Can machine learning correct microwave humidity radiances for the influence of clouds?
Inderpreet Kaur et al.
Model code and software
atmtools/typhon: Typhon Release 0.8.0Oliver 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
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.
Currently, cloud contamination in microwave humidity channels is addressed using filtering...