Application of Rescaled Range and Wavelet Analysis on Climate Prediction: A Case Study in the Lower Yellow River Region

DOI

10.2991/mmsta-19.2019.26How to use a DOI?

Keywords

rescaled range analysis; wavelet analysis; climate prediction; application

Abstract

Rescaled range analysis is a new method to reveal the natural self-similarity. Wavelet analysis shows the frequency characteristics on whole frequency domain, also can give the overall characteristics on time domain. Fengqiu is selected as the representative study area of lower Yellow River. Predicting climatic change trend based on the non-linear mathematical method, analyzing time series lone-run memory effects and memory period by Hurst index, fractal dimension and non-period cycle average cycle length. Then, analyzing the multi-time scale characteristics of the temperature time series by the Morlet wavelet transformation method. Results show that the temperature trend is fluctuating upward, warming rate is 0.09℃/10a, 5a moving average curve has a peak value in 1990s; the Hurst value is 0.9503, the fractal dimension value is 1.0497; variation trend of temperature will inherit the past trend; the annual mean temperature mainly has three different scales of oscillation period, they are 3-5a, 17a and 32a respectively. The case study shows that the rescaled range analysis and wavelet analysis can get satisfactory results for revealing some multiscale, multilevel, and multiresolution problems.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Guodong Li
AU  - Guifen Shi
AU  - Junhua Zhang
AU  - Qiongqiong Kang
AU  - Yapeng Ding
AU  - Man Liu
PY  - 2019/12
DA  - 2019/12
TI  - Application of Rescaled Range and Wavelet Analysis on Climate Prediction: A Case Study in the Lower Yellow River Region
BT  - Proceedings of the 2019 2nd International Conference on Mathematics, Modeling and Simulation Technologies and Applications (MMSTA 2019)
PB  - Atlantis Press
SP  - 124
EP  - 127
SN  - 2352-538X
UR  - https://doi.org/10.2991/mmsta-19.2019.26
DO  - 10.2991/mmsta-19.2019.26
ID  - Li2019/12
ER  -