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Korean Meteorological Society (한국기상학회)
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Predictability Study of Snowfall Case over South Korea Using TIGGE Data on 28 December 2012

TIGGE 자료를 이용한 2012년 12월 28일 한반도 강설사례 예측성 연구

  • Lee, Sang-Min (Forecast Research Laboratory, National Institute of Meteorological Research, KMA) ;
  • Han, Sang-Un (Forecast Technology Division, Forecast Bureau, KMA) ;
  • Won, Hye Young (Forecast Research Laboratory, National Institute of Meteorological Research, KMA) ;
  • Ha, Jong-Chul (Forecast Research Laboratory, National Institute of Meteorological Research, KMA) ;
  • Lee, Jeong-Soon (Forecast Research Laboratory, National Institute of Meteorological Research, KMA) ;
  • Sim, Jae-Kwan (Forecast Research Laboratory, National Institute of Meteorological Research, KMA) ;
  • Lee, Yong Hee (Forecast Research Laboratory, National Institute of Meteorological Research, KMA)
  • 이상민 (기상청 국립기상연구소 예보연구과) ;
  • 한상은 (기상청 예보국 예보기술분석과) ;
  • 원혜영 (기상청 국립기상연구소 예보연구과) ;
  • 하종철 (기상청 국립기상연구소 예보연구과) ;
  • 이정순 (기상청 국립기상연구소 예보연구과) ;
  • 심재관 (기상청 국립기상연구소 예보연구과) ;
  • 이용희 (기상청 국립기상연구소 예보연구과)
  • Received : 2013.10.12
  • Accepted : 2014.01.09
  • Published : 2014.03.31
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Abstract

This study compared ensemble mean and probability forecasts of snow depth amount associated with winter storm over South Korea on 28 December 2012 at five operational forecast centers (CMA, ECMWF, NCEP, KMA, and UMKO). And cause of difference in predicted snow depth at each Ensemble Prediction System (EPS) was investigated by using THe Observing system Research and Predictability EXperiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) data. This snowfall event occurred due to low pressure passing through South Sea of Korea. Amount of 6 hr accumulated snow depth was more than 10 cm over southern region of South Korea In this case study, ECMWF showed best prediction skill for the spatio-temporal distribution of snow depth. At first, ECMWF EPS has been consistently enhancing the indications present in ensemble mean snow depth forecasts from 7-day lead time. Secondly, its ensemble probabilities in excess of 2~5 cm/6 hour have been coincided with observation frequencies. And this snowfall case could be predicted from 5-day lead time by using 10-day lag ensemble mean 6 hr accumulated snow depth distribution. In addition, the cause of good performances at ECMWF EPS in predicted snow depth amounts was due to outstanding prediction ability of forming inversion layer with below $0^{\circ}C$ temperature in low level (below 850 hPa) according to $35^{\circ}N$ at 1-day lead time.

Keywords

References

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