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Evaluation of Long-Term Seasonal Predictability of Heatwave over South Korea Using PNU CGCM-WRF Chain

PNU CGCM-WRF Chain을 이용한 남한 지역 폭염 장기 계절 예측성 평가

  • Kim, Young-Hyun (Division of Earth Environmental System, Pusan National University) ;
  • Kim, Eung-Sup (Division of Earth Environmental System, Pusan National University) ;
  • Choi, Myeong-Ju (Division of Earth Environmental System, Pusan National University) ;
  • Shim, Kyo-Moon (National Academy of Agricultural Science, RDA) ;
  • Ahn, Joong-Bae (Division of Earth Environmental System, Pusan National University)
  • 김영현 (부산대학교 지구환경시스템학부) ;
  • 김응섭 (부산대학교 지구환경시스템학부) ;
  • 최명주 (부산대학교 지구환경시스템학부) ;
  • 심교문 (농촌진흥청 국립농업과학원) ;
  • 안중배 (부산대학교 지구환경시스템학부)
  • Received : 2019.09.05
  • Accepted : 2019.12.19
  • Published : 2019.12.31
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Abstract

This study evaluates the long-term seasonal predictability of summer (June, July and August) heatwaves over South Korea using 30-year (1989~2018) Hindcast data of the Pusan National University Coupled General Circulation Model (PNU CGCM)-Weather Research and Forecasting (WRF) chain. Heatwave indices such as Number of Heatwave days (HWD), Heatwave Intensity (HWI) and Heatwave Warning (HWW) are used to explore the long-term seasonal predictability of heatwaves. The prediction skills for HWD, HWI, and HWW are evaluated in terms of the Temporal Correlation Coefficient (TCC), Root Mean Square Error (RMSE) and Skill Scores such as Heidke Skill Score (HSS) and Hit Rate (HR). The spatial distributions of daily maximum temperature simulated by WRF are similar overall to those simulated by NCEP-R2 and PNU CGCM. The WRF tends to underestimate the daily maximum temperature than observation because the lateral boundary condition of WRF is PNU CGCM. According to TCC, RMSE and Skill Score, the predictability of daily maximum temperature is higher in the predictions that start from the February and April initial condition. However, the PNU CGCM-WRF chain tends to overestimate HWD, HWI and HWW compared to observations. The TCCs for heatwave indices range from 0.02 to 0.31. The RMSE, HR and HSS values are in the range of 7.73 to 8.73, 0.01 to 0.09 and 0.34 to 0.39, respectively. In general, the prediction skill of the PNU CGCM-WRF chain for heatwave indices is highest in the predictions that start from the February and April initial condition and is lower in the predictions that start from January and March. According to TCC, RMSE and Skill Score, the predictability is more influenced by lead time than by the effects of topography and/or terrain feature because both HSS and HR varies in different leads over the whole region of South Korea.

Keywords

References

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