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Cluster analysis by month for meteorological stations using a gridded data of numerical model with temperatures and precipitation
Journal of the Korean Data & Information Science Society 2017;28:1133-44
Published online September 30, 2017
© 2017 Korean Data & Information Science Society.

Hee-Kyung Kim1 · Kwang-Sub Kim2 · Jae-Won Lee3 · Yung-Seop Lee4

124Department of Statistics, Dongguk University
3KMA National Climate Data Center
Correspondence to: Yung-Seop Lee
Professor, Department of Statistics, Dongguk University-Seoul, Seoul 04620, Korea. Email: yung@dongguk.edu
Received April 17, 2017; Revised July 28, 2017; Accepted August 14, 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
Cluster analysis with meteorological data allows to segment meteorological region based on meteorological characteristics. By the way, meteorological observed data are not adequate for cluster analysis because meteorological stations which observe the data are located not uniformly. Therefore the clustering of meteorological observed data cannot reflect the climate characteristic of South Korea properly. The clustering of 5km×5km gridded data derived from a numerical model, on the other hand, reflect it evenly. In this study, we analyzed long-term grid data for temperatures and precipitation using cluster analysis. Due to the monthly difference of climate characteristics, clustering was performed by month. As the result of K-Means cluster analysis is so sensitive to initial values, we used initial values with Ward method which is hierarchical cluster analysis method. Based on clustering of gridded data, cluster of meteorological stations were determined. As a result, clustering of meteorological stations in South Korea has been made spatio-temporal segmentation.
Keywords : Cluster analysis, gridded data of numerical model, K-Means method, meteorological stations, precipitation, temperatures, Ward method