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Spatial analysis for the uncertainty of mountain meteorology observation system
Journal of the Korean Data & Information Science Society 2019;30:57-66
Published online January 31, 2019;
© 2019 Korean Data and Information Science Society.

Yongku Kim1 · Seungwan Cho2 · Joowon Park3

1Department of Statistics, Kyungpook National University
23School of Forest Sciences and Landscape Architecture, Kyungpook National University
Correspondence to: Associate professor, School of Forest Sciences and Landscape Architecture, Kyungpook National University, Daegu 41566, Korea. E-mail:
Received December 31, 2018; Revised January 11, 2019; Accepted January 11, 2019.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
With increasing importance for forest management, National mountain meteorology observation system are under way of expansion. Yet, the location of the additional systems should be selected to contribute to reducing uncertainty imbedded in the data from the existing systems. Thus, this study conducted a spatio-temporal analysis for understanding the spatial distribution of the uncertainty in the meteorological data obtained from the existing systems. Time-series analysis on the maxima, minima, means and medians of temperature, relative humidity, wind speed and air pressure data was done to result in the uncertainty of each individual mountain meteorological factor. As a result, nine factors except maximum temperature, minimum relative humidity, wind speed average, maximum wind speed, maximum air pressure, and air pressure minimum commonly show higher uncertainty around Baekdu-Daegan region. Based on the uncertainty map, additional systems are better to select their location among the high uncertainty areas.
Keywords : Mountain meteorology observation system, spatial distribution, spatio-temporal analysis, uncertainty.