• 환경영향평가
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• 제21권1호
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• pp.105-117
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• 2012

Future climate according to land-use change was simulated by regional climate model. The goal of study was to predict the distribution of meteorological elements using the Weather Research & Forecasting Model (WRF). The KME (Korea Ministry of Environment) medium-category land-use classification was used as dominant vegetation types. Meteorological modeling requires higher and more sophisticated land-use and initialization data. The WRF model simulations with HyTAG land-use indicated certain change in potential vegetation distribution in the future (2086-2088). Compared to the past (1986-1988) distribution, coniferous forest area was decreased in metropolitan and areas with complex terrain. The research shows a possibility to simulate regional climate with high resolution. As a result, the future climate was predicted to $4.5^{\circ}$ which was $0.5^{\circ}$ higher than prediction by Meteorological Administration. To improve future prediction of regional area, regional climate model with HyTAG as well as high resolution initial values such as urban growth and CO2 flux simulation would be desirable.

• 대기
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• 제21권1호
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• pp.17-33
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• 2011

A dynamical downscaling system for seasonal forecast has been constructed based on a regional climate model, and its predictability was investigated for 10 years' wintertime (December-January-February; DJF) climatology in East Asia. Initial and lateral boundary conditions were obtained from the operational seasonal forecasting data, which are realtime output of the Global Data Assimilation and Prediction System (GDAPS) at Korea Meteorological Administration (KMA). Sea surface temperature was also obtained from the operational forecasts, i.e., KMA El-Nino and Global Sea Surface Temperature Forecast System. In order to determine the better configuration of the regional climate model for East Asian regions, two sensitivity experiments were carried out for one winter season (97/98 DJF): One is for the topography blending and the other is for the cumulus parameterization scheme. After determining the proper configuration, the predictability of the regional forecasting system was validated with respect to 850 hPa temperature and precipitation. The results showed that mean fields error and other verification statistics were generally decreased compared to GDAPS, most evident in 500 hPa geopotential heights. These improved simulation affected season prediction, and then HSS was better 36% and 11% about 850 hPa temperature and precipitation, respectively.

• Ocean and Polar Research
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• 제43권2호
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• pp.53-63
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• 2021

Changes in a marine environment have a broad socioeconomic implication on fisheries and their relevant industries so that there has been a growing demand for the medium-range (months to years) prediction of the marine environment Using a medium-range ocean prediction model (Ocean Mid-range prediction System, OMIDAS) for the northwest Pacific, this study attempted to assess seasonal difference in the mid-range predictability of the sea surface temperature (SST), focusing on the Korea seas characterized as a complex marine system. A three-month re-forecast experiment was conducted for each of the four seasons in 2016 starting from January, forced with Climate Forecast System version 2 (CFSv2) forecast data. The assessment using relative root-mean-square-error was taken for the last month SST of each experiment. Compared to the CFSv2, the OMIDAS revealed a better prediction skill for the Korea seas SST, particularly in the Yellow sea mainly due to a more realistic representation of the topography and current systems. Seasonally, the OMIDAS showed better predictability in the warm seasons (spring and summer) than in the cold seasons (fall and winter), suggesting seasonal dependency in predictability of the Korea seas. In addition, the mid-range predictability for the Korea seas significantly varies depending on regions: the predictability was higher in the East Sea than in the Yellow Sea. The improvement in the seasonal predictability for the Korea seas by OMIDAS highlights the importance of a regional ocean modeling system for a medium-range marine prediction.

• 응용통계연구
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• 제22권6호
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• pp.1143-1152
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• 2009

A canonical correlation analysis(CCA)-based method is proposed for prediction of future climate change which combines information from ensembles of atmosphere-ocean general circulation models(AOGCMs) and observed climate values. This paper focuses on predictions of future climate on a regional scale which are of potential economic values. The proposed method is obtained by coupling the classical CCA with empirical orthogonal functions(EOF) for dimension reduction. Furthermore, we generate a distribution of climate responses, so that extreme events as well as a general feature such as long tails and unimodality can be revealed through the distribution. Results from real data examples demonstrate the promising empirical properties of the proposed approaches.

• 한국제4기학회:학술대회논문집
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• 한국제4기학회 2004년도 하계학술대회
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• pp.24-25
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• 2004

It is well known that there is an inverse relationship between the strength of Indian summer monsoon Rainfall (ISMR) and extent of Eurasian snow cover/depth in the preceding season. Tibetan snow cover/depth also affects the Asian monsoon rainy season largely. The positive correlation between Tibetan sensible heat flux and southeast Asian rainfall suggest an inverse relationship between Tibetan snow cover and southeast Asian rainfall. Developments in Regional Climate Models suggest that the effect of Tibetan snow on the ISMR can be well studied by Limited Area Models (LAMs). LAMs are used for regional climate studies and operational weather forecast of several hours to 3 days in future. The Eta model developed by the National Center for Environmental Prediction (NCEP), the Fifth-Generation NCAR/Penn State Mesoscale Model (MM5) and Regional Climate Model (RegCM) have been used for weather prediction as well as for the study of present-day climate and variability over different parts of the world. Regional Climate Model (RegCM3) has been widely . used for various mesoscale studies. However, it has not been tested to study the characteristics of circulation features and associated rainfall over India so far. In the present study, Regional Climate Model (RegCM-3) has been integrated from 1 st April to 30th September for the years 1993-1996 and monthly mean monsoon circulation features and rainfall simulated by the model at 55km resolution have been studied for the Indian summer monsoon season. Characteristics of wind at 850hPa and 200hPa, temperature at 500hPa, surface pressure and rainfall simulated by the model have been examined for two convective schemes such as Kuo and Grell with Arakawa-Schubert as the closure scheme, Model simulated monsoon circulation features have been compared with those of NCEP/NCAR reanalyzed fields and the rainfall with those of India Meteorological Department (IMD) observational rainfall datasets, Comparisons of wind and temperature fields show that Grell scheme is closer to the NCEP/NCAR reanalysis, The influence of Tibetan snowdepth in spring season on the summer monsoon circulation features and subsequent rainfall over India have been examined. For such sensitivity experiment, NIMBUS-7 SMMR snowdepth data have been used as a boundary condition in the RegCM3, Model simulation indicates that ISMR is reduced by 30% when 10cm of snow has been introduced over Tibetan region in the month of previous April. The existence of Tibetan snow in RegCM3 also indicates weak lower level monsoon westerlies and upper level easterlies.

• Wind and Structures
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• 제36권2호
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• pp.121-131
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• 2023

Aiming at the problem that fatigue characteristics of metal roof rely on local physical tests and lacks the cyclic load sequence matching with regional climate, this paper proposed a method of constructing the fatigue load spectrum based on integration of wind load model, measured data of long-span metal roof and climate statistical data. According to the turbulence characteristics of wind, the wind load model is established from the aspects of turbulence intensity, power spectral density and wind pressure coefficient. Considering the influence of roof configuration on wind pressure distribution, the parameters are modified through fusing the measured data with least squares method to approximate the actual wind pressure load of the roof system. Furthermore, with regards to the wind climate characteristics of building location, Weibull model is adopted to analyze the regional meteorological data to obtain the probability density distribution of wind velocity used for calculating wind load, so as to establish the cyclic wind load sequence with the attributes of regional climate and building configuration. Finally, taking a workshop's metal roof as an example, the wind load spectrum is constructed according to this method, and the fatigue simulation and residual life prediction are implemented based on the experimental data. The forecasting result is lightly higher than the design standards, consistent with general principles of its conservative safety design scale, which shows that the presented method is validated for the fatigue characteristics study and health assessment of metal roof.

• 한국대기환경학회지
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• 제28권6호
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• pp.615-631
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• 2012

This study provides an evaluation for capability of Integrated Climate and Air quality Modeling System (ICAMS) on future regional scale climate projection. Temperature and precipitation are compared between ground-level observation data and results of regional models (MM5) for the past 30 years over the Korean peninsula. The ICAMS successfully simulates the local-scale spatial/seasonal variation of the temperature and precipitation. The probability distribution of simulated daily mean and minimum temperature agree well with the observed patterns and trends, although mean temperature shows a little cold bias about $1^{\circ}C$ compared to observations. It seems that a systematic cold bias is mostly due to an underestimation of maximum temperature. In the case of precipitation, the rainfall in winter and light rainfall are remarkably simulated well, but summer precipitation is underestimated in the heavy rainfall phenomena of exceeding 20 mm/day. The ICAMS shows a tendency to overestimate the number of washout days about 7%. Those results of this study indicate that the performance of ICAMS is reasonable regarding to air quality predication over the Korean peninsula.

• 대기
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• 제25권4호
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• pp.669-683
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• 2015

In this study, we investigated the prediction skills of four multiple linear regression methods for monthly air temperature over South Korea. We used simulation results from four regional climate models (RegCM4, SNURCM, WRF, and YSURSM) driven by two boundary conditions (NCEP/DOE Reanalysis 2 and ERA-Interim). We selected 15 years (1989~2003) as the training period and the last 5 years (2004~2008) as validation period. The four regression methods used in this study are as follows: 1) Homogeneous Multiple linear Regression (HMR), 2) Homogeneous Multiple linear Regression constraining the regression coefficients to be nonnegative (HMR+), 3) non-homogeneous multiple linear regression (EMOS; Ensemble Model Output Statistics), 4) EMOS with positive coefficients (EMOS+). It is same method as the third method except for constraining the coefficients to be nonnegative. The four regression methods showed similar prediction skills for the monthly air temperature over South Korea. However, the prediction skills of regression methods which don't constrain regression coefficients to be nonnegative are clearly impacted by the existence of outliers. Among the four multiple linear regression methods, HMR+ and EMOS+ methods showed the best skill during the validation period. HMR+ and EMOS+ methods showed a very similar performance in terms of the MAE and RMSE. Therefore, we recommend the HMR+ as the best method because of ease of development and applications.

• 한국농공학회논문집
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• 제53권6호
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• pp.31-41
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• 2011

This study was conducted to predict future land-cover changes under climate change scenarios and to cluster analysis of regional land-cover characteristics. To simulate the future land-cover according to climate change scenarios - A1B, A2, and B1 of the Special Report on Emissions Scenarios (SRES), Dyna-CLUE (Conversion of Land Use Change and its Effects) was applied for modeling of competition among land-use types in relation with socioeconomic and biophysical driving factors. Gyeonggi-do were selected as study areas. The simulation results from 2010 to 2040 suggested future land-cover changes under the scenario conditions. All scenarios resulted in a gradual decrease in paddy area, while upland area continuously increased. A1B scenario showed the highest increase in built-up area, but all scenarios showed only slight changes in forest area. As a result of cluster analysis with the land-cover component scores, 31 si/gun in Gyeonggi-do were classified into three clusters. This approach is expected to be useful for evaluating and simulating land-use changes in relation to development constraints and scenarios. The results could be used as fundamental basis for providing policy direction by considering regional land-cover characteristics.

• 한국농공학회논문집
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• 제51권4호
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• pp.7-13
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• 2009

The paddy irrigation demand for Nakdong river basin in Korea due to the climate change have been analyzed using regional climate model outputs. High-resolution (27 ${\times}$ 27 km) climate data for SRES A2 scenario produced by the Meteorological Research Institute (METRI), South Korea, and the observed baseline climatology dataset (1971-2000) were used. The outputs from the ECHO-G GCM model were dynamically downscaled using the MM5 regional model by METRI. Maps showing the predicted spatial variations of changes in climate parameters and paddy irrigation requirements have been produced using the geographic information system. The results of this study showed that the average growing season temperature will increase steadily by 1.5 $^{\circ}C$ (2020s A2), 3.2 $^{\circ}C$ (2050s A2) and 5.2 $^{\circ}C$ (2080s A2) from the baseline (1971-2000) 19.8 $^{\circ}C$. The average growing season rainfall will change by -3.4 % (2020s A2), 0.0 % (2050s A2) and +16.5 % (2080s A2) from the baseline value 886 mm. Assuming paddy area and cropping pattern remain unchanged the average volumetric irrigation demands were predicted to increase by 5.3 % (2020s A2), 8.1 % (2050s A2) and 2.2 % (2080s A2) from the baseline value 1.159 ${\times}$ $10^6\; m^3$. These projections are different from the previous study by Chung (2009) which used a different GCM and downscaling method and projected decreasing irrigation demands. This indicates that one should be careful in interpreting the results of similar studies.