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Korean Meteorological Society (한국기상학회)
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Evaluation of Sea Surface Temperature Prediction Skill around the Korean Peninsula in GloSea5 Hindcast: Improvement with Bias Correction

GloSea5 모형의 한반도 인근 해수면 온도 예측성 평가: 편차 보정에 따른 개선

  • Gang, Dong-Woo (School of Earth and Environmental Sciences, Seoul National University) ;
  • Cho, Hyeong-Oh (School of Earth and Environmental Sciences, Seoul National University) ;
  • Son, Seok-Woo (School of Earth and Environmental Sciences, Seoul National University) ;
  • Lee, Johan (Operational Systems Development Department, National Institute of Meteorological Sciences) ;
  • Hyun, Yu-Kyung (Operational Systems Development Department, National Institute of Meteorological Sciences) ;
  • Boo, Kyung-On (Operational Systems Development Department, National Institute of Meteorological Sciences)
  • 강동우 (서울대학교 지구환경과학부) ;
  • 조형오 (서울대학교 지구환경과학부) ;
  • 손석우 (서울대학교 지구환경과학부) ;
  • 이조한 (국립기상과학원 현업운영개발부) ;
  • 현유경 (국립기상과학원 현업운영개발부) ;
  • 부경온 (국립기상과학원 현업운영개발부)
  • Received : 2021.04.27
  • Accepted : 2021.06.07
  • Published : 2021.06.30
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Abstract

The necessity of the prediction on the Seasonal-to-Subseasonal (S2S) timescale continues to rise. It led a series of studies on the S2S prediction models, including the Global Seasonal Forecasting System Version 5 (GloSea5) of the Korea Meteorological Administration. By extending previous studies, the present study documents sea surface temperature (SST) prediction skill around the Korean peninsula in the GloSea5 hindcast over the period of 1991~2010. The overall SST prediction skill is about a week except for the regions where SST is not well captured at the initialized date. This limited prediction skill is partly due to the model mean biases which vary substantially from season to season. When such biases are systematically removed on daily and seasonal time scales the SST prediction skill is improved to 15 days. This improvement is mostly due to the reduced error associated with internal SST variability during model integrations. This result suggests that SST around the Korean peninsula can be reliably predicted with appropriate post-processing.

Keywords

Acknowledgement

심사과정에서 본 논문의 개선을 위해 좋은 의견을 제시해주신 심사위원분들께 감사드립니다. 이 연구는 2021년 해양수산부 재원으로 해양수산과학기술진흥원(과학기술기반 해역이용영향평가 기술개발, 20210427)의 지원을 받아 수행하였습니다.

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