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

Model code and software

typhon - Tools for atmospheric research John Mrziglod, Lukas Kluft, Oliver Lemke, Gerrit Holl, Simon Pfreundschuh, Richard Larsson, Takayoshi Yamada, and Jakob Doerr

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.