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

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

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

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

- Indicates paper has been withdrawn from meeting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