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Stochastic weather generator for diurnal temperature range
Journal of the Korean Data & Information Science Society 2021;32:455-62
Published online March 31, 2021;
© 2021 Korean Data and Information Science Society.

Sangwan Kim1 · Yongku Kim2

12Department of Statistics, Kyungpook National University
Correspondence to: This research was supported by the Research Grants of Korea Forest Service (Korea Forestry Promotion Institute) project (No.2019149B10-2123-0301).
1Graduate student, Department of Statistics, Kyungpook National University, Daegu 41566, Korea
2Corresponding author: Associate professor, Department of Statistics, Kyungpook National University, Daegu 41566, Korea. E-mail:
Received January 17, 2021; Revised February 6, 2021; Accepted February 13, 2021.
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.
Stochastic weather generator is a statistical model that generates maximum temperature (Tmax), minimum temperature (Tmin) and daily precipitation using information of weather and climate. This model is one of the ways to reflect long-term climate change or to generate a consistent daily weather sequence for weather forecast which have a seasonality. Recently, a generalized linear model (GLM) has been considered to fit stochastic weather generators to the daily data, which is useful for explaining the characteristics or trends of climate periodicity. It is also useful to add various phenomena that affect the daily weather to variables and use them to relate them to the results. Therefore, we introduce the model that generate a precipitation occurrence, Tmax and Tmin using GLM and then generate a diurnal temperature range (DTR) using proposed model.
Keywords : Diurnal temperature range, generalized linear model, maximum temperature, minimum temperature, stochastic weather generator.