Articles | Volume 14, issue 4
Atmos. Meas. Tech., 14, 2957–2979, 2021
https://doi.org/10.5194/amt-14-2957-2021
Atmos. Meas. Tech., 14, 2957–2979, 2021
https://doi.org/10.5194/amt-14-2957-2021

Research article 20 Apr 2021

Research article | 20 Apr 2021

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

Inderpreet Kaur et al.

Model code and software

atmtools/typhon: Typhon Release 0.8.0 Oliver Lemke, Lukas Kluft, John Mrziglod, Simon Pfreundschuh, Gerrit Holl, Richard Larsson, Takayoshi Yamada, Theresa Mieslinger, and Jakob Doerr https://doi.org/10.5281/zenodo.3626449

QRNN-CloudCorrection: Correcting cloud impact in microwave radiances I. Kaur https://doi.org/10.5281/zenodo.4677560

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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.