풍력자원평가를 수행할 때 풍력자원의 특성이 동질한 바람권역을 파악하여 측정지점을 선택하여야 한다. 본 연구에서는 풍력자원의 공간적인 동질성을 파악하기 위하여 시계열 바람벡터의 유사성, 시계열 풍속의 피어슨 상관계수, 시계열 풍향의 코사인, 시 계열 풍속의 일치도 지수, 24시간 자기상관함수, 풍력자원 요인의 주성분을 유사도 척도로 이용하여 바람권역을 군집분석하였다. 주성 분분석을 수행하여 와이블 풍속분포의 척도계수 및 형상계수가 제 1 주성분으로, 지형고도와 24시간 자기상관함수가 제 2 주성분인 것으로 파악되었다. 단순지형인 제주도와 복잡한 산지지형인 포항지역에 여러 가지 유사도 척도를 적용하여 바람권역을 분류하였으며, 다연상관계수와 상자그림으로 군집내 풍력자원 요인이 유의미한 통계적 차이가 존재하는가를 평가하였다. 결론적으로 시계열 바람벡 터의 유사도와 풍력자원 요인의 주성분 거리가 가장 효과적인 군집분석의 척도임을 확인하였다.

한반도 풍력자원지도의 시계열 풍속벡터의 유사성을 유클리디안 거리로 정의하여 군집분석에 의해 바람권역을 분류하는 방법을 포항지역에 적용하였다. 풍력자원지도는 포항지역 기상탑 측정자료와의 비교를 통해 정확도를 검증하였다. 이때 풍력자원지도 와 기상탑 측정자료의 시간범위가 서로 상이하여, 재해석자료와의 측정-상관-예측을 이용하여 동기간으로 변환한 후 비교검증 하였다. 포항지역에 대한 바람권역 분류 결과, 계절별로 바람권역의 변화가 매우 큰 것으로 나타났으며, 이로부터 우리나라의 바람권역은 계절특성을 고려해야함을 확인하였다. 풍력자원지도의 공간해상도에 따른 바람권역 분류에서는 상대적으로 지형고도가 낮지만 바 람의 특성에 민감한 지형이 존재하며, 이러한 지형요소의 수치해석이 정확하게 반영되어야 바람권역 분류의 실질적인 설명력이 향상됨을 확인할 수 있었다. 본 연구를 통해 구축된 국지적 바람권역 분류방법은 풍력발전소 설계, 대기환경영향평가, 풍환경평가 등에 유용하게 활용될 것으로 기대된다.

We classified wind sectors according to the wind features in South Korea. In order to get the information of wind speed and wind direction, we used and improved on the atmospheric numerical model. We made use of detailed topographical data such as terrain height data of an interval of 3 seconds and landuse data produced at ministry of environment, Republic of Korea. The result of simulated wind field was improved. We carried out the cluster analysis to classify the wind sectors using the K-means clustering. South Korea was classified as 8 wind sectors to the annual wind field.

The air quality data is important for understanding and analyzing a surrounding influence. In that light, it is positively necessary for a propriety assessment to determine a location of the air quality monitoring sites. In this study, the climate analysis about temperature and wind, using the meteorological data in the Pohang, is conducted to do that. In the next stage, we distinguished the wind by east-west or north-south component, which has less correlation than temperature, analyzed and divided the wind sector. As the result, the wind circumstance of the Pohang is divided into major 5 wind sector; that is the urban area, the northeast coastal area, the east ocean and the west mountainous area. We think that an analysis on detailed wind sector by utilizing the numerical simulation is needed.

We have investigated coarse wind sectors in Busan metropolitan area and simulated detailed wind field using local atmospheric circulation model, RAMS in preceding studies (Part I, Part II). In this study, we divided and analyzed local wind sector in Busan according to the preceding results. We found that Busan metropolitan area is divided into 2 or 3 local wind sector in each coarse wind sector. The 9 coarse wind sectors were classified into 20 local wind sectors in total. But three local wind sectors were finally excluded because of these sectors were located on the complex hill area and the sea. Local wind sectors, therefore, in Busan metropolitan area were defined as 17 regimes. We assessed the location of air quality monitoring sites at Busan metropolitan area using the information of these wind sectors. Most of these were located at proper points, but 6 sites were placed at 3 local wind sectors as a couple and no site was set up at 3 other sectors. Hence the location of these sites was in need of rearrange.

We have analysed the observed surface and vertical meteorological data to get atmospheric information over the Busan metropolitan area. For this, we have selected 10 days in all season such as spring, summer I(Jangma season), summer II(hot season), autumn and winter. The result which have performed cluster analysis using atmospheric data represented that these days are included to most frequently appeared synoptic cluster. We have simulated wind field around Busan metropolitan area which is assigned as 1km2 using RAMS. The calculated air temperature and the wind speed was similar to the observed the that, and the trends of daily variation showed good agreement. RMSE and IOA also showed reliable value. These results indicated the RAMS is able to simulate and predict detailed atmospheric phenomenon.

In this study, climate analysis and wind sector division were conducted for a propriety assessment to determine the location of air quality monitoring sites in the Busan metropolitan area. The results based on the meteorological data(2000～2004) indicated hat air temperature is strongly correlated between 9 atmospheric monitoring sites, while wind speed and direction are not. This is because wind is strongly affected by the surrounding terrain and the obstacles such as building and tree. In the next stage, we performed cluster analysis to divide wind sector over the Busan metropolitan area. The cluster analysis showed that the Busan metropolitan area is divided into 6 wind sectors. However 1 downtown and 2 suburbs an area covering significantly broad region in Busan are not divided into independent sectors, because of the absence of atmospheric monitoring site. As such, the Busan metropolitan area is finally divided into 9 sectors.