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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Cited articles

Aires, F., Prigent, C., and Rossow, W. B.: Neural network uncertainty assessment using Bayesian statistics with application to remote sensing: 2. Output errors, J. Geophys. Res., 109, d10304, https://doi.org/10.1029/2003JD004174, 2004. a
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Brath, M., Fox, S., Eriksson, P., Harlow, R. C., Burgdorf, M., and Buehler, S. A.: Retrieval of an ice water path over the ocean from ISMAR and MARSS millimeter and submillimeter brightness temperatures, Atmos. Meas. Tech., 11, 611–632, https://doi.org/10.5194/amt-11-611-2018, 2018. a
Buehler, S. A., Mendrok, J., Eriksson, P., Perrin, A., Larsson, R., and Lemke, O.: ARTS, the Atmospheric Radiative Transfer Simulator – version 2.2, the planetary toolbox edition, Geosci. Model Dev., 11, 1537–1556, https://doi.org/10.5194/gmd-11-1537-2018, 2018. a
Cannon, A. J.: Quantile regression neural networks: Implementation in R and application to precipitation downscaling, Comput. Geosci., 37, 1277–1284, https://doi.org/10.1016/j.cageo.2010.07.005, 2011. a
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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.