Predictability for Heavy Rainfall over the Korean Peninsula during the Summer using TIGGE Model

Hwang, Yoon-Jeong; Kim, Yeon-Hee; Chung, Kwan-Young; Chang, Dong-Eon

doi:10.14191/atmos.2012.22.3.287

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…Yamagughi and Majumdar (2010) investigated the dynamic mechanism of perturbation growth in a tropical cyclone (Sinlaku, the typhoon in 2008) using ECMWF, NCEP, and JMA ensembles and found that the vertical and horizontal distributions of the initial perturbations, as well as the amplitude, were quite different among three NWP centers before, during, and after the recurvature of Typhoon Sinlaku. Hwang et al (2012) compared the performances of six ensemble models and a grand ensemble (GE) of the three best ensemble models (ECMWF, UKMO, and CMA) for inconsistencies, jumpiness, and rootmean square difference (RMSD) for 500 hPa geopotential height, 850 hPa temperature, and mean sea-level pressure and verified that the GE was more consistent than each of the single ensemble models. Additionally, in a case study of a heavy rainfall event using the GE, it was found that the GE was more skillful than the single ensemble model, which could lead to early warnings for heavy rainfall in the medium range.…”

Section: Introductionmentioning

confidence: 90%

A study on the predictability of the transition day from the dry to the rainy season over South Korea

Lee

Nam

Choi

et al. 2015

Theor Appl Climatol

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This study was conducted to evaluate the prediction accuracies of THe Observing system Research and Predictability EXperiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) data at six operational forecast centers using the root-mean square difference (RMSD) and Brier score (BS) from April to July 2012. And it was performed to test the precipitation predictability of ensemble prediction systems (EPS) on the onset of the summer rainy season, the day of withdrawal in spring drought over South Korea on 29 June 2012 with use of the ensemble mean precipitation, ensemble probability precipitation, 10-day lag ensemble forecasts (ensemble mean and probability precipitation), and effective drought index (EDI). The RMSD analysis of atmospheric variables (geopotential-height at 500 hPa, temperature at 850 hPa, sea-level pressure and specific humidity at 850 hPa) showed that the prediction accuracies of the EPS at the Meteorological Service of Canada (CMC) and China Meteorological Administration (CMA) were poor and those at the European Center for Medium-Range Weather Forecasts (ECMWF) and Korea Meteorological Administration (KMA) were good. Also, ECMWF and KMA showed better results than other EPSs for predicting precipitation in the BS distributions. It is also evaluated that the onset of the summer rainy season could be predicted using ensemble-mean precipitation from 4-day leading time at all forecast centers. In addition, the spatial distributions of predicted precipitation of the EPS at KMA and the Met Office of the United Kingdom (UKMO) were similar to those of observed precipitation; thus, the predictability showed good performance. The precipitation probability forecasts of EPS at CMA, the National Centers for Environmental Prediction (NCEP), and UKMO (ECMWF and KMA) at 1-day lead time produced overforecasting (under-forecasting) in the reliability diagram. And all the ones at 2∼4-day lead time showed under-forecasting. Also, the precipitation on onset day of the summer rainy season could be predicted from a 4-day lead time to initial time by using the 10-day lag ensemble mean and probability forecasts. Additionally, the predictability for withdrawal day of spring drought to be ended due to precipitation on onset day of summer rainy season was evaluated using Effective Drought Index (EDI) to be calculated by ensemble mean precipitation forecasts and spreads at five EPSs.

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Section: Introductionmentioning

confidence: 90%

A study on the predictability of the transition day from the dry to the rainy season over South Korea

Lee

Nam

Choi

et al. 2015

Theor Appl Climatol

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“…5개의 지역기후모델 중 HadGEM3-RA는 ERA-interim과 다른 지역기후모델에 비해 육지 에서 풍속을 과소 모의하는 반면 (Fig. 2b) (Zoster et al, 2009;Hwang et al, 2012) 0에 가까울수록 일관성이 높은 것을 의미한다. 계산식은 Hwang et al (2012) (Fig.…”

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Variability of Wind Energy in Korea Using Regional Climate Model Ensemble Projection

Kim¹,

Kim²,

Kim³

et al. 2016

Atmosphere

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The future variability of Wind Energy Density (WED) over the Korean Peninsula under RCP climate change scenario is projected using ensemble analysis. As for the projection of the future WED, changes between the historical period (1981~2005) and the future projection (2021~2050) are examined by analyzing annual and seasonal mean, and Coefficient of Variation (CV) of WED. The annual mean of WED in the future is expected to decrease compared to the past ones in RCP 4.5 and RCP 8.5 respectively. However, the CV is expected to increase in RCP 8.5. WEDs in spring and summer are expected to increase in both scenarios RCP 4.5 and RCP 8.5. In particular, it is predicted that the variation of CV for WED in winter is larger than other seasons. The time series of WED for three major wind farms in Korea exhibit a decrease trend over the future period (2021~2050) in Gochang for autumn, in Daegwanryeong for spring, and in Jeju for autumn. Through analyses of the relationship between changes in wind energy and pressure gradients, the fact that changes in pressure gradients would affect changes in WED is identified. Our results can be used as a background data for devising a plan to develop and operate wind farm over the Korean Peninsula.

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The Synoptic Meteorological Characteristics of Spring Rainfall in South Korea during 2008~2012

Park¹,

Lee²,

Kim³

et al. 2013

Journal of Environmental Science International

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Spring rainfall events were comprehensively analyzed based on the distribution of precipitation amount and the related synoptic weather between 2008~2012. Forty-eight cases are selected among the rain events of the entire country, and each distribution of the 24-hour accumulated precipitation amount is classified into three types: evenly distributed rain(Type 1), more rain in the southern area and south coast region (Type 2), and more rain in the central region (Type 3), respectively. Type 1 constitutes the largest part(35 cases, 72.9%) with mean precipitation amount of 19.4 mm, and the 17 cases of Type 1 are observed in March. Although Type B and C constitutes small parts (11 cases, 22.9% and 2 cases, 4.2%), respectively. The precipitation amount of these types is greater than 34.5 mm and occurred usually in April. The main synoptic weather patterns affecting precipitation distribution are classified into five patterns according to the pathway of low pressures. The most influential pattern is type 4, and this usually occurs in March, April, and May (Low pressures from the north and the ones from the west and south consecutively affect South Korea, 37.5%). The pattern 3(Low pressures from the south affect South Korea, 25%) happens mostly in April, and the average precipitation is usually greater than 30 mm. This value is relatively higher than the values in any other patterns.

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Predictability for Heavy Rainfall over the Korean Peninsula during the Summer using TIGGE Model

Cited by 3 publications

References 9 publications

A study on the predictability of the transition day from the dry to the rainy season over South Korea

A study on the predictability of the transition day from the dry to the rainy season over South Korea

Variability of Wind Energy in Korea Using Regional Climate Model Ensemble Projection

The Synoptic Meteorological Characteristics of Spring Rainfall in South Korea during 2008~2012

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