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Analysis of extreme wind speed and precipitation using copula
Journal of the Korean Data & Information Science Society 2017;28:797-810
Published online July 31, 2017
© 2017 Korean Data & Information Science Society.

Taeyong Kwon1 · Sanghoo Yoon2

1Department of Statistics, Daegu University
2Department of Statistics and Computer Science, Daegu University & Institute of Basic Science, Deagu University
Correspondence to: Sanghoo Yoon
Assistant professor, Department of Statistics and Computer Science, Daegu University, Gyeongbuk 38453, Republic of Korea. E-mail: statstar@daegu.ac.kr
Received May 2, 2017; Revised June 22, 2017; Accepted June 29, 2017.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
The Korean peninsula is exposed to typhoons every year. Typhoons cause huge socioeconomic damage because tropical cyclones tend to occur with strong winds and heavy precipitation. In order to understand the complex dependence structure between strong winds and heavy precipitation, the copula links a set of univariate distributions to a multivariate distribution and has been actively studied in the field of hydrology. In this study, we carried out analysis using data of wind speed and precipitation collected from the weather stations in Busan and Jeju. Log-Normal, Gamma, and Weibull distributions were considered to explain marginal distributions of the copula. Kolmogorov-Smirnov, Cramer-von-Mises, and Anderson-Darling test statistics were employed for testing the goodness-of-fit of marginal distribution. Observed pseudo data were calculated through inverse transformation method for establishing the copula. Elliptical, archimedean, and extreme copula were considered to explain the dependence structure between strong winds and heavy precipitation. In selecting the best copula, we employed the Cramer-von-Mises test and cross-validation. In Busan, precipitation according to average wind speed followed t copula and precipitation just as maximum wind speed adopted Clayton copula. In Jeju, precipitation according to maximum wind speed complied Normal copula and average wind speed as stated in precipitation followed Frank copula and maximum wind speed according to precipitation observed Husler-Reiss copula.
Keywords : Copula, extreme value, k-fold cross validation


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