Numerical Regularization for Atmospheric Inverse Problems
Libreria AbeBooks dal 11 gennaio 2012Quantità: 1
Libreria AbeBooks dal 11 gennaio 2012Quantità: 1
Riguardo questo articolo
Titolo: Numerical Regularization for Atmospheric ...
Casa editrice: Springer-Verlag Gmbh Okt 2010
Data di pubblicazione: 2010
The retrieval problems arising in atmospheric remote sensing belong to the class of the - called discrete ill-posed problems. These problems are unstable under data perturbations, and can be solved by numerical regularization methods, in which the solution is stabilized by taking additional information into account. The goal of this research monograph is to present and analyze numerical algorithms for atmospheric retrieval. The book is aimed at physicists and engineers with some ba- ground in numerical linear algebra and matrix computations. Although there are many practical details in this book, for a robust and ef?cient implementation of all numerical algorithms, the reader should consult the literature cited. The data model adopted in our analysis is semi-stochastic. From a practical point of view, there are no signi?cant differences between a semi-stochastic and a determin- tic framework; the differences are relevant from a theoretical point of view, e.g., in the convergence and convergence rates analysis. After an introductory chapter providing the state of the art in passive atmospheric remote sensing, Chapter 2 introduces the concept of ill-posedness for linear discrete eq- tions. To illustrate the dif?culties associated with the solution of discrete ill-posed pr- lems, we consider the temperature retrieval by nadir sounding and analyze the solvability of the discrete equation by using the singular value decomposition of the forward model matrix.From the Back Cover:
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.
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
Allgemeine Geschäftsbedingungen (abebooks.de)
Rhein-Team Lörrach, Inhaber Ivano Narducci e.K., Mühlestr. 1
D-79539 Lörrach, nachfolgend als Verkäufer bezeichnet.
§ 1 Allgemeines, Begriffsbestimmungen
(1) Der Verkäufer bietet unter dem Nutzernamen rhein-team unter der Plattform abebooks.de insbesondere Bücher an. Die folgenden Allgemeinen Geschäftsbedingungen (AGB) gelten für die Geschäftsbeziehung zwischen dem Verkäufer und dem Kunden in ihrer zum Zeitpunkt der Bestellung gültigen Fassung. Ferne...Ulteriori informazioni
Die Ware wird innerhalb von 1-3 Tagen nach Bestelleingang verschickt. Bitte entnehmen Sie den voraussichtlichen Liefertermin Ihrer Bestellbestätigung. Die Versandkostenpauschalen basieren auf Durchschnittswerten für 1 kg schwere Bücher. Über abweichende Kosten (z.B. wegen eines sehr schweren Buches) werden Sie gegebenenfalls vom Verkäufer informiert.
Metodi di pagamento
accettati dalla libreria