• Journal of The Korean Society of Agricultural Engineers
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• v.55 no.6
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• pp.19-30
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• 2013

This study is to evaluate the use of dual-polarization radar data for storm runoff modeling in Namgang dam (2,293 $km^2$) watershed using KIMSTORM (Grid-based KIneMatic wave STOrm Runoff Model). The Bisl dual-polarization radar data for 3 typhoons (Khanun, Bolaven, Sanba) and 1 heavy rain event in 2012 were obtained from Han River Flood Control Office. Even the radar data were overall less than the ground data in areal average, the spatio-temporal pattern between the two data was good showing the coefficient of determination ($R^2$) and bias with 0.97 and 0.84 respectively. For the case of heavy rain, the radar data caught the rain passing through the ground stations. The KIMSTORM was set to $500{\times}500$ m resolution and a total of 21,372 cells (156 rows${\times}$137 columns) for the watershed. Using 28 ground rainfall data, the model was calibrated using discharge data at 5 stations with $R^2$, Nash and Sutcliffe Model Efficiency (ME) and Volume Conservation Index (VCI) with 0.85, 0.78 and 1.09 respectively. The calibration results by radar rainfall showed $R^2$, ME and VCI were 0.85, 0.79, and 1.04 respectively. The VCI by radar data was enhanced by 5 %.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.4
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• pp.131-144
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• 2015

This research proposes a prototype for visualizing radar rainfall data using the unity 3D engine. The mashup of radar data with topographic information is necessary for the 3D visualization of the radar data with high quality. However, the mashup of a huge amount of radar data and topographic data causes the overload of data processing and low quality of the visualization results. This research utilized the Unitiy 3D engine, a widely used engine in the game industry, for visualizing the 3D topographic data such as the satellite imagery/the DEM(Digital Elevation Model) and radar rainfall data. The satellite image segmentation technique and the image texture layer mashup technique are employed to construct the 3D visualization system prototype based on the topographic information. The developed protype will be applied to the disaster-prevention works by providing the radar rainfall data with the 3D visualization based on the topographic information.

• 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.54 no.5
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• pp.321-334
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• 2021

A fixed reflectivity-rainfall relationship approach, such as the Marshall-Palmer relationship, for an entire year and different seasons, can be problematic in cases where the relationship varies spatially and temporally throughout a region. From this perspective, this study explores the use of long-term radar reflectivity for South Korea to obtain a nationwide calibrated Z-R relationship and the associated uncertainties within a Bayesian inference framework. A calibrated spatially structured pattern in the parameters exists, particularly for the wet season and parameter for the dry season. A pronounced region of high values during the wet and dry seasons may be partially associated with storm movements in that season. Overall, the radar rainfall fields based on the proposed modeling procedure are similar to the observed rainfall fields. In contrast, the radar rainfall fields obtained from the existing Marshall-Palmer relationship show a systematic underestimation. In the event of high impact weather, it is expected that the value of national radar resources can be improved by establishing an active watershed-level hydrological analysis system.

• Journal of Korea Water Resources Association
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• v.41 no.10
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• pp.1023-1034
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• 2008

The size and frequency of the natural disaster related to the severe storms are increased for recent decades in all over the globe. The damage from natural disasters such as typhoon, storm and local severe rainfall is very serious in Korea since they are concentrated on summer season. These phenomena will be more frequent in the future because of the impact of climate change related to increment of $CO_2$ concentration and the global warming. To reduce the damage from severe storms, a short-range precipitation forecasting model using a weather radar was developed. The study was conducted as following four tasks: conversion three-dimensional radar data to two-dimensional CAPPI(Constant Altitude Plan Position Indicator) efficiently, prediction of motion direction and velocity of a weather system, estimation of two-dimensional rainfall using operational calibration. Results demonstrated that two-dimensional estimation using weather radar is useful to analyze the spatial characteristics of local storms. If the precipitation forecasting system is linked to the flood prediction system, it should contribute the flood management and the mitigation of flood damages.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.136-136
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• 2021

In this study, a deep convolutional neural network (DCNN) model is proposed for short-term precipitation forecasting using weather radar-based images. The DCNN model is a combination of convolutional neural networks, autoencoder neural networks, and U-net architecture. The weather radar-based image data used here are retrieved from competition for rainfall forecasting in Korea (AI Contest for Rainfall Prediction of Hydroelectric Dam Using Public Data), organized by Dacon under the sponsorship of the Korean Water Resources Association in October 2020. This data is collected from rainy events during the rainy season (April - October) from 2010 to 2017. These images have undergone a preprocessing step to convert from weather radar data to grayscale image data before they are exploited for the competition. Accordingly, each of these gray images covers a spatial dimension of 120×120 pixels and has a corresponding temporal resolution of 10 minutes. Here, each pixel corresponds to a grid of size 4km×4km. The DCNN model is designed in this study to provide 10-minute predictive images in advance. Then, precipitation information can be obtained from these forecast images through empirical conversion formulas. Model performance is assessed by comparing the Score index, which is defined based on the ratio of MAE (mean absolute error) to CSI (critical success index) values. The competition results have demonstrated the impressive performance of the DCNN model, where the Score value is 0.530 compared to the best value from the competition of 0.500, ranking 16^th out of 463 participating teams. This study's findings exhibit the potential of applying the DCNN model to short-term rainfall prediction using weather radar-based images. As a result, this model can be applied to other areas with different spatiotemporal resolutions.

• Journal of the Korean Society of Hazard Mitigation
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• v.7 no.4
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• pp.19-29
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• 2007

This study compares various ground-truth designs of radar rainfall using rain-gauge data sets from Korea Meteorological Administration (KMA), AWS and Ministry of Construction and Transportation (MOCT). These Rain-gauge data sets and the Mt. Gwanak radar rainfall data for the same period were compared, and then the differences between two observed rainfall were evaluated with respect to the amount of bias. Additionally this study investigated possible differences in bias due to different storm characteristics. The application results showed no distinct differences between biases from three rain-gauge data sets, but some differences in their statistical characteristics. In overall, the design bias from MOCT was estimated to be the smallest among the three rain-gauge data sets. Among three storm events considered, the jangma with the highest spatial intermittency showed the smallest bias.

• Atmosphere
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• v.22 no.4
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• pp.387-400
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• 2012

This study aims at examining the sensitivity of numerical simulations to the resolution of initial and boundary data, and to an application of WRF (Weather Research and Forecasting) 3DVAR (Three Dimension Variational data Assimilation). To do this, we ran the WRF model by using GDAS (Global Data Assimilation System) FNL (Final analyses) and the KLAPS (Korea Local Analysis and Prediction System) analyses as the WRF's initial and boundary data, and by using an initial field made by assimilating the radar data to the KLAPS analyses. For the sensitivity experiment, we selected a heavy rainfall case of 21 September 2010, where there was localized torrential rain, which was recorded as 259.5 mm precipitation in a day at Seoul. The result of the simulation using the FNL as initial and boundary data (FNL exp) showed that the localized heavy rainfall area was not accurately simulated and that the simulated amount of precipitation was about 4% of the observed accumulated precipitation. That of the simulation using KLAPS analyses as initial and boundary data (KLAPC exp) showed that the localized heavy rainfall area was simulated on the northern area of Seoul-Gyeonggi area, which renders rather difference in location, and that the simulated amount was underestimated as about 6.4% of the precipitation. Finally, that of the simulation using an initial field made by assimilating the radar data to the KLAPS using 3DVAR system (KLAP3D exp) showed that the localized heavy rainfall area was located properly on Seoul-Gyeonggi area, but still the amount itself was underestimated as about 29% of the precipitation. Even though KLAP3D exp still showed an underestimation in the precipitation, it showed the best result among them. Even if it is difficult to generalize the effect of data assimilation by one case, this study showed that the radar data assimilation can somewhat improve the accuracy of the simulated precipitation.

• 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.

• Atmosphere
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• v.24 no.4
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• pp.457-470
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• 2014

Thermodynamic conditions related with localized torrential rainfall in the middle west region of Korean peninsula are examined using radar rain rate and radiosonde observational data. Localized torrential rainfall events in this study are defined by three criteria base on 1) any one of Automated Synoptic Observing System (ASOS) hourly rainfall exceeds $30mmhr^{-1}$ around Osan, 2) the rain (> $1mmhr^{-1}$) area estimated from radar reflectivity is less than $20,000km^2$, and 3) the rain (> $10mmhr^{-1}$) cell is detected clearly and duration is short than 24 hr. As a result, 13 cases were selected during the summer season of 10 years (2004-13). It was found that the duration, the maximum rain area, and the maximum volumetric rain rate of convective cells (> $30mmhr^{-1}$) are less than 9hr, smaller than $1,000km^2$, and $15,000{\sim}60,000m^3s^{-1}$ in these cases. And a majority of cases shows the following thermodynamic characteristics: 1) Convective Available Potential Energy (CAPE) > $800Jkg^{-1}$, 2) Convective Inhibition (CIN) < $40Jkg^{-1}$, 3) Total Precipitable Water (TPW) ${\approx}$ 55 mm, and 4) Storm Relative Helicity (SRH) < $120m^2s^{-2}$. These cases mostly occurred in the afternoon. These thermodynamic conditions indicated that these cases were caused by strong atmospheric instability, lifting to overcome CIN, and sufficient moisture. The localized torrential rainfall occurred with deep moisture convection result from the instability caused by convective heating.