Nocturnal Boundary Layer Mie–Raman Lidar Water Vapor Profiles Retrieved by Extended TCKF1D-Var framework for

Accurate characterization of boundary-layer water vapor prior to nocturnal heavy precipitation remains challenging due to limited observational capability. In this study, a thermodynamic- and cloud-microphysics-constrained Kalman filter one-dimensional variational (TCKF1D-Var) framework is extended 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 classical Raman lidar equation is developed, with a data-driven calibration component introduced to account for time-varying instrumental and aerosol-related uncertainties. In addition, dynamically estimated process and observation error covariance matrices are employed within a Kalman filter framework to improve retrieval robustness. The method is evaluated using co-located radiosonde observations from 56 MRL-colocated radiosonde stations across China in 2025. The retrieved water vapor mass mixing ratio profiles, with 30 meters vertical and 30 minutes temporal resolution, show consistently reduced mean bias and root mean square error compared to ERA5 prior profiles, with the largest improvements in the 1.2–3.0 km layer. Insights to nocturnal heavy precipitation events further demonstrates that the retrievals capture coherent pre-precipitation moisture evolution, highlighting the potential of combining physically constrained retrieval methods with Raman lidar observations for improved monitoring of boundary-layer moisture.