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CN 11-1768/O4

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Research on skeleton-based objective quantization and identifying algorithm for quasi-linear convective systems
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1.National Meteorological Center;2.Key Laboratory of Meteorological Disaster,Ministry of Education / Joint International Research Laboratory of Climate and Environment Change / Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters,Nanjing University of Information Science and Technology

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    Abstract:

    In this paper, the concept of skeleton in Computer Graphics is applied to meteorology, such as echo image preprocessing, skeleton pruning and length-width ratio quantization techniques are developed. The quasi-linear convection systems (QLCSs) in radar echo mosaics conform to the meteorological standards can automatically be identified by this method. Firstly, the detailed identifying algorithm is introduced based on a double QLCSs process in Huang-Huai area in 2016. Then, the QLCSs in Anhui Province in June 2016 are objectively identified by this method, and the moving characteristics of QLCSs are quantitatively. The comparison between disastrous weather observation and subjective identification are carried out. The results show that the shape information of meteorological echo is well preserved by using skeleton image identifying algorithm and the effective identification of QLCSs is realized base on accurately quantifying the long and short axes of convection system. On the one hand, quantitative characteristics such as the moving vectors can be applied to the classification of disastrous QLCSs, providing identifying algorithm and quantitative features for long series statistics and mechanism analysis of disastrous weather, and on the other hand, it can provide new technique for monitoring and warning of QLCSs.

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History
  • Received:September 10,2019
  • Revised:March 22,2020
  • Adopted:April 28,2020
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