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Performance Assessment of Monthly Ensemble Prediction Data Based on Improvement of Climate Prediction System at KMA

기상청 기후예측시스템 개선에 따른 월별 앙상블 예측자료 성능평가

Ham, Hyunjun;Lee, Sang-Min;Hyun, Yu-Kyug;Kim, Yoonjae
함현준;이상민;현유경;김윤재

  • Received : 2019.03.14
  • Accepted : 2019.06.17
  • Published : 2019.06.30

Abstract

The purpose of this study is to introduce the improvement of current operational climate prediction system of KMA and to compare previous and improved that. Whereas the previous system is based on GloSea5GA3, the improved one is built on GloSea5GC2. GloSea5GC2 is a fully coupled global climate model with an atmosphere, ocean, sea-ice and land components through the coupler OASIS. This is comprised of component configurations Global Atmosphere 6.0 (GA6.0), Global Land 6.0 (GL6.0), Global Ocean 5.0 (GO5.0) and Global Sea Ice 6.0 (GSI6.0). The compositions have improved sea-ice parameters over the previous model. The model resolution is N216L85 (~60 km in mid-latitudes) in the atmosphere and ORCA0.25L75 ($0.25^{\circ}$ on a tri-polar grid) in the ocean. In this research, the predictability of each system is evaluated using by RMSE, Correlation and MSSS, and the variables are 500 hPa geopotential height (h500), 850 hPa temperature (t850) and Sea surface temperature (SST). A predictive performance shows that GloSea5GC2 is better than GloSea5GA3. For example, the RMSE of h500 of 1-month forecast is decreased from 23.89 gpm to 22.21 gpm in East Asia. For Nino3.4 area of SST, the improvements to GloSeaGC2 result in a decrease in RMSE, which become apparent over time. It can be concluded that GloSea5GC2 has a great performance for seasonal prediction.

Keywords

GloSea5;seasonal prediction;monthly ensemble

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Acknowledgement

Grant : 장기예측시스템개발

Supported by : 국립기상과학원