• Title, Summary, Keyword: precipitation echo

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Design of Optimized Type-2 Fuzzy RBFNN Echo Pattern Classifier Using Meterological Radar Data (기상레이더를 이용한 최적화된 Type-2 퍼지 RBFNN 에코 패턴분류기 설계)

  • Song, Chan-Seok;Lee, Seung-Chul;Oh, Sung-Kwun
    • 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.

Design of Precipitation/non-precipitation Pattern Classification System based on Neuro-fuzzy Algorithm using Meteorological Radar Data : Instance Classifier and Echo Classifier (기상레이더를 이용한 뉴로-퍼지 알고리즘 기반 강수/비강수 패턴분류 시스템 설계 : 사례 분류기 및 에코 분류기)

  • Ko, Jun-Hyun;Kim, Hyun-Ki;Oh, Sung-Kwun
    • 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.

Design of Optimized Pattern Classifier for Discrimination of Precipitation and Non-precipitation Event (강수 및 비 강수 사례 판별을 위한 최적화된 패턴 분류기 설계)

  • Song, Chan-Seok;Kim, Hyun-Ki;Oh, Sung-Kwun
    • 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.

Design of Meteorological Radar Pattern Classifier Using Clustering-based RBFNNs : Comparative Studies and Analysis (클러스터링 기반 RBFNNs를 이용한 기상레이더 패턴분류기 설계 : 비교 연구 및 해석)

  • Choi, Woo-Yong;Oh, Sung-Kwun
    • 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.

Design of Meteorological Radar Echo Classifier Based on RBFNN Using Radial Velocity (시선속도를 고려한 RBFNN 기반 기상레이더 에코 분류기의 설계)

  • Bae, Jong-Soo;Song, Chan-Seok;Oh, Sung-Kwun
    • 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.

Design of Meteorological Radar Echo Classifier Using Fuzzy Relation-based Neural Networks : A Comparative Studies of Echo Judgement Modules (FNN 기반 신경회로망을 이용한 기상 레이더 에코 분류기 설계 : 에코판단 모듈의 비교 분석)

  • Ko, Jun-Hyun;Song, Chan-Seok;Oh, Sung-Kwun
    • 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.

Characteristics of Summer Season Precipitation Motion over Jeju Island Region Using Variational Echo Tracking (변분에코추적법을 이용한 제주도 지역 여름철 강수계의 이동 특성 분석)

  • Kim, Kwonil;Lee, Ho-Woo;Jung, Sung-Hwa;Lyu, Geunsu;Lee, GyuWon
    • Atmosphere
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    • v.28 no.4
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    • pp.443-455
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    • 2018
  • Nowcasting algorithms using weather radar data are mostly based on extrapolating the radar echoes. We estimate the echo motion vectors that are used to extrapolate the echo properly. Therefore, understanding the general characteristics of these motion vectors is important to improve the performance of nowcasting. General characteristics of radar-based motions are analyzed for warm season precipitation over Jeju region. Three-year summer season data (June~August, 2011~2013) from two radars (GSN, SSP) in Jeju are used to obtain echo motion vectors that are retrieved by Variational Echo Tracking (VET) method which is widely used in nowcasting. The highest frequency occurs in precipitation motion toward east-northeast with the speed of $15{\sim}16m\;s^{-1}$ during the warm season. Precipitation system moves faster and eastward in June-July while it moves slower and northeastward in August. The maximum frequency of speed appears in $10{\sim}20m\;s^{-1}$ and $5{\sim}10m\;s^{-1}$ in June~July and August respectively while average speed is about $14{\sim}15m\;s^{-1}$ in June~July and $8m\;s^{-1}$ in August. In addition, the direction of precipitation motion is highly variable in time in August. The speed of motion in Lee side of the island is smaller than that of the windward side.

A Study on Fuzzy Logic based Clustering Method for Radar Data Analysis (레이더 데이터 분석을 위한 Fuzzy Logic 기반 클러스터링 기법에 관한 연구)

  • Lee, Hansoo;Kim, Eun Kyeong;Kim, Sungshin
    • 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.

Chaff Echo Detecting and Removing Method using Naive Bayesian Network (나이브 베이지안 네트워크를 이용한 채프에코 탐지 및 제거 방법)

  • Lee, Hansoo;Yu, Jungwon;Park, Jichul;Kim, Sungshin
    • 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.