Numerical Regularization for Atmospheric Inverse Problems [electronic resource] / by Adrian Doicu, Thomas Trautmann, Franz Schreier.

Doicu, Adrian [Browse]
1st ed. 2010.
Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2010.
1 online resource (431 p.)


Environmental Sciences [More in this series]
Subseries of
Springer Praxis Books
Summary note
The subject of this book is a hot topic with currently no monographic support. It is more advanced, specialized and mathematical than its competitors, and a comprehensive book on regularization techniques for atmospheric science is much needed for further development in this field. Written by brilliant mathematicians, this research monograph presents and analyzes numerical algorithms for atmospheric retrieval, pulling together all the relevant material in a consistent, very powerful manner. The first chapter presents the typical retrieval problems encountered in atmospheric remote sensing. Chapter 2 introduces the concept of ill-posedness for linear discrete equations, illustrating the difficulties associated with the solution of the problems by considering a temperature retrieval test problem and analyzing the solvability of the discrete equation by using the singular value decomposition of the corresponding matrix. A detailed description of the Tikhonov regularization for linear problems is the subject of Chapter 3, in which the authors introduce a set of mathematical and graphical tools to characterize the regularized solution. The goal of Chapter 4 is to reveal the similitude between Tikhonov regularization and statistical inversion regarding the regularized solution representation, the error analysis, and the design of parameter choice methods. The following chapter briefly surveys some classical iterative regularization methods such as the Landweber iteration and semi-iterative methods, and then treats the regularization effect of the conjugate gradient method applied to the normal equations. Having set the stage in the first part of the book, the remaining chapters dealing with nonlinear ill-posed problems. The authors introduce four test problems that are used throughout the rest of the book to illustrate the behaviour of the numerical algorithms and tools. These deal with the retrieval of ozone and BrO in the visible spectral region, and of CO and temperature in the infared spectral domain. Chapter 6 looks at the practical aspects of Tikhonov regularization for nonlinear problems, while Chapter 7 presents the relevant iterative regularization methods for nonlinear problems. The following chapter reviews the truncated and the regularized total least squares method for solving linear ill--posed problems, and include the similarity with the Tikhonov regularization. Chapter 9 brings the list of nonlinear methods to a close. It describes the Backus-Gilbert approach as a representative member of mollifier methods and finally, addresses the maximum entropy regularization. For the sake of completeness and in order to emphasize the mathematical techniques which are used in the classical regularization theory, five appendices at the end of the book present direct and iterative methods for solving linear and nonlinear ill-posed problems.
Description based upon print version of record.
Bibliographic references
Includes bibliographical references (p. [407]-422) and index.
Language note
  • Numerical Regularization for Atmospheric Inverse Problems; Table of Contents; Preface; 1 Remote sensing of the atmosphere; 2 Ill-posedness of linear problems; 3 Tikhonov regularization for linear problems; 4 Statistical inversion theory; 5 Iterative regularization methods for linear problems; 6 Tikhonov regularization for nonlinear problems; 7 Iterative regularization methods for nonlinear problems; 8 Total least squares; 9 Two direct regularization methods; A Analysis of continuous ill-posed problems; B Standard-form transformation for rectangular regularization matrices
  • C A general direct regularization method for linear problems D Chi-square distribution; E A general iterative regularization method for linear problems; F Residual polynomials of the LSQR method; G A general direct regularization method for nonlinear problems; H A general iterative regularization method for nonlinear problems; I Filter factors of the truncated total least squares method; J Quadratic programming; References; Index
  • 1-282-92637-3
  • 9786612926372
  • 3-642-05439-0
  • 10.1007/978-3-642-05439-6
Statement on language in description
Princeton University Library aims to describe library materials in a manner that is respectful to the individuals and communities who create, use, and are represented in the collections we manage. Read more...
Other views
Staff view