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.
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.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
23 Jan 2026
Development and technical paper |
| 23 Jan 2026
| 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
Heavy Rain and Drought-Flood Disasters in Plateau and Basin Key Laboratory of Sichuan Province, Institute of Tibetan Plateau Meteorology, China Meteorological Administration, Chengdu, 610213, China
Institute of Tibetan Plateau Meteorology, Chinese Academy of Meteorological Sciences, Beijing, 100081, China
Wei Sun
CORRESPONDING AUTHOR
State Key Laboratory of Severe Weather Meteorological Science and Technology (LaSW), Chinese Academy of Meteorological Sciences (CAMS), Beijing, 100081, China
Institute of Tibetan Plateau Meteorology, Chinese Academy of Meteorological Sciences, Beijing, 100081, China
Zhiquan Liu
National Center for Atmospheric Research, Boulder, 80307-3000, USA
Jiajia Mao
Meteorological Observation Center, China Meteorological Administration, Beijing, 100081, China
Jieying He
National Space Science Center, Chinese Academy of Sciences, Beijing, 100190, China
Jian Li
State Key Laboratory of Severe Weather Meteorological Science and Technology (LaSW), Chinese Academy of Meteorological Sciences (CAMS), Beijing, 100081, China
Institute of Tibetan Plateau Meteorology, Chinese Academy of Meteorological Sciences, Beijing, 100081, China
Xingwen Jiang
Heavy Rain and Drought-Flood Disasters in Plateau and Basin Key Laboratory of Sichuan Province, Institute of Tibetan Plateau Meteorology, China Meteorological Administration, Chengdu, 610213, China
Institute of Tibetan Plateau Meteorology, Chinese Academy of Meteorological Sciences, Beijing, 100081, China
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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.
Ground-based microwave radiometers (GMWRs) offer high temporal resolution observations with...
