• Publications of The Korean Astronomical Society
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• v.15 no.spc2
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• pp.1-13
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• 2000

Through the coupling between the near-earth space environment and the polar ionosphere via geomagnetic field lines, the variations occurred in the magnetosphere are transferred to the polar region. According to recent studies, however, the polar ionosphere reacts not only passively to such variations, but also plays active roles in modifying the near-earth space environment. So the study of the polar ionosphere in terms of geomagnetic disturbance becomes one of the major elements in space weather research. Although it is an indirect method, ground magnetic disturbance data can be used in estimating the ionospheric current distribution. By employing a realistic ionospheric conductivity model, it is further possible to obtain the distributions of electric potential, field-aligned current, Joule heating rate and energy injection rate associated with precipitating auroral particles and their energy spectra in a global scale with a high time resolution. Considering that the ground magnetic disturbances are recorded simultaneously over the entire polar region wherever magnetic station is located, we are able to separate temporal disturbances from spatial ones. On the other hand, satellite measurements are indispensible in the space weather research, since they provide us with in situ measurements. Unfortunately it is not easy to separate temporal variations from spatial ones specifically measured by a single satellite. To demonstrate the usefulness of ground magnetic disturbance data in space weather research, various ionospheric quantities are calculated through the KRM method, one of the magneto gram inversion methods. In particular, we attempt to show how these quantities depend on the ionospheric conductivity model employed.

• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.6
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• pp.173-183
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• 2015

The aim of this study was to develop a soil moisture simulation model equipped with meteorological data enhanced by WRF (Weather Research and Forecast) model, and this soil moisture model was applied for quantifying soil moisture content and irrigation requirement. The WRF model can provide grid based meteorological data at various resolutions. For applicability assessment, comparative analyses were conducted using WRF data and weather data obtained from weather station located close to test bed. Water balance of each upland grid was assessed for soils represented with four layers. The soil moisture contents simulated using the soil moisture model were compared with observed data to evaluate the capacity of the model qualitatively and quantitatively with performance statistics such as correlation coefficient (R), coefficient of determination (R2) and root mean squared error (RMSE). As a result, R is 0.76, $R^2$ is 0.58 and RMSE 5.45 mm in soil layer 1 and R 0.61, $R^2$ 0.37 and RMSE 6.73 mm in soil layer 2 and R 0.52, $R^2$ 0.27 and RMSE 8.64 mm in soil layer 3 and R 0.68, $R^2$ 0.45 and RMSE 5.29 mm in soil layer 4. The estimated soil moisture contents and irrigation requirements of each soil layer showed spatiotemporally varied distributions depending on weather and soil texture data incorporated. The estimated soil moisture contents using weather station data showed uniform distribution about all grids. However the estimated soil moisture contents from WRF data showed spatially varied distribution. Also, the estimated irrigation requirements applied WRF data showed spatial variabilities reflecting regional differences of weather conditions.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.1
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• pp.1-7
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• 2019

In this study, we estimated the tropospheric delay of GNSS (Global Navigation Satellite System) signals during passing through the atmosphere in relation to weather and seasonal factors. For this purpose, we chose four CORS (Continuously Operating Reference Station) stations from inland (CCHJ and PYCH) and on the coast (GEOM and CHJU). A total of 48 days for each station (one set of data for each week) were downloaded from the NGII (National Geographic Information Institute) and processed it using the scientific GNSS software. The average tropospheric delays in winter are less than 2,400 mm, which is about 200 mm less than those in summer. The estimated tropospheric delay shows a similar pattern from all stations except the absolute bias in magnitude, while a large delay was observed for the station located on the coast. In addition, the delay during the day was relatively stable in winter, and the average tropospheric delay was strongly related to the orthometric height. The inland stations have tropospheric delays by the precipitation rather than humidity due to dry weather and difference in temperature. On the contrary, it was primarily caused by the humidity on the sea. The correlation between temperature and water vapor pressure is 0.9 or larger for all stations, and the tropospheric delay showed a high linear relationship with temperature. It is necessary to analyze the GNSS data with higher temporal resolution (e.g. all RINEX data of the year) to improve the stability and reliability of the correlation results.

• Journal of the Korean Solar Energy Society
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• v.26 no.2
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• pp.27-34
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• 2006

In this paper, a forecasting model of wind speed at Walryong Site, Jeju Island is presented, which has been developed and evaluated as a first step toward establishing Korea Forecasting Model of Wind Power Generation. The forecasting model is constructed based on neural network and is trained with wind speed data observed at Cosan Weather Station located near by Walryong Site. Due to short period of measurements at Walryong Site for training statistical model Gosan Weather Station's long-term data are substituted and then transplanted to Walryong Site by using Measure-Correlate-Predict technique. One to three-hour advance forecasting of wind speed show good agreements with the monitoring data of Walryong site with the correlation factors 0.96 and 0.88, respectively.

• Corrosion Science and Technology
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• v.2 no.5
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• pp.207-211
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• 2003

The weather resistance of five coatings systems based on alkyd, chlorinated rubber, epoxy, polyurethane and fluoropolymer were studied by natural exposure test and accelerated test. The coatings were exposed at Hanoi station with urban industry atmosphere and at Baichay station with marine atmosphere. The degradation of coatings was evaluated by gloss measurement and surface analysis by scanning electronic microscopy. The results obtained show that among coatings tested the gloss of polyurethane and fluoropolymer coatings remained highly and those of alkyd, chlorinated rubber and epoxy coatings were very low after two years of atmospheric exposure. Under accelerating conditions the gloss of fluoropolymer coatings remained highly after 80 cycles of testing. By comparison with accelerating test in UV-condensation chamber the conditions at atmospheric stations are more aggressive.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.388-391
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• 2005

The characteristics of the total ozone variations measured by the ground-based Brewer Ozone Spectrophotometer and the Total Ozone Mapping Spectrometer (TOMS) over Pohang are statistically examined from January 1994 to December 2004. First of all, in the correlation analysis of the total ozone measured from the Brewer Ozone Spectrophotometer and the TOMS, the correlation coefficient was 0.88 and the used data were 2190. The annual mean value of the total ozone is 311 DU with the standard deviation of 13 DU. The maximum and the minimum value were found in March (343 DU) and in September (282 DU), respectively. It was also revealed that the longest seasonal variation is in Spring (341 DU) and the smallest is in Autumn (283 DU). The time series data of the total ozone indicates that the annual variation is significant and the variations for three months and six months are relatively weak. Finally, the annual mean total ozones in Pohang (Brewer), Seoul (Brewer) and Busan (TOMS) are 312 DU, 324 DU and 304 DU, respectively.

• Journal of Korean Society on Water Environment
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• v.32 no.3
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• pp.310-317
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• 2016

It has been a big problem to estimate rainfall for the studies of mud-debris flows because the estimated rainfall from the nearest AWS (Automatic Weather Station) can tend to be quite inaccurate at individual sites. This study attempts to improve this problem through accurate rainfall depth estimation by applying an artificial neural network with radar rainfall data. For this, three models were made according to utilizing methodologies of rainfall data. The first model uses the nearest rainfall, observing the site from an ungauged site. The second uses only radar rainfall data and the third model integrates the above two models using both radar and observed rainfall at the sites around the ungauged site. This methodology was applied to the metropolitan area in Korea. It appeared as though the third model improved rainfall estimations by the largest margin. Therefore, the proposed methodology can be applied to forecast mud-debris flows in ungageed sites.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.410-413
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• 2007

Water vapor in the atmosphere is an influential factor of the hydrosphere cycle, which exchanges heat through phase change and is essential to precipitation. Because of its significance in altering weather, the estimation of water vapor amount and distribution is crucial to determine the precision of the weather forecasting and the understanding of regional/local climate. It is shown that it is reliable to measure precipitable water (PW) using long baseline (500-2000km) GPS observations. However, it becomes infeasible to derive absolute PW from GPS observations in Taiwan due to geometric limitation of relatively short-baseline network. In this study, a method of deriving Near-Real-Time PW from short baseline GPS observations is proposed. This method uses a reference station to derive a regression model for wet delay, and to interpolate the difference of wet delay among stations. Then, the precipitable water is obtained by using a conversion factor derived from radiosondes. The method has been tested by using the reference station located on Mt. Ho-Hwan with eleven stations around Taiwan. The result indicates that short baseline GPS observations can be used to precisely estimate the precipitable water in near-real-time.

• Journal of the Korean Solar Energy Society
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• v.31 no.6
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• pp.72-77
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• 2011

The purpose for installation of offshore weather station is a measurement of wind resources and so on. If weather station is operated, it will be possible to analysis for wind resource and arrangement of wind farm by using measured data. In this paper, we carried out the development of offshore wind resource measurement system for measuring offshore wind resource. Also, In order to monitor for real-time wind data with 1 Hz, we installed the wireless transmission system. All wind characteristic data are sent to the server PC through the this system is connected outport of DataLogger. Transmitted wind data were used in order to look at in the Web-page and tablet PC on a real time basis in a graph. In this paper, we will introduce about the wind resource measurement and remote monitoring system that is the result of study.

• 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%.