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Bayesian analysis of the Scatterometer Wind Retrieval Inverse Problem: Some New Approaches

2004

Article

ei


The retrieval of wind vectors from satellite scatterometer observations is a non-linear inverse problem.A common approach to solving inverse problems is to adopt a Bayesian framework and to infer the posterior distribution of the parameters of interest given the observations by using a likelihood model relating the observations to the parameters, and a prior distribution over the parameters.We show how Gaussian process priors can be used efficiently with a variety of likelihood models, using local forward (observation) models and direct inverse models for the scatterometer.We present an enhanced Markov chain Monte Carlo method to sample from the resulting multimodal posterior distribution.We go on to show how the computational complexity of the inference can be controlled by using a sparse, sequential Bayes algorithm for estimation with Gaussian processes.This helps to overcome the most serious barrier to the use of probabilistic, Gaussian process methods in remote sensing inverse problems, which is the prohibitively large size of the data sets.We contrast the sampling results with the approximations that are found by using the sparse, sequential Bayes algorithm.

Author(s): Cornford, D. and Csato, L. and Evans, D. and Opper, M.
Journal: Journal of the Royal Statistical Society B
Volume: 66
Pages: 1-17
Year: 2004
Day: 0
Series: 3

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
Institution: Dep. Schoelkopf, MPI. for Biological Cybernetics, Spemannstrasse 38, Tuebingen
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF

BibTex

@article{2628,
  title = {Bayesian analysis of the Scatterometer Wind Retrieval Inverse Problem: Some New Approaches},
  author = {Cornford, D. and Csato, L. and Evans, D. and Opper, M.},
  journal = {Journal of the Royal Statistical Society B},
  volume = {66},
  pages = {1-17},
  series = {3},
  organization = {Max-Planck-Gesellschaft},
  institution = {Dep. Schoelkopf, MPI. for Biological Cybernetics, Spemannstrasse 38, Tuebingen},
  school = {Biologische Kybernetik},
  year = {2004}
}