Articles | Volume 14, issue 4
https://doi.org/10.5194/amt-14-2957-2021
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, Patrick Eriksson, Simon Pfreundschuh, and David Ian Duncan

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Cited articles

Abel, S. and Boutle, I.: An improved representation of the raindrop size distribution for single-moment microphysics schemes, Q. J. R. Meteorol. Soc., 138, 2151–2162, 2012. a
Aires, F., Prigent, C., Bernardo, F., Jiménez, C., Saunders, R., and Brunel, P.: A Tool to Estimate Land-Surface Emissivities at Microwave frequencies (TELSEM) for use in numerical weather prediction, Q. J. R. Meteorol. Soc., 137, 690–699, 2011. a
Barlakas, V. and Eriksson, P.: Three dimensional radiative effects in passive millimeter/sub-millimeter all-sky observations, Remote Sensing, 12, 531, https://doi.org/10.3390/rs12030531, 2020. a
Bennartz, R. and Bauer, P.: Sensitivity of microwave radiances at 85–183 GHz to precipitating ice particles, Radio Sci., 38, 8075, https://doi.org/10.1029/2002RS002626, 2003. a
Berg, W., Bilanow, S., Chen, R., Datta, S., Draper, D., Ebrahimi, H., Farrar, S., Jones, W. L., Kroodsma, R., McKague, D., Payne, V., Wang, J., Wilheit, T., and Yang, J. X.: Intercalibration of the GPM microwave radiometer constellation, J. Atmos. Ocean. Tech., 33, 2639–2654, 2016. a
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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.