Inverse Methods for Atmospheric Sounding: Theory and PracticeWorld Scientific, 2000 - 238 pages Remote sounding of the atmosphere has proved to be a fruitful method of obtaining global information about the atmospheres of the earth and other planets. This book treats comprehensively the inverse problem of remote sounding, and discusses a wide range of retrieval methods for extracting atmospheric parameters of interest from the quantities (thermal emission, for example) that can be measured remotely. Inverse theory is treated in depth from an estimation-theory point of view, but practical questions are also emphasized, such as designing observing systems to obtain the maximum quantity of information, efficient numerical implementation of algorithms for processing large quantities of data, error analysis and approaches to the validation of the resulting retrievals. The book is targeted at graduate students as well as scientists. |
Table des matières
Chapter 1 Introduction | 1 |
Chapter 2 Information Aspects | 13 |
Chapter 3 Error Analysis and Characterisation | 43 |
Chapter 4 Optimal Linear Inverse Methods | 65 |
Chapter 5 Optimal Methods for Nonlinear Inverse Problems | 81 |
Chapter 6 Approximations Short Cuts and Adhoc Methods | 101 |
Chapter 7 The Kalman Filter | 121 |
Chapter 8 Global Data Assimilation | 129 |
Chapter 10 Construction and Use of Prior Constraints | 159 |
Chapter 11 Designing an Observing System | 175 |
Chapter 12 Testing and Validating an Observing System | 185 |
Appendix A Algebra of Matrices and Vectors | 197 |
Appendix B Answers to Exercises | 205 |
Appendix C Terminology and Notation | 223 |
| 229 | |
| 235 | |
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Expressions et termes fréquents
algebra approximation assimilation atmospheric averaging kernel matrix characterisation Cholesky decomposition components computed consider contribution function convergence coordinate corresponding cost function covariance matrix degrees of freedom derivative described diagonal Eacercise eigenvalues eigenvectors elements ensemble entropy equation error analysis estimate evaluated example expected value forward model parameters freedom for signal give grid hydrostatic equation independent information content instrument integral interpolation inverse method inverse problem iteration Kalman filter linear problem mean measured signal measurement noise measurement space measurement vector minimise modelling error nonlinear null space observing system obtain optimal optimisation paſ pressure prior information priori probability density probability density function quantity radiance random representation resolution retrieval error retrieval method retrieval noise row space scalar scale simulated singular values smoothing error spectral temperature thermal emission transformed transmittance variance vector decomposition weighting function matrix weighting functions zero