Open Access

ARTICLE

Poisson-Gumbel Model for Wind Speed Threshold Estimation of Maximum Wind Speed

by Wenzheng Yu¹, Yang Gao¹, Zhengyu Yuan¹, Xin Yao^1,*, Mingxuan Zhu¹, Hanxiaoya Zhang²

1 School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing, 210044, China
2 Faculty of Science, The University of Auckland, 1010, New Zealand

* Corresponding Author: Xin Yao. Email:

Computers, Materials & Continua 2022, 73(1), 563-576. https://doi.org/10.32604/cmc.2022.027008

Received 08 January 2022; Accepted 22 March 2022; Issue published 18 May 2022

Abstract

Poisson-Gumbel joint distribution model uses maximum wind speed corresponding to multiple typhoons to construct sample sequence. Thresholds are usually used to filter sample sequences to make them more consistent with Poisson distribution. However, few studies have discussed the threshold setting and its impact on Poisson-Gumbel joint distribution model. In this study, a sample sequence based on the data of Qinzhou meteorological station from 2005 to 2018 were constructed. We set 0%, 5%, 10%, 20% and 30% gradient thresholds. Then, we analyzed the influence of threshold change on the calculation results of maximum wind speed in different return periods. The results showed that: (1) When the threshold increases, the maximum wind speed of each return period will decrease gradually. This indicates that the length of the sample series may have a positive effect on the return period wind speed calculation in Gumbel and Poisson-Gumbel methods. Although the augment of the threshold increases the average value of the maximum wind speed of the sample sequence, it shortens the length of the sample sequence, resulting in a lower calculated value of the maximum wind speed. However, this deviation is not large. Taking the common 10% threshold as an example, the maximum wind speed calculation deviation in the 50 a return period is about 1.9%; (2) Theoretically, the threshold is set to make the sample sequence more consistent with Poisson distribution, but this example showed that the effect is worth further discussion. Although the overall trend showed that the increase of the threshold can make χ2 decrease, the correlation coefficient of linear fitting was only 0.182. Taking Qinzhou meteorological station data as an example, the χ2 of 20% threshold was as high as 6.35, meaning that the selected sample sequence was not ideal.

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Keywords

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