Session 13A Applications of Artificial Intelligence for Improved Estimation and Prediction of Extreme Precipitation and/or Drought in Weather and Climate I

Thursday, 1 February 2024: 8:30 AM-10:00 AM
345/346 (The Baltimore Convention Center)
Hosts: (Joint between the 23rd Conference on Artificial Intelligence for Environmental Science; and the Presidential Conference )
Cochairs:
Philippe E. Tissot and Alexandra Mazurek, Colorado State University, Department of Atmospheric Science, Fort Collins, CO

This session invites abstracts leveraging AI tools to improve understanding and/or forecasting abilities of extreme rainfall and drought events.

Papers:
8:30 AM
13A.1
Identifying Dominant Southeastern United States Warm-Season Precipitation Features from Numerical Weather Prediction Forecasts
Andrew E. Mercer, Mississippi State Univ., Mississippi State, MS; and J. Dyer

8:45 AM
13A.2
A Machine Learning Emulator for West African Monsoon Prediction
Charlotte Merchant, Princeton University, Princeton, NJ; and W. Yang and G. A. Vecchi

9:00 AM
13A.3
Enhancing Regional Quantitative Precipitation Forecasts Using Machine Learning in Western US Watersheds
Weiming Hu, Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, UC San Diego, La Jolla, CA; James Madison University, Harrisonburg, VA; and M. Ghazvinian, PhD, A. Sengupta, M. Pan, and L. Delle Monache

9:15 AM
13A.4
Bias Correction of Global Blended Satellite Precipitation Products over Taiwan with Deep Learning
Liping Wang, Colorado State University, Fort Collins, CO; Colorado State University, Fort Collins, CO; and H. Chen, Y. Chen, P. Xie, C. R. Chen, and J. Q. Stewart

9:30 AM
13A.5
Radar Based Quantitative Precipitation Estimation Using Deep Learning over the UAE region
EunYeol Kim, Colorado State University, Fort Collins, CO; and V. Chandrasekar, C. Radhakrishnan, L. Delle Monache, and D. Axisa

9:45 AM
13A.6
Evaluation of a Convolutional Neural Network to Predict Wintertime Orographic Precipitation Gradients of the Western CONUS
Savanna Wolvin, Univ. of Utah, Salt Lake City, UT; and C. Strong, S. Brewer, and J. Steenburgh

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- Indicates paper has been withdrawn from meeting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