English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

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

MPS-Authors
There are no MPG-Authors in the publication available
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Cornford, D., Csato, L., Evans, D., & Opper, M. (2004). Bayesian analysis of the Scatterometer Wind Retrieval Inverse Problem: Some New Approaches. Journal of the Royal Statistical Society. Series B: Statistical Methodology, 66(3), 609-626. doi:10.1111/j.1467-9868.2004.02054.x.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-D809-0
Abstract
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.