Tropical Cyclone Size Identification over the Western North Pacific Using Support Vector Machine and General Regression Neural Network
Author:
Affiliation:
1. Shanghai Typhoon Institute and Key Laboratory of Numerical Modeling for Tropical Cyclone, China Meteorological Administration, China
2. Hong Kong Observatory, Hong Kong, China
3. Shanghai Ocean University, China
Publisher
Meteorological Society of Japan
Subject
Atmospheric Science
Link
https://www.jstage.jst.go.jp/article/jmsj/100/6/100_2022-048/_pdf
Reference54 articles.
1. Bai, L. N., H. Yu, P. G. Black, Y. L. Xu, M. Ying, J. Tang, and R. Guo, 2019: Reexamination of the tropical cyclone wind-pressure relationship based on pre-1987 aircraft data in the western North Pacific. Wea. Forecasting, 34, 1939-1954.
2. Brand, S., 1972: Very large and very small typhoon of the western North Pacific Ocean. J. Meteor. Soc. Japan, 50, 332-341.
3. Chan, K. T. F., and J. C. L. Chan, 2012: Size and strength of tropical cyclones as inferred from QuikSCAT data. Mon. Wea. Rev., 140, 811-824.
4. Chan, K. T. F., and J. C. L. Chan, 2015: Global climatology of tropical cyclone size as inferred from QuikSCAT data. Int. J. Climatol., 35, 4843-4848.
5. Chavas, D. R., and K. A. Emanuel, 2010: A QuikSCAT climatology of tropical cyclone size. Geophys. Res. Lett., 37, L18816, doi:10.1029/2010GL044558.
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