Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 14, issue 4
Articles | Volume 14, issue 4
https://doi.org/10.5194/amt-14-2957-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-14-2957-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 14, issue 4
Research article
 | 
20 Apr 2021
Research article |  | 20 Apr 2021

Can machine learning correct microwave humidity radiances for the influence of clouds?

Inderpreet Kaur, Patrick Eriksson, Simon Pfreundschuh, and David Ian Duncan

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Short summary
Currently, cloud contamination in microwave humidity channels is addressed using filtering schemes. We present an approach to correct the cloud-affected microwave humidity radiances using a Bayesian machine learning technique. The technique combines orthogonal information from microwave channels to obtain a probabilistic prediction of the clear-sky radiances. With this approach, we are able to predict bias-free clear-sky radiances with well-represented case-specific uncertainty estimates.
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