Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
Articles | Volume 11, issue 8
Articles | Volume 11, issue 8
Atmos. Meas. Tech., 11, 4627–4643, 2018
https://doi.org/10.5194/amt-11-4627-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Atmos. Meas. Tech., 11, 4627–4643, 2018
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 et al.

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Latest update: 17 May 2022
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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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