Analyzing extreme diurnal temperature range in spring on the South Korea using a sliding window approach†
Jae-Heon Lee1 · Song-Lak Kang2
12Department of Atmospheric & Environmental Sciences, Gangneung-Wonju National University
Correspondence to: † This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2021R1A6A1A03044326 and 2021R1I1A3044379).
1 Researcher, Multiscale ABL Laboratory, Department of Atmospheric & Environmental Sciences, Gangneung-Wonju National University, Gangneung 25457, Korea.
2 Corresponding author: Professor, Multiscale ABL Laboratory, Department of Atmospheric & Environmental Sciences, Gangneung-Wonju National University, Gangneung 25457, Korea. E-mail: slkang@gwnu.ac.kr
1 Researcher, Multiscale ABL Laboratory, Department of Atmospheric & Environmental Sciences, Gangneung-Wonju National University, Gangneung 25457, Korea.
2 Corresponding author: Professor, Multiscale ABL Laboratory, Department of Atmospheric & Environmental Sciences, Gangneung-Wonju National University, Gangneung 25457, Korea. E-mail: slkang@gwnu.ac.kr
Received October 17, 2023; Revised December 3, 2023; Accepted December 20, 2023.
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
- We analyzed extreme values of the diurnal temperature range (DTR) during the spring season. The data were collected from 16 automated synoptic observation system (ASOS) stations in South Korea over a 59-year period spanning from 1964 to 2022. The sliding window approach was employed to capture the nonlinearity and nonstationarity of the extreme values. By applying the r-largest order statistic (r-LOS) method to sample the extremes, we model to the generalized extreme value (GEV) distribution. Trends in extreme values are detected using the Mann-Kendall trend test. Our results demonstrated that the selection of study period and methodology could significant affect in extreme value analysis, due to the extreme DTR shows the nonlinearity and nonstationarity with time-varying. While the extreme DTR is related to the linear change of location parameter, there also represent larger variation in shape and scale parameters. The extreme DTR, as indicated by the return periods, suggests the potential occurrence in some stations. Despite the reduction in extreme DTR due to climate change, major stations still exhibited a high DTR, which could pose physical risks.
- Keywords : Extreme diurnal temperature range, GEV, rLOS, sliding window approach.
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