• Journal of Korea Water Resources Association
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• v.52 no.1
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• pp.71-82
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• 2019

The purpose of this study is to analyze the Mekong River streamflow alteration due to climate change. The future climate change scenarios were produced by bias corrections of the data from East Asia RCP 4.5 and 8.5 scenarios, given by HadGEM3-RA. Then, SWAT model was used for discharge simulation of the Kratie, the main point of the Mekong River (watershed area: $646,000km^2$, 88% of the annual average flow rate of the Mekong River). As a result of the climate change analysis, the annual precipitation of the Kratie upper-watershed increase in both scenarios compared to the baseline yearly average precipitation. The monthly precipitation increase is relatively large from June to November. In particular, precipitation fluctuated greatly in the RCP 8.5 rather than RCP 4.5. Monthly average maximum and minimum temperature are predicted to be increased in both scenarios. As well as precipitation, the temperature increase in RCP 8.5 scenarios was found to be more significant than RCP 4.5. In addition, as a result of the duration curve comparison, the streamflow variation will become larger in low and high flow rate and the drought will be further intensified in the future.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.224-224
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• 2019

국제기상기구의 Global Framework for Climate Services (GFCS)의 관점에서 살펴보면 국내의 기상 기후 정보는 기상청을 중심으로 관측 자료와 중장기 예측 및 기후변화 시나리오 정보 등의 다양한 시간규모로 생산되고 있다. 하지만 사용자가 직접적으로 다양한 기후정보를 상세화하여 활용하기 위해서는 기후정보의 구축 및 전처리를 수행해야하는 어려움이 있다. 따라서 APEC Climate Center (APCC)에서 다학제 융합 기반 기후정보 서비스를 중심으로 사용자 인터페이스 플랫폼 (User Interface Platform: UIP)의 기술적 플랫폼으로 APCC Integrated Modeling Solution (AIMS)를 개발하였다. AIMS는 사용자의 관점으로 상세화를 수행할 수 있고, 다양한 응용 분야에 적용하기 쉽게 데이터를 생성하여 연구에 도움을 주고 있다. 본 연구는 AIMS에서 제공하고 있는 기존의 국가별로 제공하는 제 5차 결합 기후모델 비교사업 (The $5^{th}$ phase of the coupled model intercomparision project, CMIP5)에서 해석한 전구기후모델 (General Circulation Model, GCM)의 통계적 상세화 방법인 Simple Quantile Mapping (SQM)과 Spatial Disaggregation Quantile Delta Mapping (SDQDM)를 포함하여 AIMS에 새롭게 추가 된 통계적 상세화 방법인 Bias Correction and Stochastic Analog (BCSA) 방법을 소개하고자 한다. 또한 60개의 종관기상관측 (Automated Surface Observing System, ASOS)자료를 중심으로 생성한 세 가지 통계적 상세화방법의 과거재현성과 RCP4.5, RCP8.5 시나리오를 활용한 미래 불확실성 평가 결과를 이용하여 연구자들의 맞춤형 자료를 생산하고 평가하는데 도움을 줌으로써 다양한 기후자료의 효과적인 활용이 가능할 것으로 기대된다.

• Korean Journal of Remote Sensing
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• v.29 no.2
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• pp.263-274
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• 2013

In this study we evaluated the hydrological applicability of multi-satellite precipitation estimates. Three high-resolution global multi-satellite precipitation products, the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), the Global Satellite Mapping of Precipitation (GSMaP), and the Climate Precipitation Center (CPC) Morphing technique (CMORPH), were applied to the Coupled Routing and Excess Storage (CREST) model for the evaluation of their hydrological utility. The CREST model was calibrated from 2002 to 2005 and validated from 2006 to 2009 in the Chungju Dam watershed, including two years of warm-up periods (2002-2003 and 2006-2007). Areal-averaged precipitation time series of the multi-satellite data were compared with those of the ground records. The results indicate that the multi-satellite precipitation can reflect the seasonal variation of precipitation in the Chungju Dam watershed. However, TMPA overestimates the amount of annual and monthly precipitation while GSMaP and CMORPH underestimate the precipitation during the period from 2002 to 2009. These biases of multi-satellite precipitation products induce poor performances in hydrological simulation, although TMPA is better than both of GSMaP and CMORPH. Our results indicate that advanced rainfall algorithms may be required to improve its hydrological applicability in South Korea.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.1147-1151
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• 2009

In this study, we address the problem of producing probability forecasts of summer seasonal rainfall, on the basis of Hindcast experiments from a ensemble of GCMs(cwb, gcps, gdaps, metri, msc_gem, msc_gm2, msc_gm3, msc_sef and ncep). An advanced Hierarchical Bayesian weighting scheme is developed and used to combine nine GCMs seasonal hindcast ensembles. Hindcast period is 23 years from 1981 to 2003. The simplest approach for combining GCM forecasts is to weight each model equally, and this approach is referred to as pooled ensemble. This study proposes a more complex approach which weights the models spatially and seasonally based on past model performance for rainfall. The Bayesian approach to multi-model combination of GCMs determines the relative weights of each GCM with climatology as the prior. The weights are chosen to maximize the likelihood score of the posterior probabilities. The individual GCM ensembles, simple poolings of three and six models, and the optimally combined multimodel ensemble are compared.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2015.08a
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• pp.98-101
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• 2015

• Journal of Korea Water Resources Association
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• v.52 no.7
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• pp.475-482
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• 2019

Subseasonal-to-Seasonal (S2S) prediction information which have 2 weeks to 2 months lead time are expected to be used through many parts of industry fields, but utilizability is not reached to expectation because of lower predictability than weather forecast and mid- /long-term forecast. In this study, we used multi-layer perceptron (MLP) which is one of machine learning technique that was built for regression training in order to improve predictability of S2S precipitation data at South Korea through post-processing. Hindcast information of ECMWF was used for MLP training and the original data were compared with trained outputs based on dichotomous forecast technique. As a result, Bias score, accuracy, and Critical Success Index (CSI) of trained output were improved on average by 59.7%, 124.3% and 88.5%, respectively. Probability of detection (POD) score was decreased on average by 9.5% and the reason was analyzed that ECMWF's model excessively predicted precipitation days. In this study, we confirmed that predictability of ECMWF's S2S information can be improved by post-processing using MLP even the predictability of original data was low. The results of this study can be used to increase the capability of S2S information in water resource and agricultural fields.

• Atmosphere
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• v.29 no.5
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• pp.627-637
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• 2019

Severe and long-lasting heat waves over Korea and many regions in the Northern Hemisphere (NH) during the 2016 summer, have been attributed to global warming and atmospheric teleconnection coupled with tropical convective activities. Yet, what controls subseasonsal time scale of heat wave has not been well addressed. Here we show a critical role of two dominant boreal summer intraseasonal oscillation (BSISO) modes, denominated as BSISO1 and BSISO2, on modulating temporal structure of heat waves in the midst of similar climate background. The 2016 summer was characterized by La Nina development following decay of strong 2015/2016 El Nino. The NH circumglobal teleconnection pattern (CGT) and associated high temperature anomalies and heat waves were largely driven by convective activity over northwest India and Pakistan during summer associated with La Nina development. However, the heat wave event in Korea from late July to late August was accompanied by the phase 7~8 of 30~60-day BSISO1 characterized by convective activity over the South China Sea and Western North Pacific and anticyclonic circulation (AC) anomaly over East Asia. Although the 2010 summer had very similar climate anomalies as the 2016 summer with La Nina development and CGT, short-lasting but frequent heat waves were occurred during August associated with the phase 1~2 of 10~30-day BSISO2 characterized by convective activity over the Philippine and South China Sea and AC anomaly over East Asia. This study has an implication on importance of BSISO for better understanding mechanism and temporal structure of heat waves in Korea.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1784-1788
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• 2010

APEC 기후센터(APEC Climate Center, APCC)에서 제공하는 다중모형앙상블(Multi-model Ensemble, MME) 형태의 계절예측정보를 이용하여 3개월 가뭄전망을 수행하였다. APCC MME는 기후예측모형이 가지는 불확실성을 최소화하기 위한 방법으로, 아시아 태평양 지역 내 9개 회원국 16개 기관 21개 기후모형의 계절예측정보를 활용하여, 개별 모형이 가지는 계통오차(Systematic error)를 앙상블 기법을 통하여 상쇄함으로써 최적의 예측자료를 도출한다. 또한, 기후예측 모형이 예측한 대기순환장은 관측 지점변수와 경험적 통계적 관련성을 가지므로, 이를 바탕으로 상세지역의 이상기후에 대한 정보를 도출할 수 있다. 본 연구에서는 가뭄 관리 및 전망을 위한 입력 자료로서, 기상전문 기관인 APEC 기후센터 (APEC Climate Center, APCC)에서 제공하는 전구 규모의 기온 및 강수 전망자료를 기상청 산하 59개 지점의 전망자료로 통계적 규모 축소화 기법을 통해 3개월 예보를 실시하였다. APCC 계절예측자료를 가뭄모니터링시스템의 자료입력 포맷에 따라 적절히 가공한 뒤, 가뭄 관리 및 전망을 위하여 SPI(Standard Precipitation Index) 및 PDSI(Palmer Drought Severity Index)지수의 입력자료로 사용하여 SPI 및 PDSI 지수를 산정하였다. 또한 분위사상법(Quantile Mapping)을 이용하여 총 59개 지점의 과거 월평균 관측값과 최근 2009년에 대한 모의값의 누적확률분포값을 계산하고 모의값의 확률분포를 관측값의 확률분포에 사상시켜 가뭄 전망을 위한 기상변수의 오차를 보정하고자 하였다. 이러한 계절예측정보를 이용하여 가뭄 전망에 대한 신뢰도가 높아진다면, 사전예방 및 피해완화로 가뭄상황에 대한 신속한 대처 및 피해의 경감이 이루어질 수 있을 것이다.

• Journal of Korea Water Resources Association
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• v.48 no.10
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• pp.817-830
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• 2015

This study performed prediction of extreme rainfall uncertainty and its frequency analysis based on climate change scenarios by Coupled Model Intercomparison Project Phase 5 (CMIP5) for the selected nine-General Circulation Models (GCMs) in the near future (2011-2040) over the Korean Peninsula (KP). We analysed uncertainty of scenarios by multiple model ensemble (MME) technique using non-parametric quantile mapping method and bias correction method in the basin scale of the KP. During the near future, the extreme rainfall shows a significant gradually increasing tendency with the annual variability and uncertainty of extreme ainfall in the RCP4.5, and RCP8.5 scenarios. In addition to the probability rainfall frequency (such as 50 and 100-year return periods) has increased by 4.2% to 10.9% during the near future in 2040. Therefore, in the longer-term water resources master plan, based on the various climate change scenarios (such as CMIP5 GCMs) and its uncertainty can be considered for utilizing of the support tool for decision-makers in water-related disasters management.

• Journal of Climate Change Research
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• v.7 no.3
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• pp.315-324
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• 2016

The relevant ministries, including the Ministry of Environment in Korea, provided Post-2020 Long-term Mitigation Target and Implementation Plan. The plan consisted of four Business As Usual (BAU) reduction levels by 14.7%, 19.2%, 25.7%, and 31.3% until 2030. The Korean government finalized the mitigation target of 37%. But all the initial alternatives were below the goal, 30% from BAU, that has been promised to the international community as well as set out in the Framework Act on Low Carbon Green Growth. In order to achieve a specific goal, performance management should pursue "Justify doing the right things." Otherwise, performance management would not work properly. According to Kingdon's Policy Stream Framework, abnormal alternatives are difficult to be presented as scenarios because alternative building should focus on the role of the need to adhere to the basic principles and professionals. Such a result is possible only when the policy actors does not balance themselves. Performance management statistics has been analyzed by 6 years CCPI data since 2011, taking into account the impact after enactment. This study also has been complemented by a variety of sources, including the media, documents, and artifacts during the period. As a result, raising awareness about climate change was analyzed as one of the solutions because the climate change issue affects the normal performance management throughout the life of the people to stay linked to the environment.