• Atmosphere
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• v.30 no.4
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• pp.361-376
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• 2020

This study evaluates multiple Regional Climate Models (RCMs) in simulating temperature and precipitation over the Far East Asia (FEA) and estimates the portions of the total uncertainty originating in the RCMs and the driving Global Climate Models (GCMs) using nine present-day (1981~2000) climate data obtained from combinations of three GCMs and three RCMs in the CORDEX-EA phase2. Downscaling using the RCMs generally improves the present temperature and precipitation simulated in the GCMs. The mean temperature climate in the RCM simulations is similar to that in the GCMs; however, RCMs yield notably better spatial variability than the GCMs. In particular, the RCMs generally yield positive added values to the variability of the summer temperature and the winter precipitation. Evaluating the uncertainties by the GCMs (VARGCM) and the RCMs (VARRCM) on the basis of two-way ANOVA shows that VARRCM is greater than VARGCM in contrast to previous studies which showed VARGCM is larger. In particular, in the winter temperature, the ocean has a very large VARRCM of up to 30%. Precipitation shows that VARRCM is greater than VARGCM in all seasons, but the difference is insignificant. In the following study, we will analyze how the uncertainty of the climate model in the present-day period affects future climate change prospects.

• Journal of the Korean earth science society
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• v.42 no.2
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• pp.135-148
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• 2021

The purpose of this study was to evaluate the urban heat island (UHI) intensity and the corresponding surface temperature forecast obtained using the local data assimilation and prediction system (LDAPS) of the Korea Meteorological Administration (KMA) against the AWS observation. The observed UHI intensity in Seoul increases during spring and winter, while it decreases during summer. It is found that the diurnal variability of the UHI intensity peaks at dawn but reaches a minimum in the afternoon. The LDAPS overestimates the UHI intensity in summer but underestimates it in winter. In particular, the model tends to overestimate the UHI intensity during the daytime in summer but underestimate it during the nighttime in winter. Moreover, surface temperature errors decrease in summer but increase in winter. The underestimation of the winter UHI intensity appears to be associated with weak forecasting of urban temperature in winter. However, the overestimated summer UHI intensity results from the underestimation of the suburban temperature forecast in summer. In order to improve the predictability of the UHI intensity, an urban canopy model (MORUSES) that considers urban effects was combined with LDAPS and used for simulation for the summer of 2017. The surface temperature forecast for the city was improved significantly by adopting MORUSES, and there were remarkable improvements in urban surface temperature morning forecasts. The urban canopy model produced an improvement effect that weakened the intensity of the UHI, which showed an overestimation during summer.

• Atmosphere
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• v.22 no.2
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• pp.267-277
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• 2012

The objectives of this study are to develop a real-time drought monitoring and prediction system on the East Asia domain and to evaluate the performance of the system by using past historical drought records. The system is mainly composed of two parts: drought monitoring for providing current drought indices with meteorological and hydrological conditions; drought outlooks for suggesting future drought indices and future hydrometeorological conditions. Both parts represent the drought conditions on the East Asia domain (latitude $21.15{\sim}50.15^{\circ}$, longitude $104.40{\sim}149.65^{\circ}$), Korea domain (latitude $30.40{\sim}43.15^{\circ}$, longitude $118.65{\sim}135.65^{\circ}$) and South Korea domain (latitude $30.40{\sim}43.15^{\circ}$, longitude $118.65{\sim}135.65^{\circ}$), respectively. The observed meteorological data from ASOS (Automated Surface Observing System) and AWS (Automatic Weather System) of KMA (Korean Meteorological Administration) and model-driven hydrological data from LSM (Land Surface model) are used for the real-time drought monitoring, while the monthly and seasonal weather forecast information from UM (Unified Model) of KMA are utilized for drought outlooks. For the evaluation of the system, past historical drought records occurred in Korea are surveyed and are compared with the application results of the system. The results demonstrated that the selected drought indices such as KMA drought index, SPI (3), SPI (6), PDSI, SRI and SSI are reasonable, especially, the performance of SRI and SSI provides higher accuracy that the others.

• Nuclear Engineering and Technology
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• v.11 no.2
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• pp.127-132
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• 1979

A model is considered in order to evaluate the potential risk from a nuclear facility directly combining the on-site meteorological data. The model is utilized to evaluate the environmental consequences from the routine releases during normal plant operation as well as following postulated accidental releases. The doses to individual and risks to the population-at-large are also analyzed in conjunction with design of rad-waste management and safety systems. It is observed that the conventional analysis, which is done in two separate unaffiliated phases of releases and atmospheric dispersion tends to result in unnecessary over-design of the systems because of high resultant doses calculated by multiplication of two extreme values.

• Journal of Advanced Navigation Technology
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• v.25 no.6
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• pp.569-575
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• 2021

Deep learning shows differences in prediction performance depending on data quality and model. This study uses various input data and multiple deep learning models to build an optimal deep learning model for predicting solar radiation, which has the most influence on power generation prediction. did. As the input data, the weather data of the Korea Meteorological Administration and the clairvoyant meteorological image were used by segmenting the image of the Korea Meteorological Agency. , comparative evaluation, and predicting solar radiation by constructing multiple deep learning models connecting the models with the best error rate in each model. As an experimental result, the RMSE of model A, which is a multiple deep learning model, was 0.0637, the RMSE of model B was 0.07062, and the RMSE of model C was 0.06052, so the error rate of model A and model C was better than that of a single model. In this study, the model that connected two or more models through experiments showed improved prediction rates and stable learning results.

• Journal of Industrial Technology
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• v.27 no.B
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• pp.59-64
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• 2007

This study introduces an environmental noise evaluation procedure and results for a wind turbine (W/T) system. Test and evaluation are required by the international standard IEC 61400-11 in the aspect of environmental effect. Test and evaluation are performed on U-50 WT model which is first developed by the domestic W/T manufacturer. W/T test model is under operation in Daekwanryung wind test site. An integrated monitoring system in the test site is utilized for the evaluation. With the noise signal, meteorological data and W/T operational data are monitored in real time by the integrated monitoring system using LabVIEW. From the measured noise data, acoustic power level are estimated and compared with those of other similar size WT under the wind speeds required by international standard.

• Korean Journal of Agricultural Science
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• v.38 no.1
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• pp.129-143
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• 2011

In this study, to select the incoming solar radiation equation which is most suitable for the estimation of Penman evaporation, 12 incoming solar radiation equations were selected. The Penman evaporation rates were estimated using 12 selected incoming solar radiation equations, and the estimated Penman evaporation rates were compared with measured pan evaporation rates. The monthly average daily meteorological data measured from 17 meteorological stations (춘천, 강능, 서울, 인천, 수원, 서산, 청주, 대전, 추풍령, 포항, 대구, 전주, 광주, 부산, 목포, 제주, 진주) were used for this study. To evaluate the reliability of estimated evaporation rates, mean absolute bias error(MABE), root mean square error(RMSE), mean percentage error(MPE) and Nash-Sutcliffe equation were applied. The study results indicate that to estimate pan evaporation using Penman evaporation equation, incoming solar radiation equation using meteorological data such as precipitation, minimum air temperature, sunshine duration, possible duration of sunshine, and extraterrestrial radiation are most suitable for 11 study stations out of 17 study stations.

• Journal of Korean Society for Atmospheric Environment
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• v.18 no.1
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• pp.59-65
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• 2002

The application of local air circulation models in the field of air pollution research has become more and more popular with increasing demands of detailed wind data for obtaining precise information on spatial and temporal variations. However the prediction of air circulation near the surface is generally not a simple task because of intricate interactions between surface and air. Particularly in Korea, many areas are mountainous with a complicated shoreline. Because considerable errors could be introduced into the model predictions, it is necessary to confirm their feasibility by comparing model predictions with observations. In this paper, the results from the evaluation of model predictions in selected publications in Korea as well as their procedures were reviewed. Various aspects of errors in the model predictions. such as possible sources, vulnerable conditions, and reduction methods, were discussed.

• Atmosphere
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• v.31 no.2
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• pp.215-227
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• 2021

The necessity of the prediction on the Seasonal-to-Subseasonal (S2S) timescale continues to rise. It led a series of studies on the S2S prediction models, including the Global Seasonal Forecasting System Version 5 (GloSea5) of the Korea Meteorological Administration. By extending previous studies, the present study documents sea surface temperature (SST) prediction skill around the Korean peninsula in the GloSea5 hindcast over the period of 1991~2010. The overall SST prediction skill is about a week except for the regions where SST is not well captured at the initialized date. This limited prediction skill is partly due to the model mean biases which vary substantially from season to season. When such biases are systematically removed on daily and seasonal time scales the SST prediction skill is improved to 15 days. This improvement is mostly due to the reduced error associated with internal SST variability during model integrations. This result suggests that SST around the Korean peninsula can be reliably predicted with appropriate post-processing.

• Atmosphere
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• v.28 no.3
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• pp.291-303
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• 2018

In this study, a new consensus technique for predicting tropical cyclone (TC) intensity in the western North Pacific was developed. The most important feature of the present consensus model is to select and combine the guidance numerical models with the best performance in the previous years based on various evaluation criteria and averaging methods. Specifically, the performance of the guidance models was evaluated using both the mean absolute error and the correlation coefficient for each forecast lead time, and the number of the numerical models used for the consensus model was not fixed. In averaging multiple models, both simple and weighted methods are used. These approaches are important because that the performance of the available guidance models differs according to forecast lead time and is changing every year. In particular, this study develops both a multi-consensus model (M-CON), which constructs the best consensus models with the lowest error for each forecast lead time, and a single best consensus model (S-CON) having the lowest 72-hour cumulative mean error, through on training process. The evaluation results of the selected consensus models for the training and forecast periods reveal that the M-CON and S-CON outperform the individual best-performance guidance models. In particular, the M-CON showed the best overall performance, having advantages in the early stages of prediction. This study finally suggests that forecaster needs to use the latest evaluation results of the guidance models every year rather than rely on the well-known accuracy of models for a long time to reduce prediction error.