• Journal of Radiation Protection and Research
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• 제48권1호
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• pp.28-43
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• 2023

Background: High-fidelity meteorological data is a prerequisite for the realistic simulation of atmospheric dispersion of radioactive materials near nuclear power plants (NPPs). However, many meteorological models frequently overestimate near-surface wind speeds, failing to represent local meteorological conditions near NPPs. This study presents a new high-resolution (approximately 1 km) meteorological downscaling method for modeling short-range (< 100 km) atmospheric dispersion of accidental NPP plumes. Materials and Methods: Six considerations from literature reviews have been suggested for a new dynamic downscaling method. The dynamic downscaling method is developed based on the Weather Research and Forecasting (WRF) model version 3.6.1, applying high-resolution land-use and topography data. In addition, a new subgrid-scale topographic drag parameterization has been implemented for a realistic representation of the atmospheric surface-layer momentum transfer. Finally, a year-long simulation for the Kori and Wolsong NPPs, located in southeastern coastal areas, has been made for 2016 and evaluated against operational surface meteorological measurements and the NPPs' on-site weather stations. Results and Discussion: The new dynamic downscaling method can represent multiscale atmospheric motions from the synoptic to the boundary-layer scales and produce three-dimensional local meteorological fields near the NPPs with a 1.2 km grid resolution. Comparing the year-long simulation against the measurements showed a salient improvement in simulating near-surface wind fields by reducing the root mean square error of approximately 1 m/s. Furthermore, the improved wind field simulation led to a better agreement in the Eulerian estimate of the local atmospheric dispersion. The new subgrid-scale topographic drag parameterization was essential for improved performance, suggesting the importance of the subgrid-scale momentum interactions in the atmospheric surface layer. Conclusion: A new dynamic downscaling method has been developed to produce high-resolution local meteorological fields around the Kori and Wolsong NPPs, which can be used in short-range atmospheric dispersion modeling near the NPPs.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.26-26
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• 2023

General circulation models (GCMs) are widely used in hydrological prediction, however their coarse grids make them unsuitable for regional analysis, therefore a downscaling method is required to utilize them in hydrological assessment. As one of the downscaling methods, convolutional neural network (CNN)-based downscaling has been proposed in recent years. The aim of this study is to generate the process of dynamic downscaling using CNNs by applying GCM output as input and RCM output as label data output. Prediction accuracy is compared between different input datasets, and model structures. Several input datasets with key atmospheric variables such as precipitation, temperature, and humidity were tested with two different formats; one is two-dimensional data and the other one is three-dimensional data. And in the model structure, the hyperparameters were tested to check the effect on model accuracy. The results of the experiments on the input dataset showed that the accuracy was higher for the input dataset without precipitation than with precipitation. The results of the experiments on the model structure showed that substantially increasing the number of convolutions resulted in higher accuracy, however increasing the size of the receptive field did not necessarily lead to higher accuracy. Though further investigation is required for the application, this paper can contribute to the development of efficient downscaling method with CNNs.

• 한국기후변화학회지
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• 제2권2호
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• pp.79-91
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• 2011

본 연구에서는 기후변화와 대기환경 사이의 통합적 상호작용 연구를 위하여 전 지구규모 기후모델(CCSM3) 결과를 지역 규모 기후모델(MM5)의 초기 및 경계 조건으로 사용할 수 있도록 역학적 상세화(Downscaling) 기법을 개발하였다. 개발된 상세화 기법에서는 위 경도 좌표계로 이루어진 CCSM3 결과를 Lambert-Conformal Arakawa-B 격자 체계로, CCSM3의 hybrid-vertical coordinate를 MM5의 sigma coordinate로 대체하는 과정과 CCSM3 모델 수행 결과와 모델 수행에 필요한 변수들 간의 일치화 과정이 포함된다. 전 지구 규모 모델 결과들이 지역 규모 모델의 입력값으로 역학적 규모 축소되는 과정을 검증하기 위해 공간 분포 및 통계분석을 수행한 결과, 여름철과 겨울철의 기온 및 강수량 패턴이 동아시아 영역 및 한반도 지역에 대해 기존 관측을 이용한 결과와 매우 유사한 패턴을 보였으며, 통계 분석 결과 모델 예측지수가 기온의 경우 0.9 이상의 좋은 값이 나타났으며, 상관성 역시 0.9 수준의 결과를 보여 인터페이스 구축이 성공적으로 수행되었음을 알 수 있다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2002년도 학술발표회 논문집(II)
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• pp.1187-1194
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• 2002

In this study, we have simulated regional climate over East Asia using dynamical downscaling For dynamic downscaling experiments for regional climate simulation, MM5method. with 27 km horizontal resolution and 18 layers of sigma-coordinate in vertical is nested within global-scale NCEP reanalysis data with 2.5。${\times}$2.5。 resolution in longitude and latitude. In regional simulation, January and July, 1979 monthly mean features have been obtained by both continuous integration and daily restart integration driven by updating the lateral boundary forcing at 6-hr intervals from the NCEP reanalysis data using a nudging scheme with the updating design of initial and boundary conditions in both continuous and restart integrations. In result, we may successfully generated regional detail features which might be forced by topography, lake, coastlines and land use distribution from a regional climate. There is no significant difference in monthly mean features either integrate continuously or integrate with daily restart. For climatologically long integration, the initial condition may not be significantly important. Accordingly, MM5 can be integrated for a long period without restart frequently, if a proper lateral boundary forcing is given.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2012년도 학술발표회
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• pp.276-276
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• 2012

일반적으로 기후변화영향에 관한 연구수행을 위해 전지구기후모형(GCM; Global Climate Model)이 사용되고 있다. 하지만 GCM은 공간해상도(Spatial resolution)가 거칠기 때문에 수문학 분야에서 주로 사용되는 유역규모의 지역적인 스케일특성과 물리적 특징을 표현하는데 한계가 있다. 또한 GCM 기후변수들 중 강수량의 경우 한반도 지역의 6월과 10월 사이에 연강수량의 67% 이상이 집중되는 계절성을 반영하지 못하고 있으며, 높은 불확실성을 보이고 있다. 본 연구에서는 GCM 기반의 다지점 인공신경망기법을 적용한 상세화(Downscaling)를 실시하였다. GCM의 24개 2D변수에 대한 주성분분석을 실시하여 신경망의 학습인자로 사용하였으며, 학습, 검증 및 예측기간은 각각 1981~1995년, 1996~2000년, 2011~2100년으로 A1B 시나리오를 대상으로 상세화를 실시하였다. 또한, 여름철 태풍사상을 모의하기 위한 Stochastic Typhoon Simulation기법과 Baseline과 Projection 사이의 강수량 보정을 위한 Dynamic Quantile Mapping 기법을 적용하여, 강수량의 불확실성을 최소화 하고자 하였다.

• 한국농림기상학회지
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• 제12권1호
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• pp.1-10
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• 2010

본 연구에서는 기상예측 모형을 이용하여 상세한 농업기후지수를 모사하고자 하였다. 이를 위해서 NCEP/NCAR 재분석 자료를 지역기후모형인 WRF의 초기 및 경계조건으로 하여 2002년 3월부터 7년간 상세한 기후 자료를 생산하고, 이렇게 구한 기후 자료를 통계적 보정을 거쳐 계통적 오차를 제거함으로써 그 기간의 기후를 재현하였으며 이를 이용하여 상세한 농업기후지수로 생산하였다. 수치 실험을 통해 생산된 상세 지역기후자료는 대순환 모형이 모사할 수 없는 남한의 복잡한 지형적 구조와 전체적인 관측 공간 패턴을 모사하였다. 통계적 보정은 모형결과가 관측에 비해 과소모사 되던 경향을 제거함으로써 보다 상세하고 관측에 가까운 시 공적 기후자료의 생산을 가능하게 하였다. 이렇게 모사된 기후 자료를 이용하여 식물온도 출 현초일, 작물온도 출현초일, 벼 이앙기의 저온 출현율, 종상일 등의 농업기후지수들에 대한 상세한 분포를 생산하였다. 보정 전 모형 결과에서는 계통적 오차인 모형의 기온 과소모사 경향에 의해 전반적인 유효온도와 종상일이 늦게 출현하였으며, 저온 출현율의 빈도가 높게 나타났다. 보정 후 모형 결과에서는 계통적 오차의 보정에 의해 유효온도 $10^{\circ}C$ 출현일을 제외한 유효 온도 출현일과 종상일이 앞당겨졌으며, 저온 출현일 빈도가 감소하였다. 보정 후 모형 결과에서 유도된 유 효온도 $10^{\circ}C$ 출현일은 보정 전 모형결과보다 3일 늦게 모사되고 있으나 보정 전 모형 결과에서 모사하지 못한 지역적 특징을 모사하고 있어 국지적으로 나타나는 작물온도 출현초일의 세부적인 패턴을 이해하는데 유용한 결과라고 판단된다. 모형의 결과로 유도된 농업기후지수는 복잡한 지역적 편차를 가지면서 정량적 정성적으로 관측에서 유도한 결과와 유사하게 나타났다. 반면 통계적 보정을 적용하여도 중부 논농사 지대의 작물온도 출현초일은 여전히 잘 모사되지 못하고 있는데 이는 모형의 결과가 계통적 오차 이외에도 또 다른 불확실성에 의한 문제를 내제하고 있음을 보여주는 결과이다. 향후 물리적 모수화 과정의 개선, 역학적 규모축소방법의 최적화 그리고 통계적 보정 방법의 다양한 적용을 통해 보다 향상된 농업기후지수를 생산할 수 있을 것으로 판단된다. 이러한 실험 결과는 농업 경영자들에게 상세 농업기후지수 분포의 이해를 도와줄 뿐만 아니라 본 연구의 실험 방식이 농업 예측에 활용될 경우 장기 예측 및 기후변화에 따른 예측을 위한 정보에 긴요하게 사용될 수 있을 것으로 생각된다.

• 한국제4기학회지
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• 제19권2호
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• pp.74-79
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• 2005

Recently, several attempts have been made to provide reasonable information on unusual severe weather phenomena such as tolerant heavy rains and very wild typhoons. Quantitative precipitation forecasts and probabilistic quantitative precipitation forecasts (QPFs and PQPFs, respectively) might be one of the most promising methodologies for early warning on the flesh floods because those diagnostic precipitation models require less computational resources than fine-mesh full-dynamics non-hydrostatic mesoscale model. The diagnostic rainfall model used in this study is the named QPM(Quantitative Precipitation Model), which calculates the rainfall by considering the effect of small-scale topography which is not treated in the mesoscale model. We examine the capability of probabilistic diagnostic rainfall model in terms of how well represented the observed several rainfall events and what is the most optimistic resolution of the mesoscale model in which diagnostic rainfall model is nested. Also, we examine the integration time to provide reasonable fine-mesh rainfall information. When we apply this QPM directly to 27 km mesh meso-scale model (called as M27-Q3), it takes about 15 min. while it takes about 87 min. to get the same resolution precipitation information with full dynamic downscaling method (called M27-9-3). The quality of precipitation forecast by M27-Q3 is quite comparable with the results of M27-9-3 with reasonable threshold value for precipitation. Based on a series of examination we may conclude that the proosed QPM has a capability to provide fine-mesh rainfall information in terms of time and accuracy compared to full dynamical fine-mesh meso-scale model.

• 대기
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• 제31권5호
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• pp.637-656
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• 2021

The United States has been known as the world's major producer of crops such as wheat, corn, and soybeans. Therefore, using meteorological long-term forecast data to project reliable crop yields in the United States is important for planning domestic food policies. The current study is part of an effort to improve the seasonal predictability of regional-scale precipitation across the United States for estimating crop production in the country. For the purpose, a dynamic downscaling method using Weather Research and Forecasting (WRF) model is utilized. The WRF simulation covers the crop-growing period (March to October) during 2000-2020. The initial and lateral boundary conditions of WRF are derived from the Pusan National University Coupled General Circulation Model (PNU CGCM), a participant model of Asia-Pacific Economic Cooperation Climate Center (APCC) Long-Term Multi-Model Ensemble Prediction System. For bias correction of downscaled daily precipitation, empirical quantile mapping (EQM) is applied. The downscaled data set without and with correction are called WRF_UC and WRF_C, respectively. In terms of mean precipitation, the EQM effectively reduces the wet biases over most of the United States and improves the spatial correlation coefficient with observation. The daily precipitation of WRF_C shows the better performance in terms of frequency and extreme precipitation intensity compared to WRF_UC. In addition, WRF_C shows a more reasonable performance in predicting drought frequency according to intensity than WRF_UC.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2011년도 학술발표회
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• pp.18-18
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• 2011

Climate change is impacting and will increasingly impact both the quantity and quality of the world's water resources in a variety of ways. In some areas warming climate results in increased rainfall, surface runoff, and groundwater recharge while in others there may be declines in all of these. Water quality is described by a number of variables. Some are directly impacted by climate change. Temperature is an obvious example. Notably, increased atmospheric concentrations of $CO_2$ triggering climate change increase the $CO_2$ dissolving into water. This has manifold consequences including decreased pH and increased alkalinity, with resultant increases in dissolved concentrations of the minerals in geologic materials contacted by such water. Climate change is also expected to increase the number and intensity of extreme climate events, with related hydrologic changes. A simple framework has been developed in New Zealand for assessing and predicting climate change impacts on water resources. Assessment is largely based on trend analysis of historic data using the non-parametric Mann-Kendall method. Trend analysis requires long-term, regular monitoring data for both climate and hydrologic variables. Data quality is of primary importance and data gaps must be avoided. Quantitative prediction of climate change impacts on the quantity of water resources can be accomplished by computer modelling. This requires the serial coupling of various models. For example, regional downscaling of results from a world-wide general circulation model (GCM) can be used to forecast temperatures and precipitation for various emissions scenarios in specific catchments. Mechanistic or artificial intelligence modelling can then be used with these inputs to simulate climate change impacts over time, such as changes in streamflow, groundwater-surface water interactions, and changes in groundwater levels. The Waimea Plains catchment in New Zealand was selected for a test application of these assessment and prediction methods. This catchment is predicted to undergo relatively minor impacts due to climate change. All available climate and hydrologic databases were obtained and analyzed. These included climate (temperature, precipitation, solar radiation and sunshine hours, evapotranspiration, humidity, and cloud cover) and hydrologic (streamflow and quality and groundwater levels and quality) records. Results varied but there were indications of atmospheric temperature increasing, rainfall decreasing, streamflow decreasing, and groundwater level decreasing trends. Artificial intelligence modelling was applied to predict water usage, rainfall recharge of groundwater, and upstream flow for two regionally downscaled climate change scenarios (A1B and A2). The AI methods used were multi-layer perceptron (MLP) with extended Kalman filtering (EKF), genetic programming (GP), and a dynamic neuro-fuzzy local modelling system (DNFLMS), respectively. These were then used as inputs to a mechanistic groundwater flow-surface water interaction model (MODFLOW). A DNFLMS was also used to simulate downstream flow and groundwater levels for comparison with MODFLOW outputs. MODFLOW and DNFLMS outputs were consistent. They indicated declines in streamflow on the order of 21 to 23% for MODFLOW and DNFLMS (A1B scenario), respectively, and 27% in both cases for the A2 scenario under severe drought conditions by 2058-2059, with little if any change in groundwater levels.