• Proceedings of the Korea Water Resources Association Conference
• /
• 2020.06a
• /
• pp.166-166
• /
• 2020

Recently, As the frequency of localized heavy rains increases, the use of high-resolution radar data is increasing. The produced radar rainfall has still gaps of spatial and temporal compared to gauge observation rainfall, and in many studies, various statistical techniques are performed for correct rainfall. In this study, the precipitation correction of the S-band Dual Polarization radar in use in the flood forecast was performed using the ConvAE algorithm, one of the Convolutional Neural Network. The ConvAE model was trained based on radar data sets having a 10-min temporal resolution: radar rainfall data, gauge rainfall data for 790minutes(July 2017 in Cheongju flood event). As a result of the validation of corrected radar rainfall were reduced gaps compared to gauge rainfall and the spatial correction was also performed. Therefore, it is judged that the corrected radar rainfall using ConvAE will increase the reliability of the gridded rainfall data used in various physically-based distributed hydrodynamic models.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2015.05a
• /
• pp.180-180
• /
• 2015

Weather radar has been widely used in measuring precipitation and discharge and predicting flood risks. The radar rainfall estimate has one of the essential problems in terms of uncertainty and accuracy. Previous study analyzed radar errors to reduce its uncertainty or to improve its accuracy. Furthermore, a recent analyzed the effect of radar error on rainfall-runoff using spatial error model (SEM). SEM appropriately reproduced radar error including spatial correlation. Since the SEM does not take the time dependence into account, its time variability was not properly investigated. Therefore, in the current study, we extend the SEM including time dependence as well as spatial dependence, named after Spatial-Temporal Error Model (STEM). Radar rainfall events generated with STEM were tested so that the peak runoff from the response of a basin could be investigated according to dependent error. The Nam River basin, South Korea, was employed to illustrate the effects of STEM on runoff peak flow.

• Journal of Integrative Natural Science
• /
• v.4 no.3
• /
• pp.229-237
• /
• 2011

Today they use a weather radar with spatially high resolution in predicting rainfall intensity and utilizing the information for super short-range forecast in order to make predictions of such severe meteorological phenomena as heavy rainfall and snow. For a weather radar, they use the Z-R relation between the reflectivity factor(Z) and rainfall intensity(R) by rainfall particles in the atmosphere in order to estimate intensity. Most used among the various Z-R relation is $Z=200R^{1.6}$ applied to stratiform rain. It's also used to estimate basic rainfall intensity of a weather radar run by the weather center. This study set out to compare rainfall intensity between the reflectivity of a weather radar and the ground rainfall of ASOS(Automatic Surface Observation System) by analyzing many different cases of heavy rain, analyze the errors of different weather radars and identify their problems, and investigate their applicability to nowcasting in case of severe weather.

• Journal of Korea Multimedia Society
• /
• v.22 no.12
• /
• pp.1430-1437
• /
• 2019

In this paper, we proposed a methodology for estimating rainfall intensity using a W-band FMCW automotive radar signal which is the core technology of autonomous driving car. By comparing and analyzing the results of rainfall and non-rainfall observation, we found that the reflection intensity of the automotive radar is changed with rainfall intensity. We could confirm the possibility of deriving the quantitative precipitation estimation using the methodology derived from this result. In addition it can be possible to develop a new paradigm of precipitation observation technique by observing various events together with the weather radar and the ground rainfall observation equipment.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2016.05a
• /
• pp.164-164
• /
• 2016

Radar rainfall estimates have been widely used in calculating rainfall amount approximately and predicting flood risks. The radar rainfall estimates have a number of error sources such as beam blockage and ground clutter hinder their applications to hydrological flood forecasting. Moreover, it has been reported in paper that those errors are inter-correlated spatially and temporally. Therefore, in the current study, we tested influence about spatio-temporal errors in radar rainfall estimates. Spatio-temporal errors were simulated through a stochastic simulation model, called Multivariate Autoregressive (MAR). For runoff simulation, the Nam River basin in South Korea was used with the distributed rainfall-runoff model, Vflo. The results indicated that spatio-temporal dependent errors caused much higher variations in peak discharge than spatial dependent errors. To further investigate the effect of the magnitude of time correlation among radar errors, different magnitudes of temporal correlations were employed during the rainfall-runoff simulation. The results indicated that strong correlation caused a higher variation in peak discharge. This concluded that the effects on reducing temporal and spatial correlation must be taken in addition to correcting the biases in radar rainfall estimates. Acknowledgements This research was supported by a grant from a Strategic Research Project (Development of Flood Warning and Snowfall Estimation Platform Using Hydrological Radars), which was funded by the Korea Institute of Construction Technology.

• 한국방재학회:학술대회논문집
• /
• 2008.02a
• /
• pp.97-100
• /
• 2008

This study estimated the error involved in the areal average rainfall derived incomplete radar information due to radar partial coverage of a basin or sub-basin. This study considers the Han River Basin as an application example for the rainfall observation using the Ganghwa rain radar. Among the total of 24 mid-sized sub-basins of the Han River Basin evaluated in this study, only five sub-basins are fully covered by the radar and three are totally uncovered. Remaining 16 sub-basins are partially covered by the radar leading incomplete radar information available. When only partial radar information is available, the sampling error decreases proportional to the size of the radar coverage, which also varies depending on the number of clusters. It is general that smaller sampling error can be expected when the number of clusters increases if the total area coverage remains the same. This study estimated the sampling error of the areal average rainfall of partially-covered mid-sized sub-basins of the Han River Basin, and the results show that the sampling error could be at least several % to maximum tens % depending on the relative coverage area.

• Journal of Wetlands Research
• /
• v.12 no.1
• /
• pp.51-61
• /
• 2010

Changes in climate have largely increased concentrated heavy rainfall, which in turn is causing enormous damages to humans and properties. Therefore, it is important to understand the spatial-temporal features of rainfall. In this study, RADAR rainfall was used to calculate gridded areal rainfall which reflects the spatial-temporal variability. In addition, Kalman-filter method, a stochastical technique, was used to combine ground rainfall network with RADAR rainfall network to calculate areal rainfall. Thiessen polygon method, Inverse distance weighting method, and Kriging method were used for calculating areal rainfall, and the calculated data was compared with adjusted areal RADAR rainfall measured using the Kalman-filter method. The result showed that RADAR rainfall adjusted with Kalman-filter method well-reproduced the distribution of raw RADAR rainfall which has a similar spatial distribution as the actual rainfall distribution. The adjusted RADAR rainfall also showed a similar rainfall volume as the volume shown in rain gauge data. Anseong-Cheon basin was used as a study area and the RADAR rainfall adjusted with Kalman-filter method was applied in $Vflo^{TM}$ model, a physical-based distributed model, and ModClark model, a semi-distributed model. As a result, $Vflo^{TM}$ model simulated peak time and peak value similar to that of observed hydrograph. ModClark model showed good results for total runoff volume. However, for verifying the parameter, $Vflo^{TM}$ model showed better reproduction of observed hydrograph than ModClark model. These results confirmed that flood runoff simulation is applicable in domestic settings(in South Korea) if highly accurate areal rainfall is calculated by combining gauge rainfall and RADAR rainfall data and the simulation is performed in link to the distributed hydrological model.

• Journal of Wetlands Research
• /
• v.14 no.2
• /
• pp.243-254
• /
• 2012

Recently, the use of radar rainfall data that can help tracking of the development and movement of rainfall's spatial distribution is drawing much attention in hydrology. The reliability of existing radar rainfall compared to gauge rainfall data on the ground has not yet been confirmed and so we have difficulties to apply the radar rainfall in hydrology. The radar rainfall for the applications in hydrology are adjusted merging method derived from gage. This study uses the Mean-Field Bias (MFB) and Statistical Objective Analysis (SOA) as correction methods to create adjusted grid-based radar rainfall data which can represent the temporal and spatial distribution of rainfall. This study used a storm event occurred in August 2010 for the adjustment of radar rainfall. In addition, the grid-based distributed rainfall-runoff model (Vflo), which enables more detailed examinations of spatial flux changes in the basin rather than the lumped hydrological models, has been applied to Gamcheon river basin which is a tributary of Nakdong River located in south-eastern part of the Korean peninsular and the basin area is $1005km^2$. The simulated runoff was compared with the observed runoff in an attempt to evaluate the usability of radar rainfall data and the reliability of the correction methods. The error range of peak discharge using each correction method was within 20 percent and the efficiency of the model was between 60 and 80 percent. In particular, the SOA method showed better results than MFB method. Therefore, the SOA method could be used for the adjustment of grid-based radar rainfall and the adjusted radar rainfall can be used as an input data of rainfall-runoff models.

• Journal of Korea Water Resources Association
• /
• v.50 no.1
• /
• pp.17-28
• /
• 2017

A recent increase in extreme weather events and flash floods associated with the enhanced climate variability results in an increase in climate-related disasters. For these reasons, various studies based on a high resolution weather radar system have been carried out. The weather radar can provide estimates of precipitation in real-time over a wide area, while ground-based rain gauges only provides a point estimate in space. Weather radar is thus capable of identifying changes in rainfall structure as it moves through an ungauged basin. However, the advantage of the weather radar rainfall estimates has been limited by a variety of sources of uncertainty in the radar reflectivity process, including systematic and random errors. In this study, we developed an ensemble radar rainfall estimation scheme using the multivariate copula method. The results presented in this study confirmed that the proposed ensemble technique can effectively reproduce the rainfall statistics such as mean, variance and skewness (more importantly the extremes) as well as the spatio-temporal structure of rainfall fields.

• Journal of Multimedia Information System
• /
• v.5 no.4
• /
• pp.245-256
• /
• 2018

This study estimated rainfall information more effectively by image signals through the information system of weather radar. Based on this, we suggest the way to estimate quantitative precipitation utilizing overlapped observation area of radars. We used the overlapped observation range of ground hyetometer observation network and radar observation network which are dense in our country. We chose the southern coast where precipitation entered from seaside is quite frequent and used Sungsan radar installed in Jeju island and Gudoksan radar installed in the southern coast area. We used the rainy season data generated in 2010 as the precipitation data. As a result, we found a reflectivity bias between two radar located in different area and developed the new quantitative precipitation estimation method using the bias. Estimated radar rainfall from this method showed the apt radar rainfall estimate than the other results from conventional method at overall rainfall field.