• Korean Journal of Agricultural and Forest Meteorology
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• v.6 no.4
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• pp.211-217
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• 2004

The instrument used in this study consists of a lkg capacity loadcell and a Imm tipping bucket rain gauge. There are two signals: one is the weight of the water in the tipping bucket and the other is the pulse from the reversing mechanism of the tipping bucket. The loadcell measures the weight of water with a 0.0lmm resolution up to 1mm rainfall and the bucket reverses beyond 1mm. From this point, a pulse signal generates and the loadcell starts measuring the weight again. A field test was carried out with the range of rainfall intensity from 42mm/h to 250mm/h. The result shows an error range from -2.2% to + 2.6% in 12 measurement cases with a rainfall of l00mm or more. This result satisfies the WMO recommendation for rainfall intensity instrumentation which allows a 5% range. In a field experiment during 17 to 19 August, 2004, more than 100mm/h rainfall intensity was observed by this instrument, confirming that our instrument has a sufficient capacity of rainfall intensity measurement under extreme conditions like Jangma (Bai-u season). Compared with existing commercial models which employ a water drop measurement method, our method can give a practical solution for diagnostic check of remote rain gauges using two independent signals.

• Journal of Korea Water Resources Association
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• v.46 no.11
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• pp.1129-1140
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• 2013

Precipitation is one of the important factors in the hydrological cycle. It needs to understand accurate of spatial precipitation field because it has large spatio-temporal variability. Precipitation data obtained through the Tropical Rainfall Monitoring Mission (TRMM) 3B43 product is inaccurate because it has 25 km space scale. Downscaling of TRMM 3B43 product can increase the accuracy of spatial precipitation field from 25 km to 1 km scale. The relationship between precipitation and the normalized difference vegetation index(NDVI) (1 km space scale) which is obtained from the Moderate Resolution Imaging Spectroradiometers (MODIS) sensor loaded in Terra satellite is variable at different scales. Therefore regression equations were established and these equations apply to downscaling. Two renormalization strategies, Geographical Difference Analysis (GDA) and Geographical Ratio Analysis (GRA) are implemented for correcting the differences between remote sensing-derived and rain gauge data. As for considering the GDA method results, biases, the root mean-squared error (RMSE), MAE and Index of agreement (IOA) is equal to 4.26 mm, 172.16 mm, 141.95 mm, 0.64 in 2009 and 17.21 mm, 253.43 mm, 310.56 mm, 0.62 in 2011. In this study, we can see the 1km spatial precipitation field map over Korea. It will be possible to get more accurate spatial analysis of the precipitation field through using the additional rain gauges or radar data.

• Journal of Korea Water Resources Association
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• v.50 no.1
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• pp.17-28
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• 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 The Korean Society of Agricultural Engineers
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• v.62 no.2
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• pp.39-52
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• 2020

A high quality, long-term, high-resolution precipitation dataset is an essential in climate analyses and global water cycles. Rainfall data from station observations are inadequate over many parts of the world, especially North Korea, due to non-existent observation networks, or limited reporting of gauge observations. As a result, satellite-based rainfall estimates have been used as an alternative as a supplement to station observations. The Climate Hazards Group Infrared Precipitation (CHIRP) and CHIRP combined with station observations (CHIRPS) are recently produced satellite-based rainfall products with relatively high spatial and temporal resolutions and global coverage. CHIRPS is a global precipitation product and is made available at daily to seasonal time scales with a spatial resolution of 0.05° and a 1981 to near real-time period of record. In this study, we analyze the applicability of CHIRPS data on the Korean Peninsula by supplementing the lack of precipitation data of North Korea. We compared the daily precipitation estimates from CHIRPS with 81 rain gauges across Korea using several statistical metrics in the long-term period of 1981-2017. To summarize the results, the CHIRPS product for the Korean Peninsula was shown an acceptable performance when it is used for hydrological applications based on monthly rainfall amounts. Overall, this study concludes that CHIRPS can be a valuable complement to gauge precipitation data for estimating precipitation and climate, hydrological application, for example, drought monitoring in this region.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.11
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• pp.2745-2752
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• 2013

The tipping-bucket and weight measuring type precipitation gauge has long been used worldwide for measuring rainfall. However, the conventional gauge has observation errors and its measurement range is limited by the device's resolution. In this paper, a new type of precipitation gauge that uses an innovative method by applying a new ultrasonic flow measuring technique was developed. This is the first time this technique is being used to gauge rainfall. The prototype was tested in the laboratory designated by the Korea Laboratory Accreditation Scheme (KOLAS). The rainfall intensity condition was 20~420 mm/H and the Standard Correction System for Precipitation Gauges was used. Results of the laboratory experiment showed that the proposed gauge has a ${\pm}2%$ margin of error. Consequently, it was proven that the proposed gauge is quite accurate and reliable for measuring precipitation.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.5B
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• pp.267-272
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• 2012

To reduce the flood risk caused by unexpected heavy rainfall, many prediction methods for flood have been developed. A major constituent of flood prediction is an accurate rainfall estimation which is an input of hydrologic models. In this study, a regional-scale weather model which can provide relatively longer lead time for flood mitigation compared to the Nowcasting based on radar system will be introduced and applied to the Chongmi river basin located in central part of South Korea. The duration of application of a regional weather model is from July 11 to July 23 in 2006. The estimated rainfall amounts were compared with observations from rain gauges (Sangkeuk, Samjook, and Sulsung). For this rainfall event at Chongmi river basin, Thomson and Kain-Frisch Schemes for microphysics and cumulus parameterization, respectively, were selected as optimal physical conditions to present rainfall fall amount in terms of Mean Absolute Relative Errors (MARE>0.45).

• Magazine of the Korean Society of Agricultural Engineers
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• v.43 no.4
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• pp.57-68
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• 2001

It is matter of common knowledge to give impetus to the water resources development to cope with increasing demand and supply for the water utilization project including agricultural living and industrial water owing to the economic and civilization development in recent years. Regional design rainfall is necessary or the design of the dam reservoir levee and drainage facilities for the development of various kinds of essential waters including agricultural water. For the estimation of the regional design rainfall classification of the climatologically an geographically homogeneous regions should be preceded preferentially This study was mainly conducted to derive the optimal regionalization of the precipitation data which can be classified by the climatologically and geographically homogeneous regions all over the regions except Cheju and Wulreung islands in Korea. A total of 65 rain gauges were used to regional analysis of precipitation. Annual maximum series for the consecutive durations of 1, 3, 6, 12, 24, 36, 48 and 72hr were used for various statistical analysis. Both K-means clustering and mean annual precipitation methods are used to identify homogeneous regions all over the regions. Nine and five homogeneous regions for the precipitation were classified by the K-means clustering and mean annual methods, respectively. Finally, Five homogeneous regions were established by the trial and error method with homogeneity test using statistics of $\chi$$^2$ distribution.

• Journal of Korea Water Resources Association
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• v.48 no.12
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• pp.995-1009
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• 2015

Weather radar rainfall data has been recognized for making valuable contributions to short-term flood forecasting and management over the past decades. There are several advantages to better monitoring rainfall in ungauged area compared to ground-based rain gauges with which spatial patterns of the rainfall are not effectively identified. Hence, this study aims to develop a new scheme to forecast spatio-temporal rainfall field. The proposed model was based on an advection scheme to track wind patterns and velocity. The results showd a promising forecasting skill with quantitative and qualitative measures. It was confirmed that the forecasted rainfall may be effectively used an input data for a distributed hydrological model.

• Asian Journal of Atmospheric Environment
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• v.7 no.1
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• pp.8-16
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• 2013

The fog water chemistry and deposition in northern Japan were investigated by fog water and throughfall measurements in 2010. Fog water was sampled weekly by an active-string fog sampler at Lake Mashu from May to November. Throughfall measurements were conducted using rain gauges under three deciduous trees along the somma of the lake from August to October. The mean fog deposition rate (flux) was calculated using throughfall data to estimate the total fog water deposition amount for the entire sampling period. $NH_4{^+}$ and $SO{_4}^{2-}$ were the most abundant cation and anion, respectively, in the fog water samples. A mean pH of 5.08 in the fog water, which is higher than those in rural areas in Japan, was observed. The [$NH_4{^+}$]/[$SO{_4}^{2-}$] equivalent ratio in fog water was larger than 1.0 throughout the study period, indicating that $NH_3$ gas was the primary neutralizing agent for fog water acidity. The mean rate and total amount of fog water deposition were estimated as 0.15 mm $h^{-1}$ and 164 mm, respectively. The amounts of nitrogen and sulfate deposition via fog water deposition were corresponded to those reported values of the annual deposition amounts via rainfall.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.140-140
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• 2021

Urban flood management is a crucial and challenging task, particularly in developed cities. Therefore, accurate prediction of urban flooding under heavy precipitation is critically important to address such a challenge. In recent years, machine learning techniques have received considerable attention for their strong learning ability and suitability for modeling complex and nonlinear hydrological processes. Moreover, a survey of the published literature finds that hybrid computational intelligent methods using nature-inspired algorithms have been increasingly employed to predict or simulate the streamflow with high reliability. The present study is aimed to propose a novel approach, an ensemble tree, Bayesian Additive Regression Trees (BART) model incorporating a nature-inspired algorithm to predict hourly multi-step ahead streamflow. For this reason, a hybrid intelligent model was developed, namely GA-BART, containing BART model integrating with Genetic algorithm (GA). The Jungrang urban basin located in Seoul, South Korea, was selected as a case study for the purpose. A database was established based on 39 heavy rainfall events during 2003 and 2020 that collected from the rain gauges and monitoring stations system in the basin. For the goal of this study, the different step ahead models will be developed based in the methods, including 1-hour, 2-hour, 3-hour, 4-hour, 5-hour, and 6-hour step ahead streamflow predictions. In addition, the comparison of the hybrid BART model with a baseline model such as super vector regression models is examined in this study. It is expected that the hybrid BART model has a robust performance and can be an optional choice in streamflow forecasting for urban basins.