• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.810-814
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• 2012

최근 들어 이상기후로 인하여 대규모 또는 국지적인 호우의 발생빈도 및 강도가 증가하고 있다. 이로 인해 개수율이 높은 국가하천 및 지방하천보다는 중 소하천에서 홍수로 인한 피해가 극심해 지고 있는 상황이다. 또한 국지적 호우는 지역적인 편차가 매우 심하게 나타나고 있어 홍수 발생지역에 대한 예측이 어려우며, 대부분 홍수관리는 유역단위로 이루어져 있으나 중 소하천의 홍수대응 및 복구는 지자체별로 시행되고 있다. 따라서 홍수 발생 시에 지자체의 특성 및 재난 상황을 종합적으로 고려한 피재자의 체계적인 구호를 위하여 효율적인 피재자 구원시스템 체계 구축이 필요하다. 본 연구에서는 지자체별 피재자 구원시스템 체계 현황에 대한 조사 및 분석을 통해 피재자 구원시스템 체계 항목을 조직, 시설, 물자, 피해복구로 분류하였으며, 항목 별 체크리스트를 작성하여 홍수에 따른 피재자의 효율적인 구원시스템 체계 구축을 하고자 하였다. 체계별 항목 분류와 항목 별 체크리스트는 피재자 구원에 대한 관련법규의 현황 조사 및 분석을 통한 법규 권고 사항을 근거로 작성하였다. 또한 표준 조사표의 항목 및 체크리스트에 대한 평가 기준으로서 표준안을 제시하고자 국내 지자체별 피재자 구원시스템 체계 현황과 국외의 피재자 구원 시스템 체계의 현황에 대한 비교분석을 수행하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.77-77
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• 2015

기상이변으로 인한 사회 경제적 피해의 증가로 기상정보에 대한 중요성이 커지면서 해외에서는 민간 기업이 기상 관측망을 구축하는 사례가 나타났다. 미국의 Earth Network은 전 세계에 1만개의 기상 관측센서를 설치하였고, 일본의 통신회사인 NTT DoCoMo는 일본에 4000여 개의 기상 및 환경관측 센서를 구축하였다. 국내에서는 SK플래닛이 자사의 플랫폼 기술과 SK텔레콤의 기지국 인프라를 활용하여 수도권 지역에 국지기상 관측망을 구축하였다. SK플래닛은 2013년 서울지역에 1km 간격으로 264개의 기상센서를 설치하고, 2014년 인천 경기지역에 3km 간격으로 825개의 기상센서를 추가 설치하여, 현재 1089개의 국지기상 관측망을 운용하고 있다. 관측에 사용한 센서는 우량계와 복합 기상센서로 강수량, 기온, 습도, 바람, 기압을 측정한다. 관측된 자료는 데이터로거에서 기상청의 자료처리 표준규격에 따라 처리한 후 M2M 모뎀을 통해 1분마다 서버로 전송한다. 전송된 자료는 기상정보 플랫폼의 수집 서버에서 프로토콜 변환 후 원본자료 DB에 저장하고, 실시간 품질관리를 마친 후 품질관리 자료 DB에 저장한다. 관측 지점의 기본정보 및 작업이력은 메타데이터 DB에 저장되고 관리자 페이지를 통해 조회 및 수정 된다. 관측 자료의 품질 보증은 제조사의 센서 Calibration부터 서비스 모니터링 까지 각 단계별로 체계적인 품질관리를 통해 이루어진다. 품질관리를 마친 국지기상 관측 데이터는 응용프로그램 개발자가 편리하게 사용할 수 있는 API(Application Programming Interface)형태로 제공된다. 2013년 여름부터 수집된 1~3km 해상도의 SK플래닛 국지기상 관측 자료를 통해 그 동안 정량적으로 확인하지 못한 국지성 호우 시의 강수량 편차에 대해 알 수 있었다. 2014년 7월 31일 양평지역에 내린 국지성 호우는 시간당 최대 90mm 이상의 비가 내린 사례로, 귀여리 관측소(SK 플래닛)에 시간당 93.1mm가 내리는 동안 퇴촌 관측소(기상청)에는 17.5mm의 비가 내려, 두 관측지점 간 거리가 3.4km 임에도 불구하고 시간당 75mm 이상의 강수량 차이를 보였다. 앞으로 SK플래닛의 국지기상 관측 자료가 국지성 호우의 조기 경보 및 예측 정확도 향상에 활용되어 재난으로부터 국민의 생명과 재산을 지키는데 많은 도움이 되기를 기대한다.

• Journal of the Korean earth science society
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• v.30 no.6
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• pp.744-758
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• 2009

The characteristics of brightness temperature (BT) of infrared and water vapor channels from MTSAT-1R have been investigated using 12 persistent and frequent lightning cases selected from the summer lightnings of 2006-2008. The infrared (IR1, 10.3-11.3 ${\mu}M$) and water vapor (WV, 6.5-7.0 ${\mu}M$) channels from the MTSAT-1R and the lightning observation data from Korea Meteorological Administration are used. When there is no lightning, the BTs of the IR1 and WV channels show the largest frequency at around 290-295K and 245K, respectively. On the other hand, the BTs of two channels show the largest frequency at 215K caused by strong convection when there is lightning. As a result, the WV-IR1 difference (BTDWI) sharply increases from -50K to 0K. Although it depends on the evolution stage of thunderstorms, the lightning mainly occurs at the core of circular convection in the mesoscale convective complex (MCC), whereas the lightning occurs by concentrated line-shape in the squall line. A strong positive correlation exists between the lightning frequency and the BT in the MCC regardless of the BT, but only at the very cold BT in the squall line. In general, the characteristics of BT are well defined for the lightning occurring in the concentrated line, but they are not well defined in the MCC, especially during the decaying stage of MCC. When they are defined well, the lightning occurs when the BTs of IR1 and WV are lower than 215K, BTDWI is near -3 to 1K, and local standard deviation of IR1 decreases to around 1K.

• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.793-806
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• 2020

In this study,for YSU (Yonsei University), MYJ(Mellor-Yamada-Janjic), ACM2 (Asymmetric Convective Model), and BouLac (Bougeault-Lacarrere) PBL schemes, numerical experiments were performed for the case period (June 26-30, 2014). The PBLH calculated by using the backscatter signal produced by the mobile vehicle-mounted lidar system (LIVE) and the PBLH calculated by the prediction of each PBL schemes of WRF were compared and analyzed. In general, the experiments using the non-local schemes showed a higher correlation than the local schemes for lidar observation. The standard deviation of the PBLH difference for daylight hours was small in the order of YSU (≈0.39 km), BouLac (≈0.45 km), ACM2 (≈0.47 km), MYJ (≈0.53 km) PBL schemes. In the RMSE comparison for the case period, the YSU PBL scheme was found to have the highest precision. The meteorological lider mounted on the vehicle is expected to provide guidance for the analysis of the planetary boundary layer in a numerical model under various weather conditions.

• Korean Journal of Agricultural and Forest Meteorology
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• v.3 no.2
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• pp.96-104
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• 2001

Usage of ecosystem models has been extended to landscape scales for understanding the effects of environmental factors on natural and agro-ecosystems and for serving as their management decision tools. Accurate prediction of spatial variation in daily temperature is required for most ecosystem models to be applied to landscape scales. There are relatively few empirical evaluations of landscape-scale temperature prediction techniques in mountainous terrain such as Korean Peninsula. We derived a periodic function of seasonal lapse rate fluctuation from analysis of elevation effects on daily temperatures. Observed daily maximum and minimum temperature data at 63 standard stations in 1999 were regressed to the latitude, longitude, distance from the nearest coastline and altitude of the stations, and the optimum models with $r^2$ of 0.65 and above were selected. Partial regression coefficients for the altitude variable were plotted against day of year, and a numerical formula was determined for simulating the seasonal trend of daily lapse rate, i.e., partial regression coefficients. The formula in conjunction with an inverse distance weighted interpolation scheme was applied to predict daily temperatures at 267 sites, where observation data are available, on randomly selected dates for winter, spring and summer in 2000. The estimation errors were smaller and more consistent than the inverse distance weighting plus mean annual lapse rate scheme. We conclude that this method is simple and accurate enough to be used as an operational temperature interpolation scheme at landscape scale in Korea and should be applicable to elsewhere.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.359-359
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• 2018

이상치는 표본자료에서 크게 어긋나 다른 자료들로부터 떨어져 표시되는 자료로써, 실제로 발생할 확률이 매우 낮은 자료로 정의되고 있다. 설계홍수량을 산정하기 위하여 적용하고 있는 극치계열의 연최대치 강우자료에는 기계오작동 및 엔지니어의 표독오류가 발생하고 있으며, 기후변화에 따른 거대태풍 및 국지적인 집중호우 발생 등으로 인한 극치값 등에서 이상치가 관측되고 있다. 통상 이상치들은 통계분석시 자료 본연의 특성을 왜곡시켜 편향된 결과를 산정할 수 있으므로 빈도해석시 이상치해석 절차를 수행하여 자료의 적정성을 확인하여야 한다. 현재 실무에서는 설계홍수량 산정요령과 하천설계기준 해설 등에서 관련 내용을 기술하고 있지만, 국내 강우자료의 기록연수의 부족으로 인하여 빈도해석시 이상치 해석이 미수행되고 있어 이상치에 따른 자료편의가 발생하면 결과물인 확률강우량이 왜곡되게 산정될 수 있다. 따라서, 본 연구에서는 국내 주요 도시의 강우자료를 이용하여 이상치검정을 수행하였다. 대상지점으로는 서울, 부산, 대전, 대구, 인천, 광주, 울산 등의 비교적 긴 관측년수를 보유하고 있는 광역시를 선정하였으며, 지속기간은 10분, 1~24시간의 25개 강우자료를 적용하였다. 이상치검정 방법으로는 타 방법에 비하여 이상치 검정력이 뛰어난 것으로 알려진 2가지 방법을 채택하였으며, 표본자료의 평균과 표준편차로 표준화된 z값을 이용하여 상 하 한계선를 초과하는 값을 확인하는 z-Score 방법중 향상된 중위수 절대편차(MAD)에 의한 수정 z-Score 방법(Hoaglin, 1993)과 Box-Plot 방법(Tukey, 1969)을 적용하였다. Box-Plot 방법(Tukey, 1969)은 전체 자료를 25%씩 사분위로 구분하는 방법으로 정렬된 자료계열을 중앙값, 박스, 수염(whiskers), 이상치로 구분한다. 정렬된 25~75% 값들을 박스로 포함하여 외곽의 수염값들을 이상치로 분류하며, 특히 사분위수의 도식화로 데이터의 분포를 파악하기 좋으며, 이상치들의 위치와 자료의 비대칭 여부를 쉽게 파악할 수 있다. 본 연구의 수행으로 수정 z-Score 방법의 경우에는 서울과 대구지점에는 이상치가 없으며, 부산지점에는 13개, 대전지점 7개, 인천지점 5개, 광주지점 32개, 울산지점 26개가 나타났다. Box-Plot 방법으로는 서울지점 35개, 부산지점 39개, 대전지점 32개, 대구지점 38개, 인천지점 51개, 광주지점 61개, 울산지점 65개의 이상치가 분석되었다. 연구를 수행한 결과, 수정 z-Score 방법에 비하여 Box-Plot 방법에 의한 이상치가 더 많이 발생하였으며, 각각의 방법으로 지속기간 및 연도별 이상치 발생자료를 확인하였다. 방법별 이상치 발생현황 등을 분석하여 지점별 발생횟수를 분석하였으며, 추후 지점 및 자료의 보완이 수행되면 활용성을 증대시킬 수 있을 것으로 판단된다.

• Korean Journal of Agricultural and Forest Meteorology
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• v.2 no.4
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• pp.175-182
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• 2000

Spatial interpolation of daily temperature forecasts and observations issued by public weather services is frequently required to make them applicable to agricultural activities and modeling tasks. In contrast to the long term averages like monthly normals, terrain effects are not considered in most spatial interpolations for short term temperatures. This may cause erroneous results in mountainous regions where the observation network hardly covers full features of the complicated terrain. We developed a spatial interpolation model for daily minimum temperature which combines inverse distance squared weighting and elevation difference correction. This model uses a time dependent function for 'mountain slope lapse rate', which can be derived from regression analyses of the station observations with respect to the geographical and topographical features of the surroundings including the station elevation. We applied this model to interpolation of daily minimum temperature over the mountainous Korean Peninsula using 63 standard weather station data. For the first step, a primitive temperature surface was interpolated by inverse distance squared weighting of the 63 point data. Next, a virtual elevation surface was reconstructed by spatially interpolating the 63 station elevation data and subtracted from the elevation surface of a digital elevation model with 1 km grid spacing to obtain the elevation difference at each grid cell. Final estimates of daily minimum temperature at all the grid cells were obtained by applying the calculated daily lapse rate to the elevation difference and adjusting the inverse distance weighted estimates. Independent, measured data sets from 267 automated weather station locations were used to calculate the estimation errors on 12 dates, randomly selected one for each month in 1999. Analysis of 3 terms of estimation errors (mean error, mean absolute error, and root mean squared error) indicates a substantial improvement over the inverse distance squared weighting.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.4
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• pp.111-124
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• 2010

To monitor the environment of land surface change is considered as an important research field since those parameters are related with land use, climate change, meteorological study, agriculture modulation, surface energy balance, and surface environment system. For the change detection, many different methods have been presented for distributing more detailed information with various tools from ground based measurement to satellite multi-spectral sensor. Recently, using high resolution satellite data is considered the most efficient way to monitor extensive land environmental system especially for higher spatial and temporal resolution. In this study, we use two different spatial resolution satellites; the one is SPOT/VEGETATION with 1 km spatial resolution to detect coarse resolution of the area change and determine objective threshold. The other is Landsat satellite having high resolution to figure out detailed land environmental change. According to their spatial resolution, they show different observation characteristics such as repeat cycle, and the global coverage. By correlating two kinds of satellites, we can detect land surface change from mid resolution to high resolution. The K-mean clustering algorithm is applied to detect changed area with two different temporal images. When using solar spectral band, there are complicate surface reflectance scattering characteristics which make surface change detection difficult. That effect would be leading serious problems when interpreting surface characteristics. For example, in spite of constant their own surface reflectance value, it could be changed according to solar, and sensor relative observation location. To reduce those affects, in this study, long-term Normalized Difference Vegetation Index (NDVI) with solar spectral channels performed for atmospheric and bi-directional correction from SPOT/VEGETATION data are utilized to offer objective threshold value for detecting land surface change, since that NDVI has less sensitivity for solar geometry than solar channel. The surface change detection based on long-term NDVI shows improved results than when only using Landsat.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.3
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• pp.13-29
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• 2021

The purpose of this study was to analyze the spatial distribution and location factors of startups by generation (young and middle-aged) in Seoul. To this end, a research model was established that included factors of industry, population, and startup institutions by generation in 424 administrative districts using the Seoul Business Enterprise Survey(2018), which includes data on the age group of entrepreneurs. As an analysis method, descriptive statistics were conducted to confirm the frequency, average and standard deviation of startups by generation and major variables in the administrative districts of Seoul, and spatial distribution and characteristics of startups by generation were analyzed through global and local spatial autocorrelation analysis. In particular, the spatial distribution of startups in Seoul was confirmed in-depth by categorizing and analyzing startups by major industries. Afterwards, an appropriate spatial regression analysis model was selected through the Lagrange test, and based on this, the location factors affecting startups by generation were analyzed. The main results derived from the research results are as follows. First, there was a significant difference in the spatial distribution of young and middle-aged startups. The young people started to startups in the belt-shaped area that connects Seocho·Gangnam-Yongsan-Mapo-Gangseo, while middle-aged people were relatively active in the southeastern region represented by Seocho, Gangnam, Songpa, and Gangdong. Second, startups by generation in Seoul showed various spatial distributions according to the type of business. In the knowledge high-tech industries(ICT, professional services) in common, Seocho, Gangnam, Mapo, Guro, and Geumcheon were the centers, and the manufacturing industry was focused on existing clusters. On the other hand, in the case of the life service industry, young people were active in startups near universities and cultural centers, while middle-aged people were concentrated on new towns. Third, there was a difference in factors that influenced the startup location of each generation in Seoul. For young people, high-tech industries, universities, cultural capital, and densely populated areas were significant factors for startup, and for middle-aged people, professional service areas, low average age, and the level of concentration of start-up support institutions had a significant influence on startup. Also, these location factors had different influences for each industry. The implications suggested through the study are as follows. First, it is necessary to support systematic startups considering the characteristics of each region, industry, and generation in Seoul. As there are significant differences in startup regions and industries by generation, it is necessary to strengthen a customized startup support system that takes into account these regional and industrial characteristics. Second, in terms of research methods, a follow-up study is needed that comprehensively considers culture and finance at the large districts(Gu) level through data accumulation.