중규모 기상 모델을 이용하여 안개와 같은 미세규모 국지현상을 정확히 재현하는 것은 매우 어려운 실정이다. 특히, 수치모델의 초기 입력 자료의 불확도는 수치모델의 예측 정확도에 결정적인 영향을 미치기 때문에 이를 보완하기 위한 자료동화 과정이 요구되어진다. 본 연구에서는 WRF (Weather Research and Forecasting) 모델을 이용하여 낙동강 지역에서 발생한 여름철 안개사례 재현실험을 대상으로 중규모 기상 모델의 한계를 검증하였다. 중규모 기상 모델에서 초기 및 경계장으로 사용되는 KLAPS (Korea Local Analysis and Prediction System)와 LDAPS (Local Data Assimilation and Prediction System) 분석장 자료를 이용하여 수치모델 모의 정확도 민감도 분석을 수행하였다. 또한 AWS (Automatic Weather System) 자료를 이용한 자료동화(Four-Dimensional Data Assimilation)에 의한 수치모델의 정확도 개선 정도를 평가하였다. 초기 및 경계장 민감도 분석 결과에서 LDAPS 자료를 입력 자료로 사용한 경우가 KLAPS 자 료 보다 기온과 이슬점온도, 상대습도에서 높은 정확도를 보였고, 풍속은 더 낮은 수준을 나타내었다. 특히, 상대습도에 서 LDAPS의 경우는 RMSE (Root Mean Square Error)가 15.9%, KLAPS는 35.6%의 수준을 보여 그 차이가 매우 크 게 나타났다. 또한 자료동화를 통하여 기온, 풍속, 상대습도의 RMSE가 각각 0.3 o C, 0.2ms−1 , 2.2% 수준으로 개선되었다.

Visibility and Automatic Weather System(AWS) data near Nakdong river were analyzed to characterize fog formation during 2012-2013. The temperature was lower than its nearby city – Daegu, whereas the humidity was higher than the city. 157 fog events were observed in total during the 2 year period. About 65% of the events occurred in fall (September, October, and November) followed by winter, summer, and spring. 94 early morning fog events of longer than 30 minutes occurred when south westerly wind speed was lower than 2 m/s. During these events, the water temperature was highest followed by soil surface and air temperatures due to the advection of cold and humid air from nearby hill. The observed fog events were categorized using a fog-type classification algorithm, which used surface cooling, wind speed threshold, rate of change of air temperature and dew point temperature. As a result, frontal fog observed 6 times, radiation 4, advection 13, and evaporation 66. The evaporation fog in the study area lasted longer than other reports. It is due to the interactions of cold air drainage flow and warm surface in addition to the evaporation from the water surface. In particular, more than 60% of the evaporation fog events were accompanied with cold air flows over the wet and warm surface. Therefore, it is needed for the identification of the inland fog mechanism to evaluate the impacts of nearby topography and land cover as well as water body.

This study introduces a novel approach to the differentiation of two phenomena, Asian Dust and haze, which are extremely difficult to distinguish based solely on comparisons of PM10 concentration, through use of the Optical Particle Counter (OPC), which simultaneously generates PM10, PM2.5 and PM1.0 concentration. In the case of Asian Dust, PM10 concentration rose to the exclusion of PM2.5 and PM1.0 concentration. The relative ratios of PM2.5 and PM1.0 concentration versus PM10 concentration were below 40%, which is consistent with the conclusion that Asian Dust, as a prime example of the coarse-particle phenomenon, only impacts PM10 concentration, not PM2.5 and PM1.0 concentration. In contrast, PM10, PM2.5 and PM1.0 concentration simultaneously increased with haze. The relative ratios of PM2.5 and PM1.0 concentration versus PM10 concentration were generally above 70%. In this case, PM1.0 concentration varies because a haze event consists of secondary aerosol in the fine-mode, and the relative ratios of PM10 and PM2.5 concentration remain intact as these values already subsume PM1.0 concentration. The sequential shift of the peaks in PM10, PM2.5 and PM1.0 concentrations also serve to individually track the transport of coarse-mode versus fine-mode aerosols. The distinction in the relative ratios of PM2.5 and PM1.0 concentration versus PM10 concentration in an Asian Dust versus a haze event, when collected on a national or global scale using OPC monitoring networks, provides realistic information on outbreaks and transport of Asian Dust and haze.