Filtering Complex Turbulent Systems
Cambridge University Press
Edition: Illustrated, 2/23/2012
EAN 9781107016668, ISBN10: 1107016665
Hardcover, 368 pages, 25.1 x 18 x 2.3 cm
Language: English
Many natural phenomena ranging from climate through to biology are described by complex dynamical systems. Getting information about these phenomena involves filtering noisy data and prediction based on incomplete information (complicated by the sheer number of parameters involved), and often we need to do this in real time, for example for weather forecasting or pollution control. All this is further complicated by the sheer number of parameters involved leading to further problems associated with the 'curse of dimensionality' and the 'curse of small ensemble size'. The authors develop, for the first time in book form, a systematic perspective on all these issues from the standpoint of applied mathematics. The book contains enough background material from filtering, turbulence theory and numerical analysis to make the presentation self-contained and suitable for graduate courses as well as for researchers in a range of disciplines where applied mathematics is required to enlighten observations and models.
Preface
1. Introduction and overview
mathematical strategies for filtering turbulent systems
Part I. Fundamentals
2. Filtering a stochastic complex scalar
the prototype test problem
3. The Kalman filter for vector systems
reduced filters and a three-dimensional toy model
4. Continuous and discrete Fourier series and numerical discretization
Part II. Mathematical Guidelines for Filtering Turbulent Signals
5. Stochastic models for turbulence
6. Filtering turbulent signals
plentiful observations
7. Filtering turbulent signals
regularly spaced sparse observations
8. Filtering linear stochastic PDE models with instability and model error
Part III. Filtering Turbulent Nonlinear Dynamical Systems
9. Strategies for filtering nonlinear systems
10. Filtering prototype nonlinear slow-fast systems
11. Filtering turbulent nonlinear dynamical systems by finite ensemble methods
12. Filtering turbulent nonlinear dynamical systems by linear stochastic models
13. Stochastic parameterized extended Kalman filter for filtering turbulent signal with model error
14. Filtering turbulent tracers from partial observations
an exactly solvable test model
15. The search for efficient skilful particle filters for high dimensional turbulent dynamical systems
References
Index.