Numerical Regularization for Atmospheric Inverse Problems - Info and Reading Options
By Adrian Doicu
"Numerical Regularization for Atmospheric Inverse Problems" is published by Springer in Oct 30, 2014 - Berlin, Heidelberg, the book is classified in Science genre, it has 440 pages and the language of the book is English.
“Numerical Regularization for Atmospheric Inverse Problems” Metadata:
- Title: ➤ Numerical Regularization for Atmospheric Inverse Problems
- Author: Adrian Doicu
- Language: English
- Number of Pages: 440
- Is Family Friendly: Yes - No Mature Content
- Publisher: Springer
- Publish Date: Oct 30, 2014
- Publish Location: Berlin, Heidelberg
- Genres: Science
“Numerical Regularization for Atmospheric Inverse Problems” Subjects and Themes:
- Subjects: ➤ Atmosphere - Regularisierungsverfahren - Remote sensing - Mathematical models - Satellitenfernerkundung - Atmosphäre - Inverse problems (Differential equations) - Inverses Problem - Environmental Monitoring/Analysis - Ecology - Euthenics - Nature and nurture - Adaptation (Biology) - Environmental sciences
Edition Specifications:
- Format: paperback
Edition Identifiers:
- Google Books ID: lXUEogEACAAJ
- The Open Library ID: OL27981743M - OL16615341W
- ISBN-13: 9783642424014 - 9783642054396
- ISBN-10: 3642424015
- All ISBNs: 3642424015 - 9783642424014 - 9783642054396
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"Numerical Regularization for Atmospheric Inverse Problems" Description:
Google Books:
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.
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