TROPICAL GEOGRAPHY ›› 2015, Vol. 35 ›› Issue (4): 601-606.doi: 10.13284/j.cnki.rddl.002726

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Applications of EEMD in the Trends Analysis of the Thunderstorm Days

CHEN Zehuang1,ZHANG Yufeng1,XIE Fei2,HUO Guang3,CAO Hongliang1   

  1. (1.Nanjing University of Information Science & Technology,Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters// Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration,Nanjing 210044,China;2.College of Meteorology and Oceanography,PLA University of Science and Technology,Nanjing 211101,China;3.Qinggang County Meteorological Bureau,Suihua 152002,China)
  • Online:2015-07-03 Published:2015-07-03

Abstract:

The EEMD (Ensemble empirical mode decomposition) was used to analyze the thunderstorm days of Hong Kong to clarify the applicability in the trends analysis of lightning day. At first, the IMF (intrinsic mode function) components were decomposed based on EEMD, and then the Hilbert transform was also used to extract the features of each IMF component of the thunderstorm days. Also both the Hilbert spectrum and the marginal spectrum were showed in this paper to illustrate the variation features of the days. Lastly, the significance test of the thunderstorm days of IMF component was also made to illustrate the reliability of the IMFs in analyzing the trends. According to the research the conclusions can be drawn as follows: the thunderstorm days in Hong Kong could be decomposed into a trend term and five IMF components with different center frequencies. And the energies of the IMFs were mainly concentrated in 0.35~0.5 Hz and 0~0.05 Hz. Based on the analysis of the energy spectrum density and period distribution of the IMFs, it proved that 2.8 a interannual variation and 25 a generational variation of lightning day were the main cycles, and 4.5 and 7.1 a were the secondary cycle. Besides, from the trend term, the thunderstorm days in Hong Kong grown in wave-like form. From the results, the EEMD algorithm can be applied to the analysis of the characteristics of thunderstorm day’s trend better.

Key words: thunderstorm day, EEMD algorithm, HHT, white noise, Hong Kong