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Numerical Regularization for Atmospheric Inverse Problems (Hardcover, 2010 Ed.)
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Numerical Regularization for Atmospheric Inverse Problems (Hardcover, 2010 Ed.)
Series: Springer Praxis Books
Expected to ship within 10 - 15 working days
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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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