• Journal of Korea Water Resources Association
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• v.47 no.10
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• pp.867-878
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• 2014

Due to the nature of weather radar, blank areas occur due to impediments to observation, such as the ground clutter. Radar beam blockages have resulted in the underestimation rainfall amounts. To overcome these limitations, this study developed the Hybrid Scan Reflectivity (HSR) technique and compared the HSR results with existing methods. As a result, the HSR technique was able to estimate rainfalls in areas from which no reflectivity information was observable using existing methods. In case of estimating rainfalls depending on reflectivity scan techniques and beam-blockage/non beam-blockage, the HSR accuracy is superior. Furthermore, rainfall amounts derived from each method was inputted to the HEC-HMS to examine the accuracy of the flood simulations. The accuracy of the results using the HSR technique in contrast to the RAR calculation system and M-P relation was improved by 7% and 10%(based on correlation coefficients), and 18% and 34%(based on Nash-Sutcliffe Efficiency), on average, respectively. Therefore, it is advised that the HSR technique be utilized in the hydrology field to estimate flood discharge more accurately.

• Journal of Korea Water Resources Association
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• v.54 no.7
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• pp.453-462
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• 2021

As the frequency of localized heavy rainfall has increased during recent years, the importance of high-resolution radar data has also increased. This study aims to correct the bias of Dual Polarization radar that still has a spatial and temporal bias. In many studies, various statistical techniques have been attempted to correct the bias of radar rainfall. In this study, the bias correction of the S-band Dual Polarization radar used in flood forecasting of ME was implemented by a Convolutional Autoencoder (CAE) algorithm, which is a type of Convolutional Neural Network (CNN). The CAE model was trained based on radar data sets that have a 10-min temporal resolution for the July 2017 flood event in Cheongju. The results showed that the newly developed CAE model provided improved simulation results in time and space by reducing the bias of raw radar rainfall. Therefore, the CAE model, which learns the spatial relationship between each adjacent grid, can be used for real-time updates of grid-based climate data generated by radar and satellites.

• Journal of Korea Water Resources Association
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• v.48 no.5
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• pp.393-407
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• 2015

This study analyzed the characteristics of orographic effect using radar data for the Chungju dam basin. First, independent rainfall events were selected by applying the IETD (Interevent Time Definition) and rainfall threshold. Among those independent rainfall events, rather strong events were selected to decide the occurrence condition of orographic effect. Also, the average reflectivity was calculated for the entire period and for the period of storm center, and the change in reflectivity was analyzed by comparing the average reflectivity to that in the mountain area. Important rainfall factors were selected and applied to the logistic regression model to decide the occurrence condition of orographic effect. Summarizing the results is as follows. First, evaluation of the radar data along the passing line of a storm showed the increase of radar reflectivity in the mountain area. Second, the result of logistic regression analysis showed that the orographic effect in the Chungju Dam Basin mostly occurred when the rainfall intensity was higher than 4 mm/hr, the storm velocity was lower than 4 km/hr, and the approach angle was $90^{\circ}{\pm}5^{\circ}$.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2001.10a
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• pp.335-339
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• 2001

The Tropical Rainfall Measuring Mission(TRMM) Satellite was launched in November 1997, carrying into orbit the first space-borne Precipitation Radar(PR). The purpose of this study is to identify the relationship between TRMM/PR and AWS raingage data, and test the possibility to apply storm runoff prediction. Four TRMM/PR data in 1999 for Yongdam watershed was adopted and made a simple linear regression equation using AWS data. By using the equation, the storm runoff was estimated with the adjusted rainfall. TRMM/PR rainfall and runoff was overall underestimated by the carry-over effect of rainfall error and SCS-CN value selection.

• 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 Environmental Science International
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• v.24 no.4
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• pp.525-540
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• 2015

Spatial distribution of precipitation has been estimated based on the local gauge correction (LGC) with a fixed inverse distance weighting (IDW), which is not optimized in taking effective radius into account depending on the radar error. We developed an algorithm, improved local gauge correction (ILGC) which eliminates outlier in radar rainrate errors and optimize distance power for IDW. ILGC was statistically examined the hourly cumulated precipitation from weather for the heavy rain events. Adjusted radar rainfall from ILGC is improved to 50% compared with unadjusted radar rainfall. The accuracy of ILGC is higher to 7% than that of LGC, which resulted from a positive effect of the optimal algorithm on the adjustment of quantitative precipitation estimation from weather radar.

• Journal of Korea Water Resources Association
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• v.35 no.1
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• pp.25-36
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• 2002

Window Probability Matching Method(WPMM) is achieved by matching identical probability density of rain intensities and radar reflectivities taken only from small window centered about the gage. The equation of $Z_{e}-R$ relationship is obtained and compared with data between a DWSR-88C radar and high density rain gage networks within 150km from radar site in summer season, 1998. The probability density of radar effective reflectivity is distributed with high frequency near 15dBZ. The frequency distribution of rain intensities shows that rain intensity is lower than 10mm/hr in most part of radar coverage area. As the result of $Z_{e}-R$ relationship using WPMM, curved line has shown to the log scale spatially and it can be explained more flexible than any straight-line power laws at the transformation to the rainfall amount from $Z_e$ value. During 3 months, total radar cumulative rainfall amount estimated by $Z=200R^{1.6}$ and WPMM relationships are 44 and 80 percentages of total raingage amount, respectively. Therefore, $Z_{e}-R$ relationships by WPMM may be widely needed a statistical method for the computation of accumulated precipitation.

• Journal of Navigation and Port Research
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• v.46 no.6
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• pp.517-524
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• 2022

This paper analyzes wave measurement using X-band navigation (ship) radar, changes in radar signal due to snowfall and precipitation, and factors that obstruct wave measurement. Data obtained from the radar installed at Sokcho Beach were used, and data from the Korea Meteorological Administration and the Korea Hydrographic and Oceanographic Agency were used for the meteorological data needed for comparative verification. Data from the Korea Meteorological Administration are measured at Sokcho Meteorological Observatory, which is about 7km away from the radar, and data from the Korea Hydrographic and Oceanographic Agency are measured at a buoy about 3km away from the radar. To this point, changes in radar signals due to rainfall or snowfall have been transmitted empirically, and there is no case of an analysis comparing the results to actual weather data. Therefore, in this paper, precipitation, snowfall data, CCTV, and radar signals from the Korea Meteorological Administration were comprehensively analyzed in time series. As a result, it was confirmed that the wave height measured by the radar according to snowfall and rainfall was reduced compared to the actual wave height, and a decrease in the radar signal strength according to the distance was also confirmed. This paper is meaningful in that it comprehensively analyzes the decrease in the signal strength of radar according to snowfall and rainfall.

• Journal of Korea Water Resources Association
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• v.47 no.9
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• pp.839-852
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• 2014

This study analyzed the applicability of a rainfall downscaling algorithm in space-time multifractal framework (RDSTMF) in Korean Peninsula. To achieve this purpose, the 8 heavy rainfall events that occurred in Korea during the period between 2008 and 2012 were analyzed using the radar rainfall imagery. The result of the analysis indicated that there is a strong tendency of the multifractality for all 8 heavy rainfall events. Based on the multifractal exponents obtained from the analysis, the parameters of the RDSTMF were obtained and the relationship between the average intensity of the rainfall events and the parameters of the RDSTMF was developed. Based on this relationship, the synthetic space-time rainfall fields were generated using the RDSTMF. Then, the generated synthetic space-time rainfall fields were compared to the observation. The result of the comparison indicated that the RDSTMF can accurately reproduce the multifractal exponents of the observed rainfall field up to 3rd order and the cumulative density function of the observed space-time rainfall field with a reasoable accuracy.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.6
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• pp.1219-1228
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• 2015

The Fixed Area ARFs (Area Reduction Factors) method has limitations in providing exact information about spatial distribution due to the lack of enough density of rain gauge stations. In this study the storm-centered ARF was evaluated between frontal and typhoon storm events utilizing radar precipitation. In estimating storm-centered ARFs, in order to consider the horizontal advection, direction, and spatial distribution of rain cells, the rotational angle of rainfall of each rainfall event and the optimum areal rainfall within the spatial rain cell envelope was taken into account. Compared with the frontal storm, the ARF of typhoon storm shows narrow range of variability. It is noted that the ARFs of frontal storm increases with the rainfall duration, but those of typhoon storm shows opposite pattern. As a result the typhoon ARFs appear greater than frontal ARFs for 1~3 hours of duration, but less for more than 6 hours of duration.