응용통계연구 (The Korean Journal of Applied Statistics)

  • 제26권3호
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  • Pages.413-420
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  • 2013
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  • 1225-066X(pISSN)
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  • 2383-5818(eISSN)
한국통계학회 (The Korean Statistical Society)
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GLM 날씨 발생기를 이용한 서울지역 일일 기온 모형

A Modeling of Daily Temperature in Seoul using GLM Weather Generator

  • Kim, Hyeonjeong (Department of Statistics, Yeungnam University) ;
  • Do, Hae Young (Department of Statistics, Kyungpook National University) ;
  • Kim, Yongku (Department of Statistics, Yeungnam University)
  • 투고 : 2013.02.15
  • 심사 : 2013.04.26
  • 발행 : 2013.06.30
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초록

확률적 날씨 발생기(Stochastic weather generator)는 일일 날씨를 생성하는데 일반적으로 사용되는 방법으로 최근에는 일반화선형모형에 기초한 확률적 날씨 발생 방법이 제안되었다. 본 논문에서는 서울지역의 일일 기온을 모형화하하기 위해서 일반화선형모형에 기초한 확률적 날씨 발생기를 고려하였다. 이 모형에서는 계절성을 나타내는 변수와 강우발생 유무가 공변수로 사용되었다. 일반적으로 확률적 날씨 발생기에서는 생성된 일일 날씨가 월별 또는 계절별 총강우량이나 평균온도에 충분한 변동을 만들어 내지 못하는 과대산포 현상이 발생하는데, 이러한 한계를 극복하기 위해 본 연구에서는 평활된 계절별 평균 온도를 일반화선형모형의 공변수로 추가하였다. 그리고 제안된 모형을 1961년부터 2011년까지 51년 동안의 서울지역 일일 평균 기온자료에 적용하였다.

Stochastic weather generator is a commonly used tool to simulate daily weather time series. Recently, a generalized linear model(GLM) has been proposed as a convenient approach to tting these weather generators. In the present paper, a stochastic weather generator is considered to model the time series of daily temperatures for Seoul South Korea. As a covariate, precipitation occurrence is introduced to a relate short-term predictor to short-term predictands. One of the limitations of stochastic weather generators is a marked tendency to underestimate the observed interannual variance of monthly, seasonal, or annual total precipitation. To reduce this phenomenon, we incorporate a time series of seasonal mean temperatures in the GLM weather generator as a covariate.

키워드

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피인용 문헌

  1. Analysis of statistical models on temperature at the Seosan city in Korea vol.25, pp.6, 2014, https://doi.org/10.7465/jkdi.2014.25.6.1293
  2. Analysis of statistical models on temperature at the Suwon city in Korea vol.26, pp.6, 2015, https://doi.org/10.7465/jkdi.2015.26.6.1409