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Korean Meteorological Society (한국기상학회)
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Assessment of the Prediction Derived from Larger Ensemble Size and Different Initial Dates in GloSea6 Hindcast

기상청 기후예측시스템(GloSea6) 과거기후 예측장의 앙상블 확대와 초기시간 변화에 따른 예측 특성 분석

  • Kim, Ji-Yeong (Climate Research Department, National Institute of Meteorological Sciences) ;
  • Park, Yeon-Hee (Climate Research Department, National Institute of Meteorological Sciences) ;
  • Ji, Heesook (Climate Research Department, National Institute of Meteorological Sciences) ;
  • Hyun, Yu-Kyung (Climate Research Department, National Institute of Meteorological Sciences) ;
  • Lee, Johan (Climate Research Department, National Institute of Meteorological Sciences)
  • 김지영 (국립기상과학원 기후연구부) ;
  • 박연희 (국립기상과학원 기후연구부) ;
  • 지희숙 (국립기상과학원 기후연구부) ;
  • 현유경 (국립기상과학원 기후연구부) ;
  • 이조한 (국립기상과학원 기후연구부)
  • Received : 2022.10.19
  • Accepted : 2022.12.13
  • Published : 2022.12.31
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Abstract

In this paper, the evaluation of the performance of Korea Meteorological Administratio (KMA) Global Seasonal forecasting system version 6 (GloSea6) is presented by assessing the effects of larger ensemble size and carrying out the test using different initial conditions for hindcast in sub-seasonal to seasonal scales. The number of ensemble members increases from 3 to 7. The Ratio of Predictable Components (RPC) approaches the appropriate signal magnitude with increase of ensemble size. The improvement of annual variability is shown for all basic variables mainly in mid-high latitude. Over the East Asia region, there are enhancements especially in 500 hPa geopotential height and 850 hPa wind fields. It reveals possibility to improve the performance of East Asian monsoon. Also, the reliability tends to become better as the ensemble size increases in summer than winter. To assess the effects of using different initial conditions, the area-mean values of normalized bias and correlation coefficients are compared for each basic variable for hindcast according to the four initial dates. The results have better performance when the initial date closest to the forecasting time is used in summer. On the seasonal scale, it is better to use four initial dates, where the maximum size of the ensemble increases to 672, mainly in winter. As the use of larger ensemble size, therefore, it is most efficient to use two initial dates for 60-days prediction and four initial dates for 6-months prediction, similar to the current Time-Lagged ensemble method.

Keywords

Acknowledgement

이 연구는 기상청 국립기상과학원 「기후예측 현업시스템 개발」 (KMA2018-00322)의 지원으로 수행되었습니다.

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