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Asia-Pacific Journal of Atmospheric Sciences

, Volume 54, Issue 4, pp 563–573 | Cite as

Development of the Expert Seasonal Prediction System: an Application for the Seasonal Outlook in Korea

  • WonMoo KimEmail author
  • Sae-Rim Yeo
  • Yoojin Kim
Original Article
  • 99 Downloads

Abstract

An Expert Seasonal Prediction System for operational Seasonal Outlook (ESPreSSO) is developed based on the APEC Climate Center (APCC) Multi-Model Ensemble (MME) dynamical prediction and expert-guided statistical downscaling techniques. Dynamical models have improved to provide meaningful seasonal prediction, and their prediction skills are further improved by various ensemble and downscaling techniques. However, experienced scientists and forecasters make subjective correction for the operational seasonal outlook due to limited prediction skills and biases of dynamical models. Here, a hybrid seasonal prediction system that grafts experts’ knowledge and understanding onto dynamical MME prediction is developed to guide operational seasonal outlook in Korea. The basis dynamical prediction is based on the APCC MME, which are statistically mapped onto the station-based observations by experienced experts. Their subjective selection undergoes objective screening and quality control to generate final seasonal outlook products after physical ensemble averaging. The prediction system is constructed based on 23-year training period of 1983–2005, and its performance and stability are assessed for the independent 11-year prediction period of 2006–2016. The results show that the ESPreSSO has reliable and stable prediction skill suitable for operational use.

Keywords

Seasonal prediction Seasonal forecast Dynamical seasonal prediction Statistical seasonal prediction Multi-model ensemble 

Notes

Acknowledgements

This work is supported by the APEC Climate Center (APCC). The authors are very grateful to the APCC MME producing centers for making their hindcast/forecast data available for analysis and the APCE Climate Center for collecting and archiving them and for organizing APCC MME prediction.

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Copyright information

© Korean Meteorological Society and Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Climate Services and Research DepartmentAPEC Climate CenterBusanSouth Korea

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