Adaptive observation weighting in TCKF1D-Var for ground-based multi-sensor thermodynamic retrievals prior to nocturnal heavy precipitation over China
- 1Chinese Academy of Meteorological Sciences (Beijing, China)
Abstract
Ground-based microwave radiometers (GMWRs) and Mie–Raman lidars (MRLs) provide valuable thermodynamic observations for atmospheric profiling, but conventional variational retrieval frameworks typically rely on static observation weighting assumptions that may not adequately represent varying observation quality under precipitation conditions. To address this limitation, an adaptive observation weighting framework based on the Thermodynamic-Constrained Kalman Filter 1D-Var framework (TCKF1D-Var) is developed and evaluated using 107 nocturnal heavy-precipitation cases. The proposed method dynamically estimates the relative contribution of individual observations during the retrieval process and is applied to GMWR, MRL, and GMWR–MRL synergistic retrievals. Retrieval performance is assessed against radiosonde observations and compared with that of a conventional static-weighting TCKF1D-Var framework. Results show that the adaptive weighting approach consistently improves retrieval accuracy, with the most significant improvements found for water vapor mass mixing ratio profiles. For both GMWR and MRL retrievals, reductions in mean bias and root-mean-square error are obtained relative to the static-weighting framework. The synergistic retrieval further improves moisture-profile retrievals and generally achieves the best overall performance among all experiments. Diagnostic analyses reveal that the adaptive framework dynamically adjusts the utilization of observational information according to sensor characteristics and atmospheric conditions, while redistributing observational influence between GMWR and MRL measurements during synergistic retrievals. These results demonstrate that adaptive observation weighting provides an effective strategy for improving thermodynamic profile retrievals under heavy-precipitation pre-onset conditions.
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BibTeX
@article{zhang2026adaptive,
title = {{Adaptive observation weighting in TCKF1D-Var for ground-based multi-sensor thermodynamic retrievals prior to nocturnal heavy precipitation over China}},
author = {Qi Zhang and Tianmeng Chen and Jianping Guo and Bin Deng and Han Li and Yu Wu},
journal = {Geoscientific Model Development},
year = {2026},
month = oct,
doi = {10.5194/gmd-19-9555-2026},
url = {https://gmd.copernicus.org/articles/19/9555/2026/},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Adaptive observation weighting in TCKF1D-Var for ground-based multi-sensor thermodynamic retrievals prior to nocturnal heavy precipitation over China
AU - Qi Zhang
AU - Tianmeng Chen
AU - Jianping Guo
AU - Bin Deng
AU - Han Li
AU - Yu Wu
PY - 2026
DA - 2026/10/09
JO - Geoscientific Model Development
DO - 10.5194/gmd-19-9555-2026
UR - https://gmd.copernicus.org/articles/19/9555/2026/
AB - Ground-based microwave radiometers (GMWRs) and Mie–Raman lidars (MRLs) provide valuable thermodynamic observations for atmospheric profiling, but conventional variational retrieval frameworks typically rely on static observation weighting assumptions that may not adequately represent varying observation quality under precipitation conditions. To address this limitation, an adaptive observation weighting framework based on the Thermodynamic-Constrained Kalman Filter 1D-Var framework (TCKF1D-Var) is developed and evaluated using 107 nocturnal heavy-precipitation cases. The proposed method dynamically estimates the relative contribution of individual observations during the retrieval process and is applied to GMWR, MRL, and GMWR–MRL synergistic retrievals. Retrieval performance is assessed against radiosonde observations and compared with that of a conventional static-weighting TCKF1D-Var framework. Results show that the adaptive weighting approach consistently improves retrieval accuracy, with the most significant improvements found for water vapor mass mixing ratio profiles. For both GMWR and MRL retrievals, reductions in mean bias and root-mean-square error are obtained relative to the static-weighting framework. The synergistic retrieval further improves moisture-profile retrievals and generally achieves the best overall performance among all experiments. Diagnostic analyses reveal that the adaptive framework dynamically adjusts the utilization of observational information according to sensor characteristics and atmospheric conditions, while redistributing observational influence between GMWR and MRL measurements during synergistic retrievals. These results demonstrate that adaptive observation weighting provides an effective strategy for improving thermodynamic profile retrievals under heavy-precipitation pre-onset conditions.
SN - 1991-9603
ER -