Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 19, issue 2
Articles | Volume 19, issue 2
https://doi.org/10.5194/gmd-19-731-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-731-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 2
Development and technical paper
 | 
23 Jan 2026
Development and technical paper |  | 23 Jan 2026

Direct assimilation of ground-based microwave radiometer observations with machine learning bias correction based on developments of RTTOV-gb v1.0 and WRFDA v4.5

Qing Zheng, Wei Sun, Zhiquan Liu, Jiajia Mao, Jieying He, Jian Li, and Xingwen Jiang

Viewed

Total article views: 5,512 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
4,419 920 173 5,512 167 225
  • HTML: 4,419
  • PDF: 920
  • XML: 173
  • Total: 5,512
  • BibTeX: 167
  • EndNote: 225
Views and downloads (calculated since 23 Jan 2025)
Cumulative views and downloads (calculated since 23 Jan 2025)

Viewed (geographical distribution)

Total article views: 5,512 (including HTML, PDF, and XML) Thereof 5,452 with geography defined and 60 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Saved (final revised paper)

Latest update: 23 Aug 2026
Download
Short summary
Ground-based microwave radiometers (GMWRs) offer high temporal resolution observations with strong sensitivity to the lower atmosphere, making them valuable for data assimilation. However, their assimilation has traditionally focused on retrieved profiles. This study implemented the direct assimilation of brightness temperatures from GMWRs with a machine learning-based bias correction scheme. The results show improvements in the low-level atmospheric structure and precipitation predictions.
Read more
Share