• Title, Summary, Keyword: ASOS/AWS

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Analysis of runoff according to the time and space characteristics of hourly rainfall data in Seoul (서울 강우자료의 시·공간적 특성에 따른 유출분석)

  • Hyun, Jung Hoon;Park, Hee Seong;Chung, Gun Hui
    • Proceedings of the Korea Water Resources Association Conference
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    • pp.216-216
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    • 2019
  • 최근 이상 기후로 인하여 홍수피해가 많이 발생 하고 있다. 특히 도시유역의 도시화로 인해 불투수면적이 증가하여 내수 침수도 증가하였다. 이로 인하여 재산피해와 인명피해가 증가하면서 전 세계적으로 홍수 저감 연구가 진행 되고 있다. 강우의 시 공간적인 특성을 파악 하여 강우 사상을 정의 한다면 도시홍수 저감 에 있어 도움이 될 것이라 판단된다. 우리나라 서울 지역의 설계 강우량을 산정하기 위해 서울기상청에서 제공하고 있는 ASOS(Automated Surface Observing System) 를 사용해 왔다. 하지만 ASOS을 사용하게 되면 강수량의 공간 특성을 고려하기 어렵지만 AWS(Automatic Weather Stations) 는 세밀한 관측망을 가지고 있어 공간적 특성을 고려할 수 있다. 본 연구에서는 서울 기상청에서 제공하고 있는 강우 자료의 20개년 연속된 강우자료를 통해 강우자료를 구축 하였다. 서울지역의 유역을 선정하였으며 도시유역 강우-유출 해석에 많이 사용되는 EPA-SWMM 모형에 ASOS 와 AWS 강우자료를 적용하여 유출 분석을 하였다. 이러한 자료를 바탕으로 공간 특성 분석을 실시하여 더욱 세밀한 설계 강우량 산정에 도움을 있을 것으로 판단된다.

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Correlation Analysis of UA Using Wind Data of AWS/ASOS and SST in Summer in the East Sea (AWS/ASOS 바람자료를 이용한 여름철 동해 연안역의 용승지수와 수온과의 상관성)

  • Kim, Ju-Yeon;Han, In-Seong;Ahn, Ji-Suk
    • Journal of the Korean Society of Marine Environment & Safety
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    • v.24 no.6
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    • pp.773-784
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    • 2018
  • In this study, we examined the UA (upwelling age) using wind data of AWS/ASOS in the East Sea coast and the correlation between UA and SST (sea surface temperature) from May to August in 1995 to 2016. The data used the 6 observations of the wind data of AWS/ASOS and the SST data of the COD/RISA provided by the National Institute and Fisheries Science near the East Sea coast. The UA was calculated quantitatively low but it rose when the actual cold water mass occurred. Correlation analysis between UA and SST showed the negative (-) r (correlation coefficient) predominately. At the time of cold-water mass in June to August 2013, the r had a very high negative value of -0.65 to -0.89 in the 6 observations. It proved that as the UA increases, the SST is lower. By knowing the UA, we were able to evaluate the trend of upwelling in the cold-water mass of the East Sea coast in the long term and it will contribute to minimizing the damage to aquatic organisms according to the size and intensity of the upwelling.

Urban Runoff According to Rainfall Observation Locations (강우 측정 지점에 따른 도시 유역 유출량 변화 분석)

  • Hyun, Jung Hoon;Chung, Gunhui
    • Journal of Wetlands Research
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    • v.21 no.4
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    • pp.305-311
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    • 2019
  • Recently, global climate change causes abnormal weather and disaster countermeasures do not provide sufficient defense and mitigation because they were established according to the historical climate condition. Repeated torrential rains, in particular, are causing damage even in the robust urban flood defense system. Therefore, in this study, the change of runoff considering the spatial distribution of rainfall and urban characteristics was analyzed. For rainfall concentrated in small catchment, rainfall in the watershed must be accurately measured. This study is based on the rainfall data observed with Automated Surface Observing System (ASOS) and Automatic Weather Stations (AWS) provided by the Seoul Meteorological Administration. Effluent from the pumping station was estimated using the EPA-SWMM model and compared and analyzed. Catchments with rainwater pumping station are small with large portion of impermeable areas. Thus, when the ASOS data where is located from from the chatchment, runoff is often calculated using rainfall data that is different from rainfall in the catchment. In this study, the difference between rainfall data observed in the AWS near the catchment and ASOS away from the catchment was calculated. It was found that accurate rainfall should be used to operate rainwater pumping stations or forecast urban flooding floods. In addition, the results of this study may be helpful for estimating design rainfall and runoff calculation.

Rainfall analysis considering watershed characteristics and temporal-spatial characteristics of heavy rainfall (집중호우의 시·공간적 특성과 유역특성을 고려한 강우분석 연구)

  • Kim, Min-Seok;Choi, Ji-Hyeok;Moon, Young-Il
    • Journal of Korea Water Resources Association
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    • v.51 no.8
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    • pp.739-745
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    • 2018
  • Recently, the incidence of heavy rainfall is increasing. Therefore, a rainfall analysis should be performed considering increasing frequency. The current rainfall analysis for hydrologic design use the hourly rainfall data of ASOS with a density of 36 km on the Korean Peninsula. Therefore, medium and small scale watershed included Thiessen network at the same rainfall point are analyzed with the same design rainfall and time distribution. This causes problem that the watershed characteristics can not be considered. In addition, there is a problem that the temporal-spatial change of the heavy rainfall occurring in the range of 10~20 km can not be considered. In this study, Author estimated design rainfall considering heavy rainfall using minutely rainfall data of AWS, which are relatively dense than ASOS. Also, author analyzed the time distribution and runoff of each case to estimate the huff's method suitable for the watershed. The research result will contribute to the estimation of the design hydrologic data considering the heavy rainfall and watershed characteristics.

Production of Agrometeorological Information in Onion Fields using Geostatistical Models (지구 통계 모형을 이용한 양파 재배지 농업기상정보 생성 방법)

  • Im, Jieun;Yoon, Sanghoo
    • Journal of Environmental Science International
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    • v.27 no.7
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    • pp.509-518
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    • 2018
  • Weather is the most influential factor for crop cultivation. Weather information for cultivated areas is necessary for growth and production forecasting of agricultural crops. However, there are limitations in the meteorological observations in cultivated areas because weather equipment is not installed. This study tested methods of predicting the daily mean temperature in onion fields using geostatistical models. Three models were considered: inverse distance weight method, generalized additive model, and Bayesian spatial linear model. Data were collected from the AWS (automatic weather system), ASOS (automated synoptic observing system), and an agricultural weather station between 2013 and 2016. To evaluate the prediction performance, data from AWS and ASOS were used as the modeling data, and data from the agricultural weather station were used as the validation data. It was found that the Bayesian spatial linear regression performed better than other models. Consequently, high-resolution maps of the daily mean temperature of Jeonnam were generated using all observed weather information.

Generation and Verification on the Synthetic Precipitation/Temperature Data

  • Oh, Jai-Ho;Kang, Hyung-Jeon
    • Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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    • pp.25-28
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    • 2016
  • Recently, because of the weather forecasts through the low-resolution data has been limited, the demand of the high-resolution data is sharply increasing. Therefore, in this study, we restore the ultra-high resolution synthetic precipitation and temperature data for 2000-2014 due to small-scale topographic effect using the QPM (Quantitative Precipitation Model)/QTM (Quantitative Temperature Model). First, we reproduce the detailed precipitation and temperature data with 1km resolution using the distribution of Automatic Weather System (AWS) data and Automatic Synoptic Observation System (ASOS) data, which is about 10km resolution with irregular grid over South Korea. Also, we recover the precipitation and temperature data with 1km resolution using the MERRA reanalysis data over North Korea, because there are insufficient observation data. The precipitation and temperature from restored current climate reflect more detailed topographic effect than irregular AWS/ASOS data and MERRA reanalysis data over the Korean peninsula. Based on this analysis, more detailed prospect of regional climate is investigated.

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Distribution Analysis of Land Surface Temperature about Seoul Using Landsat 8 Satellite Images and AWS Data (Landsat 8 위성영상과 AWS 데이터를 이용한 서울특별시의 지표면 온도 분포 분석)

  • Lee, Jong-Sin;Oh, Myoung-Kwan
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.20 no.1
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    • pp.434-439
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    • 2019
  • Recently, interest in urban temperature change and ground surface temperature change has been increasing due to weather phenomenon due to global warming, heat island phenomenon caused by urbanization in urban areas. In Korea, weather data such as temperature and precipitation have been collected since 1904. In recent years, there are 96 ASOS stations and 494 AWS weather observation stations. However, in the case of terrestrial networks, terrestrial meteorological data except measurement points are predicted through interpolation because they provide point data for each installation point. In this study, to improve the resolution of ground surface temperature measurement, the surface temperature using satellite image was calculated and its applicability was analyzed. For this purpose, the satellite images of Landsat 8 OLI TIRS were obtained for Seoul Metropolitan City by seasons and transformed to surface temperature by applying NASA equation to the thermal bands. The ground measurement data was based on the temperature data measured by AWS. Since the AWS temperature data is station based point data, interpolation is performed by Kriging interpolation method for comparison with Landsat image. As a result of comparing the satellite image base surface temperature with the AWS temperature data, the temperature difference according to the season was calculated as fall, winter, summer, based on the RMSE value, Spring, in order of applicability of Landsat satellite image. The use of that attribute and AWS support starts at $2.11^{\circ}C$ and RMSE ${\pm}3.84^{\circ}C$, which reflects information from the extended NASA.

Estimation of the return period of statistical method for probable maximum precipitation (통계학적 가능최대강수량의 재현기간 추정)

  • Kim, Sangdan;Sim, Inkyeong;Lee, Okjeong
    • Proceedings of the Korea Water Resources Association Conference
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    • pp.180-180
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    • 2018
  • 가능최대강수량(PMP)은 대규모 수공구조물의 설계 시 기준이 되는 강수량으로, 최근 대규모 거대재난에 대비한 대피계획수립에 PMP를 활용하려는 움직임이 있으며 PMP에 대한 국내 연구가 활발히 수행되고 있다. PMP를 추정하기 위해 Hershfield의 통계적 방법에 대한 간단한 대안이 제안되었다. PMP는 물리적인 강우량 상한계로, 확률론적 개념과는 모순적이다. 또한, Hershfield의 PMP는 연 최대 시계열 평균의 선형함수로 주어지는 모양 매개변수를 가지는 GEV 분포의 약 60,000년 빈도임이 밝혀졌다. 따라서 본 연구에서는 Hershfield의 방법을 확률론적으로 해석하는 것이 바람직할 것으로 판단하였고, 기상청 ASOS 및 AWS 자료를 이용하여 우리나라 각 지점자료 중 10년 이상의 자료를 사용하여 Hershfield 방법을 적용하여 PMP를 산정하였다. 각 지점의 빈도계수를 구하여 우리나라 자료에 적합한 확률분포의 형태를 적용하였고, 분포형의 매개변수 값을 추정하였다. 또한, Hershfield의 빈도계수와, 우리나라 자료에 해당하는 빈도계수가 몇 년 빈도로 계산되는지 각각 확인해 보았다. ASOS 및 AWS 자료를 이용하여 연 최대 강수량 시계열 평균과 모양 매개변수의 관계 공식 또한 구성하였다. 본 연구의 방법을 검증하기 위하여 우리나라에서 제일 오래된 자료(57년)인 서울지점 자료를 이용하여 경험적인 분포함수와 본 연구에서 제안하고 있는 방법을 비롯한 다양한 방법을 통하여 구한 분포함수를 비교하여 도시하였다.

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The Characteristics of Air Temperature according to the Location of Automatic Weather System (AWS 설치장소에 따른 기온 특성)

  • Joo, Hyong-Don;Lee, Mi-Ja;Ham, In-Wha
    • Atmosphere
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    • v.15 no.3
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    • pp.179-186
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    • 2005
  • Due to several difficulties, a number of Automatic Weather Systems (AWS) operated by Korea Meteorological Administration (KMA) are located on the rooftop so that the forming of standard observation environment to obtain the accuracy is needed. Therefore, the air temperature of AWSs on the synthetic lawn and the concrete of the rooftop is compared with the standard observation temperature. The hourly mean temperature is obtained by monthly and hourly mean value and the difference of temperature is calculated according to the location, the weather phenomenon, and cloud amount. The maximum and the minimum temperatures are compared by the conditions, such as cloud amount, the existence of precipitation or not. Consequently, the temperature on the synthetic lawn is higher than it on the concrete so that it is difficult to obtain same effect from ASOS, on the contrary the installation of AWS on the synthetic lawn seem to be inadequate due to heat or cold source of the building.

Availability of AWS data from KMA for real-time river flow forecast (실시간 하천유량 예측을 위한 기상청 AWS 자료의 활용성 평가)

  • Lee, Byong-Ju;Chang, Ki-Ho;Choi, Young-Jean
    • Proceedings of the Korea Water Resources Association Conference
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    • pp.131-131
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    • 2011
  • 기후변화로 인한 기상이변 현상이 빈번하게 발생하면서 홍수와 같은 자연재해의 피해규모가 증가하고 있다. 이를 극복하기 위해 최근에는 구조적 대책뿐만 아니라 홍수예측시스템과 같은 비구조적 대책에도 많은 관심과 연구가 이루어지고 있다. 통상 홍수예측을 위해서는 예측강우의 정확도가 중요하게 부각되지만 중규모 이상의 유역에서는 수 시간의 지체시간 효과로 인해 AWS 실황강우만으로도 어느정도 선행시간에 대해서 하천유량예측이 가능하다고 할 수 있다. 본 연구에서는 기상청 AWS 실황강우를 이용하여 하천유량을 예측할 경우 어느정도 선행시간과 정확도를 확보할 수 있는지에 대해서 분석하고자 한다. 분석을 위한 시단위 강우자료와 기상자료는 각각 AWS와 ASOS 자료를 이용하였다. 또한 하천유량 모의를 위한 강우-유출모형으로는 SURF 모델(Sejong University River Forecast Model)을 이용하였다. 이 모형은 저류함수모형 기반의 연속형 강우-유출모형으로 미래에 대한 유출모의결과의 정확도를 향상시키기 위해 앙상블 칼만필터링 기법을 연계한 모형이다. 그림 1은 충주댐유역에 대해서 2009.7.8~17일(240시간)에 대해서 관측유량 자료동화 전후의 결과를 나타낸 것이다. 현시점을 100, 105, 110, 115시간으로 가정하고 미래기간에 대해서는 관측강우를 0으로 가정했을 때 대략 첨두유량 발생 5시간 전에 예측된 모의유량이 관측유량과 거의 일치함을 확인할 수 있다. 따라서 실황강우와 관측유량 자료동화 기법을 연계할 경우 수 시간의 선행시간에 대해서 유량예측이 가능한 것으로 판단된다.

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