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기상청 전지구예측시스템 자료에서의 2016~2017년 북반구 블로킹 예측성 분석
노준우(Joon-Woo Roh),조형오(Hyeong-Oh Cho),손석우(Seok-Woo Son),백희정(Hee-Jeong Baek),부경온(Kyung-On Boo),이정경(Jung-Kyung Lee) 한국기상학회 2018 대기 Vol.28 No.4
Predictability of Northern Hemisphere blocking in the Korea Meteorological Administration (KMA) Global Data Assimilation and Prediction System (GDAPS) is evaluated for the period of July 2016 to May 2017. Using the operational model output, blocking is defined by a meridional gradient reversal of 500-hPa geopotential height as Tibaldi-Molteni Index. Its predictability is quantified by computing the critical success index and bias score against ERA-Interim data. It turns out that Northwest Pacific blockings, among others, are reasonably well predicted with a forecast lead time of 2~3 days. The highest prediction skill is found in spring with 3.5 lead days, whereas the lowest prediction skill is observed in autumn with 2.25 lead days. Although further analyses are needed with longer dataset, this result suggests that Northern Hemisphere blocking is not well predicted in the operational weather prediction model beyond a short-term weather prediction limit. In the spring, summer, and autumn periods, there was a tendency to overestimate the Western North Pacific blocking.
GloSea5 모형의 한반도 인근 해수면 온도 예측성 평가: 편차 보정에 따른 개선
강동우(Dong-Woo Gang),조형오(Hyeong-Oh Cho),손석우(Seok-Woo Son),이조한(Johan Lee),현유경(Yu-Kyung Hyun),부경온(Kyung-On Boo) 한국기상학회 2021 대기 Vol.31 No.2
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.