklahoma, Norman, OK; and M. M. Madsen and A. McGovern

900
Analyzing and Exploring Training Recipes for Large-Scale Transformer-Based Weather Prediction
Jared Daniel Willard, LBNL, Berkeley, CA; and P. Harrington, S. Subramanian, A. Mahesh, T. A. O'Brien, and W. D. Collins
Manuscript (320.2 kB)

901
Advancing Global Land Surface Albedo Parameterization with Physics-Informed Machine Learning Methods
Akarsh Ralhan, University of Maryland, College Park, College Park, MD; and C. Sun and X. Z. Liang

902
Exploring Data-Driven Equation Discovery to Model Moisture Flux
Rebecca Z Porter, UCAR, Olathe, KS; and Y. Huang and P. Gentine

903
Testing Machine Learning Methods for Downscaling in the 3D RTMA project
Miodrag Rancic, Lynker, College Park, MD; and A. M. Gibbs, M. Pondeca, R. J. Purser, T. lei, E. Colon, M. T. Morris, and G. Zhao

904
Using Machine Learning to Produce Watch-to-Warning Severe Weather Guidance
Sam Varga, University of Oklahoma, Norman, OK; Cooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, OK; National Severe Storms Laboratory, Norman, OK; and M. L. Flora and C. K. Potvin

Handout (3.2 MB)

905
Educational Project on Machine Learning-Based Prediction of Tornados Under DHS STEM Enhancement Program
Francis Tuluri, Jackson State University, Jackson, MS; and R. S. Reddy Sr., E. Reddy, and B. Blanton

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