• Journal of the Korean earth science society
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• v.34 no.1
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• pp.1-12
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• 2013

To effectively remove chaff echoes, which are often misidentified as precipitation echoes on weather radars, this study examines the relationship between the radar reflectivity and each of dual polarimetric parameters. The dual polarimetric parameters are collected only for the echo areas identified as chaff echoes on the NIMR X-band dual polarization radar. Overall, the polarimetric parameters (i.e., reflectivity, differential reflectivity, cross correlation coefficient, standard deviation of differential reflectivity and specific differential phase) for chaff echoes have a wider range of values than those for precipitation echoes and the chaff filaments tend to be horizontally oriented to radar beams. There appears to be a considerable overlap in the cross correlation coefficient range of chaff and precipitation echoes since some precipitation echoes have cross correlation coefficient lower than 0.8. Therefore, although the cross correlation coefficient is known to be a good variable in identifying and separating chaff echoes from precipitation echoes, it is suggested that additional care should be taken when using the cross correlation coefficient solely in removing chaff echoes.

• Journal of Korea Water Resources Association
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• v.46 no.8
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• pp.857-871
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• 2013

The interest in rainfall observation and forecasting using remote sensing method like RADAR (Radio Detection and Ranging) and satellite image is increased according to increased damage by rapid weather change like regional torrential rain and flash flood. In this study, the basin runoff was calculated using adaptive neuro-fuzzy technique, one of the data driven model and MAPLE (McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation) forecasted precipitation data as one of the input variables. The flood estimation method using neuro-fuzzy technique and RADAR forecasted precipitation data was evaluated. Six rainfall events occurred at flood season in 2010 and 2011 in Chungju Reservoir basin were used for the input data. The flood estimation results according to the rainfall data used as training, checking and testing data in the model setup process were compared. The 15 models were composed of combination of the input variables and the results according to change of clustering methods were compared and analysed. From this study was that using the relatively larger clustering radius and the biggest flood ever happened for training data showed the better flood estimation. The model using MAPLE forecasted precipitation data showed relatively better result at inflow estimation Chungju Reservoir.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.1
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• pp.99-106
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• 2015

In this study, we propose Radial basis function Neural Network(RBFNN) using Recursive Weighted Least Square Estimation(RWLSE) to effectively deal with big data class meteorological radar data. In the condition part of the RBFNN, Fuzzy C-Means(FCM) clustering is used to obtain fitness values taking into account characteristics of input data, and connection weights are defined as linear polynomial function in the conclusion part. The coefficients of the polynomial function are estimated by using RWLSE in order to cope with big data. As recursive learning technique, RWLSE which is based on WLSE is carried out to efficiently process big data. This study is experimented with both widely used some Machine Learning (ML) dataset and big data obtained from meteorological radar to evaluate the performance of the proposed classifier. The meteorological radar data as big data consists of precipitation echo and non-precipitation echo, and the proposed classifier is used to efficiently classify these echoes.

• Korean Journal of Agricultural and Forest Meteorology
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• v.15 no.3
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• pp.171-177
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• 2013

The purpose of this study is to find a scheme to scale down the KMA (Korea Meteorological Administration) digital precipitation maps to the grid cell resolution comparable to the rural landscape scale in Korea. As a result, we suggest two steps procedure called RATER (Radar Assisted Topography and Elevation Revision) based on both radar echo data and a mountain precipitation model. In this scheme, the radar reflection intensity at the constant altitude of 1.5 km is applied first to the KMA local analysis and prediction system (KLAPS) 5 km grid cell to obtain 1 km resolution. For the second step the elevation and topography effect on the basis of 270 m digital elevation model (DEM) which represented by the Parameter-elevation Regressions on Independent Slopes Model (PRISM) is applied to the 1 km resolution data to produce the 270 m precipitation map. An experimental watershed with about $50km^2$ catchment area was selected for evaluating this scheme and automated rain gauges were deployed to 13 locations with the various elevations and slope aspects. 19 cases with 1 mm or more precipitation per day were collected from January to May in 2013 and the corresponding KLAPS daily precipitation data were treated with the second step procedure. For the first step, the 24-hour integrated radar echo data were applied to the KLAPS daily precipitation to produce the 1 km resolution data across the watershed. Estimated precipitation at each 1 km grid cell was then regarded as the real world precipitation observed at the center location of the grid cell in order to derive the elevation regressions in the PRISM step. We produced the digital precipitation maps for all the 19 cases by using RATER and extracted the grid cell values corresponding to 13 points from the maps to compare with the observed data. For the cases of 10 mm or more observed precipitation, significant improvement was found in the estimated precipitation at all 13 sites with RATER, compared with the untreated KLAPS 5 km data. Especially, reduction in RMSE was 35% on 30 mm or more observed precipitation.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.180-180
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• 2015

Weather radar has been widely used in measuring precipitation and discharge and predicting flood risks. The radar rainfall estimate has one of the essential problems in terms of uncertainty and accuracy. Previous study analyzed radar errors to reduce its uncertainty or to improve its accuracy. Furthermore, a recent analyzed the effect of radar error on rainfall-runoff using spatial error model (SEM). SEM appropriately reproduced radar error including spatial correlation. Since the SEM does not take the time dependence into account, its time variability was not properly investigated. Therefore, in the current study, we extend the SEM including time dependence as well as spatial dependence, named after Spatial-Temporal Error Model (STEM). Radar rainfall events generated with STEM were tested so that the peak runoff from the response of a basin could be investigated according to dependent error. The Nam River basin, South Korea, was employed to illustrate the effects of STEM on runoff peak flow.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.149-153
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• 2006

Precipitation is the most important component and critical to the study of water and energy cycle. This study investigates the propagation of precipitation retrieval uncertainty in the simulation of hydrologic variables for varying spatial resolution on two different vegetation cover. We explore two remotely sensed rain retrievals (space-borne IR-only and radar rainfall) and three spatial grid resolutions. An offline Community Land Model (CLM) was forced with in situ meteorological data In turn, radar rainfall is replaced by the satellite rain estimates at coarser resolution $(0.25^{\circ},\;0.5^{\circ}\;and\;1^{\circ})$ to determine their probable impact on model predictions. Results show how uncertainty of precipitation measurement affects the spatial variability of model output in various modelling scales. The study provides some intuition on the uncertainty of hydrologic prediction via interaction between the land surface and near atmosphere fluxes in the modelling approach.

• Atmosphere
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• v.24 no.1
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• pp.39-48
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• 2014

This study provides a comparative analysis of cloud top heights observed by a Ka-band cloud radar and the Communication, Ocean and Meteorological Satellite (COMS) at Boseong National Center for Intensive Observation of severe weather (NCIO) from May 25, 2013 (1600 UTC) to May 27. The rainfall duration is defined as the period of rainfall from start to finish, and the no rainfall duration is defined as the period other than the rainfall duration. As a result of the comparative analysis, the cloud top heights observed by the cloud radar have been estimated to be lower than that observed by the COMS for the rainfall duration due to the signal attenuation caused by raindrops. The stronger rainfall intensity gets, the more the difference grows. On the other hand, the cloud top heights observed by the cloud radar have been relatively similar to that observed by the COMS for the no rainfall duration. In this case, the cloud radar can effectively detect cloud top heights within the range of its observation. The COMS indicates the cloud top heights lower than the actual ones due to the upper thin clouds under the influence of ground surface temperature. As a result, the cloud radar can be useful in detecting cloud top heights when there are no precipitation events. The COMS data can be used to correct the cloud top heights when the radar gets beyond the valid range of observation or there are precipitation events.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.10
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• pp.901-906
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• 2013

Chaff is a kind of matter spreading atmosphere with the purpose of preventing aircraft from detecting by radar. The chaff is commonly composed of small aluminum pieces, metallized glass fiber, or other lightweight strips which consists of reflecting materials. The chaff usually appears on the radar images as narrow bands shape of highly reflective echoes. And the chaff echo has similar characteristics to precipitation echo, and it interrupts weather forecasting process and makes forecasting accuracy low. In this paper, the chaff echo recognizing and removing method is suggested using Bayesian network. After converting coordinates from spherical to Cartesian in UF (Universal Format) radar data file, the characteristics of echoes are extracted by spatial and temporal clustering. And using the data, as a result of spatial and temporal clustering, a classification process for analyzing is performed. Finally, the inference system using Bayesian network is applied. As a result of experiments with actual radar data in real chaff echo appearing case, it is confirmed that Bayesian network can distinguish between chaff echo and non-chaff echo.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.5
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• pp.676-682
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• 2014

In this paper, precipitation echo(PRE) and non-precipitaion echo(N-PRE)(including ground echo and clear echo) through weather radar data are identified with the aid of neuro-fuzzy algorithm. The accuracy of the radar information is lowered because meteorological radar data is mixed with the PRE and N-PRE. So this problem is resolved by using RBFNN and judgement module. Structure expression of weather radar data are analyzed in order to classify PRE and N-PRE. Input variables such as Standard deviation of reflectivity(SDZ), Vertical gradient of reflectivity(VGZ), Spin change(SPN), Frequency(FR), cumulation reflectivity during 1 hour(1hDZ), and cumulation reflectivity during 2 hour(2hDZ) are made by using weather radar data and then each characteristic of input variable is analyzed. Input data is built up from the selected input variables among these input variables, which have a critical effect on the classification between PRE and N-PRE. Echo judgment module is developed to do echo classification between PRE and N-PRE by using testing dataset. Polynomial-based radial basis function neural networks(RBFNNs) are used as neuro-fuzzy algorithm, and the proposed neuro-fuzzy echo pattern classifier is designed by combining RBFNN with echo judgement module. Finally, the results of the proposed classifier are compared with both CZ and DZ, as well as QC data, and analyzed from the view point of output performance.

• Journal of information and communication convergence engineering
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• v.4 no.1
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• pp.46-52
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• 2006

Precipitation measurement with ground-based radar needs an information of the raindrop size distribution (RSD) characteristics. A 10-month dataset was collected in tropical Atlantic coastal zone of northeastern Brazil where the weather radar was installed. The number of drop was mainly recorded in 300 - 500 drop $mm^{-3}$, of which the maximum was registered around 1.1 mm drop diameter.