• Journal of Korea Water Resources Association
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• v.50 no.1
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• pp.1-15
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• 2017

Information on radar rainfall with high spatio-temporal resolution over large areas has been used to mitigate climate-related disasters such as flash floods. On the other hand, a well-known problem associated with the radar rainfall using the Marshall-Palmer relationship is the underestimation. In this study, we develop a new bias correction scheme based on the quantile regression method. This study employed a bivariate copula function method for the joint simulation between radar and ground gauge rainfall data to better characterize the error distribution. The proposed quantile regression based bias corrected rainfall showed a good agreement with that of observed. Moreover, the results of our case studies suggest that the copula function approach was useful to functionalize the error distribution of radar rainfall in an effective way.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2468-2473
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• 2005

Because the rain gauges of tipping bucket type can easily use the digital signal, the rain gauges are widely used for the meteorological observation. In general, the resolution of rain gauges of tipping bucket type can be categorized by the 0.1mm, 0.5mm, and 1.0mm classes. But, the error of the tipping bucket rain gauges is made by the intensity of rainfalls and is expected to make the standard calibration method for error measurement. Thus, we developed the hardware of standard calibration facility for rain gauges by weighting measurement method and proposed the standard procedure by rainfall intensity in this study Also, we calculated the error for the rainfall intensity and obtained useful result through the proposed calibration method.

• Journal of Korea Water Resources Association
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• v.41 no.5
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• pp.545-558
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• 2008

This study estimated the error involved in the areal average rainfall derived from 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 20 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 12 sub-basins are partially covered by the radar to result in 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. Conditioned that the total area coverage remains the same, the sampling error decreases as the number of clusters increases. 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 Korea Water Resources Association
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• v.37 no.3
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• pp.233-240
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• 2004

Most of hydrological analyses in the field of water resources are launched by gathering and analyzing rainfall data. Several methods have been developed to estimate areal rainfall from point rainfall data and to fill missing or ungaged data. Thiessen and Reciprocal Distance Squared(RDS) methods whose parameters are only dependent on inter-station distance are classical work in hydrology, but these techniques do not provide a continuous representation of the hydrologic process involved. In this study, kriging technique was applied to rainfall analysis in Nakdong river basin in order to complement the defects of these classical methods and to reflect spatial characteristics of regional rainfall. After spatial correlation and semi-variogram analyses were performed to perceive regional rainfall property, kriging analysis was performed to interpolate rainfall data for each grid Thus, these procedures were enable to estimate average rainfall of subbasins. In addition, poor region of rainfall observation was analyzed by spatial interpolation error for each grid and mean error for each subbasin.

• Journal of Korea Water Resources Association
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• v.30 no.5
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• pp.441-447
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• 1997

Rainfall observation using rain gage network or satellites includes the sampling error depending on the observation methods or plans. For example, the sampling using rain gages is continuous in time but discontinuous in space, which is nothing but the source of the sampling error. The sampling using satellites is the reverse case that continuous in space and discontinuous in time. The sampling error may be quantified by use of the temporal-spatial characteristics of rainfall and the sampling design. One of recent works on this problem was done by North and Nakamoto (1989), who derived a formulation for estimating the sampling error based on the temporal-spatial rainfall spectrum and the design scheme. The formula enables us to design an optimal rain gage network or a satellite operation plan providing the statistical characteristics of rainfall. In this paper the formula is reviewed and applied for the sampling error problems using several multi-dimensional precipitation models. The results show the limitation of the formulation, which cannot distinguish the model difference in case the model parameters can reproduce similar second order statistics of rainfall. The limitation can be improved by developing a new way to consider the higher order statistics, and eventually the probability density function (PDF) of rainfall.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.1798-1802
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• 2007

The practice of business estimate flood discharge by rainfall-flow relation that is easy collection of observation data. The important factor is rainfall, coefficient of runoff, and drainage area for analysis of runoff-flow relation.The practice of business usually use probability rainfall that use a weighted average value after each observation post estimate probability of non-same time. It has more error than same time probability rainfall, and it can excess of estimation because it can't consider space distribution of rainfall.The study of result showed similar aspect with existing ARF but width of coefficient become smaller. And the comparison of peak flow did not different what used by ARF and same time probability rainfall(A group). But non-same time probability rainfall is bigger 25% more than another(B group). Between A group and B group of the difference increased with the lapse of time.

• Journal of The Korean Society of Agricultural Engineers
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• v.47 no.6
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• pp.3-14
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• 2005

Accurate estimation of the spatial distribution of rainfall is critical to the successful modeling of hydrologic processes. The objective of this study is to evaluate the applicability of spatially distributed rainfall data. Spatially distributed rainfall was calculated using Kriging method and Thiessen method. The application of spatially distributed rainfall was appreciated to the runoff response from the watershed. The results showed that for each method the coefficient of determination for observed hydrograph was $0.92\~0.95$ and root mean square error was $9.78\~10.89$ CMS. Ordinary Kriging method showed more exact results than Simple Kriging, Universal Kriging and Thiessen method, based on comparison of observed and simulated hydrograph. The coefncient of determination for the observed peak flow was 0.9991 and runoff volume was 0.9982. The accuracy of rainfall-runoff prediction depends on the extent of spatial rainfall variability.

• Journal of Industrial Technology
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• v.27 no.A
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• pp.95-102
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• 2007

The practice of business estimate flood discharge by rainfall-flow relation that is easy collection of observation data. The important factor is rainfall, coefficient of runoff, and drainage area for analysis of runoff-flow relation. The practice of business usually use probability rainfall that use a weighted average value after each observation post estimate probability of non-same time. It has more error than same time probability rainfall, and it can excess of estimation because it can't consider space distribution of rainfall. The study of result showed similar aspect with existing ARF but width of coefficient become smaller. And the comparison of peak flow did not different what used by ARF and same time probability rainfall(A group). But non-same time probability rainfall is bigger 25% more than another(B group). Between A group and B group of the difference increased with the lapse of time.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.135-135
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• 2021

In water resources management, rainfall prediction with high accuracy is still one of controversial issues particularly in countries facing heavy rainfall during wet seasons in the monsoon climate. The aim of this study is to develop an artificial neural network (ANN) for predicting future six months of rainfall data (from April to September 2020) from daily meteorological data (from 1971 to 2019) such as rainfall, temperature, wind speed, and humidity at Seoul, Korea. After normalizing these data, they were trained by using a multilayer perceptron (MLP) as a class of the feedforward ANN with 15,000 neurons. The results show that the proposed method can analyze the relation between meteorological datasets properly and predict rainfall data for future six months in 2020, with an overall accuracy over almost 70% and a root mean square error of 0.0098. This study demonstrates the possibility and potential of MLP's applications to predict future daily rainfall patterns, essential for managing flood risks and protecting water resources.

• Communications for Statistical Applications and Methods
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• v.14 no.1
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• pp.1-9
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• 2007

A three-state Markov logistic regression model is suggested to forecast the probability of tomorrow's precipitation based on the current meteorological situation. The suggested model turns out to be better than Markov regression model in the sense of the mean squared error of forecasting for the rainfall data of Seoul area.