• Journal of Korea Water Resources Association
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• v.44 no.12
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• pp.941-954
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• 2011

In this study, past method and recent method for flood discharge with domestic multi-purpose dams in Korea were compared and analyzed with respect to the scale of watershed. Rainfall depth, temporal pattern, rainfall excess, rainfall-runoff model, parameter estimation and base flow were selected as the principal factors affecting flood discharge and effects on flood discharge were analyzed quantitatively by using sensitivity analysis. The results showed that the flood discharges calculated by past and recent method increased and decreased with a wide range of discharge with respect to the scale of watershed. The reason for decrease of flood discharge is the exchange of temporal pattern of rainfall and the principal reasons for increase of flood discharge are the increase of rainfall depth by unusual weather phenomena and the difference of estimation method for parameters of unit hydrograph.

• 한국방재학회:학술대회논문집
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• 2011.02a
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• pp.171-171
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• 2011

급격한 기상이변과 자연현상의 변화에 의해 자연재해가 발생하며, 이러한 변화로 인해 전 세계적으로 환경뿐만 아니라 방재의 중요성 또한 대두되고 있으나, 강우 강도의 예측 분야에서는 여유추정시간의 차이로 인해 방재현상에 대한 구체적인 협력을 하지 못했던 것이 사실이다. 현재 가장 많이 사용하고 있는 레이더 강우자료와 지상 우량계의 강우자료는 여유추정시간이 3시간에서 4시간 사이의 단기 예측만을 가능하게 한다. 본 연구에서는 이의 개선을 위하여 청미천 유역을 대상으로 GIS를 이용하여 CN value를 추출하고, 지역 강우 모형인 Weather Research Forecast(WRF) - Advanced Research WRF(ARW)를 통하여 모의한 강우자료에 대하여 과거 같은 기간의 강우자료와 비교 검증한 후 지역 강우 모형을 통하여 모의한 자료를 HEC-HMS의 Input자료로 활용하여 지역 유출량을 산정한다. 또한 유역의 지표면 유출 모의를 통하여 강우-유출현상과 수리-수문학적 과정을 상호 연결하고, 강우에 의한 유역 지표면에서의 유출을 도출하며, 최적화된 매개변수들의 조합을 개선하여 대상유역의 현상을 보다 유사하게 나타낼 것이다. 이와 함께 WRF-ARW 모형을 통하여 여유추정시간의 증가를 모색하며 그로인한 홍수예측 및 경계체계를 확립하기 위해 연구를 진행한다. 이 지역강우 모형의 대한민국 지형의 적용성 즉, 대한민국 지형에 가장 잘 어울리는 최적화된 매개변수들의 조합을 알아내고 그의 적용현실성을 찾아내려 한다. 더 나아가 강우에 대한 예측을 통해 홍수 경보 체계를 위한 자료로 활용할 수 있는 방안 또한 모색할 것이다.

• Atmosphere
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• v.28 no.2
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• pp.211-222
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• 2018

Cloud droplet activation process is well described by $K{\ddot{o}}hler$ theory and several parameterizations based on $K{\ddot{o}}hler$ theory are used in a wide range of models to represent this process. Here, we test the two different method of calculating the solute effect in the $K{\ddot{o}}hler$ equation, i.e., osmotic coefficient method (OSM) and ${\kappa}-K{\ddot{o}}hler$ method (KK). To do that, each method is implemented in the cloud droplet activation parameterization module of WRF-CHEM (Weather Research and Forecasting model coupled with Chemistry) model. It is assumed that aerosols are composed of five major components (i.e., sulfate, organic matter, black carbon, mineral dust, and sea salt). Both methods calculate similar representative hygroscopicity parameter values of 0.2~0.3 over the land, and 0.6~0.7 over the ocean, which are close to estimated values in previous studies. Simulated precipitation, and meteorological variables (i.e., specific heat and temperature) show good agreement with reanalysis. Spatial patterns of precipitation and liquid water path from model results and satellite data show similarity in general, but on regional scale spatial patterns and intensity show some discrepancy. However, meteorological variables, precipitation, and liquid water path do not show significant differences between OSM and KK simulations. So we suggest that the relatively simple KK method can be a good alternative to the OSM method that requires various information of density, molecular weight and dissociation number of each individual species in calculating the solute effect.

• Atmosphere
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• v.28 no.4
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• pp.479-490
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• 2018

This study assesses the prediction skill of regional scale model for the mean temperature anomaly over South Korea produced by Pusan National University Coupled General Circulation Model (PNU CGCM)-Weather Research and Forecasting (WRF) chain. The initial and boundary conditions of WRF are derived from PNU CGCM. The hindcast period is 11 years from 2007 to 2017. The model's prediction skill of mean temperature anomaly is evaluated in terms of the temporal correlation coefficient (TCC), root mean square error (RMSE) and skill scores which are Heidke skill score (HSS), hit rate (HR), false alarm rate (FAR). The predictions of WRF and PNU CGCM are overall similar to observation (OBS). However, TCC of WRF with OBS is higher than that of PNU CGCM and the variation of mean temperature is more comparable to OBS than that of PNU CGCM. The prediction skill of WRF is higher in March and April but lower in October to December. HSS is as high as above 0.25 and HR (FAR) is as high (low) as above (below) 0.35 in 2-month lead time. According to the spatial distribution of HSS, predictability is not concentrated in a specific region but homogeneously spread throughout the whole region of South Korea.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.392-392
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• 2021

자료동화(data assimilation) 기법은 관측 자료와 예측 모형의 정보를 동시에 활용, 모형의 상태량(state variables)이나 매개변수(model parameters)를 실시간으로 업데이트하는 Bayesian 필터링 이론에 근거한 방법으로, 최근 이를 활용한 수문 모의 정확도 향상 기술이 빠르게 발전하고 있다. 본 연구에서는 앙상블 자료동화의 정확성을 향상시키기 위한 세부 방법인 along-the-stream localization과 inflation 기법의 분포형 수문 모형에 대한 적용성을 대규모 지역 단위(regional-scale) 모의를 통해 검토한다. 분포형 수문모형과 자료동화 framework로는 WRF-Hydro(Weather Research and Forecasting Model Hydrological Modeling System)와 DART(Data Assimilation Research Testbed)를 각각 적용한다. WRF-Hydro는 미국의 전 대륙지역(CONUS; continental United States)에 대한 수문 모델링 framework인 National Water Model의 핵심엔진이고, DART는 미국 National Center for Atmospheric Research(NCAR) 연구소에서 개발한 범용 자료동화 도구이다. 본 연구에서는 지표수 수문모형의 자료동화를 위해 개발된 기법인 along-the-stream localization과 inflation 기법이 하도 추적에 미치는 영향을 분석한다. along-the stream localization 기법은 공간적 근접도 외에 하도의 수문학적 연관관계를 고려하는 localization 기법으로, 상대적으로 수문학적 상관도가 떨어지는 하도에 대한 과도한 자료동화를 줄여줄 수 있다. inflation 기법은 앙상블의 다양성을 증가시키는 기법으로, 칼만 필터(Kalman filter)에 의한 업데이트의 이전이나 이후 적용하여 앙상블 예측의 정확도를 추가적으로 향상시킬 수 있다. 본 고에서는 앙상블 자료동화 기법을 지표수 수문 모의에 적용할 경우 남아 있는 난제와 적용 가능한 방법에 대해 중점적으로 논의한다.

• Spatial Information Research
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• v.22 no.1
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• pp.55-63
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• 2014

Near surface air temperature data which are one of the essential factors in hydrology, meteorology and climatology, have drawn a substantial amount of attention from various academic domains and societies. Meteorological observations, however, have high spatio-temporal constraints with the limits in the number and distribution over the earth surface. To overcome such limits, many studies have sought to estimate the near surface air temperature from satellite image data at a regional or continental scale with simple regression methods. Alternatively, we applied various Kriging methods such as ordinary Kriging, universal Kriging, Cokriging, Regression Kriging in search of an optimal estimation method based on near surface air temperature data observed from automatic weather stations (AWS) in South Korea throughout 2010 (365 days) and MODIS land surface temperature (LST) data (MOD11A1, 365 images). Due to high spatial heterogeneity, auxiliary data have been also analyzed such as land cover, DEM (digital elevation model) to consider factors that can affect near surface air temperature. Prior to the main estimation, we calculated root mean square error (RMSE) of temperature differences from the 365-days LST and AWS data by season and landcover. The results show that the coefficient of variation (CV) of RMSE by season is 0.86, but the equivalent value of CV by landcover is 0.00746. Seasonal differences between LST and AWS data were greater than that those by landcover. Seasonal RMSE was the lowest in winter (3.72). The results from a linear regression analysis for examining the relationship among AWS, LST, and auxiliary data show that the coefficient of determination was the highest in winter (0.818) but the lowest in summer (0.078), thereby indicating a significant level of seasonal variation. Based on these results, we utilized a variety of Kriging techniques to estimate the surface temperature. The results of cross-validation in each Kriging model show that the measure of model accuracy was 1.71, 1.71, 1.848, and 1.630 for universal Kriging, ordinary Kriging, cokriging, and regression Kriging, respectively. The estimates from regression Kriging thus proved to be the most accurate among the Kriging methods compared.

• Korean Journal of Agricultural and Forest Meteorology
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• v.9 no.3
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• pp.209-215
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• 2007

To improve the accuracy of frost warning system for pear orchard in a complex terrain in Naju area, the daily minimum temperature forecasted by Korea Meteorological Administration (KMA) was interpolated using a regional climate model based on topoclimatic estimation and optimum scale interpolation from 2004 to 2005. Based on the validation experiments done for three pear orchards in the spring of 2004, the results showed a good agreement between the observed and predicted values, resulting in improved predictability compared to the forecast from Korea Meteorological Administration. The differences between the observed and the predicted temperatures were $-2.1{\sim}2.7^{\circ}C$ (on average $-0.4^{\circ}C$) in the valley, $-1.6{\sim}2.7^{\circ}C$ (on average $-0.4^{\circ}C$) in the riverside and $-1.1{\sim}3.5^{\circ}C$ (on average $0.6^{\circ}C$) in the hills. Notably, the errors have been reduced significantly for the valley and riverside areas that are more affected by the cold air drainage and more susceptible to frost damage than hills.

• Journal of Korea Water Resources Association
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• v.56 no.5
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• pp.311-323
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• 2023

In order to reduce disaster damage by localized heavy rains, floods, and urban inundation, it is important to know in advance whether natural disasters occur. Currently, heavy rain watch and heavy rain warning by the criteria of the Korea Meteorological Administration are being issued in Korea. However, since this one criterion is applied to the whole country, we can not clearly recognize heavy rain damage for a specific region in advance. Therefore, in this paper, we tried to reset the current criteria for a special weather report which considers the regional characteristics and to predict the damage caused by rainfall after 1 hour. The study area was selected as Gyeonggi-province, where has more frequent heavy rain damage than other regions. Then, the rainfall inducing disaster or hazard-triggering rainfall was set by utilizing hourly rainfall and heavy rain damage data, considering the local characteristics. The heavy rain damage prediction model was developed by a decision tree model and a random forest model, which are machine learning technique and by rainfall inducing disaster and rainfall data. In addition, long short-term memory and deep neural network models were used for predicting rainfall after 1 hour. The predicted rainfall by a developed prediction model was applied to the trained classification model and we predicted whether the rain damage after 1 hour will be occurred or not and we called this as 1ST-Model. The 1ST-Model can be used for preventing and preparing heavy rain disaster and it is judged to be of great contribution in reducing damage caused by heavy rain.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.4
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• pp.312-326
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• 2020

Generation of weather forecasts at 100 m resolution through a statistical downscaling process was implemented by Korea Meteorological Administration Post- Processing (KMAPP) system. The KMAPP data started to be used in various industries such as hydrologic, agricultural, and renewable energy, sports, etc. Cheorwon area and Jeonbuk area have horizontal planes in a relatively wide range in Korea, where there are many complex mountainous areas. Cheorwon, which has a large number of in-situ and remotely sensed phenological data over large-scale rice paddy cultivation areas, is considered as an appropriate area for verifying KMAPP prediction performance in agricultural areas. In this study, the performance of predicting KMAPP temperature changes according to ecological changes in agricultural areas in Cheorwon was compared and verified using KMA and National Center for AgroMeteorology (NCAM) observations. Also, during the heat wave in Jeonbuk Province, solar radiation forecast was verified using Automated Synoptic Observing System (ASOS) data to review the usefulness of KMAPP forecast data as input data for application models such as livestock heat stress models. Although there is a limit to the need for more cases to be collected and selected, the improvement in post-harvest temperature forecasting performance in agricultural areas over ordinary residential areas has led to indirect guesses of the biophysical and phenological effects on forecasting accuracy. In the case of solar radiation prediction, it is expected that KMAPP data will be used in the application model as detailed regional forecast data, as it tends to be consistent with observed values, although errors are inevitable due to human activity in agricultural land and data unit conversion.