Preliminary research on the relationship between long-range correlations and predictability

doi:10.1088/1674-1056/20/1/019201

中国物理B ›› 2011, Vol. 20 ›› Issue (1): 19201-019201.doi: 10.1088/1674-1056/20/1/019201

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Preliminary research on the relationship between long-range correlations and predictability

胡经国¹, 张志森², 封国林³, 龚志强⁴, 支蓉⁴

(1)College of Physics Science and Technology, Yangzhou University, Yangzhou 225002, China; (2)College of Physics Science and Technology, Yangzhou University, Yangzhou 225002, China;Key Laboratory of Regional Climate-Environment for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; (3)College of Physics Science and Technology, Yangzhou University, Yangzhou 225002, China;Key Laboratory of Regional Climate-Environment for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China;National Cl; (4)National Climate Center, China Meteorological Administration, Beijing 100081, China

收稿日期:2010-08-05 修回日期:2010-09-04 出版日期:2011-01-15 发布日期:2011-01-15
基金资助:
Project supported by the National Natural Science Foundation of China (Grant Nos. 40930952, 40875040, and 41005043), the Special Project for Public Welfare Enterprises (Grant No. GYHY200806005) and the National Science/Technology Support Program of China (Grant Nos. 2007BAC29B01 and 2009BAC51B04).

Preliminary research on the relationship between long-range correlations and predictability

Zhang Zhi-Sen(张志森)^a)b), Gong Zhi-Qiang(龚志强)^c), Zhi Rong(支蓉)^c), Feng Guo-Lin(封国林)^a)b)c)†, and Hu Jing-Guo(胡经国)^a)‡

^a College of Physics Science and Technology, Yangzhou University, Yangzhou 225002, China; ^b Key Laboratory of Regional Climate-Environment for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; ^c National Climate Center, China Meteorological Administration, Beijing 100081, China

Received:2010-08-05 Revised:2010-09-04 Online:2011-01-15 Published:2011-01-15
Supported by:
Project supported by the National Natural Science Foundation of China (Grant Nos. 40930952, 40875040, and 41005043), the Special Project for Public Welfare Enterprises (Grant No. GYHY200806005) and the National Science/Technology Support Program of China (Grant Nos. 2007BAC29B01 and 2009BAC51B04).

摘要/Abstract

摘要： By establishing the Markov model for a long-range correlated time series (LRCS) and analysing its evolutionary characteristics, this paper defines a physical effective correlation length (ECL) τ, which reflects the predictability of the LRCS. It also finds that the ECL has a better power law relation with the long-range correlated exponent γ of the LRCS: τ= Kexp (-γ/0.3)+Y, (0 < γ < 1) -- the predictability of the LRCS decays exponentially with the increase of γ. It is then applied to a daily maximum temperature series (DMTS) recorded at 740 stations in China between the years 1960--2005 and calculates the ECL of the DMTS. The results show the remarkable regional distributive feature that the ECL is about 10--14 days in west, northwest and northern China, and about 5--10 days in east, southeast and southern China. Namely, the predictability of the DMTS is higher in central-west China than in east and southeast China. In addition, the ECL is reduced by 1--8 days in most areas of China after subtracting the seasonal oscillation signal of the DMTS from its original DMTS; however, it is only slightly altered when the decadal linear trend is removed from the original DMTS. Therefore, it is shown that seasonal oscillation is a significant component of daily maximum temperature evolution and may provide a basis for predicting daily maximum temperatures. Seasonal oscillation is also significant for guiding general weather predictions, as well as seasonal weather predictions.

Abstract: By establishing the Markov model for a long-range correlated time series (LRCS) and analysing its evolutionary characteristics, this paper defines a physical effective correlation length (ECL) $\tau$, which reflects the predictability of the LRCS. It also finds that the ECL has a better power law relation with the long-range correlated exponent $\gamma$ of the LRCS: $\tau$ = Kexp (-$\gamma$/0.3)+Y, ($0<\gamma <1$)-the predictability of the LRCS decays exponentially with the increase of γ. It is then applied to a daily maximum temperature series (DMTS) recorded at 740 stations in China between the years 1960–2005 and calculates the ECL of the DMTS. The results show the remarkable regional distributive feature that the ECL is about 10–14 days in west, northwest and northern China, and about 5–10 days in east, southeast and southern China. Namely, the predictability of the DMTS is higher in central-west China than in east and southeast China. In addition, the ECL is reduced by 1–8 days in most areas of China after subtracting the seasonal oscillation signal of the DMTS from its original DMTS; however, it is only slightly altered when the decadal linear trend is removed from the original DMTS. Therefore, it is shown that seasonal oscillation is a significant component of daily maximum temperature evolution and may provide a basis for predicting daily maximum temperatures. Seasonal oscillation is also significant for guiding general weather predictions, as well as seasonal weather predictions.

Key words: long-range correlation, information entropy, effective correlation length, predictability

中图分类号: (Weather analysis and prediction)

92.60.Wc

张志森, 龚志强, 支蓉, 封国林, 胡经国. Preliminary research on the relationship between long-range correlations and predictability[J]. 中国物理B, 2011, 20(1): 19201-019201.

Zhang Zhi-Sen(张志森), Gong Zhi-Qiang(龚志强), Zhi Rong(支蓉), Feng Guo-Lin(封国林), and Hu Jing-Guo(胡经国). Preliminary research on the relationship between long-range correlations and predictability[J]. Chin. Phys. B, 2011, 20(1): 19201-019201.

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Preliminary research on the relationship between long-range correlations and predictability

Preliminary research on the relationship between long-range correlations and predictability

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