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 ...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.展开更多
The chaoticity of daily mean temperature time series is investigated with complex systems theory. The data set that has been used in this analysis consists of daily mean temperature recorded at four stations in China....The chaoticity of daily mean temperature time series is investigated with complex systems theory. The data set that has been used in this analysis consists of daily mean temperature recorded at four stations in China. The power spectrums axe used to obtain some preliminaxy information regaxding the temperature time series. R/S analysis provides evidence for fractaiity in temperature time series. Cao method and correlation dimension, as well as the largest Lyapunov exponent, give consistent results, which does not exclude the possibility of deterministic chaos for the four daily mean temperature series. The research provides a principled basis for further study of temperature data with nonlinear dynamical methods.展开更多
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11102114,11172179 and 11332006)the Inovation Program of Shanghai Municipal Education Commission(Grant No.13YZ124)
文摘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.
文摘The chaoticity of daily mean temperature time series is investigated with complex systems theory. The data set that has been used in this analysis consists of daily mean temperature recorded at four stations in China. The power spectrums axe used to obtain some preliminaxy information regaxding the temperature time series. R/S analysis provides evidence for fractaiity in temperature time series. Cao method and correlation dimension, as well as the largest Lyapunov exponent, give consistent results, which does not exclude the possibility of deterministic chaos for the four daily mean temperature series. The research provides a principled basis for further study of temperature data with nonlinear dynamical methods.
