Articles | Volume 11, issue 8
Atmos. Meas. Tech., 11, 4627–4643, 2018
https://doi.org/10.5194/amt-11-4627-2018
Atmos. Meas. Tech., 11, 4627–4643, 2018
https://doi.org/10.5194/amt-11-4627-2018

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 et al.

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Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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AR: Author's response | RR: Referee report | ED: Editor decision
AR by Simon Pfreundschuh on behalf of the Authors (26 Jun 2018)  Author's response    Manuscript
ED: Publish subject to technical corrections (28 Jun 2018) by Andrew Sayer
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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.