• Journal of Environmental Science International
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• v.33 no.6
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• pp.435-442
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• 2024

We investigated the accuracy of surface air temperature prediction according to the selection of land-use data and initial meteorological data using the Weather Research and Forecasting model-v4.2.1. A numerical experiment was conducted at the Daegu Dyeing Industrial Complex. We initially used meteorological input data from GFS (Global forecast system)and GDAPS (Global data assimilation and prediction system). High-resolution input data were generated and used as input data for the weather model using the land cover data of the Ministry of Environment and the digital elevation model of the Ministry of Land, Infrastructure, and Transport. The experiment was conducted by classifying the terrestrial and topographic data (land cover data) and meteorological data applied to the model. For simulations using high-resolution terrestrial data(10 m), global data assimilation, and prediction system data(CASE 3), the calculated surface temperature was much closer to the automatic weather station observations than for simulations using low-resolution terrestrial data(900 m) and GFS(CASE 1).

• 한국태양에너지학회:학술대회논문집
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• 2011.11a
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• pp.342-347
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• 2011

Solar energy is attenuated by absorbing gases (ozone, aerosol, water vapour and mixed gas) and cloud in the atmosphere. And these are measured with solar instruments (pyranometer, phyheliometer). However, solar energy is insufficient to represent detailed energy distribution, because the distributions of instruments are limited on spatial. If input data of solar radiation model is accurate, the solar energy reaches at the surface can be calculated accurately. Recently a variety of satellite measurements are available to TERA/AQUA (MODIS), AURA (OMI) and geostationary satellites (GMS-5, GOES-9, MTSAT-1R, MTSAT-2 and COMS). Input data of solar radiation model can be used aerosols and surface albedo of MODIS, total ozone amount of OMI and cloud fraction of meteorological geostationary satellite. The solar energy reaches to the surface is calculated hourly by solar radiation model and those are accumulated monthly and annual. And these results are verified the spatial distribution and validated with ground observations.

• Journal of Environmental Impact Assessment
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• v.20 no.3
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• pp.337-348
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• 2011

Urban environmental problem became one of major issues during its urbanization processes. Environmental impacts are assessed during recent urban planning and development. Though the environmental impact assessment considers meteorological impact as a minor component, changes in wind environment during development can largely affect the distribution pattern of air temperature, humidity, and pollutants. Impact assessment of local wind is, therefore, a major element for impact assessment prior to any other meteorological impact assessment. Computational Fluid Dynamics (CFD) models are utilized in various fields such as in wind field assessment during a construction of a new building and in post analysis of a fire event over a mountain. CFD models require specially formatted input data and produce specific output files, which can be analyzed using special programs. CFD's huge requirement in computing power is another hurdle in practical use. In this study, a CFD model and related software processors were automated and integrated as a microscale wind environmental impact assessment system. A supercomputer system was used to reduce the running hours of the model. Input data processor ingests development plans in CAD or GIS formatted files and produces input data files for the CFD model. Output data processor produces various analytical graphs upon user requests. The system was used in assessing the impacts of a new building near an observatory on wind fields and showed the changes by the construction visually and quantitatively. The microscale wind assessment system will evolve, of course, incorporating new improvement of the models and processors. Nevertheless the framework suggested here can be utilized as a basic system for the assessment.

• Journal of the Korean Solar Energy Society
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• v.28 no.2
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• pp.10-18
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• 2008

This study aims to figure out typical meteorological data according to Korean time in order to evaluate building energy performance. Various methods of calculating typical meteorological data were compared and examined to improve accuracy and reliability of this study. This study analyzed and examined such methodologies as typical meteorological data for HASP/ACLD-8001, UK CIBSE TRY developed by CIBSE and prEN ISO 15927-4, (=ISO TRY) an international standard to evaluate annual energy demand of cooling and heating devices. In addition, actual data of KMA corresponding to Seoul in $1985{\sim}2005$ were statistically analyzed according to calculation methodology. The calculated typical meteorological data were compared te actual data using MBE, RMSE and t-Statistic. As a result, According to the comparison between average annual for HASP/ACLD-8001 and ISO TRY standard year, the average annual for HASP/ACLD-8001 is closer to actual measurement, showing that the use of typical meteorological data for HASP/ACLD-8001 is preferred. However, since the input format requested by current simulation is the same international standard as TRY. Therefore, it is necessary to improve accuracy of TRY calculation methodology and accordingly figure out Korean typical meteorological data based on average year.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.7
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• pp.1114-1124
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• 2015

In this paper, precipitation / non-precipitation pattern classification of meteorological radar data is conducted by using neuro-fuzzy algorithm. Structure expression of meteorological radar data information is analyzed in order to effectively classify precipitation and non-precipitation. Also diverse input variables for designing pattern classifier could be considered by exploiting the quantitative as well as qualitative characteristic of meteorological radar data information and then each characteristic of input variables is analyzed. Preferred pattern classifier can be designed by essential input variables that give a decisive effect on output performance as well as model architecture. As the proposed model architecture, neuro-fuzzy algorithm is designed by using FCM-based radial basis function neural network(RBFNN). Two parts of classifiers such as instance classifier part and echo classifier part are designed and carried out serially in the entire system architecture. In the instance classifier part, the pattern classifier identifies between precipitation and non-precipitation data. In the echo classifier part, because precipitation data information identified by the instance classifier could partially involve non-precipitation data information, echo classifier is considered to classify between them. The performance of the proposed classifier is evaluated and analyzed when compared with existing QC method.

• 한국방재학회:학술대회논문집
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• 2007.02a
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• pp.315-318
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• 2007

In order to reduce the amount of damage from natural disasters, we needs prevention meteorological database classified into the cause of disaster, damage elements etc. For this, we have analyzed four data, such as Statistical yearbook of calamities issued by the National Emergency Management Agency and Annual Climatological Report issued by the Korea Meteorological Administration and Recently 10 years for natural disaster damage and Statistics Yearbook from the Ministry of Government Administration and Human affairs. Through the analysis of disaster data, we have selected input variables, such as causes and elements, occurrence frequencies, vulnerable areas of natural disaster, etc. In order to reduce damage from natural disaster, the prevention activities and forecasting based on meteorological parameters and damage datas are required. In addition, it is necessary to process meteorological information for disaster prevention activities. Through these procedure, we have established the foundation of database about natural disasters. This database will be used to assess the natural disasters and build risk model and natural disasters mitigation plan.

• Journal of Environmental Impact Assessment
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• v.22 no.6
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• pp.609-626
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• 2013

Air quality models have been widely used to study and simulate many air quality issues. In the simulation, it is important to raise the accuracy of meteorological predicted data because the results of air quality modeling is deeply connected with meteorological fields. Therefore in this study, we analyzed the effects of meteorological fields on the air quality simulation. This study was designed to evaluate MM5 predictions by using different initial condition data and different observations utilized in the data assimilation. Among meteorological scenarios according to these input data, the results of meteorological simulation using National Centers for Environmental Prediction (Final) Operational Global Analysis data were in closer agreement with the observations and resulted in better prediction on ozone concentration. And in Seoul, observations from Regional Meteorological Office for data assimilations of MM5 were suitable to predict ozone concentration. In other areas, data assimilation using both observations from Regional Meteorological Office and Automatical Weather System provided valid method to simulate the trends of meteorological fields and ozone concentrations. However, it is necessary to vertify the accuracy of AWS data in advance because slightly overestimated wind speed used in the data assimilation with AWS data could result in underestimation of high ozone concentrations.

• Journal of Korean Society for Atmospheric Environment
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• v.18 no.6
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• pp.437-452
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• 2002

The Metropolitan Tracer Experiment (METREX) was performed over the Washington, D.C. area using two inert, non-deposition perfluorocarbon gases for over 1 year period (November 1983∼December 1984). Two perfluorocarbon gas tracers (PDCH, PMCH) were released simultaneously at intervals of every 36 hours for 6 hours, regardless of the meteorological conditions in metropolitan area. Samples were collected continuously for 8 hours at a central downtown and two adjacent suburban locations. Monthly air samples were collected at 93 sites across the whole region (at urban, suburban, and rural locations). The purpose of this study is to simulate INPUFF and ISCST model using METREX data, and to compare calculated and observed concentrations. In the case of INPUFF simulation, two meteorological input data were used. One is result data from wind field model which was calculated by diagnostic wind model (DWM), the other is meteorological data observed at single station. Here, three kinds of model calculation were performed during April and July 1984; they include (1) INPUFF model using DWM data (2) INPUFF model using single meteorological data (3) ISCST model. The monthly average concentration data were used for statistic analysis and to draw their horizontal distribution patterns. Eight-hour-averaged concentration was used to describe movement of puff during the episode period. The results showed that the concentrations calculated by puff model (INPUFF) were better than plume model (ISCST). In the case of puff model (INPUFF), a model run using wind field data produced better results than that derived by single meteorological data.

• 한국신재생에너지학회:학술대회논문집
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• 2011.11a
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• pp.56.1-56.1
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• 2011

The solar and meteorological resources map is calculated using by one-layer solar radiation model (GWNU model), satellites data and numerical model output on the Korean peninsula. The Meteorological input data to perform the GWNU model are retrieved aerosol optical thickness from MODIS (TERA/AQUA), total ozone amount from OMI (AURA), cloud fraction from geostationary satellites (MTSAT-1R) and temperature, pressure and total precipitable water from output of RDAPS (Regional Data Assimilation and Prediction System) and KLAPS (Korea Local Analysis and Prediction System) model operated by KMA (Korea Meteorological Administration). The model is carried out every hour using by the meteorological data (total ozone amount, aerosol optical thickness, temperature, pressure and cloud amount) and the basic data (surface albedo and DEM). And the result is analyzed the distribution in time and space and validated with 22 meteorological solar observations. The solar resources map is used to the solar energy-related industries and assessment of the potential resources for solar plant. The National Institute of Meteorological Research in KMA released $4km{\times}4km$ solar map in 2008 and updated solar map with $1km{\times}1km$ resolution and topological effect in 2010. The meteorological resources map homepage (http://www.greenmap.go.kr) is provided the various information and result for the meteorological-solar resources map.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.491-493
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• 2022

Flood damage due to torrential rains and typhoons is occurring in many parts of the world. In this paper, we propose a water level prediction model using water level, precipitation, and humidity data, which are key parameters for flood prediction, as input data. Based on the LSTM and GRU models, which have already proven time-series data prediction performance in many research fields, different input datasets were constructed using the ASOS(Automated Synoptic Observing System) data and AWS(Automatic Weather System) data provided by the Korea Meteorological Administration, and performance comparison experiments were conducted. As a result, the best results were obtained when using ASOS data. Through this paper, a performance comparison experiment was conducted according to the input data, and as a future study, it is thought that it can be used as an initial study to develop a system that can make an evacuation decision in advance in connection with the flood risk determination model.