Stochastic Analysis of Scaling Time Series
From Turbulence Theory to Applications

This book provides a thorough understanding of the techniques used to retrieve multi-scale information from turbulent and complex systems, with case studies.

François G. Schmitt (Author), Yongxiang Huang (Author)

9781107067615, Cambridge University Press

Hardback, published 7 January 2016

226 pages, 148 b/w illus.
25.3 x 17.9 x 1.5 cm, 0.6 kg

Multi-scale systems, involving complex interacting processes that occur over a range of temporal and spatial scales, are present in a broad range of disciplines. Several methodologies exist to retrieve this multi-scale information from a given time series; however, each method has its own limitations. This book presents the mathematical theory behind the stochastic analysis of scaling time series, including a general historical introduction to the problem of intermittency in turbulence, as well as how to implement this analysis for a range of different applications. Covering a variety of statistical methods, such as Fourier analysis and wavelet transforms, it provides readers with a thorough understanding of the techniques and when to apply them. New techniques to analyse stochastic processes, including empirical mode decomposition, are also explored. Case studies, in turbulence and ocean sciences, are used to demonstrate how these statistical methods can be applied in practice, for students and researchers.

Preface
1. Introduction: a multiscale and turbulent-like world
2. Homogeneous turbulence and intermittency
3. Scaling and intermittent stochastic processes
4. New methodologies to deal with nonlinear and scaling time series
5. Applications: case studies in turbulence
6. Applications: case studies in ocean and atmospheric sciences
References
Index.

Subject Areas: Environmental science, engineering & technology [TQ], Engineering thermodynamics [TGMB], The environment [RN], Oceanography [seas RBKC], Earth sciences [RB], Mathematical physics [PHU], Physics [PH], Applied mathematics [PBW], Probability & statistics [PBT]