• Journal of the Korean Solar Energy Society
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• v.31 no.2
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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%.

• Korean Journal of Agricultural and Forest Meteorology
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• v.15 no.4
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• pp.304-311
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• 2013

There have been increasing cases for farmers to install automated weather stations (AWS) at their farms and orchards in order to take countermeasures to more frequent weather disasters caused by climate variability and weather extremes. Although raw data are the same, the additive values as agrometeorological information may vary depending on data processing methods. User demands on appropriate information could also be different among crop species, cropping systems and even cultivars. We designed an internet based AWS data processing and display system to help diverse users (e.g., farmers), extension workers to access their weather data on specific demands. The system was implemented at a rural catchment with 52 $km^2$ land area where 14 automated weather stations are in operation. This note introduces the system and describes the major modules in detail. By linking regional AWS networks, a feasibility for this system as an early warning system is also discussed.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1071-1081
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• 2021

In addition to the current quality control procedures for the weather observation performed by the Korea Meteorological Administration (KMA), this study proposes quality inspection standards for Internet of Things (IoT) urban weather observed data based on machine learning that can be used in smart cities of the future. To this end, in order to confirm whether the standards currently set based on ASOS (Automated Synoptic Observing System) and AWS (Automatic Weather System) are suitable for urban weather, usability was verified based on SKT AWS data installed in Seoul, and a machine learning-based quality control algorithm was finally proposed in consideration of the IoT's own data's features. As for the quality control algorithm, missing value test, value pattern test, sufficient data test, statistical range abnormality test, time value abnormality test, spatial value abnormality test were performed first. After that, physical limit test, stage test, climate range test, and internal consistency test, which are QC for suggested by the KMA, were performed. To verify the proposed algorithm, it was applied to the actual IoT urban weather observed data to the weather station located in Songdo, Incheon. Through this, it is possible to identify defects that IoT devices can have that could not be identified by the existing KMA's QC and a quality control algorithm for IoT weather observation devices to be installed in smart cities of future is proposed.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.244-255
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• 2022

This study estimated and evaluated the high resolution (1km) gridded mountain meteorology data of daily mean, maximum and minimum temperature based on ASOS (Automated Surface Observing System), AWS (Automatic Weather Stations) and AMOS (Automatic Mountain Meteorology Observation System) in South Korea. The ASOS, AWS, and AMOS meteorology data which were located above 200m was classified as mountainous area. And the ASOS, AWS, and AMOS meteorology data which were located under 200m was classified as non-mountainous area. The bias-correction method was used for correct air temperature over complex mountainous area and the performance of enhanced daily coefficients based on the AMOS and mountainous area observing meteorology data was evaluated using the observed daily mean, maximum and minimum temperature. As a result, the evaluation results show that RMSE (Root Mean Square Error) of air temperature using the enhanced coefficients based on the mountainous area observed meteorology data is smaller as 30% (mean), 50% (minimum), and 37% (maximum) than that of using non-mountainous area observed meteorology data. It indicates that the enhanced weather coefficients based on the AMOS and mountain ASOS can estimate mean, maximum, and minimum temperature data reasonably and the temperature results can provide useful input data on several climatological and forest disaster prediction studies.

• KIEAE Journal
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• v.16 no.6
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• pp.69-75
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• 2016

Climate change has resulted in the urban heat island (UHI) effect throughout the globe, contributing to heat-related illness and fatalities. In order to reduce such damage, it is necessary to improve the climate observation network for precise observation of the urban thermal environment and quick UHI forecasting system. Purpose: This study analyzed the effective range of the climate observation network and the distribution of the existing Automatic Weather Stations (AWS) in Seoul to propose optimal locations for additional installment of AWS. Method: First, we performed quality analysis to pinpoint missing values and outliers within the high-density temperature data measured. With the result from the analysis, a spatial autocorrelation structure in the temperature data was tested to draw the effective range and correlation distance for each major time period. Result: As a result, it turned out that the optimal effective range for the climate observation network in Seoul in July was a radius of 2.8 kilometers. Based on this result, population density, and temperature data, we selected the locations for additional installment of AWS. This study is expected to be used to generate urban temperature maps, select and move measurement locations since it is able to suggest valid, specific spatial ranges when the data measured in point is converted into surface data.

• Journal of Korea Water Resources Association
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• v.49 no.1
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• pp.11-18
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• 2016

It is very important to decide probability precipitation that is used as hydraulic structure design and target rainfall for urban disaster prevention. Especially, National Emergency Management Agency (NAMA) announced target rainfall from probability precipitation in korea on city and district level. It make use to performance evaluation of disaster prevention and planning of development for disasters prevention capacity target. In this study was calculated target rainfall that is duration 1~3 hour based unit of gu (borough) by point and regional frequency analysis using rainfall data of Surface Synoptic Stations (SSS) and Automatic Weather Stations (AWS). The result of this study can utilized as a reference to related business such as disaster capability assessment and achievement of prevention capacity target against disasters. And it also will be contribute to establishment of prevention capacity target against disasters.

• Journal of Digital Convergence
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• v.16 no.12
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• pp.317-325
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• 2018

With the aim of creating new economic values and strengthening national competitiveness, governments and major private companies around the world are continuing their interest in big data and making bold investments. In order to collect objective data, such as news, securing data integrity and quality should be a prerequisite. For researchers or practitioners who wish to make decisions or trend analyses based on objective and massive data, such as portal news, the problem of using the existing Crawler method is that data collection itself is blocked. In this study, we implemented a method of collecting web data by addressing existing crawler-style problems using the cloud service platform provided by Amazon Web Services (AWS). In addition, we collected 'gas safety' articles and analyzed issues related to gas safety. In order to ensure gas safety, the research confirmed that strategies for gas safety should be established and systematically operated based on five categories: accident/occurrence, prevention, maintenance/management, government/policy and target.

• Journal of the Korean earth science society
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• v.39 no.6
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• pp.533-544
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• 2018

Rainfall is one of the most important meteorological variables in meteorology, agriculture, hydrology, natural disaster, construction, and architecture. Recently, satellite remote sensing is essential to the accurate detection, estimation, and prediction of rainfall. In this study, the accuracy of Integrated Multi-satellite Retrievals for GPM (IMERG) product, a composite rainfall information based on Global Precipitation Measurement (GPM) satellite was evaluated with ground observation data in the inland of Korea. The Automatic Weather Station (AWS)-based rainfall measurement data were used for validation. The IMERG and AWS rainfall data were collocated and compared during one year from January 1, 2016 to December 31, 2016. The coastal regions and islands were also evaluated irrespective of the well-known uncertainty of satellite-based rainfall data. Consequently, the IMERG data showed a high correlation (0.95) and low error statistics of Bias (15.08 mm/mon) and RMSE (30.32 mm/mon) in comparison to AWS observations. In coastal regions and islands, the IMERG data have a high correlation more than 0.7 as well as inland regions, and the reliability of IMERG data was verified as rainfall data.

• 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.

• Atmosphere
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• v.24 no.3
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• pp.433-444
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• 2014

Although the Radar-AWS Rainrate (RAR) calculation system operated by Korea Meteorological Administration estimated precipitation using 2-dimensional composite components of single polarization radars, this system has several limitations in estimating the precipitation accurately. To to overcome limitations of the RAR system, the Korea Meteorological Administration developed and operated the RMQ (Radar-based Multi-sensor Quantitative Precipitation Estimation) system, the improved version of NMQ (National Mosaic and Multi-sensor Quantitative Precipitation Estimation) system of NSSL (National Severe Storms Laboratory) for the Korean Peninsula. This study introduced the RMQ system domestically for the first time and verified the precipitation estimation performance of the RMQ system. The RMQ system consists of 4 main parts as the process of handling the single radar data, merging 3D reflectivity, QPE, and displaying result images. The first process (handling of the single radar data) has the pre-process of a radar data (transformation of data format and quality control), the production of a vertical profile of reflectivity and the correction of bright-band, and the conduction of hydrid scan reflectivity. The next process (merger of 3D reflectivity) produces the 3D composite reflectivity field after correcting the quality controlled single radar reflectivity. The QPE process classifies the precipitation types using multi-sensor information and estimates quantitative precipitation using several Z-R relationships which are proper for precipitation types. This process also corrects the precipitation using the AWS position with local gauge correction technique. The last process displays the final results transformed into images in the web-site. This study also estimated the accuracy of the RMQ system with five events in 2012 summer season and compared the results of the RAR (Radar-AWS Rainrate) and RMQ systems. The RMQ system ($2.36mm\;hr^{-1}$ in RMSE on average) is superior to the RAR system ($8.33mm\;hr^{-1}$ in RMSE) and improved by 73.25% in RMSE and 25.56% in correlation coefficient on average. The precipitation composite field images produced by the RMQ system are almost identical to the AWS (Automatic Weather Statioin) images. Therefore, the RMQ system has contributed to improve the accuracy of precipitation estimation using weather radars and operation of the RMQ system in the work field in future enables to cope with the extreme weather conditions actively.