장마 강수를 위한 앙상블 통계 예측 모델 개발
The Development of Ensemble Statistical Prediction Model for Changma Precipitation
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초록
Statistical forecast models for the prediction of the summertime Changma precipitationhave been developed in this study. As effective predictors for the Changma precipitation,the springtime sea surface temperature (SST) anomalies over the North Atlantic (NA1),the North Pacific (NPC) and the tropical Pacific Ocean (CNINO) has been suggested in Leeand Seo (2013). To further improve the performance of the statistical prediction scheme, weselect other potential predictors and construct 2 additional statistical models. The selected predictorsare the Northern Indian Ocean (NIO) and the Bering Sea (BS) SST anomalies, and thespring Eurasian snow cover anomaly (EUSC). Then, using the total three statistical predictionmodels, a simple ensemble-mean prediction is performed. The resulting correlation skill scorereaches as high as ~0.90 for the last 21 years, which is ~16% increase in the skill compared tothe prediction model by Lee and Seo (2013). The EUSC and BS predictors are related to astrengthening of the Okhotsk high, leading to an enhancement of the Changma front. The NIO predictor induces the cyclonic anomalies to the southwest of the Korean peninsula and southeasterly flows toward the peninsula, giving rise to an increase in the Changma precipitation.
Abstract
Statistical forecast models for the prediction of the summertime Changma precipitationhave been developed in this study. As effective predictors for the Changma precipitation,the springtime sea surface temperature (SST) anomalies over the North Atlantic (NA1),the North Pacific (NPC) and the tropical Pacific Ocean (CNINO) has been suggested in Leeand Seo (2013). To further improve the performance of the statistical prediction scheme, weselect other potential predictors and construct 2 additional statistical models. The selected predictorsare the Northern Indian Ocean (NIO) and the Bering Sea (BS) SST anomalies, and thespring Eurasian snow cover anomaly (EUSC). Then, using the total three statistical predictionmodels, a simple ensemble-mean prediction is performed. The resulting correlation skill scorereaches as high as ~0.90 for the last 21 years, which is ~16% increase in the skill compared tothe prediction model by Lee and Seo (2013). The EUSC and BS predictors are related to astrengthening of the Okhotsk high, leading to an enhancement of the Changma front. The NIO predictor induces the cyclonic anomalies to the southwest of the Korean peninsula and southeasterly flows toward the peninsula, giving rise to an increase in the Changma precipitation.
참고문헌 (15)
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