• Journal of Wetlands Research
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• v.16 no.1
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• pp.113-124
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• 2014

The observed rainfall may be different along with the altitude of rain gauge, resulting in the fact that the characteristics of rainfall events occurred in urban or mountainous areas are different. Due to the mountainous effects, in higher altitude, the uncertainty involved in the rainfall observation gets higher so that the density of rain gauges should be more dense. Basically, a methodology for the rain gauge network evaluation, considering this altitude effect of rain gauges can account for the mountainous effects and becomes an important step for forecasting flash flood and calibrating of the radar rainfall. For this reason, in this study, we suggest a methodology for rain gauge network evaluation with consideration of the rain gauge's altitude. To explore the density of rain gauges at each level of altitude, the Equal-Altitude-Ratio of the density of rain gauges, which is based on the fixed amount of elevation and the Equal-Area-Ratio of the density of rain gauges, which is based on the fixed amount of basin area are designed. After these two methods are applied to a real watershed, it is found that the Equal-Area-Ratio generates better results for evaluation of a rain gauge network with consideration of rain gauge's altitude than the Equal-Altitude-Ratio does. In addition, for comparison between the soundness of rain gauge networks in other watersheds, the Coefficient of Variation (CV) of the rain gauge density by the Equal-Area-Ratio is served as the index for the evenness of the distribution of the rain gauge's altitude. The suggested method is applied to the five large watersheds in Korea and it is found that rain gauges installed in a watershed having less value of the CV shows more evenly distributed than the ones in a watershed having higher value of the CV.

• Journal of Wetlands Research
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• v.19 no.1
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• pp.103-111
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• 2017

In this study, we estimate the NNI(Nearest Neighbor Index) which is considered altitude of rain gauge network as a method for evaluating appropriateness of spatial distribution and the current rain gauge network is evaluated. The altitude is divided by equal-area-ratio and optimal NNI within given basin condition is estimated using harmony search method for considering geographical conditions that vary from altitude to altitude. After calculating current state and optimal NNI for each altitude, the distribution of the rain gauge network is evaluated based on the difference between the two NNIs. As a result, it founds that the density of rain gauge networks is relatively thin as the altitude increases. Furthermore, it will be possible to construct an efficient rain gauge network if the characteristics of different altitudes are considered when a new rain gauge network is newly constructed.

• Journal of Korea Water Resources Association
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• v.40 no.11
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• pp.841-849
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• 2007

This study evaluated the effect of combined rainfall observation of using rain gauge and rain radar. The effect of combined observations is to be evaluated by considering the decrease of measurement error due to combined use of design orthogonal observation methods. As an example, this study evaluated the rain gauge network of the Keum river basin, and showed how the density of rain gauges could be decreased by combining the radar observation. This study applied the researches on sampling error by North and Nakamoto(1989), Yoo et al. (1996) and Yoo (1997), also the simple NFD model for representing the rainfall field. The model parameters were decided using the rainfall characteristics (correlation time and length) estimated using the data collected in the Keum River Basin by 28 rain gauges and the operation rule of radar was assumed arbitrarily. This study considered the rain gauge density criteria provided by WMO(1994) and the rain gauge density installed in the Keum river basin to decrease the rain gauge density under the condition of introducing the radar.

• Journal of Korea Water Resources Association
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• v.42 no.6
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• pp.493-503
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• 2009

Estimation of the mean-field bias of radar rainfall is to determine the difference between the areal means of radar and rain gauge rainfall, where the rain gauge rainfall is assumed to be the truth. To exactly determine this bias, the variance of the difference between two observations must be small enough, thus, enough number of observations is indispensible. So, the problem becomes to determine the number of rain gauges to satisfy the level of variance of the difference between two observations. Especially, this study focuses on the case when the rain gauges are disproportionate spatially. This is the problem for the Ganghwa rain radar for the observation of rainfall within the Imjin river basin and the same problem also occurs when a radar is located in between land and ocean. This study considered the Imjin river basin, and compared two cases when rain gauges are available only within the downstream part, about one third of the whole basin, and over the whole basin. Based on the results derived, the rain gauge density within the downstream part of the Imjin river basin was proposed to secure the same accuracy obtained when the rain gauges are available over the whole Imjin river basin.

• Journal of Wetlands Research
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• v.15 no.3
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• pp.413-422
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• 2013

In recent, the rainfall is showing different properties in space and time but the ground rain gauge only can observe rainfall at a point. This means the ground rain gauge has the limitations in spatial and temporal resolutions to measure rainfall and so there is a need to utilize radar rainfall which can consider spatial distribution of rainfall This study tried to apply radar rainfall for runoff simulation on an urban drainage system. The study area is Guro-gu, Seoul and we divided study area into subbasins based on rain gauge network of AWS(Automatic Weather station). Then the radar rainfalls were adjusted using rainfall data of rain gauge stations the areal rainfalls were obtained. The runoffs were simulated by using XP-SWMM model in subbasins of an urban drainage system. As the results, the adjusted radar rainfalls were underestimated in the range of 60 to 95% of rain gauge rainfalls and so the simulated runoffs from the adjusted radar and gauge rainfalls also showed the differences. The runoff peak time from radar rainfall was occurred more fast than that from gauge rainfall.

• Journal of Korea Water Resources Association
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• v.48 no.4
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• pp.245-255
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• 2015

This study used high density network of integrated meteorological sensor, which are operated by SK Planet, with KMA weather stations to estimate the quantitative precipitation field in Seoul area. We introduced SK Planet network and analyzed quality of the observed data for 3 months data from 1 July to 30 September 2013. As the quality analysis result, we checked most SK Planet stations observed similar with previous KMA stations. We developed the real-time quality check and adjustment method to reduce the error effect for hydrological application by missing and outlier value and we confirmed the developed method can be corrected the missing and outlier value. Through this method, we used the 190 stations(KMA 34 stations, SK Planet 156 stations) that missing ratio is less than 20% and the effect of the outlier was the smallest for quantitative precipitation estimation. Moreover, we evaluated reproducibility of rainfall field high density rain gauge network has $3km^2$/gauge. As the result, the spatial relative frequency of rainfall field using SK Planet and KMA stations is similar with radar rainfall field. And, it supplement the blank of KMA observation network. Especially, through this research we will take advantage of the density of the network to estimate rainfall field which can be considered as a very good approximation of the true value.

• Journal of the Korean earth science society
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• v.34 no.1
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• pp.13-27
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• 2013

Quantitative precipitation estimation (QPE) is one of the most important elements in meteorological and hydrological applications. In this study, we adjusted the QPE from an S-band weather radar based on co-kriging method using the geostatistical structure function of error distribution of radar rainrate. In order to estimate the accurate quantitative precipitation, the error of radar rainrate which is a primary variable of co-kriging was determined by the difference of rain rates from rain gauge and radar. Also, the gauge rainfield, a secondary variable of co-kriging is derived from the ordinary kriging based on raingauge network. The error distribution of radar rain rate was produced by co-kriging with the derived theoretical variogram determined by experimental variogram. The error of radar rain rate was then applied to the radar estimated precipitation field. Locally heavy rainfall case during 6-7 July 2009 is chosen to verify this study. Correlation between adjusted one-hour radar rainfall accumulation and rain gauge rainfall accumulation improved from 0.55 to 0.84 when compared to prior adjustment of radar error with the adjustment of root mean square error from 7.45 to 3.93 mm.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.09a
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• pp.95-96
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• 2005

• 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 Korea Water Resources Association
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• v.39 no.4 s.165
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• pp.335-346
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• 2006

Among various input data to hydrologic models, rainfall measurements arguably have the most critical influence on the performance of hydrologic model. Traditionally, hydrologic models have relied on point gauge measurements to provide the area-averaged rainfall information. However, rainfall estimates from gauges become inadequate due to their poor representation of areal rainfall, especially in situations with sparse gauge network. Alternatively, radar that covers much larger areas has become an attractive instrument for providing area- averaged precipitation information. Despite of the limitation of the QPE(Quantitative Precipitation Estimation) using radar, we can get the better information of spatial variability of rainfall fields. Also, rain-gauges give us the better quantitative information of rainfall field. Therefore, in this study, we developed improved methodologies tu estimate rainfall fields using an ordinary cokriging technique which optimally merges radar reflectivity data into rain-gauges data.