• The Korean Journal of Applied Statistics
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• v.15 no.2
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• pp.213-222
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• 2002

The 3-hour-interval prediction of ground-level temperature up to +45 hours in Seoul area is performed using dynamic linear models(DLM). Numerical outputs and observations we used as input values of DLM. According to compare DLM forecasts to RDAPS forecasts using RMSE, DLM improve the accuracy of prediction and systematic error of numerical model outputs are eliminated by DLM.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.616-617
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• 2011

최대전력수요를 분석함에 있어 발생 당시의 기온 실적이 반영된 실적 최대전력만을 사용함으로 다양한 통계적 착시현상이 나타나고 있다. 평균적인 기상 상태에서의 최대전력수요를 측정하기 어려워 신뢰성있는 예측수요를 도출하기에도 많은 한계가 발생한다. 따라서 역사적 기온데이터에 기반한 정상적인 기온분포를 "표준기온분포"로 새롭게 정의하고, 이를 반영한 최대전력수요를 "기온보정 최대전력 수요"로 규정함으로써, 기존의 통계적 착시현상을 배제하고, 정확도 높은 최대전력 수요 예측치를 도출하여, 안정적 전력수급에 큰 기여가 있을 것으로 기대한다. 또한 본 연구에서는 기온보정 최대전력을 도출하기 위해 공적분 및 오차수정이론을 반영하여 모형화하였고, 엄격한 통계적 방법론을 이용하여 관련 모형을 검증하였다.

• Journal of Korea Water Resources Association
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• v.34 no.4
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• pp.347-356
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• 2001

Even though the increase of greenhouse gases such as $CO_2$ is thought to be the main cause for global warming, its impact on global climate has not been revealed clearly in rather quantitative manners. However, researches using Genral Circulation Model(GCM) has shown that the accumulation of greenhouse gases increases the global mean temperature, which in turn impacts on the global water circulation pattern. A climate predictive capability is limited by lack of understanding of the different process governing the climate and hydrologic systems. The prediction of the complex responses of the fully coupled climate and hydrologic systems can be achieved only through development of models that adequately describe the relevant process at a wide range of spatial and temporal scales. These models must ultimately couple the atmospheres, oceans, and lad and will involve many submodels that properly represent the individual processes at work within the coupled components of systems. So far, there are no climate and related hydrologic models except local rainfall-runoff models in Korea. The purpose of this research is to predict the change of temperature in Korean Peninsula using regional scale model(IRSHAM96 model) and GCM data obtained from the increasing scenarios of $CO_2$ Korean Peninsula increased by $2.5^{\circ}C$ and the duration of Winter in $lxCO_2$ condition would be shorter the $2xCo_2$ condition due to global warming.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.405-414
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• 2021

This study predicted waxy corn harvest date in South Korea using 30-year (1991-2020) hindcasts (1-6 month lead) produced by the Pusan National University Coupled General Circulation Model (PNU CGCM)-Weather Research and Forecasting (WRF) chain. To estimate corn harvest date, the cumulative temperature is used, which accumulated the daily observed and predicted temperatures from the seeding date (5 April) to the reference temperature (1,650~2,200℃) for harvest. In terms of the mean air temperature, the hindcasts with a bias correction (20.2℃) tends to have a cold bias of about 0.1℃ for the 6 months (April to September) compared to the observation (20.3℃). The harvest date derived from bias-corrected hindcasts (DOY 187~210) well simulates one from observation (DOY 188~211), despite a slight margin of 1.1~1.3 days. The study shows the possibility of obtaining the gridded (5 km) daily temperature and corn harvest date information based on the cumulative temperature in advance for all regions of South Korea.

• Environmental and Resource Economics Review
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• v.29 no.4
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• pp.419-442
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• 2020

The majority of the natural gas demand in South Korea is mainly determined by the heating demand. Accordingly, there is a distinct seasonality in which the gas demand increases in winter and decreases in summer. Moreover, the degree of sensitiveness to temperature on gas demand has changed over time. This study firstly introduces changing temperature response function (TRF) to capture effects of changing seasonality. The temperature effect (TE), estimated by integrating temperature response function with daily temperature density, represents for the amount of gas demand change due to variation of temperature distribution. Also, this study presents an innovative way in forecasting daily temperature density by employing functional principal component analysis based on daily max/min temperature forecasts for the five big cities in Korea. The forecast errors of the temperature density and gas demand are decreased by 50% and 80% respectively if we use the proposed forecasted density rather than the average daily temperature density.

• Journal of the Korean Society of Marine Environment & Safety
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• v.20 no.5
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• pp.526-534
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• 2014

In this study, single-input transfer function model is applied to forecast monthly mean sea surface temperature(SST) in 2010 at Yeosu in Korean coastal waters. As input series, monthly mean air temperature series for ten years(2000-2009) at Yeosu in Korea is used, and Monthly mean SST at Yeosu station in Korean coastal waters is used as output series(the same period of input). To build transfer function model, first, input time series is prewhitened, and then cross-correlation functions between prewhitened input and output series are determined. The cross-correlation functions have just two significant values at time lag at 0 and 1. The lag between input and output series, the order of denominator and the order of numerator of transfer function, (b, r, s) are identified as (0, 1, 0). The selected transfer function model shows that there does not exist the lag between monthly mean air temperature and monthly mean SST, and that transfer function has a first-order autoregressive component for monthly mean SST, and that noise model was identified as $ARIMA(1,0,1)(2,0,0)_{12}$. The forecasted values by the selected transfer function model are generally $0.3-1.3^{\circ}C$ higher than actual SST in 2010 and have 6.4 % mean absolute percentage error(MAPE). The error is 2 % lower than MAPE by ARIMA model. This implies that transfer function model could be more available than ARIMA model in terms of forecasting performance of SST.

• Journal of Korea Water Resources Association
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• v.54 no.9
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• pp.731-745
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• 2021

In this study, the monthly temperature of the Han River basin was predicted by statistical multiple regression models that use global climate indices and weather data of the target region as predictors. The optimal predictors were selected through teleconnection analysis between the monthly temperature and the preceding patterns of each climate index, and forecast models capable of predicting up to 12 months in advance were constructed by combining the selected predictors and cross-validating the past period. Fore each target month, 1000 optimized models were derived and forecast ranges were presented. As a result of analyzing the predictability of monthly temperature from January 1992 to December 2020, PBIAS was -1.4 to -0.7%, RSR was 0.15 to 0.16, NSE was 0.98, and r was 0.99, indicating a high goodness-of-fit. The probability of each monthly observation being included in the forecast range was about 64.4% on average, and by month, the predictability was relatively high in September, December, February, and January, and low in April, August, and March. The predicted range and median were in good agreement with the observations, except for some periods when temperature was dramatically lower or higher than in normal years. The quantitative temperature forecast information derived from this study will be useful not only for forecasting changes in temperature in the future period (1 to 12 months in advance), but also in predicting changes in the hydro-ecological environment, including evapotranspiration highly correlated with temperature.

• Journal of Environmental Policy
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• v.13 no.2
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• pp.21-38
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• 2014

In response to global warming, the effect of the air temperature on water temperature has been noticed. The change in water temperature in river environment results in the change in water quality and ecosystem, especially Dissolved Oxygen (DO) level, and shifts in aquatic biota. Efforts need to be made to predict future water temperature in order to understand the timing of the projected river temperature. To do this, the data collected by the Ministry of Environment and the Korea Meteororlogical Administration has been used to build a nonlinear relationship between air and water temperature. The logistic function that includes four different parameters was selected as a working model and the parameters were optimized using SCE algorithm. Weekly average values were used to remove time scaling effect because the time scale affects maximum and minimum temperature and then river environment. Generally speaking nonlinear logistic model shows better performance in NSC and RMSE and nonlinear logistic function is recommendable to build a relationship between air and water temperature in Korea. The results will contribute to determine the future policy regarding water quality and ecosystem for the decision-driving organization.

• Proceedings of the Plant Resources Society of Korea Conference
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• 2020.08a
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• pp.31-31
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• 2020

본 연구는 대표적인 봄 꽃 식물인 진달래(Rhododendron mucronulatum Turcz.)의 개화시기를 예측하기 위해 지난 9년간(2011년-2019년) 주왕산 지역에 생육하는 진달래의 식물계절자료(파열·개화·개엽·만개·낙엽)와 기상자료(일평균기온·일최고기온·일최저기온)를 토대로 이탈리아 생물기상연구소(IBMET)의 Chill Day 개화 예측모형인 생물계절모형을 실시하였다. 생물계절모형에 의한 예상 발아일간 편차의 제곱을 최소로 하는 조합은 기준온도 5℃, 저온요구량과 가온요구량은 97.94로 나타났다. 즉, 휴면해제일로부터 기준온도 5℃로 Chill Day를 누적시켜 97.94에 도달하는 날짜가 낙엽~내생휴면해제일이자 내생휴면해제일~발아기간까지의 값이며, 내생휴면해제일을 기점으로 개화일까지 102.93이 개화에 필요한 가온량으로 나타났다. 2011년부터 2019년까지 개화예상일을 기상청 회귀모형을 실관측기온에 적용한 결과 오차는 MAE=1.44이며, 생물계절모형을 적용할 경우 오차는 MAE=1.39, 기준온도 5℃일 경우 MAE=4.23, 기준온도 6℃일 경우 MAE=5.47, 기준온도 7℃일 경우 MAE=5.05로 나타나 생물계절에 의한 관측과 기상청의 회귀모형이 가장 유사한 것으로 나타났다. 가장 최근인 2018년과 2019년의 기상청 회귀모형와 생물계절모형의 개화 예측일을 비교한 결과, 2018년의 경우 청송지역의 진달래는 기상청 회귀모형에서 3월 30일 전후로 개화를 예상하였고 생물계절모형은 기준온도 5℃에 적용할 경우 내생휴면일에 가장 근접한 날은 3월 26일이였으며 이를 기준으로 가온량의 합이 102.93에 가깝게 되는 날인 4월 2일을 전후로 개화를 예측하였다. 실제 청송 주왕산의 진달래는 4월 3일에 개화를 시작하여 생물계절모형과 매우 유사함을 확인하였다. 2019년의 경우 청송지역의 진달래는 기상청 회귀모형에서 3월 25일 전후로 개화를 예상하였고 생물계절모형은 기준온도 5℃에 적용할 경우 내생휴면일에 가장 근접한 날은 3월 8일이였으며 이를 기준으로 가온량의 합이 102.93에 가깝게 되는 날인 3월 29일을 전후로 개화를 예측하였다. 실제 청송 주왕산의 진달래는 4월 5일에 개화를 시작하여 오히려 생물계절모형과 더욱 유사함을 확인하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.139-139
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• 2020

Intergovernmental Panel on Climate Change(IPCC)에 따르면 지난 1세기 반 동안 전 세계 평균 기온은 약 1℃가 상승하였으며, 온실가스 축적에 따라 평균기온은 21세기 중반에서 21세기 말까지 1~3℃가 증가할 것으로 전망되고 있다. 이러한 기온의 상승으로 인한 하천의 수온 변화는 수중에서 온도에 민감한 생화학적 반응의 변화를 유발하여 수질 및 수생태 변화에 영향을 미칠 수 있다. 따라서 효과적인 수질 및 수생태 관리를 위해서는 기온과 수온 사이의 명확한 관계 정립을 통해 수질변화를 정확하게 예측하는 것이 중요하다. 본 연구에서는 국내·외로 널리 활용되고 있는 SWAT(Soil and Water Assessment Tool, SWAT) 모형을 통해 기온-수온 회귀식이 하천 수질변화에 미치는 영향을 정량적으로 분석하고자 하였다. 그러나 기존 SWAT 모형에서의 기온-수온 회귀식은 미국 유역의 환경 특성을 바탕으로 도출되었기 때문에 국내 유역에 적용하기에 한계점이 있다. 따라서 본 연구의 목적은 국내 유역에서의 실측 기온자료와 수온자료를 사용하여 SWAT 모형 내 기온-수온 회귀식을 재도출하고 적용성을 평가하는 것이다.