AI Advances in Tropical Meteorology: Tropical Cyclones, Sub-seasonal Phenomena, and More


Description:

This covers topics in which AI is advancing the science of tropical cyclones, sub-seasonal phenomena, and other areas in tropical meteorology.


Papers:
Formation Mechanism of TC Secondary Eyewall by Numerical Experiments: Role of Dry Air Inflow from the Middle and Upper Troposphere and Cooling by Evaporation and Sublimation
Kyohei Kasami, The University of Tokyo, Kashiwa-shi, Japan; and M. Satoh

Tropical Surface Analysis Using Deep Learning
Will Downs, University of Miami Rosenstiel School of Marine, Atmospheric, and Earth Science, Miami, FL; and S. Majumdar and A. D. Mahoney

Evaluation of Typhoon Forecasts Using the Pangu-Weather Neural Network Model: A Comparative Analysis with ECMWF and Other Numerical Prediction Models
Yuanfu na Xie, Shenzhen Institute of Meteorological Innovation, Shenzhen, Guangdong Province, China; and Z. Qin, Y. Wu, N. Wu, J. chen, X. Xu, F. Zheng, and H. Zhang

Improving Ensemble Model Tropical Cyclone Track Forecast Using Machine Learning
Nikita Agrawal, Whitney M. Young Magnet High School, Chicago, IL; and B. A. Colle

Exploring Tropical Cyclone Structure and Evolution with AI-based Synthetic Passive Microwave Data
Marie McGraw, CIRA, Fort Collins, CO; and K. Haynes, K. D. Musgrave, I. Ebert-Uphoff, C. Slocum, and J. Knaff

A Spatially-Aware North Atlantic Tropical Cyclogenesis Index Using Machine Learning
Alex Alvin Cheung, University of Maryland, College Park, MD; and M. J. Molina and S. J. Camargo

Inference-based retrieval of inner-core tropical cyclone surface winds from satellite-based microwave imagers and sounders
Anthony J. Wimmers, CIMSS, Univ. of Wisconsin−Madison, Madison, WI; and S. Griffin, D. C. Herndon, and C. Velden

Machine Learning Quality Control of Lightning Data for Tropical Cyclone Intensity Forecasting
Kyle A. Hilburn, ; and S. N. Stevenson, K. Musgrave, and B. C. Trabing

Causal Feature Selection for Tropical Cyclone Intensity Forecasting
Tom Georges Beucler, University of Lausanne, Lausanne, VD, Switzerland; and S. G. SUDHEESH, F. I. H. Tam, M. S. Gomez Jr., M. McGraw, M. DeMaria, K. Musgrave, A. Gerhardus, and J. Runge

Development of a Machine Learning-based Tropical Cyclone Track Prediction Scheme over the western North Pacific
You-Hyun Baek, National Institute of Meteorological Sciences, Seogwipo-si, 49, South korea; and H. Lee, J. R. Lee, S. Won, and S. H. Kim

Submission Deadline: Thursday, 31 August 2023



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