• Journal of Korea Water Resources Association
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• v.36 no.4
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• pp.647-659
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• 2003

The purpose of this study is to evaluate hydrological applicability of spatially observed rainfall distribution data by the TRMM/PR (Tropical Rainfall Measuring Mission / Precipitation Radar). For this study, firstly, TRMM/PR data (Y) of the Yongdam-Dam Watershed (930.38$km^2$) was extracted and secondly, TRMM/PR data and the rainfall data (X) by AWS (Automatic Weather Station) were compared by executing a correlation analysis. As a result, the regression equations were deduced as two parts (under 60mm/day : Y = 18.55X-0.53, over 60mm/day : Y = 3.11X+51.16). SCS runoff analysis was conducted using 7 rainfall events in 1999 for Yongdam-Dam watershed and the Cheon-Cheon subwatershed for the revised TRMM/PR data. TRMM/PR data showed relative errors ranging from 19.6％ ti 45.6％, and from 11.3％ to 38.9％ for Cheon-Cheon subwatershed and Yongdam-Dam watershed, respectively, AWS data showed relative errors ranging from 0.5％ to 12.8％, and from -1.6％ to -10.3％, for Cheon-Cheon subwatershed and Yongdam-Dam watershed, respectively. Futher researches are necessary to evaluate the relationship between TRMM/PR data and AWS data for practical hydrological applications.

• Journal of the Korean Solar Energy Society
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• v.32 no.1
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• pp.1-7
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• 2012

Predictions of wind speed for six different near-shore sites were made using the NCAR (National Center for Atmospheric Research) wind data. The distances between the NCAR sites and prediction sites were varied between 40km and 150km. A well-known wind energy prediction program, WindPRO, was used. The prediction results were compared with the measured data from the AWS(Automated Weather Stations). Although the NCAR wind data were located far away from the AWS sites, the prediction errors were within 9% for all the cases. In terms of sector-wise wind energy distributions, the predictions were fairly close to the measurements, and the error in predicting main wind direction was less than $30^{\circ}$. This proves that the NCAR wind data are very useful in roughly estimating wind energy in offshore or near-shore sites where offshore wind farm might be constructed in Korea.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.2
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• pp.122-133
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• 2021

Cultivar parameter calibration can be affected by the reliability of the input data to a crop growth model. In South Korea, two sets of weather stations, which are included in the automated synoptic observing system (ASOS) or the automatic weather system (AWS), are available for preparation of the weather input data. The objectives of this study were to estimate the cultivar parameter using those sets of weather data and to compare the uncertainty of these parameters. The cultivar parameters of CERES-Rice model for Shindongjin cultivar was calibrated using the weather data measured at the weather stations included in either ASO S or AWS. The observation data of crop growth and management at the experiment farms were retrieved from the report of new cultivar development and research published by Rural Development Administration. The weather stations were chosen to be the nearest neighbor to the experiment farms where crop data were collected. The Generalized Likelihood Uncertainty Estimation (GLUE) method was used to calibrate the cultivar parameters for 100 times, which resulted in the distribution of parameter values. O n average, the errors of the heading date decreased by one day when the weather input data were obtained from the weather stations included in AWS compared with ASO S. In particular, reduction of the estimation error was observed even when the distance between the experiment farm and the ASOS stations was about 15 km. These results suggest that the use of the AWS stations would improve the reliability and applicability of the crop growth models for decision support as well as parameter calibration.

• Korean Journal of Agricultural and Forest Meteorology
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• v.3 no.2
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• pp.121-125
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• 2001

농업기상정보서비스는 농촌진흥청 정보화 기술개발 사업의 일환으로 개발되었다. 이 사업은 전국의 농업기상자동관측장비(Automatic Weather Station; AWS)를 전산망에 통합하여 전국 농업기상관측망을 구축하고, 농업기상정보의 수집, 저장을 체계화하는 한편, 이를 이용하여 농업인, 정책결정자, 연구원 등에게 필요한 형태로 농업기상정보를 제공하기 위한 목적으로 수행되었다.(중략)

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2000.10a
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• pp.397-404
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• 2000

A total of 65 rain gauges with Automatic Weather Station(AWS) were used to regional analysis of precipitation. Nine cluster regions were identified using geographical locations, maximum, mean, standard deviation of 1 day maximum rainfalls, mean annual precipitation and rainfall of rainy season in Korea. The mean annual precipitation, geographical locations, and the synoptic generating mechanisms were used to identify th five climatological homogeneous regions in Korea. Number of final regions by mean annual precipitation and cluster methods divided into five regions in Korea.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.2
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• pp.319-326
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• 2015

Rainfall data is necessary component for water resources design and flood warning system. Most analysis are used long-term hourly data of surface synoptic stations from the Meteorological Administration, Ministry of land, Infrastructure and Transport and others. However, It will be used minutely data of more high density automatic weather stations than surface synoptic stations expecting to increase the frequency of heavy precipitation. But minutely data has a problem about quality of rainfall data by auto observation. This study analyzed about quality control method using automatic weather station's minutely rainfall data of meteorological administration. It was performed assessment of the quality control that was classified quality control of miss Data, outlier data and rainfall interpolation. This method will be utilized when hydrological analysis uses minute rainfall data.

• 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.

• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.1
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• pp.117-126
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• 2018

The purpose of this study is to analyze the spatial distribution characteristics and spatial changes of Automatic Weather Stations (AWS) in mountainous areas with altitude more than 200 meters in South Korea. In order to analyze the spatial distribution patterns, spatial analysis was performed on 203 Automatic Mountain Meteorology Observation Station (AMOS) points from 2012 to 2016 by Euclidean distance analysis, nearest neighbor index analysis, and Kernel density analysis methods. As a result, change of the average distance between 2012 and 2016 decreased up to 16.4km. The nearest neighbor index was 0.666632 to 0.811237, and the result of Z-score test was -4.372239 to -5.145115(P<0.01). The spatial distributions of AMOSs through Kernel density analysis were analyzed to cover 129,719ha/a station in 2012 and 50,914ha/a station in 2016. The result of a comparison between 2012 and 2016 on the spatial distribution has decreased about 169,399ha per a station for the past 5 years. Therefore it needs to be considered the mountainous regions with low density when selecting the site of AMOS.

• Fire Science and Engineering
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• v.28 no.6
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• pp.13-21
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• 2014

Forest fire has a number of variables and since the effects of wind fields are bigger than any other variables, it is essential to know wind direction and velocity for the forest fire extinguishing techniques and the prediction of fire spread. With regards to the local area that has a high chance of forest fire, the data from meteorological observatory in the area is used for the estimation of wind velocity. It is relatively easy to obtain automatic weather station (AWS) data which are available for the whole nation. There is a chance that the data from the weather station may be different with the actual data at the mountain areas. In this study simply shaped hills (Sae-byeol hill of Jeju Island and port Ma-geum in An-myeon Island in the sea side) were selected as the experimental locations to minimize the distortion of the wind field by the adjacent geographic features. Spot measurements and analysis of computational fluid dynamics (CFD) for the given geographic features were conducted to examine and compare their consistency. As a conclusion It is possible to predict wind patterns in these simple locations.

• Journal of the Korean Society of Marine Environment & Safety
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• v.18 no.6
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• pp.545-550
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• 2012

Wind resource assessment is necessary when designing wind farm. To get the assessment, we must use a long term(20 years) observed wind data but it is so hard. so that we usually measured more than a year on the planned site. From the wind data, we can calculate wind energy related with the wind farm site. However, it calculate wind energy to collect the long term data from Met-mast(Meteorology Mast) station on the site since the Met-mast is unstable from strong wind such as Typhoon or storm surge which is Non-periodic. To solve the lack of the long term data of the site, we usually derive new data from the long term observed data of AWS(Automatic Weather Station) around the wind farm area using mathematical interpolation method. The interpolation method is called MCP(Measure-Correlative-Predict). In this study, based on the MCP Regression Model proposed by us, we estimated the wind energy at Handong site using AEP(Annual Energy Production) from Gujwa AWS data in Jeju. The calculated wind energy at Handong was shown a good agreement between the predicted and the measured results based on the linear regression MCP. Short term AEP was about 7,475MW/year. Long term AEP was about 7,205MW/year. it showed an 3.6% of annual prediction different. It represents difference of 271MW in annual energy production. In comparison with 20years, it shows difference of 5,420MW, and this is about 9 months of energy production. From the results, we found that the proposed linear regression MCP method was very reasonable to estimate the wind resource of wind farm.