• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.6
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• pp.922-934
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• 2015

In this paper, The classification between precipitation echo(PRE) and non-precipitation echo(N-PRE) (including ground echo and clear echo) is carried out from weather radar data using neuro-fuzzy algorithm. In order to classify between PRE and N-PRE, Input variables are built up through characteristic analysis of radar data. First, the event classifier as the first classification step is designed to classify precipitation event and non-precipitation event using input variables of RBFNNs such as DZ, DZ of Frequency(DZ_FR), SDZ, SDZ of Frequency(SDZ_FR), VGZ, VGZ of Frequency(VGZ_FR). After the event classification, in the precipitation event including non-precipitation echo, the non-precipitation echo is completely removed by the echo classifier of the second classifier step that is built as Type-2 FCM based RBFNNs. Also, parameters of classification system are acquired for effective performance using PSO(Particle Swarm Optimization). The performance results of the proposed echo classifier are compared with CZ. In the sequel, the proposed model architectures which use event classifier as well as the echo classifier of Interval Type-2 FCM based RBFNN show the superiority of output performance when compared with the conventional echo classifier based on RBFNN.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.9
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• pp.1337-1346
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• 2015

In this paper, pattern classifier is designed to classify precipitation and non-precipitation events from weather radar data. The proposed classifier is based on Fuzzy Neural Network(FNN) and consists of three FNNs which operate in parallel. In the proposed network, the connection weights of the consequent part of fuzzy rules are expressed as two polynomial types such as constant or linear polynomial function, and their coefficients are learned by using Least Square Estimation(LSE). In addition, parametric as well as structural factors of the proposed classifier are optimized through Differential Evolution(DE) algorithm. After event classification between precipitation and non-precipitation echo, non-precipitation event is to get rid of all echo, while precipitation event including non-precipitation echo is to get rid of non-precipitation echo by classifier that is also based on Fuzzy Neural Network. Weather radar data obtained from meteorological office is to analysis and discuss performance of the proposed event and echo patter classifier, result of echo pattern classifier compare to QC(Quality Control) data obtained from meteorological office.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.7
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• pp.1114-1124
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• 2015

In this paper, precipitation / non-precipitation pattern classification of meteorological radar data is conducted by using neuro-fuzzy algorithm. Structure expression of meteorological radar data information is analyzed in order to effectively classify precipitation and non-precipitation. Also diverse input variables for designing pattern classifier could be considered by exploiting the quantitative as well as qualitative characteristic of meteorological radar data information and then each characteristic of input variables is analyzed. Preferred pattern classifier can be designed by essential input variables that give a decisive effect on output performance as well as model architecture. As the proposed model architecture, neuro-fuzzy algorithm is designed by using FCM-based radial basis function neural network(RBFNN). Two parts of classifiers such as instance classifier part and echo classifier part are designed and carried out serially in the entire system architecture. In the instance classifier part, the pattern classifier identifies between precipitation and non-precipitation data. In the echo classifier part, because precipitation data information identified by the instance classifier could partially involve non-precipitation data information, echo classifier is considered to classify between them. The performance of the proposed classifier is evaluated and analyzed when compared with existing QC method.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.3
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• pp.242-247
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• 2015

In this study, we propose the design of Radial Basis Function Neural Network(RBFNN) classifier in order to classify between precipitation and non-precipitation echo. The characteristics of meteorological radar data is analyzed for classifying precipitation and non-precipitation echo. Input variables is selected as DZ, SDZ, VGZ, SPN, DZ_FR, VR by performing pre-processing of UF data based on the characteristics analysis and these are composed of training and test data. Finally, QC data being used in Korea Meteorological Administration is applied to compare with the performance results of proposed classifier.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.5
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• pp.536-541
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• 2014

Data through meteorological radar includes ground echo, sea-clutter echo, anomalous propagation echo, clear echo and so on. Each echo is a kind of non-precipitation echoes and the characteristic of individual echoes is analyzed in order to identify with non-precipitation. Meteorological radar data is analyzed through pre-processing procedure because the data is given as big data. In this study, echo pattern classifier is designed to distinguish non-precipitation echoes from precipitation echo in meteorological radar data using RBFNNs and echo judgement module. Output performance is compared and analyzed by using both HCM clustering-based RBFNNs and FCM clustering-based RBFNNs.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.3
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• pp.217-222
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• 2015

Clustering is one of important data mining techniques known as exploratory data analysis and is being applied in various engineering and scientific fields such as pattern recognition, remote sensing, and so on. The method organizes data by abstracting underlying structure either as a grouping of individuals or as a hierarchy of groups. Weather radar observes atmospheric objects by utilizing reflected signals and stores observed data in corresponding coordinate. To analyze the radar data, it is needed to be separately organized precipitation and non-precipitation echo based on similarities. Thus, this paper studies to apply clustering method to radar data. In addition, in order to solve the problem when precipitation echo locates close to non-precipitation echo, fuzzy logic based clustering method which can consider both distance and other properties such as reflectivity and Doppler velocity is suggested in this paper. By using actual cases, the suggested clustering method derives better results than previous method in near-located precipitation and non-precipitation echo case.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.5
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• pp.562-568
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• 2014

There exist precipitation echo and non-precipitation echo in the meteorological radar. It is difficult to effectively issue the right weather forecast because of a difficulty in determining these ambiguous point. In this study, Data is extracted from UF data of meteorological radar used. Input and output data for designing two classifier were built up through the analysis of the characteristics of precipitation and non-precipitation. Selected input variables are considered for better performance and echo classifier is designed using fuzzy relation-based nueral network. Comparative studies on the performance of echo classifier are carried out by considering both echo judgement module 1 and module 2.

• 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 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.

• Journal of the Korean earth science society
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• v.44 no.6
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• pp.557-577
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• 2023

This study defines non-precipitation information as areas with weak precipitation or cloud particles that radar cannot detect due to weak returned signals, and suggests methods for its utilization in data assimilation. Previous studies have demonstrated that assimilating radar data from precipitation echoes can produce precipitation in model analysis and improve subsequent precipitation forecast. However, this study also recognizes the non-precipitation information as valuable observation and seeks to assimilate it to suppress spurious precipitation in the model analysis and forecast. To incorporate non-precipitation information into data assimilation, we propose observation operators that convert radar non-precipitation information into hydrometeor mixing ratios and relative humidity for the Weather Research and Forecasting Data Assimilation system (WRFDA). We also suggest a preprocessing method for radar non-precipitation information. A single-observation experiment indicates that assimilating non-precipitation information fosters an environment conducive to inhibiting convection by lowering temperature and humidity. Subsequently, we investigate the impact of assimilating non-precipitation information to a real case on July 23, 2013, by performing a subsequent 9-hour forecast. The experiment that assimilates radar non-precipitation information improves the model's precipitation forecasts by showing an increase in the Fractional Skill Score (FSS) and a decrease in the False Alarm Ratio (FAR) compared to experiments in which do not assimilate non-precipitation information.