4 Applications of Artificial Intelligence in Tropical Cyclones and Tropical Meteorology

Thursday, 9 May 2024: 3:15 PM-4:45 PM
Regency Ballroom (Hyatt Regency Long Beach)
Host: 36th Conference on Hurricanes and Tropical Meteorology

Papers:
134
Parametric and Machine Learning Approach for describing Hurricane Offshore Winds over the U.S Northeast Outer Continental Shelf
Geeta Nain, Argonne National Lab, Lemont, IL; ANL, Lemont, IL; and W. J. Pringle, J. Wang, and P. Xue

135
Updates on the Tropical Cyclone Precipitation, Infrared, Microwave, and Environmental Dataset (TC PRIMED)
Muhammad Naufal Razin, CIRA, Fort Collins, CO; and C. Slocum, J. Knaff, K. Haynes, and M. McGraw

Reaching a Wide and Diverse Audience Quickly for Weather Communication Content with Generative AI

136
Assessment of Tropical Cyclone (TC) Hazard and Risk in a Changing Climate by means of a New Hybrid TC Global Model
Roberto Ingrosso, University of Quebec in Montreal, Montreal, QC, Canada; and M. Boudreault, F. S. Pausata, and D. Carozza

137
Exploring the Use of Machine Learning in Correcting GFS Tropical Cyclone Track and Landfall Errors

138
TCBench: A Platform for the Data-Driven Prediction of Tropical Cyclones
Milton Salvador Gomez Jr., Univ. of Lausanne, Lausanne, Vaud, Switzerland; University of Lausanne, Lausanne, Vaud, Switzerland; and M. McGraw, L. Poulain-Auzeau, F. I. H. Tam, S. G. Sudheesh, S. J. Camargo, PhD, D. R. Chavas, Y. Cohen, and T. Beucler

139
Prediction of Tropical Cyclogenesis Using Vision Transformers
William Downs, University of Miami Rosenstiel School of Marine, Atmospheric, and Earth Science, Miami, FL; and S. Majumdar, Ph.D.

140
Extreme Rapid Intensification of Hurricanes Otis (2023) and Patricia (2015): Machine Learning Diagnoses
Xander Lowry, Indiana University Bloomington, Bloomington, IN; and C. Q. Kieu

141
Using a Convolutional Neural Network to Disentangle Environmental Differences between Developing and Non-Developing African Easterly Waves
Alyssa M Stansfield, Colorado State University, Fort Collins, CO; and M. J. Molina

142
Ensemble Forecasting with Global Machine Learning Models

143
Can Deep Learning Predict the Chaos of Tropical Cyclone Intensity?
Chanh Q Kieu, Indiana University Bloomington, Bloomington, IN

144
Machine Learning-Driven Enhancement of Predictors and Prediction for Tropical Cyclone Activity
Michael Maier-Gerber, ECMWF, Bonn, NW, Germany; and L. Magnusson, P. Bonetti, A. M. Metelli, and M. Restelli

Poster 145 will also be presented as Paper 19C.3A

146
Improved “First-Guess” Forecast Guidance for National Hurricane Center Forecasters

147
What Does Lightning Tell Us About Tropical Cyclone Structure Relevant to Forecasting Intensity Change?

148
Surveillance Camera Based Extreme Rainfall Observation Using Deep Learning Model
xing wang, Nanjing university, nanjing, 32, China; and J. Zhao

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