• Journal of Korean Society for Atmospheric Environment
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• v.14 no.2
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• pp.81-94
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• 1998

Six complex terrain dispersion models recommended by the U. S. Environmental Protection Agency were investigated using a hypothetical case in which a plume approaches complex terrain. The six models considered were Valley, CTSCREEN, COMPLEX 1, SHORTZ, RTDM, and CTDMPLUS, the latter four being closely studied. Highest concentrations were predicted for 48 receptors and plume behaviors were compared for stable and unstable meteorological conditions. Under stable conditions, ground-level concentrations were determined by the height of the plume centerline above the terrain. The concentrations estimated by SHORTZ and COMPLEX I were higher than those estimated by CTSCREEN, with CTDMPLUS predicting the lowest concentrations. In particular, the height of the lift midpoint, as well as the co.nterline of the plume, are important in the model calculation of CTDMPLUS. Under unstable conditions, the vertical dispersion plays a key role in determining ground -level concentrations. For this case, concentrations predicted by CTDMPLUS were the 'highest, whereas those predicted by SHORTZ were the lowest. Concentration distributions predicted by CTDMPLUS are quite similar to typical Gaussian distributions even on complex terrain, except for a slight shift of the plume centerline due to the of(tract of the geostrophic wind. In addition,24-hour average concentrations were estimated for comparison with results from the Valley model. Among the four models studied closely, CTDMPLUS predicted the lowest 24-hour average concentrations, but the concentrations estimated by Valley were lower than those estimated by CTDMPLUS.

• Journal of Korean Society for Atmospheric Environment
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• v.13 no.1
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• pp.79-89
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• 1997

In order to reduce the outbreaks of short-term high concentrations and its impacts, we developed the models which predicted tomorrow's maximum hourly concentrations of $O_3$, TSP, SO$_2$, NO$_2$ and CO. Statistical methods like multi regressions were used because it must be operated easily under the present conditions. 47 independent variables were used, which included observed concentrations of air pollutants, observed and forcasted meteorological data in 1994 at Seoul and its surrounding areas. We subdivided Seoul into 4 areas coinciding with the present ozone warning areas. 4 kinds of seasonal models were developed due to the seasonal variations of observed concentrations, and 2 kinds of data models for the unavailable case of forecasted meteorological data. By comparing the $R^2$and root mean square error(hearafter 'RMSE') of each model, we confirmed that the models including forecasted data showed higher accuracy than ones using observed only. It was also shown that the higher the seasonal mean concentrations, the larger the RMSE. There was no distinct difference between the results of 4 areal models. In case of test run using 1995's data, the models predicted well the trends of daily variation of concentrations and the days when the possibility of outbreak of high concentarion was high. This study showed that it was reasonable to use those models as operational ones, because the $R^2$ and RMSE of models were smaller than those of operational/research models such as in South Coast Air Basin, CA, USA.

• Journal of the Korean earth science society
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• v.21 no.4
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• pp.369-379
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• 2000

The purposes of this study is to identify the misconceptions on atmospheric pressure and to investigate the effect of conceptual change of the cognitive conflict teaching models. The subjects are 184 students in girls' high school and divided into the controlled and test group. Before instruction on atmospheric pressure concept, their concept types were identified and their conceptual changes were compared after instruction by the traditional and the cognitive conflict teaching models. The results of this study are as follows; 1 ) The students' understanding level on the atmospheric pressure was low before instruction and they had some misconceptions. But the concept levels related to their everyday life experieces and memorized concept were high. 2) The cognitive conflict teaching model were more effective than the traditional teaching model in the formation of atmospheric pressure concept. 3) Though there were some differences among the test items, the cognitive conflict teaching model was identified to be more effective than the traditional teaching model in terms of the durability of atmospheric pressure concept.

• Journal of the Korean Operations Research and Management Science Society
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• v.23 no.4
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• pp.151-169
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• 1998

Since the atmospheric clearness index is one of the main factors for evaluating atmospheric circumstances, it is necessary to estimate its characteristics all over the cities in Korea. This study was focused on the evaluation of atmospheric condition for each 15 cities in terms of respectively or mutually analyzed clearness factor that was predicted on the assumed clear day with the model using factors such as average global insolation. cloud amount, and duration of sunshine measured for two years between 1996 and 1997. The new clearness index data will be extensively used by atmospheric circumstances analysts as well as by solar application designers or users.

• Journal of the Korean Solar Energy Society
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• v.22 no.1
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• pp.55-65
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• 2002

Since the atmospheric clearness index is one of the main factors for evaluating atmospheric circumstances, it is necessary to estimate its characteristics all over the cities in Korea. This study was focused on the evaluation of atmospheric condition for each 15 cities in terms of respectively or mutually analyzed clearness factor that was predicted on the assumed clear day with the model using factors such as average global insolation, cloud amount, and duration of sunshine measured for two years between 1999 and 2000. The new clearness index data will be extensively used by atmospheric circumstances analysts as well as by solar application designers or users.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.4
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• pp.328-337
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• 2021

Air pollution, which is a class 1 carcinogen, such as asbestos and benzene, is the cause of various diseases. The spread of ultra-air pollution is one of the important causes of the spread of the corona virus. This paper analyzes and predicts fine dust and ultra-air pollution from 2015 to 2019 based on weather data such as average temperature, precipitation, and average wind speed in Seoul and atmospheric environment data such as SO2, NO2, and O3. Linear regression, SVM, and ensemble models among machine learning models were compared and analyzed to predict fine dust by grasping and analyzing the status of air pollution and ultra-air pollution by season and month. In addition, important features(attributes) that affect the generation of fine dust and ultra-air pollution are identified. The highest ultra-air pollution was found in March, and the lowest ultra-air pollution was observed from August to September. In the case of meteorological data, the data that has the most influence on ultra-air pollution is average temperature, and in the case of meteorological data and atmospheric environment data, NO2 has the greatest effect on ultra-air pollution generation.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2008.10a
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• pp.486-488
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• 2008

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2008.10a
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• pp.177-178
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• 2008

• Atmosphere
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• v.25 no.2
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• pp.249-260
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• 2015

The changes in thermodynamic and dynamic aspects on near (2025~2049) and long-term (2075~2099) future climate changes between the historical run (1979~2005) and the Representative Concentration Pathway (RCP) 4.5 run with 20 coupled models which employed in the phase five of Coupled Model Inter-comparison Project (CMIP5) over East Asia (EA) and the Korean Peninsula are investigated as an extended study for Moon et al. (2014) study noted that the 20 models' multi-model ensemble (MME) and best five models' multi-model ensemble (B5MME) have a different increasing trend of precipitation during the boreal winter and summer, in spite of a similar increasing trend of surface air temperature, especially over the Korean Peninsula. Comparing the MME and B5MME, the dynamic factor (the convergence of mean moisture by anomalous wind) and the thermodynamic factor (the convergence of anomalous moisture by mean wind) in terms of moisture flux convergence are analyzed. As a result, the dynamic factor causes the lower increasing trend of precipitation in B5MME than the MME during the boreal winter and summer over EA. However, over the Korean Peninsula, the dynamic factor causes the lower increasing trend of precipitation in B5MME than the MME during the boreal winter, whereas the thermodynamic factor causes the higher increasing trend of precipitation in B5MME than the MME during the boreal summer. Therefore, it can be noted that the difference between MME and B5MME on the change in precipitation is affected by dynamic (thermodynamic) factor during the boreal winter (summer) over the Korean Peninsula.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.112-115
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• 2009

The research of climate change impact in hydrometeorology often relies on climate change information. In this paper, neural networks models such as support vector machine neural networks model (SVM-NNM) and multilayer perceptron neural networks model (MLP-NNM) are proposed statistical downscaling of the monthly precipitation. The input nodes of neural networks models consist of the atmospheric meteorology and the atmospheric pressure data for 2 grid points including $127.5^{\circ}E/35^{\circ}N$ and $125^{\circ}E/35^{\circ}N$, which produced the best results from the previous study. The output node of neural networks models consist of the monthly precipitation data for Seoul station. For the performances of the neural networks models, they are composed of training and test performances, respectively. From this research, we evaluate the impact of SVM-NNM and MLP-NNM performances for the downscaling of the monthly precipitation data. We should, therefore, construct the credible monthly precipitation data for Seoul station using statistical downscaling method. The proposed methods can be applied to future climate prediction/projection using the various climate change scenarios such as GCMs and RCMs.