Analysis of extreme wind speed trend in the typhoon-vulnerable prefectures of Japan
This paper is an observational study that evaluates historical trend changes of extreme wind speed in Japan’s top three typhoon-vulnerable prefectures according to Japan Meteorological Agency (JMA), namely, Kagoshima, Kochi, and Wakayama. Recorded historical average daily data from 35 wind stations...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | en |
| Published: |
Corvinus University of Budapest
2025
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| Online Access: | http://psasir.upm.edu.my/id/eprint/120421/1/120421.pdf http://psasir.upm.edu.my/id/eprint/120421/ https://www.aloki.hu/pdf/2304_78737897.pdf |
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| Summary: | This paper is an observational study that evaluates historical trend changes of extreme wind speed in Japan’s top three typhoon-vulnerable prefectures according to Japan Meteorological Agency (JMA), namely, Kagoshima, Kochi, and Wakayama. Recorded historical average daily data from 35 wind stations were collected from as early as 1979 until late 2021. Extreme wind speed deterministic trends were calculated using Kendall’s τ and their significance was tested using the Mann-Kendall test, confirming whether the trend is significantly monotonically increasing or decreasing. Mann-Kendall test results across the wind stations were then corrected using the Benjamini-Hochberg method to rectify the false discovery rate. Moreover, Local Indicators of Spatial Association (LISA) method was used to determine the existence of significant spatial correlation of extreme winds across wind stations and their k-nearest neighbours. We also tested for trend-stationarity using the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test to determine whether extreme winds from these stations can be characterized with stochastic trend, i.e., if there is evidence of extreme wind speed trend fluctuations over time. Finally, we also provide spatial interpolation of extreme wind speed using the inverse distance weighting (IDW) technique to provide insight into how extreme wind speeds are clustered and spread and was also applied to every season (domain) to understand better the spatial distribution of extreme winds as season changes. |
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