• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2002년도 Proceedings of International Symposium on Remote Sensing
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• pp.358-360
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• 2002

In order to find horizontal and vertical precipitation structure in Korean peninsula, we use ground-based radar, and Automatic Weather Station (AWS) data. Radar data was selected for rain events in the Pusan and Jindo in Korea, during the spring and summer season of 2002. AWS point gauge measurements are analyzed as part of spatial structure of precipitation. TRMM/PR and ground-based radar is used vertical correlation. The results showed, as expected that the correlation decreased rapidly with distance.

• 대기
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• 제23권4호
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• pp.413-424
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• 2013

Urban Heat Island (UHI) intensity is one of vital parameters in studying urban boundary layer meteorology as well as urban planning. Because the UHI intensity is defined as air temperature difference between urban and rural sites, an objective sites selection criterion is necessary for proper quantification of the spatial variations of the UHI intensity. This study quantified the UHI intensity and its spatial pattern, and then analyzed their connections with urban structure and metabolism in Seoul metropolitan area where many kinds of land use and land cover types coexist. In this study, screen-level temperature data in non-precipitation day conditions observed from 29 automatic weather stations (AWS) in Seoul were analyzed to delineate the characteristics of UHI. For quality control of the data, gap test, limit test, and step test based on guideline of World Meteorological Organization were conducted. After classifying all stations by their own local climatological properties, UHI intensity and diurnal temperature range (DTR) are calculated, and then their seasonal patterns are discussed. Maximum UHI intensity was $4.3^{\circ}C$ in autumn and minimum was $3.6^{\circ}C$ in spring. Maximum DTR appeared in autumn as $3.8^{\circ}C$, but minimum was $2.3^{\circ}C$ in summer. UHI intensity and DTR showed large variations with different local climate zones. Despite limited information on accuracy and exposure errors of the automatic weather stations, the observed data from AWS network represented theoretical UHI intensities with difference local climate zone in Seoul.

• 한국태양에너지학회 논문집
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• 제31권2호
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• pp.72-81
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• 2011

The wind data obtained from an AWS(Automated Weather Station) was used to predict the AEP(annual energy production) of Gangwon wind farm having a total capacity of 98 MWin Korea. A wind energy prediction program based on the Reynolds averaged Navier-Stokes equation was used. Predictions were made for three consecutive years starting from 2007 and the results were compared with the actual AEPs presented in the CDM (Clean Development Mechanism) monitoring report of the wind farm. The results from the prediction program were close to the actual AEPs and the errors were within 7.8%.

• 풍력에너지저널
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• 제9권4호
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• pp.32-39
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• 2018

In this study, the annual power production of a wind farm according to obstacles and wind data was predicted for the Garyeok-do wind farm in the Saemangeum area. The Saemangeum Garyeok-do wind farm was built in December 2014 by the Korea Rural Community Corporation. Currently, two 1.5 MW wind turbines manufactured by Hyundai Heavy Industries are installed and operated. Automatic weather station data from 2015 to 2017 was used as wind data to predict the annual power production of the wind farm for three consecutive years. For prediction, a commercial computational fluid dynamics tool known to be suitable for wind energy prediction in complex terrain was used. Predictions were made for three cases with or without considering obstacles and wind direction errors. The study found that by considering both obstacles and wind direction errors, prediction errors could be substantially reduced. The prediction errors were within 2.5 % or less for all three years.

• 대기
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• 제14권3호
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• pp.19-28
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• 2004

In this study, two years Automatic Weather Station (AWS) data observed near the coast and islands are used to evaluate gust factors only when time averaged wind speed is higher than 5 ms. The gust factors are quite different in spatial and temporal domain according to analysis method. As the averaged time is increased, the gust factors are also increased. But the gust factors are decreased when wind speed is increased. It is because each wind speed is averaged one and a maximum wind is the greatest one for each time interval. The result from t-test is shown that all data are included within the 99% significance level. A sample standard deviation of ten minutes and one minute are 0.137~0.197, 0.067~0.142, respectively. Recently, the gust factor provided at the Korea Meteorological Administration (KMA) Homepage is calculated with one-hour averaged method. All though this method is hard to use directly for forecasting the strong wind over sea and coast, the result will be a great help to express Ocean Storm Flash in the Regional Meteorological Offices and the Meteorological Stations.

• Journal of Korean Society for Atmospheric Environment
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• 제18권E4호
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• pp.215-221
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• 2002

The real-time modeling system, named AirWatch System, has been developed to evaluate the environmental impact from a large source. It consists of stack TMS (TeleMetering System) that measures the emission data from the source, AWS (Automatic Weather Station) that monitors the weather data and computer system with the dispersion modeling software. The modeling theories used in the system are Gaussian plume and puff models. The Gaussian plume model is used for the dispersion in the simple terrain with a point meteorological data while the puff model is for the dispersion in complex terrain with three dimensional wind fields. The AirWatch System predicts the impact of the emitted pollutants from the large source on the near-by environment on the real -time base and the alarm is issued to control the emission rate if the calculated concentrations exceed the modeling significance level.

• 한국환경과학회지
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• 제10권2호
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• pp.135-142
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• 2001

In order to investigate horizontal wind field in the boundary layer around Pusan area, wind speed and wind direction measured at 14 AWS(Automatic Weather Station), 1997, was used. The wind direction at PRM(Pusan Regional Meterological Office) was showed that southwest and northeast wind dominated for spring and summer, northeast wind for fall and northwest for winter. Anticline flow was showed at \`Gaekumm\` which is located between Mt. Backyang(641m) and Mt. Yumkwang(503m) and affected on wind field at \`Pusanjin\`. The low wind speed and various wind direction was represented at the basin topography, \`Buckgu\`, \`Jeasong\`, \`Ilkwang\` and \`Kijang\`. The annual mean wind speed at 14 sites, 2.5ms(sup)-1, was lower than that measured at PRMO, 3.9ms(sup)-1. The wind direction analysis showed that the case of same direction in compare with that measured at PRMO is about 54% and case of opposite direction is about 12%. Annual and seasonal mean windrose showed wind direction is affected by not only synoptic weather state but also topography.

• 대기
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• 제27권1호
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• pp.67-77
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• 2017

In this study, we analyzed the characteristics of flow around the Daeyeon automatic weather station (AWS 942) and established formulas estimating inflow wind speeds at a computational fluid dynamics (CFD) model domain for the area around Pukyong national university using a computational fluid dynamics (CFD) model. Simulated wind directions at the AWS 942 were quite similar to those of inflows, but, simulated wind speeds at the AWS 942 decreased compared to inflow wind speeds except for the northerly case. The decrease in simulated wind speed at the AWS 942 resulted from the buildings around the AWS 942. In most cases, the AWS 942 was included within the wake region behind the buildings. Wind speeds at the inflow boundaries of the CFD model domain were estimated by comparing simulated wind speeds at the AWS 942 and inflow boundaries and systematically increasing inflow wind speeds from $1m\;s^{-1}$ to $17m\;s^{-1}$ with an increment of $2m\;s^{-1}$ at the reference height for 16 inflow directions. For each inflow direction, calculated wind speeds at the AWS 942 were fitted as the third order functions of the inflow wind speed by using the Marquardt-Levenberg least square method. Estimated inflow wind speeds by the established formulas were compared to wind speeds observed at 12 coastal AWSs near the AWS 942. The results showed that the estimated wind speeds fell within the inter quartile range of wind speeds observed at 12 coastal AWSs during the nighttime and were in close proximity to the upper whiskers during the daytime (12~15 h).

• 대한원격탐사학회지
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• 제31권2호
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• pp.65-75
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• 2015

본 연구에서는 전산유체역학 모델과 지리정보시스템 자료를 이용하여 밀양시 내이동에 위치한 자동지상관측소(AWS 288)의 지상 바람 관측환경을 분석하였다. AWS 288 인근 지역에 건축 중인 아파트 단지에 의한 관측환경 변화를 분석하기 위하여 16방위의 유입류를 고려하였다. AWS 위치에서 수치 모의된 풍속과 풍향 변화를 중점적으로 분석하였고, 3가지 유입류(남남서풍, 남남동풍, 북북서풍)에 대해서는 AWS 288 주위의 흐름 특성을 상세하게 분석하였다. 남남서풍의 경우, AWS 288 지점에서는 남서쪽에 위치한 아파트 단지의 영향으로 아파트 단지 건축 전과 후의 풍속 차이가 가장 크게 나타났다. 아파트 단지 건축 전에 상대적으로 높은 풍향 빈도가 나타난 남남동풍과 북북서풍의 경우에는 아파트 단지 건축 전 대비 건축 후의 AWS 288 지점에서 수치 모의된 풍속과 풍향 차이는 크지 않았다.

• 한국수자원학회논문집
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• 제54권spc1호
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• pp.1071-1081
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• 2021

본 연구에서는 기상청에서 수행하는 기존의 기상 관측에 대한 품질관리 절차 이외에 향후 스마트시티 등에서 활용될 수 있는 머신러닝 기반의 Internet of Things (IoT) 도시기상 관측 자료에 대한 품질검사 기준을 제안한다. 현재 기상청에서 종관기상관측(Automated Synoptic Observing System, ASOS)과 방재기상관측(Automatic Weather System, AWS) 기반으로 설정한 기준이 도시기상에 적합한지 확인하기 위하여 서울시에 설치된 SKT AWS 자료를 기반으로 사용성을 검증하였고, IoT 자체의 데이터가 가지는 특성을 고려하여 최종적으로 머신러닝 기반의 품질검사 알고리즘을 제안하였다. 품질검사 방법으로는 IoT 기기 자체에 대한 결측값 검사, 값 패턴 검사, 충분 데이터 검사, 통계적 범위 이상 검사, 시간값 이상 검사, 공간값 이상 검사를 먼저 수행하고, 기상청에서 제시하고 있는 기상 관측에 대한 품질검사인 물리한계검사, 단계검사, 지속성 검사, 기후범위 검사, 내적 일치성 검사를 5가지 기상요소에 대하여 각각 수행하였다. 제안한 알고리즘의 검증을 위하여 인천광역시 송도에 위치한 관측소에 실제 IoT 도시기상관측 데이터에 이를 적용하였다. 이를 통해 기존의 기상청 QC로는 확인할 수 없었던 IoT 기기가 가질 수 있는 결함을 확인할 수 있고, 알고리즘에 대한 검증을 진행하여 향후 스마트시티에 설치될 IoT 기상관측기기에 대한 품질검사 방법을 제안한다.