000 04364nam a22004335i 4500
001 978-3-642-05439-6
003 DE-He213
005 20140220084528.0
007 cr nn 008mamaa
008 100716s2010 gw | s |||| 0|eng d
020 _a9783642054396
_9978-3-642-05439-6
024 7 _a10.1007/978-3-642-05439-6
_2doi
050 4 _aGE300-350
072 7 _aTQD
_2bicssc
072 7 _aSCI026000
_2bisacsh
082 0 4 _a363.7063
_223
100 1 _aDoicu, Adrian.
_eauthor.
245 1 0 _aNumerical Regularization for Atmospheric Inverse Problems
_h[electronic resource] /
_cby Adrian Doicu, Thomas Trautmann, Franz Schreier.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2010.
300 _aXIII, 426p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Praxis Books
520 _aThe 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.
650 0 _aEnvironmental sciences.
650 1 4 _aEnvironment.
650 2 4 _aEnvironmental Monitoring/Analysis.
700 1 _aTrautmann, Thomas.
_eauthor.
700 1 _aSchreier, Franz.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642054389
830 0 _aSpringer Praxis Books
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-05439-6
912 _aZDB-2-EES
999 _c111718
_d111718