• Journal of Korea Water Resources Association
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• v.55 no.12
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• pp.1053-1064
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• 2022

The Thiessen method, which is the current area average precipitation method, has serious structural limitations in accurately calculating the average precipitation in the watershed. In addition to the observation accuracy of the precipitation meter, errors may occur in the area average precipitation calculation depending on the arrangement of the precipitation meter and the direction of the heavy rain. When the watershed is small and the station density is sparse, in both simulation and observation history, the Thiessen method showed a peculiar tendency that the average precipitation in the watershed continues to increase and decrease rapidly for 10 minutes before and after the peak. And the average precipitation in the Thiessen basin was different from the rainfall radar at the peak time. In the case where the watershed is small but the station density is relatively high, overall, the Thiessen method did not show a trend of sawtooth-shaped over-peak, and the time-dependent fluctuations were similar. However, there was a continuous time lag of about 10 minutes between the rainfall radar observations and the ground precipitation meter observations and the average precipitation in the basin. As a result of examining the ground correction effect of the rainfall radar watershed average precipitation, the correlation between the area average precipitation after correction is rather low compared to the area average precipitation before correction, indicating that the correction effect of the current rainfall radar ground correction algorithm is not high.

• Journal of Korea Water Resources Association
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• v.55 no.12
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• pp.1105-1114
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• 2022

A study on a method to overcome the limitations of the topographical and hydrological observation environment for estimating the QPE with high consistency with the ground rainfall by utilizing the spatiotemporal observation advantages of the rainfall radar for use in flood forecasting, and quantitative observations of localized rainfall due to these limiting conditions Uncertainty should be identified in terms of flood analysis. Against this background, in this study, 22 major heavy rain events in 2016 were analyzed for each of Mt. Biseul (BSL), Mt. Sobaek (SBS), Mt. Gari (GRS), Mt. Mohu (MHS), and Mt. Seodae (SDS) to determine the observation distance and altitude. The uncertainty of observation was quantified and an error map was derived. As a result of the analysis, it was found that, on average, the rainfall radar exceeded 10% up to 100 km and 30% over 150 km. Based on the average radar operating altitude angle, it was found that the error for the altitude was approximately 10% or less up to the second altitude angle, 20% at the third or higher altitude angle, and more than 50% at the fourth altitude angle or higher.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.9
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• pp.1641-1648
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• 2016

A weather radar estimates Doppler frequency and width of Doppler spectrum from the received weather signal which represents the return echoes from rain or dust particles in a corresponding area. These estimates are very important parameters since they are directly related to precipitation, wind velocity and degree of turbulence. Therefore, these estimated values should be highly reliable to obtain accurate weather information. However, the echoes of a weather radar include both the weather signal and the clutter which occurred from ground reflection or moving objects, etc. The existence of the clutter in the echoes may cause serious errors in the estimation of weather-related parameters. Therefore, in this paper, models are developed to represent the weather signal and the clutter for the purpose of analyzing estimation errors caused by the strong clutter echoes. Using these models, various return echoes according to the weather signal and clutter power are simulated to analyze the effects of the clutter.

• Journal of Korea Water Resources Association
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• v.33 no.2
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• pp.219-227
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• 2000

In Korea, heavy rainfall is mainly induced by the Changma front or frontal system passed over Korea periodically. Both its unknown mesoscale characteristics and the lack of direct measurements make it difficult to predict precipitation reasonably. To understand its 3-dimensional structure, initiation and development mechanism of precipitation in that system will be very helpful to forecast it more accurately. A meteorological radar is specially useful because it produces direct measurement with high resolution in time and space. In this study, representative frontal system is selected and analyzed specially focused on its vertical structure using radar data. Results shows that there are convective cells with horizontal scale of 10 - 20 km in precipitation system. Melting layer located between 3 and 5 km height, maximum fall speeds of rain drops were seen just below bright band.

• Journal of Korea Water Resources Association
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• v.48 no.12
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• pp.995-1009
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• 2015

Weather radar rainfall data has been recognized for making valuable contributions to short-term flood forecasting and management over the past decades. There are several advantages to better monitoring rainfall in ungauged area compared to ground-based rain gauges with which spatial patterns of the rainfall are not effectively identified. Hence, this study aims to develop a new scheme to forecast spatio-temporal rainfall field. The proposed model was based on an advection scheme to track wind patterns and velocity. The results showd a promising forecasting skill with quantitative and qualitative measures. It was confirmed that the forecasted rainfall may be effectively used an input data for a distributed hydrological model.

• Journal of Korea Water Resources Association
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• v.46 no.8
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• pp.857-871
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• 2013

The interest in rainfall observation and forecasting using remote sensing method like RADAR (Radio Detection and Ranging) and satellite image is increased according to increased damage by rapid weather change like regional torrential rain and flash flood. In this study, the basin runoff was calculated using adaptive neuro-fuzzy technique, one of the data driven model and MAPLE (McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation) forecasted precipitation data as one of the input variables. The flood estimation method using neuro-fuzzy technique and RADAR forecasted precipitation data was evaluated. Six rainfall events occurred at flood season in 2010 and 2011 in Chungju Reservoir basin were used for the input data. The flood estimation results according to the rainfall data used as training, checking and testing data in the model setup process were compared. The 15 models were composed of combination of the input variables and the results according to change of clustering methods were compared and analysed. From this study was that using the relatively larger clustering radius and the biggest flood ever happened for training data showed the better flood estimation. The model using MAPLE forecasted precipitation data showed relatively better result at inflow estimation Chungju Reservoir.

• Journal of Korea Water Resources Association
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• v.56 no.8
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• pp.471-484
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• 2023

Deep learning models based on generative adversarial neural networks are specialized in generating new information based on learned information. The deep generative models (DGMR) model developed by Google DeepMind is an generative adversarial neural network model that generates predictive radar images by learning complex patterns and relationships in large-scale radar image data. In this study, the DGMR model was trained using radar rainfall observation data from the Ministry of Environment, and rainfall prediction was performed using an generative adversarial neural network for a heavy rainfall case in August 2021, and the accuracy was compared with existing prediction techniques. The DGMR generally resembled the observed rainfall in terms of rainfall distribution in the first 60 minutes, but tended to predict a continuous development of rainfall in cases where strong rainfall occurred over the entire area. Statistical evaluation also showed that the DGMR method is an effective rainfall prediction method compared to other methods, with a critical success index of 0.57 to 0.79 and a mean absolute error of 0.57 to 1.36 mm in 1 hour advance prediction. However, the lack of diversity in the generated results sometimes reduces the prediction accuracy, so it is necessary to improve the diversity and to supplement it with rainfall data predicted by a physics-based numerical forecast model to improve the accuracy of the forecast for more than 2 hours in advance.

• Journal of the Korean GEO-environmental Society
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• v.25 no.2
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• pp.5-14
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• 2024

Reinforced concrete bridge decks are the first to be damaged by vehicle loads and rain infiltration. Concrete deterioration primarily occurs owing to the corrosion of rebars and other metal components by chlorides used for snow and ice melting. The structural condition and concrete deterioration of the bridge decks within the pavement were evaluated using ground-penetrating radar (GPR) survey data. To evaluate concrete deterioration in bridges, it is necessary to develop GPR data analysis techniques to accurately identify deteriorated locations and rebar positions. GPR exploration involves the acquisition of reflection and diffraction wave signals due to differences in radar wave propagation velocity in geotechnical media. Therefore, a full-waveform inversion (FWI) method was developed to evaluate the deterioration of reinforced concrete bridge decks by estimating the radar wave propagation velocity in geotechnical media using GPR data. Numerical experiments using a GPR velocity model confirmed the deterioration phenomena of bridge decks, such as concrete delamination and rebar corrosion, verifying the applicability of the developed technology. Moreover, using the synthetic GPR data, FWI facilitates the determination of rebar positions and concrete deterioration locations using inverted velocity images.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.10
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• pp.1012-1017
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• 2007

Rain data and water-level data are importantly used for dam operation at flood period. Because dams are directly controlled by the water-level data, the characteristic of the water-level gauges is necessary to be managed. Thus, we developed the standard test facility and method for testing the water-level gauges which are a float type, a supersonic type and a radar type. And we calculated the uncertainty of the standard test facility to maintain the accuracy of water-level gauges. Through development of this facility, we could obtain the characteristics and the calibration factor of the water-level gauges. And, this study showed that the standard test facility can be widely used for dam operation and basin management.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.1511-1514
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• 2006

본 연구에서는 현재 건설교통부에서 설치 및 운영 중에 있는 소형강우레이더의 최적화를 위해서 지상의 강우관측소 자료와 레이더 측정 자료의 실시간 보정방법을 이용하여 강우강도를 추정하였다. 본 연구에서 이용된 실시간 Z-R 관계식 적용으로 인한 강우강도 개선 정도를 파악하기 위해서 통상 일률적으로 적용되고 있는 $Z=200R^{1.6}$에 의한 강우강도 결과와 비교.분석하였으며, 지상의 강우관측소 실측 강우량과 비교함으로써 적용성을 보였다. 본 연구에서 이용된 보정방법은 강우보정에 소요되는 시간이 짧아 실시간 적용이 가능하며, 레이더 강우량의 정확한 추정으로 유역에서의 향상된 면적강우량 산출이 가능할 것으로 판단된다.