Articles liés à Numerical Regularization for Atmospheric Inverse Problems
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- ÉditeurSpringer-Verlag Berlin and Heidelberg GmbH & Co. K
- Date d'édition2010
- ISBN 10 3642054382
- ISBN 13 9783642054389
- ReliureRelié
- Nombre de pages426
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Numerical Regularization for Atmospheric Inverse Problems (Springer Praxis Books) by Doicu, Adrian, Schreier, Franz, Trautmann, Thomas [Hardcover ]
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ISBN 10 : 3642054382
ISBN 13 : 9783642054389
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Description du livre Hardcover. Etat : new. N° de réf. du vendeur 9783642054389
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Numerical Regularization for Atmospheric Inverse Problems (Springer Praxis Books)
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(2010)
ISBN 10 : 3642054382
ISBN 13 : 9783642054389
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Description du livre Etat : New. N° de réf. du vendeur ABLIING23Mar3113020214832
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Numerical Regularization for Atmospheric Inverse Problems
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Springer Berlin Heidelberg
(2010)
ISBN 10 : 3642054382
ISBN 13 : 9783642054389
Neuf
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Description du livre Gebunden. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents regularization methods for atmospheric retrieval, based on the authors workFocuses on computational aspects but also provides some theoretical resultsSurveys the state-of-the-art numerical methods for solving discrete ill-posed problemsAnlayzes the. N° de réf. du vendeur 5044618
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Numerical Regularization for Atmospheric Inverse Problems
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(2010)
ISBN 10 : 3642054382
ISBN 13 : 9783642054389
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Description du livre Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 440 pp. Englisch. N° de réf. du vendeur 9783642054389
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Numerical Regularization for Atmospheric Inverse Problems
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Description du livre Etat : New. PRINT ON DEMAND Book; New; Fast Shipping from the UK. No. book. N° de réf. du vendeur ria9783642054389_lsuk
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Numerical Regularization for Atmospheric Inverse Problems
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Springer Berlin Heidelberg
(2010)
ISBN 10 : 3642054382
ISBN 13 : 9783642054389
Neuf
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Description du livre Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. N° de réf. du vendeur 9783642054389
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