Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 11, issue 8
Articles | Volume 11, issue 8
https://doi.org/10.5194/amt-11-4627-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-11-4627-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 11, issue 8
Research article
 | 
09 Aug 2018
Research article |  | 09 Aug 2018

A neural network approach to estimating a posteriori distributions of Bayesian retrieval problems

Simon Pfreundschuh, Patrick Eriksson, David Duncan, Bengt Rydberg, Nina Håkansson, and Anke Thoss

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Short summary
A novel neural-network-based retrieval method is proposed that combines the flexibility and computational efficiency of machine learning retrievals with the consistent treatment of uncertainties of Bayesian methods. Numerical experiments are presented that show the consistency of the proposed method with the Bayesian formulation as well as its ability to represent non-Gaussian retrieval errors. With this, the proposed method overcomes important limitations of traditional methods.
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