| EEMD在雷暴日趋势特征分析中的应用 |
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| 引用本文： | 陈则煌,张云峰,谢菲,霍光,曹洪亮.EEMD在雷暴日趋势特征分析中的应用[J].热带地理,2015,35(4):601-606. |
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| 作者姓名： | 陈则煌 张云峰 谢菲 霍光 曹洪亮 |
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| 作者单位： | （1．南京信息工程大学气象灾害预报预警与评估协同创新中心//中国气象局气溶胶-云-降水重点开放实验室，南京 210044； 2．解放军理工大学 气象海洋学院，南京 211101；3．青冈县气象局，黑龙江 绥化 152002） |
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| 基金项目： | 国家重点基础研究发展计划（973计划，2014CB441405）；国家自然科学基金项目（41075025） |
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| 摘 要： | 首先利用整体经验模态分解算法（EEMD）对加入高斯白噪声后的香港地区雷暴日时间序列进行本征模态函数（IMF）分解；其次对各IMF分量进行Hilbert变换，提取雷暴日波动特征参量，并给出雷暴日序列的Hilbert谱和边际谱；最后对各雷暴日IMF分量进行显著性检验。结果表明：香港地区近67 a雷暴日序列可分解为1项趋势项和5个中心频率不同的IMF分量，其中能量主要集中在0.35~0.5 Hz和0~0.05 Hz频段；通过分析IMF能量谱密度-周期分布，得出雷暴日变化周期为2.8 a左右的年际变化和25 a左右的代际变化为主要变化周期，其次是4.5 和7.1 a左右的年际变化为次要变化周期，从趋势项可知香港地区雷暴日呈波动上升趋势。EEMD算法可较好地用于雷暴日趋势特征分析。
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| 关 键 词： | 雷暴日 EEMD HHT变换 白噪声 香港 |
Applications of EEMD in the Trends Analysis of the Thunderstorm Days |
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| CHEN Zehuang,ZHANG Yufeng,XIE Fei,HUO Guang,CAO Hongliang.Applications of EEMD in the Trends Analysis of the Thunderstorm Days[J].Tropical Geography,2015,35(4):601-606. |
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| Authors: | CHEN Zehuang ZHANG Yufeng XIE Fei HUO Guang CAO Hongliang |
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| Institution: | （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） |
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| 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. |
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| Keywords: | thunderstorm day EEMD algorithm HHT white noise Hong Kong |
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