Analysis of temperature time series based on Hilbert-Huang Transform

• Published in: Journal of hydrodynamics. Series B 2015-10, Vol.27 (4), p.587-592
• Main Author: 马皓 邱翔 罗剑平 顾品强 刘宇陆
• Format: Article
• Language: English
• Subjects: atmospheric turbulence; empirical mode decomposition (EMD); Engineering; Engineering Fluid Dynamics; Hilbert Huang Transform; Hydrology/Water Resources; Numerical and Computational Physics; Simulation; temperature time series; 周期性变化; 希尔伯特-黄变换; 希尔伯特黄变换; 平均温度; 时间尺度; 时间序列分析; 经验模式分解; 非线性信号
• ISSN: 1001-6058; 1878-0342
• DOI: 10.1016/S1001-6058(15)60520-0

In this paper, with consideration of the nonlinear and non-stationary properties of the temperature time series, we employ the Hilbert-Huang Transform, based on the empirical mode decomposition（EMD）, to analyze the temperature time series from 1959 to 2012 in the Fengxian district of Shanghai, obtained from a certain monitoring station. The oscillating mode is drawn from the data, and its characteristics of the time series are investigated. The results show that the intrinsic modes of 1, 2 and 6 represent the periodic properties of 1 year, 2.5 years, and 27 years. The mean temperature shows periodic variations, but the main trend of this fluctuation is the rising of the temperature in the recent 50 years. The analysis of the reconstructed modes with the wave pattern shows that the variations are quite large from 1963 to 1964, from 1977 to 1982 and from 2003 to 2006, which indicates that the temperature rises and falls dramatically in these periods. The volatility from 1993 to 1994 is far more dramatic than in other periods. And the volatility is the most remarkable in recent 50 years. The log-linear plots of the mean time scales T and M show that each mode associated with a time scale almost twice as large as the time scale of the preceding mode. The Hilbert spectrum shows that the energy is concentrated in the range of low frequency from 0.05 to 0.1 Hz, and a very small amount of energy is distributed in the range of higher frequency over 0.1 Hz. In conclusion, the HHT is better than other traditional signal analysis methods in processing the nonlinear signals to obtain the periodic variation and volatility＇s properties of different time scales.