Overview
- Authors:
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Adrian Doicu
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und Raumfahrt e. V., Remote Sensing Technology Institute, DLR Deutsches Zentrum für Luft-, Weßling, Germany
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Thomas Trautmann
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Raumfahrt (DLR), Inst. Physik der Atmosphäre,, Deutsches Zentrum für Luft- und, Weßling, Germany
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Franz Schreier
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Luft- und Raumfahrt (DLR), GeoForschungsZentrum Potsdam, Deutsches Zentrum für, Weßling, Germany
- Presents regularization methods for atmospheric retrieval, based on the authors work
- Focuses on computational aspects but also provides some theoretical results
- Surveys the state-of-the-art numerical methods for solving discrete ill-posed problems
- Anlayzes the existing numerical alorithms and discusses practical implementation issues
- Illustrates with examples from atmospheric remote sensing the variouis methods in action
- Includes supplementary material: sn.pub/extras
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Table of contents (9 chapters)
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Front Matter
Pages I-XIII
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- Adrian Doicu, Thomas Trautmann, Franz Schreier
Pages 1-21
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- Adrian Doicu, Thomas Trautmann, Franz Schreier
Pages 23-38
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- Adrian Doicu, Thomas Trautmann, Franz Schreier
Pages 39-106
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- Adrian Doicu, Thomas Trautmann, Franz Schreier
Pages 107-140
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- Adrian Doicu, Thomas Trautmann, Franz Schreier
Pages 141-162
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- Adrian Doicu, Thomas Trautmann, Franz Schreier
Pages 163-220
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- Adrian Doicu, Thomas Trautmann, Franz Schreier
Pages 221-250
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- Adrian Doicu, Thomas Trautmann, Franz Schreier
Pages 251-270
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- Adrian Doicu, Thomas Trautmann, Franz Schreier
Pages 271-284
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Back Matter
Pages 424-426
About this book
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.
Authors and Affiliations
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und Raumfahrt e. V., Remote Sensing Technology Institute, DLR Deutsches Zentrum für Luft-, Weßling, Germany
Adrian Doicu
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Raumfahrt (DLR), Inst. Physik der Atmosphäre,, Deutsches Zentrum für Luft- und, Weßling, Germany
Thomas Trautmann
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Luft- und Raumfahrt (DLR), GeoForschungsZentrum Potsdam, Deutsches Zentrum für, Weßling, Germany
Franz Schreier