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Hilbert-Huang transform analysis of hydrological and climatic time series.

机译：Hilbert-Huang变换对水文和气候时间序列的分析。

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摘要

To accommodate the inherent non-linearity and non-stationarity of many natural time series, empirical mode decomposition (EMD) and Hilbert-Huang transform (HHT) provide an adaptive and efficient method. The HHT is based on the local characteristic time scale of the data. The HHT method provides not only a precise definition in time-frequency representation than the other conventional signal processing methods, but also more physically meaningful interpretation of the underlying dynamic processes. The EMD also works as a filter to extract the variability of signals with different scales and is applicable to non-linear and non-stationary processes. This promising algorithm has been applied in many fields since it was developed, but it has not been applied to hydrological and climatic time series. This study starts with several simulated data sets in order to investigate the capability of this method and to compare it to other conventional frequency-domain analysis methods that assume stationarity. Rainfall, streamflow, temperature, wind speed time series and lake temperature data are investigated in this study. The aim of the study is to investigate periodicity, long term oscillations and trends embedded in these data by using HHT. The analysis is performed in both the time and frequency domains. The results from HHT are compared to those from the multi-taper method which is based on Fourier Transform of the data. The results indicate that the HHT is clearly superior to MTM in delineating the stochastic structure of the data. Details about the data which cannot be investigated by traditional methods are clearly seen with HHT. The nonstationarities of climatic and hydrologic data are also brought out. The HHT is an excellent tool to investigate the characteristics of environmental and hydrologic time series.
机译：为了适应许多自然时间序列固有的非线性和非平稳性，经验模式分解（EMD）和希尔伯特-黄氏变换（HHT）提供了一种自适应且有效的方法。 HHT基于数据的本地特征时间标度。与其他常规信号处理方法相比，HHT方法不仅提供了时频表示的精确定义，而且还对底层动态过程进行了更有意义的物理解释。 EMD还可以用作过滤器，以提取具有不同比例的信号的可变性，并且适用于非线性和非平稳过程。自从开发以来，这种有前途的算法已应用于许多领域，但尚未应用于水文和气候时间序列。这项研究从几个模拟的数据集开始，以研究此方法的功能并将其与假定平稳性的其他常规频域分析方法进行比较。这项研究调查了降雨，水流，温度，风速时间序列和湖泊温度数据。该研究的目的是通过使用HHT来研究嵌入在这些数据中的周期性，长期振荡和趋势。在时域和频域均进行分析。将HHT的结果与基于数据的Fourier变换的多锥度方法的结果进行比较。结果表明，HHT在描述数据的随机结构方面明显优于MTM。使用HHT可以清楚地看到无法通过传统方法调查的数据的详细信息。还提出了气候和水文数据的非平稳性。 HHT是研究环境和水文时间序列特征的出色工具。

著录项

  • 作者

    Hsu, En-Ching.;

  • 作者单位

    Purdue University.;

  • 授予单位 Purdue University.;
  • 学科 Engineering Civil.
  • 学位 Ph.D.
  • 年度 2006
  • 页码 302 p.
  • 总页数 302
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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