DFA Based Predictability Indices Analysis of Climatic Dynamics in Beijing Area, China

Abstract:

In this paper, detrended fluctuation analysis is used to calculate the Hurst exponent, the fractal dimensions and finally the climate predictability indices of monthly and seasonal time series of air temperature, surface pressure, precipitation, wind speed and relative humidity for Beijing meteorological stations, in which the meteorological data cover a period from 1951 to 2009 and the precipitation data own a series of 286 years (1724~2009). And we found that at the monthly scale, the predictability of precipitation and wind speed was not controlled by temperature and pressure. A strong negative correlation showed for precipitation VS. temperature and pressure, and the persistence trait of wind speed just depended absolutely on itself. At the seasonal scale, all three meteorological parameters existed negative persistence behavior with temperature and pressure in winter. In spring, the persistence behavior of precipitation is in step with that of temperature and pressure, and for wind speed and relative humidity, it got unconformable results.

• Periodical: Advanced Materials Research (Volume 382)
• Pages: 60-64
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.382.60
• Online since: November 2011
• Authors: Miao Li, Jun Xia, De Juan Meng
• Keywords: Climate Predictability Indices, Climatic Dynamics, Detrended Fluctuation Analysis, Fractal Dimension, Hurst Exponent

References

[1] G.Y. Ren. Climate Change and China's Water Resources. (China Meteorological Press, Beijing 2007).

[2] J. Xia, C.P. Ou et al. The Analysis of Haihe River Basin Hydrometeorological Spatial-temporal Variability Based on GIS and Information Difference Measure. Journal of natural resources. Vol. 22 (2007), pp.409-415.

[3] X.Y. Ding, Y.W. Jia, H. Wang et al. Impacts of Climate Change on Water Resources in the Haihe River Basin and Corresponding Countermeasures. Journal of natural resources. Vol. 25(2010), pp.604-613.

[4] G. Matheron. Principles of geostatistics. Economic Geology. Vol. 56 (1963), p.1246–1266.

[5] M.B. Priestley. Spectral analysis and time series. (Academic Press, London 1981. ).

[6] R.F. Voss. Random fractals forgeries. In: Earnshaw Ra, editor. Fundamental algorithms for computer graphics. NATO Series. Berlin: Springer; (1985). pp.805-835.

DOI: 10.1007/978-3-642-84574-1_34

[7] H.E. Hurst, R.P. Black, Y.M. Simaika. Long-term storage: An experimental study. (Constable, London 1965).

[8] A. Grossmann, J. Morlet. Decomposition of Hardy functions into square inferable wavelets of constant shape. SIAM journal on Mathematical Analysis Vol. 15(1984), p.723–736.

DOI: 10.1137/0515056

[9] C. K. Peng, S.V. Buldyrev, A.L. Goudberger., Finite size effects on long range correlation implications for analyzing DNA sequences. Physical Review E. Vol. 47(1993), pp.3730-3733.

DOI: 10.1103/physreve.47.3730

[10] Y.M. Kuo, F.J. Chang. Dynamic Factor Analysis for Estimating Ground Water Arsenic Trends. Journal of Environmental Quality . Vol. 39 (2010), pp.176-184.

DOI: 10.2134/jeq2009.0098

[11] C. Tricot, J.F. Quiniou, D. Wehbi, C. Roques-Carmes, B. Dubuc. Evaluation de la dimension fractale d'un graphe. Revue de Physique. Vol. 23(1988), p.111–124.

DOI: 10.1051/rphysap:01988002302011100

[12] M.C. Breslin, J.A. Belward. Fractal dimensions for rainfall time series. Mathematics and Computers in Simulation. Vol. 48 (1999), pp.437-446.

DOI: 10.1016/s0378-4754(99)00023-3

[13] G. Rangarajan. A climate predictability index and its applications. Geophysical research letters. Vol. 24 (1997), pp.1239-1242.

DOI: 10.1029/97gl01058

[14] G. Rangarajan, D. A. Sant., Fractal dimensional analysis of Indian climatic dynamics. Chaos, Solution & Fractals. Vol. 19 (2004), pp.285-291.

DOI: 10.1016/s0960-0779(03)00042-0

[15] S. Rehman, Wavelet based Hurst exponent and fractal dimensional analysis of Saudi climatic dynamics. Chaos, Solution & Fractals. Vol. 40(2009), pp.1081-1090.

DOI: 10.1016/j.chaos.2007.08.063