• Atmosphere
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• v.25 no.2
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• pp.283-294
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• 2015

For urban flash flood simulation, we need the higher resolution radar rainfall than radar rainfall of KMA, which has 10 min time and 1km spatial resolution, because the area of subbasins is almost below $1km^2$. Moreover, we have to secure the high quantitative accuracy for considering the urban hydrological model that is sensitive to rainfall input. In this study, we developed the quantitative precipitation estimation (QPE), which has 250 m spatial resolution and high accuracy using KMA AWS and SK Planet stations with Mt. Gwangdeok radar data in Seoul area. As the results, the rainfall field using KMA AWS (QPE1) is showed high smoothing effect and the rainfall field using Mt. Gwangdeok radar is lower estimated than other rainfall fields. The rainfall field using KMA AWS and SK Planet (QPE2) and conditional merged rainfall field (QPE4) has high quantitative accuracy. In addition, they have small smoothed area and well displayed the spatial variation of rainfall distribution. In particular, the quantitative accuracy of QPE4 is slightly less than QPE2, but it has been simulated well the non-homogeneity of the spatial distribution of rainfall.

• Korean Journal of Remote Sensing
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• v.25 no.6
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• pp.465-474
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• 2009

Convective/stratiform radar echo classification schemes by Steiner et al. (1995) and Biggerstaff and Listemaa (2000) are examined on a monsoonal front during the summer monsoon-Changma period, which is organized as a cloud cluster with mesoscale convective complex. Target radar is S-band with wavelength of 10cm, spatial resolution of 1km, elevation angle interval of 0.5-1.0 degree, and minimum elevation angle of 0.19 degree at Jindo over the Korean Peninsula. For verification of rainfall amount retrieved from the echo classification, ground-based rain gauge observations (Automatic Weather Stations) are examined, converting the radar echo grid data to the station values using the inverse distance weighted method. Improvement from the echo classification is evaluated based on the correlation coefficient and the scattered diagram. Additionally, an optimal use method was designed to produce combined rainfalls from the radar echo and Tropical Rainfall Measuring Mission Precipitation Radar (TRMM/PR) data. Optimal values for the radar rain and TRMM/PR rain are inversely weighted according to the error variance statistics for each single station. It is noted how the rainfall distribution during the summer monsoon frontal system is improved from the classification of convective/stratiform echo and the use of the optimal use technique.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.358-360
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• 2002

In order to find horizontal and vertical precipitation structure in Korean peninsula, we use ground-based radar, and Automatic Weather Station (AWS) data. Radar data was selected for rain events in the Pusan and Jindo in Korea, during the spring and summer season of 2002. AWS point gauge measurements are analyzed as part of spatial structure of precipitation. TRMM/PR and ground-based radar is used vertical correlation. The results showed, as expected that the correlation decreased rapidly with distance.

• Journal of the Korean Society of Hazard Mitigation
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• v.8 no.6
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• pp.113-120
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• 2008

This study analyzed the spatial correlograms of minutely rainfall data with respect to various accumulation times. A bivariate mixed lognormal distribution was applied for rainfall modelling. A total of 26 minutely rainfall data sets from rain gauge stations in the central part of Korean peninsula were analyzed, also repeated for several storm types like Jang-Ma, typhoon and convective storms for their comparison. The accumulation times 1, 2, 3, 5, 10, 30 and 60 minutes were considered in this study. As results, it was found that the minutely rainfall data available was not good enough for estimating minutely rainfall intensity at ungaged locations. It seems more practical to use the hourly rainfall data with much higher rain gauge density, if proper methods for interpolation and data dis-aggregation are provided.

• Journal of Satellite, Information and Communications
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• v.12 no.4
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• pp.62-66
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• 2017

In this paper, we present the development of communication and data processing strategy for the electromagnetic wave precipitation gauge system. The electromagnetic wave precipitation gauge system is a small system for deriving area rainfall rates within 1 km radius through dual polarization radar observation at 24GHz band. It is necessary to take consider for measurement of accurate precipitation under limited computing resources originating from small systems and to minimize the use of network for the unattended operation and remote management. To overcome computational resource limitations, we adopted the fuzzy logic for quality control to eliminate non-precipitation echoes and developed the method by weighted synthesis of various rain rate fields using multiple radar QPE formulas. Also we have designed variable data packets rules to minimize the network traffic.

• Journal of Wetlands Research
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• v.16 no.1
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• pp.19-32
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• 2014

The purpose of this paper is to obtain reliable rainfall data for runoff simulation and other hydrological analysis by the calibration of gauge rainfall. The calibrated gauge rainfall could be close to the actual value with rainfall on the ground. In order to analyze the wind effect of ground rain gauge, we selected the rain gauge sites with and without a windshield and standard rain gauge data from Chupungryeong weather station installed by standard of WMO. Simple linear regression model and artificial neural networks were used for the calibration of rainfalls, and we verified the reliability of the calibrated rainfalls through the runoff analysis using $Vflo^{TM}$. Rainfall calibrated by linear regression is higher amount of rainfall in 5%~18% than actual rainfall, and the wind remarkably affects the rainfall amount in the range of wind speed of 1.6~3.3m/s. It is hard to apply the linear regression model over 5.5m/s wind speed, because there is an insufficient wind speed data over 5.5m/s and there are also some outliers. On the other hand, rainfall calibrated by neural networks is estimated lower rainfall amount in 10~20% than actual rainfall. The results of the statistical evaluations are that neural networks model is more suitable for relatively big standard deviation and average rainfall. However, the linear regression model shows more suitable for extreme values. For getting more reliable rainfall data, we may need to select the suitable model for rainfall calibration. We expect the reliable hydrologic analysis could be performed by applying the calibration method suggested in this research.

• Journal of the Korean earth science society
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• v.42 no.5
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• pp.514-523
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• 2021

Using the precipitation data observed at the Gochang Standard Weather Observatory (GSWO) during the winter seasons from 2014 to 2016, we analyzed the precipitation characteristics of the winter observation environment. For this study, we used four different types of precipitation gauges, i.e., No Shield (NS), Single Alter (SA), Double Fence Intercomparison Reference (DFIR), and Pit Gauge (PG). We analyzed the data from each to find differences in the accumulated precipitation, characteristics of the precipitation type, and the catch efficiency according to the wind speed based on the DFIR. We then classified these into three precipitation types, i.e., rain, mixed precipitation, and snow, according to temperature data from Gochang's Automated Synoptic Observing System (ASOS). We considered the DFIR to be the standard precipitation gauge for our analysis and the cumulative winter precipitation recorded by each other gauge compared to the DFIR data in the following order (from the most to least similar): SA, NS, and PG. As such, we find that the SA gauge is the most accurate when compared to the standard precipitation gauge used (DFIR), and the PG system is inappropriate for winter observations.

• Journal of Environmental Science International
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• v.33 no.1
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• pp.43-57
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• 2024

The possible experimental time for cloud seeding was analyzed in South Korea. Rain gauge and radar precipitation data collected from September 2017 to August 2022 in from the three main target stations of cloud seeding experimentation (Daegwallyeong, Seoul, and Boryeong) were analyzed. In this study, the assumption that rainfall and cloud enhancement originating from the atmospheric updraft is a necessary condition for the cloud seeding experiment was applied. First, monthly and seasonal means of the precipitation duration and frequency were analyzed and cloud seeding experiments performed in the past were also reanalyzed. Results of analysis indicated that the experiments were possible during a monthly average of 7,025 minutes (117 times) in Daegwallyeong, 4,849 minutes (81 times) in Seoul, and 5,558 minutes (93 times) in Boryeong, if experimental limitations such as the insufficient availability of aircraft is not considered. The seasonal average results showed that the possible experimental time is the highest in summer at all three stations, which seems to be owing to the highest precipitable water in this period. Using the radar-converted precipitation data, the cloud seeding experiments were shown to be possible for 970-1,406 hours (11-16%) per year in these three regions in South Korea. This long possible experimental time suggests that longer duration, more than the previous period of 1 hour, cloud seeding experiments are available, and can contribute to achieving a large accumulated amount of enhanced rainfall.

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.525-540
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• 2000

Characteristics of precipitation in Seoul have been examined by using long-term observational data. Precipitation records from modern rain gauges were used for 1908-1996, together with the traditional Korean rain gauge (called Chukwookee) observations for 1777-1907. A linear trend analysis of seasonal total rainfall shows no significant trends over the last 200 years A wavelet transform analysis was performed to figure out the transient variations of precipitation.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.413-413
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• 2015

최근 국내외적으로 정도 높은 강우장을 모의하기 위해 레이더 자료와 강우자료를 합성하려는 연구가 수행되고 있다. 이는 정밀하게 강우를 관측할 수 있는 지점 강우의 장점과 강우의 공간분포를 파악할 수 있는 레이더 자료의 장점을 결합하여 고품질의 자료를 생산할 수 있기 때문이다. 기존에는 연속수정법, 조건부 합성기법, 코크리깅, 가우시안 합성기법 등과 같은 자료합성 기법을 이용하여 레이더 자료와 지상강우를 합성한 연구가 수행되었다. 그러나 강우자료의 간헐적인 특징으로 인하여 두 자료를 합성하려는 시도는 간단하지 않은 문제로 나타났다. 이에 본 연구에서는 간헐적 특성의 자료를 고려한 자료합성 기법을 제안하였다. 먼저, 자료를 0이 포함되지 않은 경우와 0이 포함된 경우의 2가지 유형으로 구분하였다. 기존에는 주로 자료에 0에 포함되지 않은 경우에 대한 합성기법이 평가되었지만, 본 연구에서는 0이 포함된 경우에 대한 합성기법에 초점을 맞추었다. 자료합성 기법 중 코크리깅 기법을 이용하여 0이 포함된 레이더 자료와 강우자료를 합성하는 방법을 제안하였고 이를 0이 포함되지 않은 경우를 이용한 합성 결과와 비교하였다. 아울러 제안된 합성기법을 한강 유역의 자료에 적용하여 그 결과를 비교 및 평가 하였다.