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

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

doi:10.5194/gmd-19-731-2026

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

Data sets

Data for manuscript "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 https://doi.org/10.5281/zenodo.14586346

NCEP ADP Global Upper Air 40 and Surface Weather Observations (PREPBUFR format) DOC/NOAA/NWS/NCEP https://doi.org/10.5065/Z83F-N512

NCEP GDAS/FNL 0.25 Degree Global Tropospheric Analyses and Forecast Grids DOC/NOAA/NWS/NCEP https://doi.org/10.5065/D65Q4T4Z

Model code and software

Codes related to the manuscript "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 https://doi.org/10.5281/zenodo.16731169

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.