• Journal of Korea Water Resources Association
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• v.49 no.7
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• pp.625-634
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• 2016

Three leading agencies under different ministries - Korea Meteorological Administration (KMA) in the ministry of Environment, Han river control office in the Ministry of Land, Infrastructure and Transport (MOLIT) and Weather Group of ROK Air Force in the Ministry of National Defense (MND) - have been operated radars in the purpose of observing weather, hydrology and military operational weather in Korea. Eight S-band dual-pol. radars have been newly installed or replaced by these ministries over different places by 2015. However each ministry has different aims of operating radars, observation strategies, data processing algorithms, etc. Due to the differences, there is a wide level of accuracy on observed radar data as well as the composite images made of the cross governmental radar measurement. Gaining fairly high level of accuracy on radar data obtained by different agencies has been shared as a great concern by the ministries. Thus, "an agreement of harmonizing weather and hydrological radar products" was made by the three ministries in 2010. Particularly, this is very important to produce better rainfall estimation using the cross governmental radar measurement. Weather Radar Center(WRC) in KMA has been developed an empirical method using measurements observed by Yongin testbed radar. This study is aiming to examine the efficiency of the empirical method to improve the accuracies of radar rainfalls estimated from cross governmental dual-pol. radar measurements. As a result, the radar rainfalls of three radars (Baengnyeongdo, Biseulsan, and, Sobaeksan Radar) were shown improvement in accuracy (1-NE) up to 70% using data from May to October in 2015. Also, the range of the accuracies in radar rainfall estimation, which were from 30% to 60% before adjusting polarimetric variables, were decreased from 65% to 70% after adjusting polarimetric variables.

• Journal of Korea Water Resources Association
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• v.42 no.6
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• pp.493-503
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• 2009

Estimation of the mean-field bias of radar rainfall is to determine the difference between the areal means of radar and rain gauge rainfall, where the rain gauge rainfall is assumed to be the truth. To exactly determine this bias, the variance of the difference between two observations must be small enough, thus, enough number of observations is indispensible. So, the problem becomes to determine the number of rain gauges to satisfy the level of variance of the difference between two observations. Especially, this study focuses on the case when the rain gauges are disproportionate spatially. This is the problem for the Ganghwa rain radar for the observation of rainfall within the Imjin river basin and the same problem also occurs when a radar is located in between land and ocean. This study considered the Imjin river basin, and compared two cases when rain gauges are available only within the downstream part, about one third of the whole basin, and over the whole basin. Based on the results derived, the rain gauge density within the downstream part of the Imjin river basin was proposed to secure the same accuracy obtained when the rain gauges are available over the whole Imjin river basin.

• Journal of Korea Water Resources Association
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• v.50 no.12
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• pp.849-862
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• 2017

The increased frequency of meteorological disasters has been observed due to increased extreme events such as heavy rainfalls and flash floods. Numerous studies using high-resolution weather radar rainfall data have been carried out on the hydrological effects. In this study, a conditional merging technique is employed, which makes use of geostatistical methods to extract the optimal information from the observed data. In this context, three different techniques such as kriging, inverse distance weighting and spline interpolation methods are applied to conditionally merge radar and ground rainfall data. The results show that the estimated rainfall not only reproduce the spatial pattern of sub-hourly rainfall with a relatively small error, but also provide reliable temporal estimates of radar rainfall. The proposed modeling framework provides feasibility of using conditionally merged rainfall estimation at high spatio-temporal resolution in ungauged areas.

• Journal of the Korean earth science society
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• v.36 no.1
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• pp.109-124
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• 2015

A novel approach, hybrid surface rainfall (KNU-HSR) technique developed by Kyungpook Natinal University, was utilized for improving the radar rainfall estimation. The KNU-HSR technique estimates radar rainfall at a 2D hybrid surface consistings of the lowest radar bins that is immune to ground clutter contaminations and significant beam blockage. Two HSR techniques, static and dynamic HSRs, were compared and evaluated in this study. Static HSR technique utilizes beam blockage map and ground clutter map to yield the hybrid surface whereas dynamic HSR technique additionally applies quality index map that are derived from the fuzzy logic algorithm for a quality control in real time. The performances of two HSRs were evaluated by correlation coefficient (CORR), total ratio (RATIO), mean bias (BIAS), normalized standard deviation (NSD), and mean relative error (MRE) for ten rain cases. Dynamic HSR (CORR=0.88, BIAS= $-0.24mm\;hr^{-1}$, NSD=0.41, MRE=37.6%) shows better performances than static HSR without correction of reflectivity calibration bias (CORR=0.87, BIAS= $-2.94mm\;hr^{-1}$, NSD=0.76, MRE=58.4%) for all skill scores. Dynamic HSR technique overestimates surface rainfall at near range whereas it underestimates rainfall at far ranges due to the effects of beam broadening and increasing the radar beam height. In terms of NSD and MRE, dynamic HSR shows the best results regardless of the distance from radar. Static HSR significantly overestimates a surface rainfall at weaker rainfall intensity. However, RATIO of dynamic HSR remains almost 1.0 for all ranges of rainfall intensity. After correcting system bias of reflectivity, NSD and MRE of dynamic HSR are improved by about 20 and 15%, respectively.

• Journal of Korea Water Resources Association
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• v.53 no.7
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• pp.493-505
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• 2020

The rainfall-runoff model has been generally adopted to obtain a consistent runoff sequence with the use of the long-term ground-gauged based precipitation data. The Thiessen polygon is a commonly applied approach for estimating the mean areal rainfall from the ground-gauged precipitation by assigning weight based on the relative areas delineated by a polygon. However, spatial bias is likely to increase due to a sparse network of the rain gauge. This study aims to generate continuous runoff sequences with the mean areal rainfall obtained from radar rainfall estimates through a PRMS rainfall-runoff model. Here, the systematic error of radar rainfall is corrected by applying the G/R Ratio. The results showed that the estimated runoff using the corrected radar rainfall estimates are largely similar and comparable to that of the Thiessen. More importantly, one can expect that the mean areal rainfall obtained from the radar rainfall estimates are more desirable than that of the ground in terms of representing rainfall patterns in space, which in turn leads to significant improvement in the estimation of runoff.

• Journal of Korea Water Resources Association
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• v.42 no.12
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• pp.1039-1052
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• 2009

Radar measurement with high temporal and spatial resolutions can be a valuable source of data, especially in the areas where rain gauge installment is not practical. However, this kind of data brings with it many errors. The objective of this paper is to propose a method to evaluate statistically the quantitative and qualitative accuracy at different radar ranges, temporal intervals and raingage densities and use a bias adjustment technique to improve the quality of radar rainfall for the purpose of hydrological application. The method is tested with the data of 2 storm events collected at Jindo (S band) and Kwanak (C band) radar stations. The obtained results show that the accuracy of radar rainfall estimation increases when time interval rises. Radar data at the shorter range seems to be more accurate than the further one, especially for C-band radar. Using the Monte Carlo simulation experiment, we find out that the sampling error of the bias between radar and gauge rainfall reduces nonlinearly with increasing raingage density. The accuracy can be improved considerably if the real-time bias adjustment is applied, making adjusted radar rainfall to be adequately good to apply for hydrological application.

• 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 Korea Water Resources Association
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• v.48 no.1
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• pp.57-67
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• 2015

One of main benefits of a dual polarization radar is improvement of quantitative rainfall estimation. In this paper, performance of two representative rainfall estimation methods for a dual polarization radar, JPOLE and CSU algorithms, have been compared by using data from a MOLIT S-band dual polarization radar. In addition, this paper presents evaluation of specific differential phase ($K_{dp}$) retrieval algorithm proposed by Lim et al. (2013). Current $K_{dp}$ retrieval methods are based on range filtering technique or regression analysis. However, these methods can result in underestimating peak $K_{dp}$ or negative values in convective regions, and fluctuated $K_{dp}$ in low rain rate regions. To resolve these problems, this study applied the $K_{dp}$ distribution method suggested by Lim et al. (2013) and evaluated by adopting new $K_{dp}$ to JPOLE and CSU algorithms. Data were obtained from the Mt. Biseul radar of MOLIT for two rainfall events in 2012. Results of evaluation showed improvement of the peak $K_{dp}$ and did not show fluctuation and negative $K_{dp}$ values. Also, in heavy rain (daily rainfall > 80 mm), accumulated daily rainfall using new $K_{dp}$ was closer to AWS observation data than that using legacy $K_{dp}$, but in light rain(daily rainfall < 80mm), improvement was insignificant, because $K_{dp}$ is used mostly in case of heavy rain rate of quantitative rainfall estimation algorithm.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.528-530
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• 2003

We present here, some of the studies carried for estimation of rainfall over land and oceanic regions in and around South Korea. We use active and passive microwave measurements from TRMM ? TMI and Precipitation Radar (PR) respectively during a typhoon even named ? RUSA that took place during 30 Aug. 2002. We have followed due approach by Yao at. all (2002) and examined the performance of their algorithm using two main predictor variable, named as Scattering Index (SI) and Polarization Corrected Brightness Temperature (PCT) while using TMI data. The rainfall fnus estimated using PST and SI shows some Underestimation as compared to the 2A25 rainfall products from the PR in common area of overlap. A larger database thus would be used in future. To establish a new rain rate algorithm over Korean region based on the present case study.

• 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.