• Environmental Engineering Research
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• 제14권2호
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• pp.88-94
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• 2009

In this study an attempt has been made to predict the annual foggy days over Gyeongsangbuk-do of Korea, using the regional mesoscale model (MM5). The annual meteorological conditions are simulated, and the annual and seasonal foggy days are predicted from the simulated results based on the seasonal and spatial information of the observed meteorological characteristics for fog occurrence such as wind speed, relative humidity, and temperature. Most of observed inland fog over Gyeongsangbuk-do occurs in autumn under the meteorological conditions such as a cairn, a high temperature range (above $10^{\circ}C$), and a high relative humidity (above 85%). The predicted results show the various foggy days, about 10${\sim}$60 days, depending on the season and the site locations. The predicted annual foggy days at inland sites are about 30${\sim}$60 days, but at coastal sites, about 10${\sim}$20 days. Also, a higher frequency of fog occurrence at inland sites is shown in autumn (about 60% of the annual foggy days). Otherwise, a higher frequency of fog occurrence at coastal sites is shown in summer (about 60% of the annual foggy days), unlike the inland. These annual foggy days and their seasonal variations agree reasonably well with the observed values. It can be concluded that it is possible to predict the occurrence of annual or seasonal foggy days by MM5.

• 대기
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• 제16권2호
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• pp.67-83
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• 2006

The statistical analysis for the springtime windstorm in Korea shows that Yeongdong region has the highest occurrence frequency during recent 10 years. The objective of this study is to find possible mechanisms for the downslope windstorm formation in the Yeongdong region by using a mesoscale numerical model, WRF. Dynamical process, wave breaking (hereafter WB), is qualitatively investigated as the candidate mechanism for a windstorm event occurred in 5 April, 2005. WB is developed in upper troposphere downstream, since stable air is lifted by the Taebaek mountain. This process can cause and maintain the severe downslope windstorm by drawing the upper flow down to the surface. And the intensified downslope wind leads the hydraulic jump (hereafter HJ) in downstream region. Froude numbers at Chuncheon (upslope side), Seorak Mountain (crest), Yangyang (lee side), and the East Sea (distant downstream position) are estimated by about 0.4, 1.0, 1.6, and 0.6, respectively. This result implies that the accelerated and supercritical (Fr>1) flow adjusts to the ambient subcritical (Fr<1) conditions in the turbulent HJ. In addition, we find the formation of upstream inversion near top level of the mountain cause the intensification of HJ. Experiments to examine the orographic effect on the mechanisms suggest that the magnitudes of WB and HJ are larger in the experiment of higher topography, but there is no significant difference of windstorm magnitude among the experiments. Another important result from these sensitivity experiments is that the intensity of downslope windstorm strongly depends on the magnitude of upper (2~4 km) wind in upstream side.

• 대한토목학회논문집
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• 제28권5B호
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• pp.485-493
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• 2008

한반도 영역을 대상으로 RDAPS모형의 수치예보자료, AWS의 관측강수, 상층기상관측(upper-air sounding)의 관측자료를 이용하여 권역별 강수발생확률을 예측할 수 있는 인공신경망 모형을 제시하였다. 사용된 자료의 기간은 2001년 7, 8월과 2002년 6월로 홍수기를 대상으로 하였다. 500/750/1000 hPa에서의 지위고도, 500-1000 hPa에서의 층후(thickness), 500 hPa에서의 X와 Y방향 바람성분, 750 hPa에서의 X와 Y방향 바람성분, 표면풍속, 500/750 hPa/표면에서의 온도, 평균해면기압, 3시간 누적 강수, AWS관측소에서 관측된 RDAPS모형 실행전의 6시간과 12시간동안의 누적강수, 가강수량, 상대습도등을 신경망의 예측인자로 사용하였다. 신경망의 구조는 3층 MLP(Multi Layer Perceptron)로 구성하여 역전파알고리즘(Back-propagation)을 학습방법으로 사용하였다. 신경망예측결과 한반도전체에 대한 예측성과의 개선은 H가 6.8%상승하였고, 특히 TS와 POD는 각각 99.2%와 148.1% 상승함으로서 강수예측에 대한 신경망모형이 효과적인 도구가 될 수 있음을 확인하였다. KSS 역시 92.8% 개선됨으로서 RDAPS 예측에 비하여 뚜렷이 개선된 결과를 보여주고 있다.

• 한국대기환경학회지
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• 제22권5호
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• pp.661-678
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• 2006

Products developed in this research is a software which can transfer the type of shape(.shp) into the type of ascii using the land cover data and the topography data in the metropolitan area of Seoul. In addition, it can calculate the $CO_2$ flux according to distribution of plants within the land cover data. The $CO_2$ flux is calculated by the experimental equation which is compose of the meteorological parameters such as the solar radiation and the air temperature. The net flux was shown in about $-19ton/km^2$ by removing $CO_2$ through the photosynthesis during daytime, and in 2 ton/km2 by producing it through the respiration during nighttime on 10 August 2004, the maximum day of air temperature during the period of 3yr(2001 to 2004), in the metropolitan area of Seoul. Spatial distribution of the air temperature and the wind field is simulated by substituting the middle classification of the land cover map data, upgraded by the Korean Ministry of Environment(KME), for the land-use data of the United States Geological Survey(USGS) within the Meteorological Mesoscale Model Version 5(MM5) on 10 August 2006 in the metropolitan area of Seoul. Difference of the air temperature between both data was shown in the maximum range of $-2^{\circ}C\;to\;2.9^{\circ}C$, and the air temperature due to the land use data of KME was higher than that of USGS in average $0.4^{\circ}C$. Also, those of wind vectors were meanly lower than that of USGS in daytime and nighttime. Furthermore, the hourly time series of Volatile Organic Components(VOCs) is calculated by using the Biosphere Emission and Interaction System Version 2(BEIS2) including the new land cover data and the meteorological parameters such as the air temperature and so]ar insolation. It is possible to calculate the concentration of ozone due to the biogenic emission of VOCs.