• Atmosphere
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• v.25 no.3
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• pp.511-520
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• 2015

Existing studies on radar rainfall uncertainties were performed to reduce the uncertainty for each stage by using bias correction during the quantitative radar rainfall estimation process. However, the studies do not provide quantitative comparison with the uncertainties for all stages. Consequently, this study proposes a suitable approach that can quantify the uncertainties at each stage of the quantitative radar rainfall estimation process. First, the new approach can present initial and final uncertainties, increasing or decreasing the uncertainty, and the uncertainty percentage at each stage. Furthermore, Maximum Entropy (ME) was applied to quantify the uncertainty in the entire process. Second, for the uncertainty quantification of radar rainfall estimation at each stage, this study used two quality control algorithms, two rainfall estimation relations, and two bias correction techniques as post-processing and progressed through all stages of the radar rainfall estimation. For the proposed approach, the final uncertainty (ME = 3.81) from the ME of the bias correction stage was the smallest while the uncertainty of the rainfall estimation stage was higher because of the use of an unsuitable relation. Additionally, the ME of the quality control was at 4.28 (112.34%), while that of the rainfall estimation was at 4.53 (118.90%), and that of the bias correction at 3.81 (100%). However, this study also determined that selecting the appropriate method for each stage would gradually reduce the uncertainty at each stage. Finally, the uncertainty due to natural variability was 93.70% of the final uncertainty. Thus, the results indicate that this new approach can contribute significantly to the field of uncertainty estimation and help with estimating more accurate radar rainfall.

• Journal of Korea Water Resources Association
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• v.46 no.11
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• pp.1041-1052
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• 2013

In this study, we estimate parameters of a distributed hydrologic model, GRM (grid based rainfall-runoff model), using a model-independent parameter estimation tool, PEST. We implement auto calibration of model parameters such as initial soil moisture, multipliers of overland roughness and soil hydraulic conductivity in the Geumho River Catchment and the Gamcheon Catchment using radar rainfall estimates and ground-observed rainfall represented by Thiessen interpolation. Automatic calibration is performed by GRM-MP (multiple projects), a modified version of GRM without GUI (graphic user interface) implementation, and "Parallel PEST" to improve estimation efficiency. Although ground rainfall shows similar or higher cumulative amount compared to radar rainfall in the areal average, high spatial variation is found only in radar rainfall. In terms of accuracy of hydrologic simulations, radar rainfall is equivalent or superior to ground rainfall. In the case of radar rainfall, the estimated multiplier of soil hydraulic conductivity is lower than 1, which may be affected by high rainfall intensity of radar rainfall. Other parameters such as initial soil moisture and the multiplier of overland roughness do not show consistent trends in the calibration results. Overall, calibrated parameters show different patterns in radar and ground rainfall, which should be carefully considered in the rainfall-runoff modelling applications using radar rainfall.

• 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.

• Journal of Korea Water Resources Association
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• v.38 no.12 s.161
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• pp.1039-1049
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• 2005

The objective of this study is to produce optimal radar-derived rainfall for hydrologic utilization. The ground clutter and beam blockage effects from Mt. Kwanak station (E.L 608m) are removed from radar reflectivities by POD analysis. The reflectivities are used to produce radar rainfall data in the form of rain rates (mm/h) by the application of the Marshall-Palmer reflectivity versus rainfall relationship. However, these radar-derived rainfall are underestimated in temporal and spatial scale compared with observed one, so it is necessary to hire a correction scheme based on the gauge-to-radar (G/R) statistical adjustment technique. The selected watershed for studying the real-time correction of radar-rainfall estimation is the Soyang dam site, which is located approximately 100km east of Kwanak radar station. The results indicate that adjusted radar rainfall with the gauge measurement have reasonal G/R ratio ranged on 0.95-1.32 and less uncertainty with that mean standard deviation of G/R ratio are decreased by $9-28\%$. Mean areal precipitation from adjusted radar rainfall are well agreed to the observed one on the Soyang River watershed. It is concluded that the real-time bias adjustment scheme is useful to estimate accurate basin-based radar rainfall for hydrologic application.

• Journal of Korea Multimedia Society
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• v.20 no.12
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• pp.1874-1889
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• 2017

The paper aims to analyze structure of I/Q data observed from radar and reliably estimate rainfall through quality control of I/Q data that can quantify uncertainty of I/Q data occurring due to resultant errors. Radar rainfall data have strong uncertainty due to various factors influencing quality. In order to reduce this uncertainty, previously enumerated errors in quality need to be eliminated. However, errors cannot be completely eliminated in some cases as seen in random errors, so uncertainty is necessarily involved in radar rainfall data. Multi-Lag Method, one of I/Q data quality control methods, was applied to estimate precipitation with regard to I/Q data of rainfall radar in Mt. Sobaek.

• Proceedings of the Korea Water Resources Association Conference
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• 2003.05a
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• pp.93-100
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• 2003

In order to use radar rainfall data for flood management, it is necessary to study and develop a method for optimum error correction to obtain radar rainfall values that agree closely with surface rainfall data. This paper proposes an optimum estimation method for calculating rainfall in a river basin by using data from surface raingauges and radar raingauge systems. This paper also reports on recent applications of radar raingauge systems for accurate simulation of flood discharge based on river basin rainfall values obtained from radar raingauge systems.

• Proceedings of the Korea Water Resources Association Conference
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• 2001.05a
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• pp.35-44
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• 2001

As one of advanced uses of radar, a physically based rainfall prediction method which uses a conceptual rainfall model assimilated by information from volume scanning radar is shown. As another example of advanced utilization of weather radar, results from analyzing a hierarchical time-scale structure in dependency of rainfall distribution en topography are shown.

• Journal of Korea Water Resources Association
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• v.54 no.1
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• pp.27-38
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• 2021

Recently, many studies have been conducted to use the radar rainfall in hydrology. However, in the case of weather radar, the beam is blocked due to the limitation of the observation such as mountain effect, which causes underestimation of the radar rainfall. In this study, the radar rainfall was estimated using the Hybrid Sacn Reflectivity (HSR) technique for hydrological use of weather radar and the runoff analysis was performed using the GRM model which is a distributed rainfall-runoff model. As a result of performing the radar rainfall correction and runoff simulation for 5 rainfall events, the accuracy of the dual-polarization radar rainfall using the HSR technique (Q_H_KDP) was the highest with an error within 15% of the ground rainfall. In addition, the result of runoff simulation using Q_H_KDP also showed an accuracy of R2 of 0.9 or more, NRMSE of 1.5 or less and NSE of 0.5 or more. From this study, we examined the application of the dual-polarization radar and this results can be useful for studies related to the hydrological application of dual-polarization radar rainfall in the future.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.2B
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• pp.159-167
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• 2010

The purpose of this study is to get the adjusted radar rainfalls by ANN(Artificial Neural Network) method. In the case of radar rainfall, it has an advantage of spatial distribution characteristics of rainfall while point rainfall has an advantage at the point. Therefore we adjusted the radar rainfall by ANN method considering the advantages of two rainfalls of radar and point. This study constructed two ANN models of Model I and Model II for radar rainfall adjustment. We collected the three rainfall events and adjusted the radar rainfall for Anseong-cheon basin. The two events were inputted into the Modeland Model to derive the optimum parameters and the rest event was used for validation. The adjusted radar rainfalls by ANN method and the raw radar rainfall were used as the input data of ModClark model which is a semi-distributed model to simulate the runoff. As the results of the simulation, the runoff by raw radar rainfall were overestimated but the peak time and peak runoff from the adjusted rainfall by ANN were well fitted to the observed hydrograph.

• Journal of Korea Water Resources Association
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• v.48 no.4
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• pp.257-268
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• 2015

The objective of this study is to assess the dual-polarization radar for flood forecasting. First, radar rainfall has temporal and spatial errors, so estimated radar rainfall was compared with ground observation rainfall to assess accuracy improvement, especially, considering the radar range of observation and increase of the rainfall intensity. The results of this study showed that the error for estimated dual-polarization radar rainfall was less than single-polarization radar rainfall. And in this study, dual-polarization radar rainfall for flood forecasting was assessed using MAP (Mean Areal Precipitation) and SURR (Sejong University Rainfall Runoff) model in Namkang dam watershed. The results of MAP are more accurate using dual-polarization radar. And the results of runoff using dual-polarization radar rainfall showed that peak flow error was reduced approximately 12~63%, runoff volumes error was reduced by approximately 30~42%, and also the root mean square error decreased compared to the result of runoff using single-polarization radar rainfall. The results revealed that dual-polarization radar will contribute to improving the accuracy of the flood forecasting.