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

Viewed

Total article views: 2,203 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,584 568 51 2,203 56 50
  • HTML: 1,584
  • PDF: 568
  • XML: 51
  • Total: 2,203
  • BibTeX: 56
  • EndNote: 50
Views and downloads (calculated since 23 Dec 2020)
Cumulative views and downloads (calculated since 23 Dec 2020)

Viewed (geographical distribution)

Total article views: 2,203 (including HTML, PDF, and XML) Thereof 2,105 with geography defined and 98 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 17 Jul 2024
Download
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.
Read more