• Atmosphere
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• v.25 no.2
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• pp.283-294
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• 2015

For urban flash flood simulation, we need the higher resolution radar rainfall than radar rainfall of KMA, which has 10 min time and 1km spatial resolution, because the area of subbasins is almost below $1km^2$. Moreover, we have to secure the high quantitative accuracy for considering the urban hydrological model that is sensitive to rainfall input. In this study, we developed the quantitative precipitation estimation (QPE), which has 250 m spatial resolution and high accuracy using KMA AWS and SK Planet stations with Mt. Gwangdeok radar data in Seoul area. As the results, the rainfall field using KMA AWS (QPE1) is showed high smoothing effect and the rainfall field using Mt. Gwangdeok radar is lower estimated than other rainfall fields. The rainfall field using KMA AWS and SK Planet (QPE2) and conditional merged rainfall field (QPE4) has high quantitative accuracy. In addition, they have small smoothed area and well displayed the spatial variation of rainfall distribution. In particular, the quantitative accuracy of QPE4 is slightly less than QPE2, but it has been simulated well the non-homogeneity of the spatial distribution of rainfall.

• Journal of Wetlands Research
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• v.4 no.2
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• pp.43-56
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• 2002

About 12 rain gauge stations of Korea, annual maximum rainfall series of before and after 1980 whose durations are 1, 2, 3, 6, 12, 24, 48, 72 hours respectively were composed and statistical characteristics of those time series were calculated and probability rainfall were estimated by L-moment frequency analysis method and compared each other in order to investigate the recent quantitative rainfall variations. And also, distribution curves of each statistical variations for each duration were constructed by using Kigging method to look into spacial rainfall variation aspects. As a result, We could confirm recent rainfall increase in the South Korea. And spatial increase pattern of standard deviation and frequency rainfall appeared analogously each other. 1n the cases of comparatively short rainfall duration, we could see relatively low increase or decrease tendency in Chungchong Province, Cholla-bukdo, Cholla-namdo eastern part, Kyongsang-namdo western part area. While, variations happened great1y in seaside district of east coast, southwest seashore, Inchon area etc. In the cases of longer durations relatively low increase was showed in southern seashore such as Yeosoo area and as distance recedes from this area, showed gradually augmented tendency. The aspect of mean looks similar tendency of above except that the variation rate of almost seaside district are big in the case of shorter durations. In addition, rainfall increases of short durations which became the center of hydrologist and meteorologist are unconfirmed in this study.

• Journal of Korea Water Resources Association
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• v.48 no.9
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• pp.719-728
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• 2015

In this study, urban runoff analyses were performed using high resolution Quantitative Precipitation Estimation (QPE), and variation of rainfall and runoff were analyzed to evaluate QPE data for urban runoff analysis. The five drainage districts (Seocho3, 4, 5, Yeoksam and Nonhyun) around Gangnam station were chosen as study area, the area is $7.4km^2$. Rainfall data from KMA AWS (34 stations), SKP AWS (156 stations) and Gwanduk radar were used for QPEs in Seoul area. Four types of QPE(QPE1: KMA AWS, QPE2: KMA+ SKP AWS, QPE3: Gwangduk radar, QPE4: QPE2+QPE3) of 6 events in July 2013 were generated by using Krigging and conditional merging. The temporal and spatial resolution of QPEs are 10 minutes and 250 m, respectively. The complex pipe network were treated as 773 manholes, 772 sub-drainage districts and 1,059 pipelines for urban runoff analysis as input data. QPE2 and QPE4 show spatial variation of rainfall by sub-drainage districts as 1.9 times bigger than QPE1. The peak runoff of QPE2 and QPE4 also show spatial variation as 6 times bigger than Gangnam and Seocho AWS. Thus, the spatial variation of rainfall and runoff could exist in small area such as this study area, and using high-resolution rainfall data is desirable for accurate urban runoff analysis.

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

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.378-381
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• 2007

Analyses of Tropical Rainfall Measuring Mission (TRMM) microwave radiometer (TMI) and precipitation radar (PR) data show that the rainfall inhomogeneity, represented by the coefficient of variation, decreases as rain rate increases at the low resolution (the footprint size of TMI 10 GHz channel). The rainfall inhomogeneity, however, is relatively constant for all rain rates at the high resolution (the footprint size of TMI 37 GHz channel). Consequently, radiometric signatures at lower spatial resolutions are characterized by larger dynamic range and smaller variability than those at higher spatial resolution. Based on the observed characteristics, this study develops a low-resolution (${\sim}40{\times}40$ km) rainfall retrieval algorithm utilizing realistic rainfall distributions in the a-priori databases. The purpose of the low-resolution rainfall algorithm is to make more reliable climatological rainfalls from various microwave sensors, including low-resolution radiometers.

• Journal of Korea Water Resources Association
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• v.40 no.12
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• pp.943-956
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• 2007

Among various statistics, the spatial correlation function, that is "correlogram", is frequently used to evaluate or design the rain gauge network and to model the rainfall field. The spatial correlation structure of rainfall has the significant variation due to many factors. Thus, the variation of spatial correlation structure of rainfall causes serious problems when deciding the spatial correlation function of rainfall within the basin. In this study, the spatial rainfall structure was modeled using bivariate mixed distributions to derive monthly spatial correlograms, based on Gaussian and lognormal distributions. This study derived the correlograms using hourly data of 28 rain gauge stations in the Keum river basin. From the results, we concluded as following; (1) Among three cases (Case A, Case B, Case C) considered, the Case A(+,+) seems to be the most relevant as it is not distorted much by zero measurements. (2) The spatial correlograms based on the lognormal distribution, which is theoretically as well as practically adequate, is better than that based on the Gaussian distribution. (3) The spatial correlation in July exponentially decrease more obviously than those in other months. (4) The spatial correlograms should be derived considering the temporal resolution(hourly, daily, etc) of interest.

• The Korean Journal of Applied Statistics
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• v.22 no.2
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• pp.425-433
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• 2009

Spatial models suitable for describing the evolving random fields in climate and environmental systems have been developed by many researchers. In general, rainfall in South Korea is highly variable in intensity and amount across space. This study characterizes the monthly and regional variation of rainfall fields using the spatial modeling. The main objective of this research is spatial prediction with the Bayesian hierarchical modeling (kriging) in order to further our understanding of water resources over space. We use the Bayesian approach in order to estimate the parameters and produce more reliable prediction. The Bayesian kriging also provides a promising solution for analyzing and predicting rainfall data.

• Journal of Positioning, Navigation, and Timing
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• v.4 no.2
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• pp.87-95
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• 2015

In this study, the temporal and spatial variation in precipitable water vapor (PWV) was analyzed for typhoon Ewiniar which had made landfall in the Korean peninsula in 2006. To make a contour map of PWV, zenith total delay (ZTD) was calculated using about 60 GPS permanent stations in Korea, and the pressure and temperature data of nearby AWS stations were interpolated and applied to the equation for calculating the PWV. While Typhoon Ewiniar was migrating north from the southern coast to the eastern coast of Korea, the PWV migrated showing a spatial distribution similar to that of rainfall. Also, the fluctuating pattern of the normalized PWV was analyzed, and the moving speed of the PWV was estimated using the delay time of the increase/decrease pattern in the eight-test stations. The result indicated that the moving speed of the PWV was about 35 km/h, which was similar to the average moving speed of the typhoon (38.9 km/h).

• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.1047-1051
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• 2004

The design storm is a crucial element in urban drainage design and hydrological modeling. The total rainfall depth of a design storm is usually estimated by hydrological frequency analysis using historic rainfall records. The different geostatistical approaches (ordinary kriging, universal kriging) have been used as estimators and their results are compared and discussed. Variogram parameters, the sill, nugget effect and influence range, are analysis. Kriging method was applied for developing contour maps of design storm depths In bocheong stream basin. Effect to utilize weather radar data and grid-based basin model on the spatial variation characteristics of storm requires further study.

• Journal of Korea Water Resources Association
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• v.43 no.11
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• pp.933-944
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• 2010

This study compares the CEM and SGS methods which are geostatistical stochastic simulation methods for assessing the uncertainty by spatial variability in the estimation of the spatial distribution of probability rainfall. In the stochastic simulations using CEM and SGS, two methods show almost similar results for the reproduction of spatial correlation structure, the statistics (standard deviation, coefficient of variation, interquartile range, and range) of realizations as uncertainty measures, and the uncertainty distribution of basin mean rainfall. However, the CEM is superior to SGS in aspect of simulation efficiency.