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Korean Meteorological Society (한국기상학회)
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Predictability of Temperature over South Korea in PNU CGCM and WRF Hindcast

PNU CGCM과 WRF를 이용한 남한 지역 기온 예측성 검증

  • Ahn, Joong-Bae (Division of Earth Environmental System, Pusan National University) ;
  • Shim, Kyo-Moon (National Academy of Agricultural Science, RDA) ;
  • Jung, Myung-Pyo (National Academy of Agricultural Science, RDA) ;
  • Jeong, Ha-Gyu (Division of Earth Environmental System, Pusan National University) ;
  • Kim, Young-Hyun (Division of Earth Environmental System, Pusan National University) ;
  • Kim, Eung-Sup (Division of Earth Environmental System, Pusan National University)
  • 안중배 (부산대학교 지구환경시스템학부) ;
  • 심교문 (농촌진흥청 국립농업과학원) ;
  • 정명표 (농촌진흥청 국립농업과학원) ;
  • 정하규 (부산대학교 지구환경시스템학부) ;
  • 김영현 (부산대학교 지구환경시스템학부) ;
  • 김응섭 (부산대학교 지구환경시스템학부)
  • Received : 2018.08.17
  • Accepted : 2018.09.19
  • Published : 2018.12.31
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Abstract

This study assesses the prediction skill of regional scale model for the mean temperature anomaly over South Korea produced by Pusan National University Coupled General Circulation Model (PNU CGCM)-Weather Research and Forecasting (WRF) chain. The initial and boundary conditions of WRF are derived from PNU CGCM. The hindcast period is 11 years from 2007 to 2017. The model's prediction skill of mean temperature anomaly is evaluated in terms of the temporal correlation coefficient (TCC), root mean square error (RMSE) and skill scores which are Heidke skill score (HSS), hit rate (HR), false alarm rate (FAR). The predictions of WRF and PNU CGCM are overall similar to observation (OBS). However, TCC of WRF with OBS is higher than that of PNU CGCM and the variation of mean temperature is more comparable to OBS than that of PNU CGCM. The prediction skill of WRF is higher in March and April but lower in October to December. HSS is as high as above 0.25 and HR (FAR) is as high (low) as above (below) 0.35 in 2-month lead time. According to the spatial distribution of HSS, predictability is not concentrated in a specific region but homogeneously spread throughout the whole region of South Korea.

Keywords

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Fig. 1. The topography heights (in meters) of the WRF domain and locations of ASOS (bigger dots) and AWS (smaller dots) in South Korea.

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Fig. 2. Lead-times in 5-month lead hindcast experiment. Left column indicates initialized month and top line indicates predicted month. Lead2~4 (dark-gray shaded) are dynamically downscaled by WRF.

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Fig. 3. Spatial distribution of mean temperature (℃) derived from NCEP-R2 (upper), PNU CGCM (middle) and WRF (bottom) in the WRF domain for the period of hindcast (2007~2017).

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Fig. 4. Taylor Diagram of surface temperature (℃) for the period of hindcast (2007~2017) during (a) MAM, (b) JJA, (c) SON and (d) DJF for 2~4 month lead-times.

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Fig. 5. The (a) temporal correlation coefficient (TCC) and (b) root mean squared error (RMSE) of WRF prediction result. The upper x-axis indicates initialized month, lower x-axis indicates predicted month. Each line indicates prediction from lead-time 2 to lead-time 4 of initialized month. Filled (open) circle of (a) TCC indicates the values that are statistically significant at the 95% (90%) confidence level.

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Fig. 6. Same as Fig. 5 but for (a) Heidke Skill Score (HSS), (b) Hit Rate (HR) and (c) False Alarm Rate (FAR).

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Fig. 7. Spatial distribution of Heidke Skill Score (HSS) for surface temperature in March (top), April (middle) and May (bottom). The values at the top right of each figure are mean HSS.

Table 1. Description of PNU-CGCM.

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Table 2. WRF configuration used in this study.

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