• Proceedings of the Korean Institute of Building Construction Conference
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• 2007.04a
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• pp.19-23
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• 2007

In this paper, the period for the strength correction was determined with each region of south Korea based on the meteorological data of KMA(Korea meteorological administration) by applying KASS-5 regulation. In case of 28 days of strength control age, the period for strength correction with 6MPa was calculated to 50-60 days and, with 3 MPa. to around 80 days. The period for the strength correction was shown to be decreased with the rise of altitude. The period to consider the delay of the strength development due to low temperature including the period of cold weather concrete was nearly 7 months around 1 year. References for determining the strength correction factors with each region of south Korea was provided in this paper.

• Journal of Industrial Technology
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• v.22 no.A
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• pp.49-57
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• 2002

The accurate wind speed information at the hub height of a wind turbine is very essential to the exact estimation of the wind turbine power performance testing. Several methods on the site calibration, which is a technique to estimate the wind speed at the wind turbine's hub height based on the measured wind data using a reference meteorological mast, are introduced. A site calibration result and the wind resource assessment for the Taekwanryung test site are presented using a one-month wind data from a reference meteorological mast and a temporal mast installed at the site of wind turbine. From this analysis, it turns out that the current location of the reference meteorological mast is wrongly determined, and the self-developed codes for the site calibration are working properly. Besides, an analysis on the uncertainty allocation for the wind speed correction using site calibration is performed.

• Journal of Mechanical Science and Technology
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• v.18 no.12
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• pp.2250-2257
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• 2004

The accurate wind speed information at the hub height of a wind turbine is very essential to the exact estimation of the wind turbine power performance testing. Several methods on the site calibration, which is a technique to estimate the wind speed at the wind turbine's hub height based on the measured wind data using a reference meteorological mast, are introduced. A site calibration result and the wind resource assessment for the TaeKwanRyung test site are presented using three-month wind data from a reference meteorological mast and the other mast temporarily installed at the site of wind turbine. Besides, an analysis on the uncertainty allocation for the wind speed correction using site calibration is performed.

• Journal of Wind Energy
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• v.4 no.2
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• pp.34-39
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• 2013

The wind resource assessment for measured wind data over 1 year by using the meteorological mast should be a prerequisite for business feasibility of the wind farm development. Even though the direction of boom mounting the wind vane and anemometer is carefully engineered to escape the interference of wakes generated from the met-mast structures, the shadow effect is not completely avoided due to seasonal winds in the Korean Peninsula. The shadow effect should be properly calibrated because it is able to distort the wind resources. In this study a calibration method is introduced for the measured wind data at Julpo in Jeonbuk Province. Each sectoral terrain conditions along the selected wind direction nearby the met-mast is investigated, and the distorted wind data due to shadow effects can be calibrated effectively. The correction factor is adopted for quantitative calibration by carrying out the WindSim analysis.

• Journal of Navigation and Port Research
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• v.40 no.5
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• pp.271-278
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• 2016

Localized atmospheric conditions between multi-reference stations can bring the tropospheric delay irregularity that becomes an error terms affecting positioning accuracy in network RTK environment. Imbalanced network error can affect the network solutions and it can corrupt the entire network solution and degrade the correction accuracy. If an anomaly could be detected before the correction message was generated, it is possible to eliminate the anomalous satellite that can cause degradation of the network solution during the tropospheric delay anomaly. An atmospheric grid that consists of four meteorological stations was used to detect an inhomogeneous weather conditions and tropospheric anomaly applied AWSs (automatic weather stations) meteorological data. The threshold of anomaly detection algorithm was determined based on the statistical weather data of AWSs for 5 years in an atmospheric grid. From the analytic results of anomaly detection algorithm it showed that the proposed algorithm can detect an anomalous satellite with an anomaly flag generation caused tropospheric delay anomaly during localized atmospheric conditions between stations. It was shown that the different precipitation condition between stations is the main factor affecting tropospheric anomalies.

• Atmosphere
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• v.29 no.4
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• pp.463-480
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• 2019

This study classified heavy rain types from K-means clustering for the hourly relationship between rainfall intensity and cloud top height over the Korean peninsula, and then examined their statistical characteristics for the period of June~August 2013~2018. Total rainfall amount of warm-type events was 2.65 times larger than that of the cold-type, whereas the lightning frequency divided by total rainfall for the warm-type was only 46% of the cold-type. Typical cold-type cases exhibited high cloud top height around 16 km, large reflectivity in the upper layer, and frequent lightning flashes under convectively unstable condition. Phenomenally, the cold-type cases corresponded to cloud cluster or multi-cell thunderstorms. However, two warm-type cases related to Changma and typhoon were characterized by heavy rainfall due to long duration, relatively low cloud top height and upper-level reflectivity, and the absence of lightning under the convectively neutral and extremely humid conditions. This study further confirmed that the forecast skill of rainfall could be improved by applying correction factor with the overestimation for cold-type and underestimation for warm-type cases in the Local Data Assimilation and Prediction System (LDAPS) operational model (e.g., BIAS score was improved by 5%).

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.458-461
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• 2005

Surface Solar Insolation is important for vegetation productivity, hydrology, crop growth, etc. However, ground base measurement stations installed pyranometer are often sparsely distributed, especially over oceans. In this study, Surface Solar Insolation is estimated using the visible and infrared spin scan radiometer(VISSR) data on board Geostationary Meteorological Satellite (GMS)-S covering from March 2001 to December 2001 in clear and cloudy conditions. To retrieve atmospheric factor, such as, optical depth, the amount of ozone, H20, and aerosol, SMAC (Simplified Method for Atmospheric Correction) code, is adopted. The hourly Surface Solar Insolation is estimated with a spatial resolution of $5km\;\times\;5km$ grid. The daily Surface Solar Insolation is derived from the available hourly Surface solar irradiance, independently for every pixel. The pyranometer by the Korea Meteorological Agency (KMA) is used to validate the estimated Surface Solar Insolation with a spatial resolution of $3\;\times\;3Pixels.$

• Journal of Wind Energy
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• v.14 no.3
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• pp.25-33
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• 2023

In order to secure the safety of increasing offshore activities such as offshore wind farm maintenance and fishing, IMPACT, a mid-term marine weather forecasting system, was established by predicting marine weather up to 7 days in advance. Forecast data from the Korea Hydrographic and Oceanographic Agency (KHOA), which provides the most reliable marine meteorological service in Korea, was used, but wind speed and wave height forecast errors increased as the leading forecast period increased, so improvement of the accuracy of the model results was needed. The Model Output Statistics (MOS) method, a post-correction method using statistical machine learning, was applied to improve the prediction accuracy of wave height, which is an important factor in forecasting the risk of marine activities. Compared with the observed data, the wave height prediction results by the model before correction for 6 to 7 days ahead showed an RMSE of 0.692 m and R of 0.591, and there was a tendency to underestimate high waves. After correction with the MOS technique, RMSE was 0.554 m and R was 0.732, confirming that accuracy was significantly improved.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.260-260
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• 2016

Soil erosion is a very serious problem from agricultural as well as environmental point of view. Various computer models have been used to estimate soil erosion and assess erosion control practice. Universal Soil loss equation (USLE) is a popular model which has been used in many countries around the world. Erosivity (USLE R-factor) is one of the USLE input parameters to reflect impacts of rainfall in computing soil loss. Value of R factor depends upon Energy (E) and maximum rainfall intensity of specific period ($I30_{max}$) of that rainfall event and thus can be calculated using higher temporal resolution rainfall data such as 10 minute interval. But 10 minute interval rainfall data may not be available in every part of the world. In that case we can use hourly rainfall data to compute this R factor. Maximum 60 minute rainfall ($I60_{max}$) can be used instead of maximum 30 minute rainfall ($I30_{max}$) as suggested by USLE manual. But the value of Average annual R factor computed using hourly rainfall data needs some correction factor so that it can be used in USLE model. The objective of our study are to derive relation between averages annual R factor values using 10 minute interval and hourly rainfall data and to determine correction coefficient for R factor using hourly Rainfall data.75 weather stations of Korea were selected for our study. Ten minute interval rainfall data for these stations were obtained from Korea Meteorological Administration (KMA) and these data were changed to hourly rainfall data. R factor and $I60_{max}$ obtained from hourly rainfall data were compared with R factor and $I30_{max}$ obtained from 10 minute interval data. Linear relation between Average annual R factor obtained from 10 minute interval rainfall and from hourly data was derived with $R^2=0.69$. Correction coefficient was developed for the R factor calculated using hourly rainfall data.. Similarly, the relation was obtained between event wise $I30_{max}$ and $I60_{max}$ with higher $R^2$ value of 0.91. Thus $I30_{max}$ can be estimated from I60max with higher accuracy and thus the hourly rainfall data can be used to determine R factor more precisely by multiplying Energy of each rainfall event with this corrected $I60_{max}$.

• Atmosphere
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• v.24 no.4
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• pp.491-502
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• 2014

As a part of the KIAPS (Korea Institute of Atmospheric Prediction Systems) Package for Observation Processing (KPOP), we have developed the modules for Advanced Microwave Sounding Unit-A (AMSU-A) pre-processing and its bias correction. The KPOP system calculates the airmass bias correction coefficients via the method of multiple linear regression in which the scan-corrected innovation and the thicknesses of 850~300, 200~50, 50~5, and 10~1 hPa are respectively used for dependent and independent variables. Among the four airmass predictors, the multicollinearity has been shown by the Variance Inflation Factor (VIF) that quantifies the severity of multicollinearity in a least square regression. To resolve the multicollinearity, we adopted simple linear regression and Principal Component Regression (PCR) to calculate the airmass bias correction coefficients and compared the results with those from the multiple linear regression. The analysis shows that the order of performances is multiple linear, principal component, and simple linear regressions. For bias correction for the AMSU-A channel 4 which is the most sensitive to the lower troposphere, the multiple linear regression with all four airmass predictors is superior to the simple linear regression with one airmass predictor of 850~300 hPa. The results of PCR with 95% accumulated variances accounted for eigenvalues showed the similar results of the multiple linear regression.