• Title/Summary/Keyword: Radar-echo

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Classification of Convective/Stratiform Radar Echoes over a Summer Monsoon Front, and Their Optimal Use with TRMM PR Data

  • Oh, Hyun-Mi;Heo, Ki-Young;Ha, Kyung-Ja
    • Korean Journal of Remote Sensing
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    • v.25 no.6
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    • pp.465-474
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    • 2009
  • Convective/stratiform radar echo classification schemes by Steiner et al. (1995) and Biggerstaff and Listemaa (2000) are examined on a monsoonal front during the summer monsoon-Changma period, which is organized as a cloud cluster with mesoscale convective complex. Target radar is S-band with wavelength of 10cm, spatial resolution of 1km, elevation angle interval of 0.5-1.0 degree, and minimum elevation angle of 0.19 degree at Jindo over the Korean Peninsula. For verification of rainfall amount retrieved from the echo classification, ground-based rain gauge observations (Automatic Weather Stations) are examined, converting the radar echo grid data to the station values using the inverse distance weighted method. Improvement from the echo classification is evaluated based on the correlation coefficient and the scattered diagram. Additionally, an optimal use method was designed to produce combined rainfalls from the radar echo and Tropical Rainfall Measuring Mission Precipitation Radar (TRMM/PR) data. Optimal values for the radar rain and TRMM/PR rain are inversely weighted according to the error variance statistics for each single station. It is noted how the rainfall distribution during the summer monsoon frontal system is improved from the classification of convective/stratiform echo and the use of the optimal use technique.

Design of Echo Classifier Based on Neuro-Fuzzy Algorithm Using Meteorological Radar Data (기상레이더를 이용한 뉴로-퍼지 알고리즘 기반 에코 분류기 설계)

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

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.

Unusual Radar Echo from the Wake of Meteor Fireball in Nearly Horizontal Transits in the Summer Polar Lower-Thermosphere

  • Lee, Young-Sook;Kirkwood, Sheila;Kwak, Young-Sil
    • Journal of Astronomy and Space Sciences
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    • v.35 no.2
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    • pp.83-92
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    • 2018
  • The summer polar lower thermosphere (90-100 km) has an interesting connection to meteors, adjacent to the mesopause region attaining the lowest temperature in summer. Meteors supply condensation nuclei for charged ice particles causing polar mesospheric summer echoes (PMSE). We report the observation of meteor trail with nearly horizontal transit at high speed (20-50 km/s), and at last with re-enhanced echo power followed by diffusive echoes. Changes in phase difference between radar receivers aligned in meridional and zonal directions are used to determine variations in horizontal displacements and speeds with respect to time by taking advantage of radar interferometric analysis. The actual transit of echo target is observed along the straight pathway vertically and horizontally extended as much as a distance of at least 24 km and at most 29 km. The meteor trail initially has a signature similar to 'head echoes', with travel speeds from 20 - 50 km/s. It subsequently transforms into a different type of echo target including specular echo and then finally the power reenhanced. The reenhancement of echo power is followed by fume-like diffusive echoes, indicating sudden release of plasma as like explosive process probably involved. We discuss a possible role of meteor-triggered secondary plasma trail, such as fireball embedded with electrical discharge that continuously varies the power and transit speed.

A Study on Anomalous Propagation Echo Identification using Naive Bayesian Classifier (나이브 베이지안 분류기를 이용한 이상전파에코 식별방법에 대한 연구)

  • Lee, Hansoo;Kim, Sungshin
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2016.05a
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    • pp.89-90
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    • 2016
  • Anomalous propagation echo is a kind of abnormal radar signal occurred by irregularly refracted radar beam caused by temperature or humidity. The echo frequently appears in ground-based weather radar. In order to improve accuracy of weather forecasting, it is important to analyze radar data precisely. Therefore, there are several ongoing researches about identifying the anomalous propagation echo all over the world. This paper conducts researches about a classification method which can distinguish anomalous propagation echo in the radar data using naive Bayes classifier and unique attributes of the echo such as reflectivity, altitude, and so on. It is confirmed that the fine classification results are derived by verifying the suggested naive Bayes classifier using actual appearance cases of the 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.

Case Study of the Precipitation System Occurred Around Cheongju Using Convective/Stratiform Radar Echo Classification Algorithm (레이더 반사도 유형분류 알고리즘을 이용한 청주 부근에서 관측된 강우시스템의 사례 분석)

  • Nam, Kyung-Yeub;Lee, Jeong-Seog;Nam, Jae-Cheol
    • Atmosphere
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    • v.15 no.3
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    • pp.155-165
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    • 2005
  • The characteristics of six precipitation systems occurred around Cheongju in 2002 are analyzed after the convective/stratiform radar echo classification using radar reflectivity from the Meteorological Research Institute"s X-band Doppler weather radar. The Biggerstaff and Listemaa (2000) algorithm is applied for the classification and reveals a physical characteristics of the convective and stratiform rain diagnosed from the three-dimensional structure of the radar reflectivity. The area satisfying the vertical profile of radar reflectivity is well classified, while the area near the radar site and the topography-shielded area show a mis-classification. The seasonal characteristics of the precipitation system are also analyzed using the contoured frequency by altitude diagrams (CFADs). The heights of maximum reflectivity are 4 km and 5.5 km in spring and summer, respectively, and the vertical gradient of radar reflectivity from 1.5 km to the melting layer in spring is larger than in summer.

Naive Bayes Classifier based Anomalous Propagation Echo Identification using Class Imbalanced Data (클래스 불균형 데이터를 이용한 나이브 베이즈 분류기 기반의 이상전파에코 식별방법)

  • Lee, Hansoo;Kim, Sungshin
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.20 no.6
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    • pp.1063-1068
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    • 2016
  • Anomalous propagation echo is a kind of abnormal radar signal occurred by irregularly refracted radar beam caused by temperature or humidity. The echo frequently appears in ground-based weather radar due to its observation principle and disturb weather forecasting process. In order to improve accuracy of weather forecasting, it is important to analyze radar data precisely. Therefore, there are several ongoing researches about identifying the anomalous propagation echo with data mining techniques. This paper conducts researches about implementation of classification method which can separate the anomalous propagation echo in the raw radar data using naive Bayes classifier with various kinds of observation results. Considering that collected data has a class imbalanced problem, this paper includes SMOTE method. It is confirmed that the fine classification results are derived by the suggested classifier with balanced dataset using actual appearance cases of the echo.

A Study of Line-shaped Echo Detection Method using Naive Bayesian Classifier (나이브 베이지안 분류기를 이용한 선에코 탐지 방법에 대한 연구)

  • Lee, Hansoo;Kim, Sungshin
    • Journal of the Korean Institute of Intelligent Systems
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    • v.24 no.4
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    • pp.360-365
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    • 2014
  • There are many types of advanced devices for weather prediction process such as weather radar, satellite, radiosonde, and other weather observation devices. Among them, the weather radar is an essential device for weather forecasting because the radar has many advantages like wide observation area, high spatial and time resolution, and so on. In order to analyze the weather radar observation result, we should know the inside structure and data. Some non-precipitation echoes exist inside of the observed radar data. And these echoes affect decreased accuracy of weather forecasting. Therefore, this paper suggests a method that could remove line-shaped non-precipitation echo from raw radar data. The line-shaped echoes are distinguished from the raw radar data and extracted their own features. These extracted data pairs are used as learning data for naive bayesian classifier. After the learning process, the constructed naive bayesian classifier is applied to real case that includes not only line-shaped echo but also other precipitation echoes. From the experiments, we confirm that the conclusion that suggested naive bayesian classifier could distinguish line-shaped echo effectively.

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.