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Development of statistical prediction models for Changma precipitation: An ensemble approach

Development of statistical prediction models for Changma precipitation: An ensemble approach Abstract An ensemble statistical forecast scheme with a one-month lead is developed to predict year-to-year variations of Changma rainfall over the Korean peninsula. Spring sea surface temperature (SST) anomalies over the North Atlantic, the North Pacific and the tropical Pacific Ocean have been proposed as useful predictors in a previous study. Through a forward-stepwise regression method, four additional springtime predictors are selected: the northern Indian Ocean (NIO) SST, the North Atlantic SST change (NAC), the snow cover anomaly over the Eurasian continent (EUSC), and the western North Pacific outgoing longwave radiation anomaly (WNP (OLR)). Using these, three new prediction models are developed. A simple arithmetic ensemble mean produces much improved forecast skills compared to the original prediction model of Lee and Seo (2013). Skill scores measured by temporal correlation and MSSS (mean square error skill score) are improved by about 9% and 17%, respectively. The GMSS (Gerrity skill score) and hit rate based on a tercile prediction validation scheme are also enhanced by about 19% and 13%, respectively. The reversed NIO, reversed WNP (OLR), and reversed NAC are all related to the enhancement of a cyclonic circulation anomaly to the south or southwest of the Korean peninsula, which induces southeasterly moisture flux into the peninsula and increasing Changma precipitation. The EUSC predictor induces an enhancement of the Okhotsk Sea high downstream and thus strengthening of Changma front. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png "Asia-Pacific Journal of Atmospheric Sciences" Springer Journals

Development of statistical prediction models for Changma precipitation: An ensemble approach

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References (32)

Publisher
Springer Journals
Copyright
2017 Korean Meteorological Society and Springer Science+Business Media Dordrecht
ISSN
1976-7633
eISSN
1976-7951
DOI
10.1007/s13143-017-0027-2
Publisher site
See Article on Publisher Site

Abstract

Abstract An ensemble statistical forecast scheme with a one-month lead is developed to predict year-to-year variations of Changma rainfall over the Korean peninsula. Spring sea surface temperature (SST) anomalies over the North Atlantic, the North Pacific and the tropical Pacific Ocean have been proposed as useful predictors in a previous study. Through a forward-stepwise regression method, four additional springtime predictors are selected: the northern Indian Ocean (NIO) SST, the North Atlantic SST change (NAC), the snow cover anomaly over the Eurasian continent (EUSC), and the western North Pacific outgoing longwave radiation anomaly (WNP (OLR)). Using these, three new prediction models are developed. A simple arithmetic ensemble mean produces much improved forecast skills compared to the original prediction model of Lee and Seo (2013). Skill scores measured by temporal correlation and MSSS (mean square error skill score) are improved by about 9% and 17%, respectively. The GMSS (Gerrity skill score) and hit rate based on a tercile prediction validation scheme are also enhanced by about 19% and 13%, respectively. The reversed NIO, reversed WNP (OLR), and reversed NAC are all related to the enhancement of a cyclonic circulation anomaly to the south or southwest of the Korean peninsula, which induces southeasterly moisture flux into the peninsula and increasing Changma precipitation. The EUSC predictor induces an enhancement of the Okhotsk Sea high downstream and thus strengthening of Changma front.

Journal

"Asia-Pacific Journal of Atmospheric Sciences" – Springer Journals

Published: May 1, 2017

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