• Wind and Structures
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• 제13권6호
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• pp.519-528
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• 2010

A meteorological model, RAMS, and a commercial computational fluid dynamics (CFD) model, FLUENT are combined as a one-way off-line nested modeling system, namely, RAMS/FLUENT system. The system is experimentally applied in the wind simulation over a complex terrain, with which numerical simulations of wind field over Foyeding weather station located in the northwest mountainous area of Beijing metropolis are performed. The results show that the method of combining a meteorological model and a CFD model as a modeling system is reasonable. In RAMS/FLUENT system, more realistic boundary conditions are provided for FLUENT rather than idealized vertical wind profiles, and the finite volume method (FVM) of FLUENT ensures the capability of the modeling system on describing complex terrain in the simulation. Thus, RAMS/FLUENT can provide fine-scale realistic wind data over complex terrains.

• 한국방재학회지
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• 제2권4호
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• pp.40-47
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• 2002

Since it is important to understand the bio-climatic change in Seoul for ecological city planning in the future, this paper gives an overview on bio-climate analysis of urban environments at Seoul. We analyzed its characteristics in recent years using the observations of 24 of Automatic Weather Station (AWS) by Korea Meteorological Administration (KMA). In urbanization, Seoul metropolitan area is densely populated and is concentrated with high buildings. This urban activity changes land covering, which modifies the local circulation of radiation, heat and moisture, precipitation and creating a specific climate. Urban climate is evidently manifested in the phenomena of the increase of the air temperature, called urban heat Island and in addition urban sqall line of heavy rain. Since a city has its different land cover and street structure, these form their own climate character such as climate comfort zone. The thermal fold in urban area such as the heat island is produced by the change of land use and the air pollution that provide the bio-climate change of urban eco-system. The urban wind flow is the most important climate element on dispersion of air pollution, thermal effects and heavy shower. Numerical modeling indicates that the bio-climatic transition of wind wake in urban area and the dispersion of the air pollution by the simulations of the wind variation depend on the urban land cover change. The winds are separately simulated on small and micro-scale at Seoul with two kinds of kinetic model, Witrak and MUKLIMO.

• KIEAE Journal
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• 제16권1호
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• pp.11-19
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• 2016

Purpose: PMV, PET, and similar thermal comfort indices and microclimate modeling have recently become actively used to evaluate thermal comfort. This study will look at pedestrian roads with diverse spatial characteristics on university campus using the ENVI-met model as the base for onsite measurement. Method: The PET was used as the thermal comfort index. The first microclimate measures were collected on September 20, 2014, and the second microclimate measures were collected on June 1, 2015. The ENVI-met model was used at the same time. Result: As a results, Onsite measurement results differed depending on the PET spatial characteristics. The location associated with the most discomfort had a PET of $47.8^{\circ}C$. The spatial characteristics of this place included a with no shade. The most comfortable location had shade, and the PET was $24.6^{\circ}C$. When the ENVI-met model and onsite measurements were compared, similar patterns were found, but with a few differences at specific points; this was due to the limitation of using input materials such as trees, buildings, and covering materials with the ENVI-met model. This factor must be thoroughly considered when analyzing modeling results.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2021년도 학술발표회
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• pp.183-183
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• 2021

Deep learning methods and their application have become an essential part of prediction and modeling in water-related research areas, including hydrological processes, climate change, etc. It is known that application of deep learning leads to high availability of data sources in hydrology, which shows its usefulness in analysis of precipitation, runoff, groundwater level, evapotranspiration, and so on. However, there is still a limitation on microclimate analysis and prediction with deep learning methods because of deficiency of gauge-based data and shortcomings of existing technologies. In this study, a real-time rainfall prediction model was developed from a sky image data set with convolutional neural networks (CNNs). These daily image data were collected at Chung-Ang University and Korea University. For high accuracy of the proposed model, it considers data classification, image processing, ratio adjustment of no-rain data. Rainfall prediction data were compared with minutely rainfall data at rain gauge stations close to image sensors. It indicates that the proposed model could offer an interpolation of current rainfall observation system and have large potential to fill an observation gap. Information from small-scaled areas leads to advance in accurate weather forecasting and hydrological modeling at a micro scale.