Extended TCKF1D-Var framework for Mie–Raman lidar boundary layer water vapor profiling: insights into nocturnal preprecipitation moisture evolution
Year: 2026
Type: article
Abstract: . Accurate characterization of boundary-layer water vapor prior to nocturnal heavy precipitation remains challenging due to limited observational capability. In this study, we build upon a previously developed and validated thermodynamic- and cloud-microphysics-constrained Kalman filter one-dimensional variational (TCKF1D-Var) framework by extending it to incorporate nitrogen and water vapor Raman channel observations from the China Meteorological Administration Mie–Raman lidar (MRL) network. A physics-informed lidar observation operator based on the classical Raman lidar formulation is developed, together with a data-driven calibration component to account for time-varying instrumental and aerosol-related uncertainties. In addition, process and observation error covariance matrices are dynamically estimated within the Kalman filter framework to enhance retrieval robustness. The method is evaluated against co-located radiosonde observations launched prior to nocturnal heavy precipitation events at 56 MRL–radiosonde co-located stations across China in 2025. The retrieved water vapor mass mixing ratio profiles, with a vertical resolution of 30 meters and a temporal resolution of 30 minutes, exhibit consistently reduced mean bias and root mean square error compared to ERA5 prior profiles, with the largest improvements found in the 1.2–3.0 km layer. Analysis of nocturnal heavy precipitation cases further demonstrates that the retrievals capture coherent pre-precipitation moisture evolution. These results highlight the potential of combining physically constrained retrieval frameworks with Raman lidar observations for improved monitoring of boundary-layer moisture.
Authors: Qi Zhang, Tianmeng Chen, Jianping Guo, Xun Li
Language: English
Subfield: Atmospheric Science
Field: Earth and Planetary Sciences
Domain: Physical Sciences
SDG: Climate action
Open Access status: gold
Estimated APC paid: $1,585
Funders: National Natural Science Foundation of China, Chinese Academy of Meteorological Sciences, National Outstanding Youth Foundation of China, Institute of Heavy Rain, China Meteorological Administration, National Key Research and Development Program of China, State Key Laboratory of Severe Weather
OpenAlex ID: W7171531535