• Journal of the Korea Institute of Military Science and Technology
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• v.11 no.3
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• pp.169-175
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• 2008

A fine carbon fibers dispersion model is implemented to calculate the scattering range and ground level concentration of carbon fibers emitted at certain altitudes of atmospheric boundary layer. This carbon fibers dispersion model was composed by coupling a commonly used atmospheric dispersion model and an atmospheric boundary layer model. The atmospheric boundary layer model, applying the Monin-Obukov Similarity Rule obtained from measurement input data at ground level, was used to create the atmospheric boundary layer structure. In the atmospheric dispersion model, the Lagrangian Particle Model and the Markov Process were applied to calculate the trajectory of scattered carbon fibers relative to gravity and aerodynamic force, as well as carbon fibers specification.

• Journal of Korean Society for Atmospheric Environment
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• v.20 no.1
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• pp.47-58
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• 2004

The $textsc{k}$-$\varepsilon$ algebraic stress model (KEASM) was applied to atmospheric dispersion simulation using the Lagrangian particle dispersion model and was compared with the most popular turbulence closure model in the field of atmospheric simulation, the Mellor-Yamada (MY) model. KEASM has been rarely applied to atmospheric simulation, but it includes the pressure redistribution effect of buoyancy due to heat and momentum fluxes. On the other hand, such effect is excluded from MY model. In the simulation study, the difference in the two turbulence models was reflected to both the turbulent velocity and the Lagrangian time scale. There was little difference in the vertical diffusion coefficient $\sigma$$_{z}$. However, the horizontal diffusion coefficient or calculated by KEASM was larger than that by MY model, coincided with the Pasquill-Gifford (PG) chart. The applicability of KEASM to atmospheric simulations was demonstrated by the simulations.s.

• Atmosphere
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• v.14 no.2
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• pp.11-22
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• 2004

A technique on atmospheric correction algorithm to the multi-band reflectance of Landsat TM imagery has been developed using an atmospheric radiation transfer model for eliminating the atmospheric and surface diffusion effects. Despite the fact that the technique of satellite image processing has been continually developed, there is still a difference between the radiance value registered by satellite borne detector and the true value registered at the ground surface. Such difference is caused by atmospheric attenuations of radiance energy transfer process which is mostly associated with the presence of aerosol particles in atmospheric suspension and surface irradiance characteristics. The atmospheric reflectance depend on atmospheric optical depth and aerosol concentration, and closely related to geographical and environmental surface characteristics. Therefore, when the effects of surface diffuse and aerosol reflectance are eliminated from the satellite image, it is actually corrected from atmospheric optical conditions. The objective of this study is to develop an algorithm for making atmospheric correction in satellite image. The study is processed with the correction function which is developed for eliminating the effects of atmospheric path scattering and surface adjacent pixel spectral reflectance within an atmospheric radiation model. The diffused radiance of adjacent pixel in the image obtained from accounting the average reflectance in the $7{\times}7$ neighbourhood pixels and using the land cover classification. The atmospheric correction functions are provided by a radiation transfer model of LOWTRAN 7 based on the actual atmospheric soundings over the Korean atmospheric complexity. The model produce the upward radiances of satellite spectral image for a given surface reflectance and aerosol optical thickness.

• Journal of Environmental Science International
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• v.14 no.9
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• pp.851-860
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• 2005

In order to how well predict ISCST3(lndustrial Source Complex Short Term version 3) model dispersion of air pollutant at point source, sensitivity was analysed necessary parameters change. ISCST3 model is Gaussian plume model. Model calculation was performed with change of the wind speed, atmospheric stability and mixing height while the wind direction and ambient temperature are fixed. Fixed factors are wind direction as the south wind(l80") and temperature as 298 K(25 "C). Model's sensitivity is analyzed as wind speed, atmospheric stability and mixing height change. Data of stack are input by inner diameter of 2m, stack height of 30m, emission temperature of 40 "C, outlet velocity of 10m/s. On the whole, main factor which affects in atmospheric dispersion is wind speed and atmospheric stability at ISCST3 model. However it is effect of atmospheric stability rather than effect of distance downwind. Factor that exert big influence in determining point of maximum concentration is wind speed. Meanwhile, influence of mixing height is a little or almost not.

• Journal of Environmental Science International
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• v.8 no.4
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• pp.485-490
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• 1999

We will calculate concentration of air pollutants using ISCST3, FDM and AERMOD of models recommended in U. S. EPA which are able to predict concentration of short term for point source, complex like industrial complex, power plant and burn-up institution. Before executing model, as analyzing computational result of many cases according to selecting of input data, we will increasing predictable ability of model in limit range of model. Especially, we analyzed three cases-case of considering various emission rate according to time scale and not, case considering effect of atmospheric pollution materials removed by physical process. In our study, after comparing and analyzing results of three model, we choose the atmospheric dispersion model reflected well the characteristic of the area. And we will investigate how large the complex pollutant sources such as industrial complex contribute to atmospheric environment and air quality of the surrounding the area as predicting and estimating chosen model.

• Asian Journal of Atmospheric Environment
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• v.12 no.3
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• pp.215-231
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• 2018

In order to understand the variation of air quality and the concentration of atmospheric particulates in Chengdu Second Ring Road renovation project, this paper starts to investigate the surrounding residents' opinions on the influenced environment and their daily lives via questionnaires. Then the study numerically simulates the change rule of atmospheric particulates in terms of time and space by using the Gaussian dispersion-deposition model and the compartment model. The optimized scientific scheme is selected by the improved fuzzy analytical hierarchy process(FAHP) to help decision making for the future urban reconstructions. Finally, the reduced emissions of atmospheric particulates are measured when the improvement scheme is provided. According to the study, it can be concluded that the concentration of atmospheric particulates increases rapidly in central Chengdu city during the renovation project, which results in worsening air quality in Chengdu during March 2012 to March 2013. Taking related measures on energy saving and emission reduction can effectively reduce the concentration of atmospheric particulates and promote economic, environmental and social coordination.

• Journal of Korean Society for Atmospheric Environment
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• v.29 no.6
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• pp.838-848
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• 2013

The Community Multiscale Air Quality (CMAQ) model is capable of providing high quality atmospheric chemistry profiles through the utilization of high-resolution meteorology and emissions data. However, it cannot simulate air quality accurately if input data are not appropriate and reliable. One of the most important inputs required by CMAQ is the air pollutants emissions, which determines air pollutants concentrations during the simulation. For the CMAQ simulation of Korean peninsula, we, in general, use the Korean National Emission Inventory data which are estimated by Clean Air Policy Support System (CAPSS). However, since they are not provided by model-ready emission data, we should convert CAPSS emissions into model-ready data. The SMOKE is the emission model we used in this study to generate CMAQ-ready emissions. Because processing the emissions data is very monotonous and tedious work, we have developed CAPSS2SMOKE program to convert CAPSS emissions into SMOKE-ready data with ease and effective. CAPSS2SMOKE program consists of many codes and routines such as source classification code, $PM_{10}$ to $PM_{2.5}$ ratio code, map projection conversion routine, spatial allocation routine, and so on. To verify the CAPSS2SMOKE program, we have run SMOKE using the CAPSS 2009 emissions and found that the SMOKE results inherits CAPSS emissions quite well.

• International Journal of Aeronautical and Space Sciences
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• v.16 no.1
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• pp.64-76
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• 2015

This paper suggests applying pseudospectral model predictive method for exo-atmospheric guidance. The method is a fusion of pseudospectral law and model predictive control, in which a two point boundary value problem is formulated using model predictive approach and solved by applying pseudospectral law. In this work, the method is applied to exo-atmospheric guidance with specific target requirement. The existing exo-atmospheric guidance methods suffice general requirements for guidance, but cannot ensure specific target constraints; whereas, the presented method is able to do so. The proposed guidance law is assessed through simulation of perturbed cases, and the tests suggest that the method is able to operate semi-autonomously under control and thrust vector perturbations.

• Journal of Korean Society for Atmospheric Environment
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• v.15 no.6
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• pp.767-777
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• 1999

In the coastal region air flow changes due to the abrupt change of surface temperature between land and sea. So a numerical simulation for atmospheric flow fields must be considered the correct fields of sea surface temperature(SST). In this study, we used variables such as latent heat flux, sensible heat flux, short and long wave radiation of ocean and atmosphere which exchanged across the sea surface between atmosphere and ocean model. We found that this consideration simulated the more precise SST fields by comparing with those of the observated results. Simulated horizontal SST differences in season were 2.5~4$^{\circ}C$. Therefore we simulated the more precise atmospheric flow fields and the movement and dispersion of the pollutants with the Lagrangian particle dispersion model. In the daytime dispersion pattern of the pollutants emitted from ship sources moved toward inland, in the night time moved toward sea by land/sea breeze criculation. But air pollutants dispersion can be affected by inland topography, especially Yangsan and coastal area because of nocturnal wind speed decrease.

• Environmental Sciences Bulletin of The Korean Environmental Sciences Society
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• v.10 no.S_1
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• pp.23-28
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• 2001

A new numerical weather prediction and dispersion model, the Operational Multi-scale Environment model with Grid Adaptivity(OMEGA) including an embedded Atmospheric Dispersion Model(ADM), is introduced as a next generation atmospheric simulation system for real-time hazard predictions, such as severe weather or the transport of hazardous release. OMEGA is based on an unstructured grid that can facilitate a continuously varying horizontal grid resolution ranging from 100 km down to 1 km and a vertical resolution from 20 -30 meters in the boundary layer to 1 km in the free atmosphere. OMEGA is also naturally scale spanning and time. In particular, the unstructured grid cells in the horizontal dimension can increase the local resolution to better capture the topography or important physical features of the atmospheric circulation and cloud dynamics. This means the OMEGA can readily adapt its grid to a stationary surface, terrain features, or dynamic features in an evolving weather pattern. While adaptive numerical techniques have yet to be extensively applied in atmospheric models, the OMEGA model is the first to exploit the adaptive nature of an unstructured gridding technique for atmospheric simulation and real-time hazard prediction. The purpose of this paper is to provide a detailed description of the OMEGA model, the OMEGA system, and a detailed comparison of OMEGA forecast results with observed data.