• Journal of Korea Water Resources Association
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• v.39 no.8 s.169
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• pp.717-726
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• 2006

In this study, we developed a rainfall forecasting model using data from radiosonde and rain gauge network and neural networks. The primary hypothesis is that if we can consider the moving direction of the rain generating weather system in forecasting rainfall, we can get more accurate results. We assume that the moving direction of the rain generating weather system is same as the wind direction at 700mb which is measured at radiosonde networks. Neural networks are consisted of 8 different modules according to 8 different wind directions. The model was verified using 350 AWS data and Pohang radiosonde data. Correlation coefficient is improved from 0.41 to 0.73 and skill score is 0.35. Statistical performance measures of the Quantitative Precipitation Forecast (QPF) model show improved output compared to that of rainfall forecasting model using only AWS data.

• Journal of the Korean Data and Information Science Society
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• v.28 no.3
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• pp.483-492
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• 2017

Precipitation is one of key components in hydrological modeling and water balance studies. A comprehensive, optimized and sustainable water balance monitoring requires the availability of accurate precipitation data. The amount of precipitation measured in a gauge is less than the actual precipitation reaching the ground. The objective of this study is to determine the wind-induced under-catch of solid precipitation and develop a continuous adjustment function for measurements of all types of winter precipitation (from rain to dry snow), which can be used for operational measurements based on data available at standard automatic weather stations. This study provides Bayesian analysis for the systematic structure of catch ratio in precipitation measurement.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.3
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• pp.37-48
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• 2010

The purpose of this paper is to evaluate how the rainfall field effect on a runoff simulation using grid radar rainfall data and ground gauge rainfall. The Gwangdeoksan radar and ground-gauge rainfall data were used to estimate a spatial rainfall field, and a hydrologic model was used to evaluate whether the rainfall fields created by each method reproduced a realistically valid spatial and temporal distribution. Pilot basin in this paper was the Naerin stream located in Inje-gun, Gangwondo, 250m grid scale digital elevation data, land cover maps, and soil maps were used to estimate geological parameters for the hydrologic model. For the rainfall input data, quantitative precipitation estimation(QPE), adjusted radar rainfall, and gauge rainfall was used, and then compared with the observed runoff by inputting it into a $Vflo^{TM}$ model. As a result of the simulation, the quantitative precipitation estimation and the ground rainfall were underestimated when compared to the observed runoff, while the adjusted radar rainfall showed a similar runoff simulation with the actual observed runoff. From these results, we suggested that when weather radars and ground rainfall data are combined, they have a greater hydrological usability as input data for a hydrological model than when just radar rainfall or ground rainfall is used separately.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.183-183
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• 2020

Accurate quantitative precipitation estimation plays an important role in hydrological modelling and prediction. Instantaneous quantitative precipitation estimation (QPE) by utilizing the weather radar data is a great applicability for operational hydrology in a catchment. Previously, regression technique performed between reflectivity (Z) and rain intensity (R) is used commonly to obtain radar QPEs. A novel, recent approaching method which might be applied in hydrological area for QPE is Long Short-Term Memory (LSTM) Networks. LSTM networks is a development and evolution of Recurrent Neuron Networks (RNNs) method that overcomes the limited memory capacity of RNNs and allows learning of long-term input-output dependencies. The advantages of LSTM compare to RNN technique is proven by previous works. In this study, LSTM networks is used to estimate the quantitative precipitation from weather radar for an urban catchment in South Korea. Radar information and rain-gauge data are used to evaluate and verify the estimation. The estimation results figure out that LSTM approaching method shows the accuracy and outperformance compared to Z-R relationship method. This study gives us the high potential of LSTM and its applications in urban hydrology.

• Proceedings of the KSRS Conference
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• v.2
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• pp.660-663
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• 2006

Rainfall estimation is important to weather forecast, flood control, hydrological plan. The empirical and statistical methods by measured data(surface rain gauge, rainfall radar, Satellite) is commonly used for rainfall estimation. In this study, the rainfall intensity for East Asia region was estimated using the empirical relationship between SSM/I data of DMSP satellite and brightness temperature of GEOS-9(10.7${\mu}m$) with cloud types(ISCCP and MSG classification). And the empirical formula for rainfall estimation was produced by PMM (Probability Matching Method).

• Atmosphere
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• v.16 no.4
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• pp.343-350
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• 2006

The rainfall amount measured by Chugugi at Gongju was found in "Gaksadeungnok". Gaksadeungnok is ancient documents from governmental offices in Joseon dynasty. Rainfall data at Gongju are restored for 18 years of 19th century. In 1871, total rainfall amount is 1,338 mm. It is different by about 11% in the amount compared with Seoul Chugugi rainfall in 1871 and Daejeon modern raingauge measurement result during the 30 years (1971-2000). Annual march of monthly rainfall data at Gongju is similar with that of Seoul. Based on the results, restored rainfall at Gongju is consistent with Seoul Chugugi rainfall data. The rainfall amount restored in this study is measured by Chugugi which was installed at Gongju, in Chung-Cheong province. Furthermore, Gaksadeungnok includes rainfall amount reports by agricultural tool measurement in addition to Chugugi measurement. These facts prove a network of rain gauge in Joseon dynasty.

• The KIPS Transactions:PartA
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• v.17A no.4
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• pp.197-202
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• 2010

The recorder at observatory can save the measured data from water gauge and rain gauge at an interval of five minutes. And then, the RTU (Remote Terminal Unit) in observatory sends the measured data in the recorder to the TM (Telemetering) in FCO (Flood Control Office) at an interval of ten minutes using VHF or satellite communication. But the transmitted data is not the stored data at the recorder, it is just data that is measured at an interval of ten minutes. In the FCO, the transmitted data is analyzed in order to forecast the flood. And also one of the most important things is the maintenance of an observatory. In this paper, an effective management system for the flood forecast is proposed. It uses the CDMA and the Blutooth technology on PDA. The proposed system is very portable, and also easily able to send the data stored at the recorder in observatory to TM in FCO without RTU. And it allows us to view remotely the data of other observatories by downloading from the FCO. Hence the system can do efficiently the maintenance of observatory without wasting manpower and time.

• Water Engineering Research
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• v.1 no.2
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• pp.147-158
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• 2000

In this study, we characterized the seasonal variation of rainrate fields in the Han river basin using the WGR multi-dimensional precipitation model (Waymire, Gupta, and Rodriguez-Iturbe, 1984) by estimating and comparing the parameters derived for each month and for the plain area, the mountain area and overall basin, respectively. The first-and second-order statistics derived from observed point gauge data were used to estimate the model parameters based on the Davidon-Fletcher-Powell algorithm of optimization. As a result of the study, we can find that the higher rainfall amount during summer is mainly due to the arrival rate of rain bands, mean number of cells per cluster potential center, and raincell intensity. However, other parameters controlling the mean number of rain cells per cluster, the cellular birth rate, and the mean cell age are found invariant to the rainfall amounts. In the application to the downstream plain area and upstream mountain area of the Han river basin, we found that the number of storms in the mountain area was estimated a little higher than that in the plain area, but the cell intensity in the mountain area a little lower than that in the plain area. Thus, in the mountain area more frequent but less intense storms can be expected due to the orographic effect, but the total amount of rainfall in a given period seems to remain the same.

• Journal of Multimedia Information System
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• v.5 no.1
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• pp.1-8
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• 2018

The coordinate system conversion of weather radar data is a basic and important process because it can be a factor to measure the accuracy of radar precipitation rate by comparison with the ground rain gauge. We proposed a real-time coordinate system conversion method that combines the advantages of the interpolation masks of SPRINT and REORDER to use tables of predetermined radar samples for each interpolated object coordinate and also distance weights for each precomputed sample. Experimental results show that the proposed method improves the computation speed more than 20~30 times compared with the conventional method and shows that the deterioration of image quality is hardly ignored.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.6
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• pp.1765-1777
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• 2014

This study evaluated PMM (Probability Matching Method) for parameter estimation of the Z - R relation. As a first step, the sensitivity analysis was done to decide the threshold number of data pairs and the data interval for the development of a histogram. As a result, it was found that at least 1,000 number of data pairs are required to apply the PMM for the parameter estimation. This amount of data is similar to that collected for two hours. Also, the number of intervals for the histogram was found to be at least 100. Additionally, it was found that the matching the first-order moment is better than the cumulative probability, and that the data pairs comprising 30 to 100% are better for the PMM application. Finally, above findings were applied to a real rainfall event observed by the Bislsan radar and optimal parameters were estimated. The radar rain rate derived by applying these parameters was found to be well matched to the rain gauge rain rate.