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Korean Meteorological Society (한국기상학회)
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The Development of Ensemble Statistical Prediction Model for Changma Precipitation

장마 강수를 위한 앙상블 통계 예측 모델 개발

  • Kim, Jin-Yong (Department of Atmospheric Sciences, Division of Earth Environmental System, Pusan National University) ;
  • Seo, Kyong-Hwan (Department of Atmospheric Sciences, Division of Earth Environmental System, Pusan National University)
  • 김진용 (부산대학교 지구환경시스템학부 대기환경과학과) ;
  • 서경환 (부산대학교 지구환경시스템학부 대기환경과학과)
  • Received : 2014.10.29
  • Accepted : 2014.12.09
  • Published : 2014.12.31
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Abstract

Statistical forecast models for the prediction of the summertime Changma precipitation have been developed in this study. As effective predictors for the Changma precipitation, the springtime sea surface temperature (SST) anomalies over the North Atlantic (NA1), the North Pacific (NPC) and the tropical Pacific Ocean (CNINO) has been suggested in Lee and Seo (2013). To further improve the performance of the statistical prediction scheme, we select other potential predictors and construct 2 additional statistical models. The selected predictors are the Northern Indian Ocean (NIO) and the Bering Sea (BS) SST anomalies, and the spring Eurasian snow cover anomaly (EUSC). Then, using the total three statistical prediction models, a simple ensemble-mean prediction is performed. The resulting correlation skill score reaches as high as ~0.90 for the last 21 years, which is ~16% increase in the skill compared to the prediction model by Lee and Seo (2013). The EUSC and BS predictors are related to a strengthening of the Okhotsk high, leading to an enhancement of the Changma front. The NIO predictor induces the cyclonic anomalies to the southwest of the Korean peninsula and southeasterly flows toward the peninsula, giving rise to an increase in the Changma precipitation.

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References

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