Automatic tracking of convective cells and cell complexes from lightning and radar data

被引：32
|
作者：
Steinacker, R [1 ]
Dorninger, M [1 ]
Wölfelmaier, E [1 ]
Krennert, T [1 ]
机构：
[1] Univ Vienna, Inst Meteorol & Geophys, A-1190 Vienna, Austria
来源：
METEOROLOGY AND ATMOSPHERIC PHYSICS | 2000年 / 72卷 / 2-4期
关键词：
D O I：
10.1007/s007030050009
中图分类号：
P4 [大气科学（气象学）];
学科分类号：
0706 ; 070601 ;
摘要：
A new automatic tracking method for convective cells and cell complexes is introduced. The method uses a simple Gaussfilter and a selection procedure to define displacement vectors of the specific system. The change of the half width of the filter results in a separation of the system's scale. The proposed method is applied on two data sources: lightning density and RADAR reflectivity. They describe different properties of convective cells and cell complexes. A strong connection of the storm tracks to topography becomes also evident.
引用
收藏
页码：101 / 110
页数：10
相关论文
共 50 条
  • [41] Assessing the quality of DSM from ALOS optical and radar data for automatic drainage extraction
    Nikolakopoulos, Konstantinos G.
    Choussiafis, Christos
    Karathanassi, Vassileia
    [J]. EARTH SCIENCE INFORMATICS, 2015, 8 (02) : 293 - 307
  • [42] Assessing the quality of DSM from ALOS optical and radar data for automatic drainage extraction
    Konstantinos G. Nikolakopoulos
    Christos Choussiafis
    Vassileia Karathanassi
    [J]. Earth Science Informatics, 2015, 8 : 293 - 307
  • [43] Automatic detection of steel rebar in bridge decks from ground penetrating radar data
    Ma, Xin
    Liu, Hao
    Wang, Ming L.
    Birken, Ralf
    [J]. JOURNAL OF APPLIED GEOPHYSICS, 2018, 158 : 93 - 102
  • [44] Automatic Single-Cell Segmentation and Tracking of Bacterial Cells in Fluorescence Microscopy Images
    Liluashvili, Vaja
    Bergeest, Jan-Philip
    Harder, Nathalie
    Ziesack, Marika
    Mutlu, Alper
    Bischofs, Ilka B.
    Rohr, Karl
    [J]. BILDVERARBEITUNG FUR DIE MEDIZIN 2015: ALGORITHMEN - SYSTEME - ANWENDUNGEN, 2015, : 239 - 244
  • [45] Application of machine learning to large hail prediction - The importance of radar reflectivity, lightning occurrence and convective parameters derived from ERA5
    Czernecki, Bartosz
    Taszarek, Mateusz
    Marosz, Michal
    Polrolniczak, Marek
    Kolendowicz, Leszek
    Wyszogrodzki, Andrzej
    Szturc, Jan
    [J]. ATMOSPHERIC RESEARCH, 2019, 227 : 249 - 262
  • [46] CELL TRACKING AND DATA ANALYSIS OF IN VITRO TUMOUR CELLS FROM TIME-LAPSE IMAGE SEQUENCES
    Yan, Kuan
    Verbeek, Fons J.
    Le Devedec, Sylvia
    van de Water, Bob
    [J]. VISAPP 2009: PROCEEDINGS OF THE FOURTH INTERNATIONAL CONFERENCE ON COMPUTER VISION THEORY AND APPLICATIONS, VOL 1, 2009, : 281 - +
  • [47] Achieving Efficient and Realistic Full-Radar Simulations and Automatic Data Annotation by Exploiting Ray Meta Data from a Radar Ray Tracing Simulator
    Schussler, Christian
    Hoffmann, Marcel
    Wirth, Vanessa
    Eskofier, Bjoern
    Weyrich, Tim
    Stamminger, Marc
    Vossiek, Martin
    [J]. 2023 IEEE RADAR CONFERENCE, RADARCONF23, 2023,
  • [48] Feature Extraction and Automatic Material Classification of Underground Objects from Ground Penetrating Radar Data
    Lu, Qingqing
    Pu, Jiexin
    Liu, Zhonghua
    [J]. JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING, 2014, 2014
  • [49] Inferring circulations within mesoscale convective systems using single Doppler radar data from TOGA COARE
    Carpenter, SJ
    Leary, CA
    [J]. 22ND CONFERENCE ON HURRICANES AND TROPICAL METEOROLOGY, 1997, : 47 - 48
  • [50] Assimilation of Radar and Cloud-to-Ground Lightning Data Using WRF-3DVar Combined with the Physical Initialization Method——A Case Study of a Mesoscale Convective System
    Ruhui GAN
    Yi YANG
    Qian XIE
    Erliang LIN
    Ying WANG
    Peng LIU
    [J]. Journal of Meteorological Research, 2021, 35 (02) : 329 - 342
12345