Dynamic data assimilation-剑桥经典书籍亚马逊报价167美刀

yuanliang110

感谢楼主发布这么多书，还是给点介绍吧，而且全部要贡献，这个价格高了吧

andrewsoong

yuanliang110 发表于 2013-7-7 23:14

                               
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感谢楼主发布这么多书，还是给点介绍吧，而且全部要贡献，这个价格高了吧

目录如下
Dynamic Data Assimilation
A Least Squares Approach
JOHN M. LEWIS
National Severe Storms Laboratory
and
Desert Research Institute
S. LAKSHMIVARAHAN
University of Oklahoma
SUDARSHAN DHALL
University of Oklahoma

Preface page xiii
Acknowledgements xxi
PART I GENESIS OF DATA ASSIMILATION 1
1 Synopsis 3
1.1 Forecast: justification for data assimilation 3
1.2 Models 6
1.3 Observations 10
1.4 Categorization of models used in data assimilation 12
1.5 Sensitivity analysis 19
1.6 Predictability 21
2 Pathways into data assimilation: illustrative examples 27
2.1 Least squares 27
2.2 Deterministic/Static problem 27
2.3 Deterministic/Linear dynamics 30
2.4 Stochastic/Static problem 33
2.5 Stochastic/Dynamic problem 34
2.6 An intuitive view of least squares adjustment 36
2.7 Sensitivity 39
2.8 Predictability 42
2.9 Stochastic/Dynamic prediction 45
3 Applications 51
3.1 Straight line problem 51
3.2 Celestial dynamics 54
3.3 Fluid dynamics 56
3.4 Fluvial dynamics 60
3.5 Oceanography 60
3.6 Atmospheric chemistry 70
3.7 Meteorology 73
3.8 Atmospheric physics (an inverse problem) 77
4 Brief history of data assimilation 81
4.1 Where do we begin the history? 81
4.2 Laplace’s strategy for orbital determination 82
4.3 The search for Ceres 83
4.4 Gauss’s method: least squares 84
4.5 Gauss’s problem: a simplified version 85
4.6 Probability enters data assimilation 91
PART II DATA ASSIMILATION:
DETERMINISTIC/STATIC MODELS 97
5 Linear least squares estimation: method of normal equations 99
5.1 The straight line problem 100
5.2 Generalized least squares 110
5.3 Dual problem: m < n 112
5.4 A unified approach: Tikhonov regularization 115
6 A geometric view: projection and invariance 121
6.1 Orthogonal projection: basic idea 121
6.2 Ordinary least squares estimation: orthogonal projection 124
6.3 Generalized least squares estimation:
oblique projection 126
6.4 Invariance under linear transformation 127
7 Nonlinear least squares estimation 133
7.1 A first-order method 133
7.2 A second-order method 136
8 Recursive least squares estimation 141
8.1 A recursive framework 141
PART III COMPUTATIONAL TECHNIQUES 147
9 Matrix methods 149
9.1 Cholesky decomposition 149
9.2 QR-decomposition 154
9.3 Singular value decomposition 160
先贴这些吧~~~

letsdo8

同化的书下了以后读读。