• Journal of Convergence Society for SMB
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• v.6 no.4
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• pp.93-98
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• 2016

Recently, the project related to the smart grid are being actively studied around the developed world. In particular, the long-term stabilization measures distributed power supply problem has been highlighted. In this paper, we propose a three-dimensional numerical weather prediction models to compare the error rate information which combined with the physical models and statistical models to predict the output of distributed power. Proposed model can predict the system for a stable power grid-can improve the prediction information of the distributed power. In performance evaluation, proposed model was a generation forecasting accuracy improved by 4.6%, temperature compensated prediction accuracy was improved by 3.5%. Finally, the solar radiation correction accuracy is improved by 1.1%.

• Atmosphere
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• v.33 no.2
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• pp.73-104
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• 2023

Over the past 60 years, Korean numerical weather prediction (NWP) has advanced rapidly with the collaborative effort between the science community and the operational modelling center. With an improved scientific understanding and the growth of information technology infrastructure, Korea is able to provide reliable and seamless weather forecast service, which can predict beyond a 10 days period. The application of NWP has expanded to support decision making in weather-sensitive sectors of society, exploiting both storm-scale high-impact weather forecasts in a very short range, and sub-seasonal climate predictions in an extended range. This article gives an approximate chronological account of the NWP over three periods separated by breakpoints in 1990 and 2005, in terms of dynamical core, physics, data assimilation, operational system, and forecast application. Challenges for future development of NWP are briefly discussed.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.18 no.1
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• pp.89-99
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• 2010

The purpose of this study is analysis of Korean peninsula aircraft turbulence using the numerical weather prediction model, KWRF with the various turbulence index and pilot weather report data. Compared with the pilot weather report data and Calculated the turbulence index using the KWRF model result, many turbulence index show the similar horizontal distribution, except for the TUB2 and VWS. The analysis of vertical structure of turbulence, there are some difference each turbulence index respectively, but severe turbulence turn up in 15,000ft almost turbulence index. above 20,000ft height, intensity of turbulence vary each turbulence index. Through this turbulence study, It is founded on the research and development of the Korean peninsula aircraft turbulence

• Transactions of the Korean Society of Automotive Engineers
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• v.18 no.4
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• pp.121-126
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• 2010

The automotive weather strip has functions of isolating of water, dust, noise and vibration from outside. To achieve good sealing performance, weather strip should be designed to have the high contact force and wide contact area. However, these design causes excessive permanent deformation of weather strip. The causes of permanent deformation is generally explained to be the chemical material detrioration and physical variation and cyclic loading, etc. This paper introduces a numerical method to predict the permanent deformation using the time dependent viscoelastic model which is represented by Prony series in ABAQUS. Uniaxial tension and creep tests were conducted to obtain the material data. And the lab. test for the permanent deformation was accelerated during shorter time, 300 hours. The permanent deformation of weather strip was successfully predicted under the different loading conditions and different section shapes using the suggested numerical process.

• Proceedings of the Korea Concrete Institute Conference
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• 2005.11a
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• pp.785-788
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• 2005

In this paper, the influence of many factors related to the concrete mixture and the weather condition on plastic shrinkage cracking are analyzed through parametric studies using the numerical models introduced in the companion paper. First of all, through a systematic calculation of bleeding, the relationship between the bleeding constants and concrete mixture is proposed on the basis of the experimental data obtained by many researchers. Moreover, an equation, which can directly determine a critical point at which the evaporation and bleeding is balanced, is introduced, and the efficiency of the introduced equation is verified through the correlation study between the obtained results by the introduced equation and those by the rigorous analyses. The introduced equation can effectively be used to predict and to prevent plastic shrinkage cracking without any rigorous analysis and, in advance, to cope with the sudden changes in the concrete mixture and/or weather condition.

• Proceedings of the Korea Water Resources Association Conference
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• 2002.05a
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• pp.43-50
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• 2002

Accurate quantitative forecasting of rainfall for basins with a short response time is essential to predict streamflow and flash floods. Previously, neural networks were used to develop a Quantitative Precipitation Forecasting (QPF) model that highly improved forecasting skill at specific locations in Pennsylvania, using both Numerical Weather Prediction (NWP) output and rainfall and radiosonde data. The objective of this study was to improve an existing artificial neural network model and incorporate the evolving structure and frequency of intense weather systems in the mid-Atlantic region of the United States for improved flood forecasting. Besides using radiosonde and rainfall data, the model also used the satellite-derived characteristics of storm systems such as tropical cyclones, mesoscale convective complex systems and convective cloud clusters as input. The convective classification and tracking system (CCATS) was used to identify and quantify storm properties such as life time, area, eccentricity, and track. As in standard expert prediction systems, the fundamental structure of the neural network model was learned from the hydroclimatology of the relationships between weather system, rainfall production and streamflow response in the study area. The new Quantitative Flood Forecasting (QFF) model was applied to predict streamflow peaks with lead-times of 18 and 24 hours over a five year period in 4 watersheds on the leeward side of the Appalachian mountains in the mid-Atlantic region. Threat scores consistently above .6 and close to 0.8 ∼ 0.9 were obtained fur 18 hour lead-time forecasts, and skill scores of at least 4％ and up to 6％ were attained for the 24 hour lead-time forecasts. This work demonstrates that multisensor data cast into an expert information system such as neural networks, if built upon scientific understanding of regional hydrometeorology, can lead to significant gains in the forecast skill of extreme rainfall and associated floods. In particular, this study validates our hypothesis that accurate and extended flood forecast lead-times can be attained by taking into consideration the synoptic evolution of atmospheric conditions extracted from the analysis of large-area remotely sensed imagery While physically-based numerical weather prediction and river routing models cannot accurately depict complex natural non-linear processes, and thus have difficulty in simulating extreme events such as heavy rainfall and floods, data-driven approaches should be viewed as a strong alternative in operational hydrology. This is especially more pertinent at a time when the diversity of sensors in satellites and ground-based operational weather monitoring systems provide large volumes of data on a real-time basis.

• 한국지구과학회:학술대회논문집
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• 2005.02a
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• pp.182-187
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• 2005

The weather hazard by worldwide global warming rapidly increases year by year, and the damage becomes also enormous. especially, the damage by the random local severe rain in Korea is conspicuous. The forecast is difficult, because the random local severe rain arises by the complicated mechanism. However, local weather field in the Honam district where the weather hazard arises well is accurately grasped, and the systems that predict the local severe rain early are necessary. The purpose of this research is development of radar data assimilation observed at Jindo S-band radar. The accurate observational data assimilation system is required for meteorological numerical prediction of the region scale. Diagnostic analysis system LAPS(Local Analysis and Prediction System) developed by US FSL(Forecast Systems Laboratory) is adopted assimilation system of the Honam district forecasting system.

• Journal of Korean Society for Atmospheric Environment
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• v.20 no.4
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• pp.523-537
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• 2004

In order to raise the accuracy of meteorological data, several numerical experiments about the usefulness of data assimilation to prediction of air pollution was carried out. Used data for data assimilation are surface meteorological components observed by Automatical Weather System with high spatial density. The usage of surface data assimilation gives changes of temperature and wind fields and the change caused by the influence of land-use on meterological simulation is more sensitive at night than noon. The data quality in assimilation it also one of the important factors to predict the meteorological field precisely and through the static IOA (Index of Agreement), simulated meteorological components with selected limited surface data assimilation are agree well with observations.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.18 no.6
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• pp.158-169
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• 2004

A method for predicting the short-term dynamic line ratings in overhead transmission lines using real-time weather forecast data is proposed in this paper. Through some inspections for the 3-hour interval forecasting factors such as ambient temperature, wind speed grade and weather code given by KMA(Korea Meteorological Administration), correlation properties between forecast weather data and actual measured data are analyzed. To use these variable in determining the dynamic line ratings, they are changed into suitable numerical values. Furthermore adaptive neuro-fuzzy systems to improve reliabilities for wind speed and solar heat radiation ate designed It was verified that the forecast weather data can be used to predict the line rating with reliable. As a result it can be possible that the proposed predicting system can be effectively utilized by their anticipation a short-time in advance.

• Journal of Environmental Science International
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• v.25 no.7
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• pp.1017-1028
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• 2016

Wind information is one of the major inputs for the prediction of urban air flow using computational fluid dynamic (CFD) models. Therefore, the numerical characteristics of the wind data formed at their mother domains should be clarified to predict the urban air flow more precisely. In this study, the formation characteristics of the wind data in the Seoul region were used as the inlet wind information for a CFD based simulation and were analyzed using numerical weather prediction models for weather research and forecasting (WRF). Because air flow over the central part of the Korean peninsula is often controlled not only by synoptic scale westerly winds but also by the westerly sea breeze induced from the Yellow Sea, the westerly wind often dominates the entire Seoul region. Although simulations of wind speed and air temperature gave results that were slightly high and low, respectively, their temporal variation patterns agreed well with the observations. In the analysis of the vertical cross section, the variation of wind speed along the western boundary of Seoul is simpler in a large domain with the highest horizontal resolution as compared to a small domain with the same resolution. A strong convergence of the sea breeze due to precise topography leads to the simplification of the wind pattern. The same tendency was shown in the average vertical profiles of the wind speed. The difference in the simulated wind pattern of two different domains is greater during the night than in the daytime because of atmospheric stability and topographically induced mesoscale forcing.