Enhancing Boundary-Layer Forecast Skill through KF1D-Var Assimilation of Raman Lidar Thermodynamic Profiles

Existing PBL observations, particularly during the pre-convective environment, remain sparse and underutilized, thereby constraining the predictive skill of mesoscale NWP systems. This study presents a Kalman Filter One-Dimensional Variational (KF1D-Var) data assimilation framework designed to retrieve atmospheric thermodynamic profiles from water vapor and nitrogen channel observations of Raman lidar within the China Meteorological Administration ground-based vertical profiling network. Leveraging Global Forecast System (GFS) forecasts as the background field, the framework generates high-resolution atmospheric profiles at 30-min temporal and 15-m vertical resolutions. The retrievals are evaluated by using nighttime observations collected at Sheyang station, Jiangsu Province during 1–31 August 2024. The results indicate that the KF1D-Var assimilation reduces GFS biases in temperature, humidity, and pressure within the planetary boundary layer. Water vapor corrections reach magnitudes of −5% (drying above 300 m) and +1.5% (moistening below 300 m during precipitation events). A month-long sensitivity experiment with a Weather Research and Forecasting (WRF) model–based observation-nudging Numerical Weather Prediction (NWP) system shows improved forecast skill at 1–10 h lead times, relative to the control run. The forecasts exhibit enhanced relative humidity between the surface and 650 hPa, as well as improved horizontal wind and precipitation predictions. These results demonstrate the potential of assimilating Raman lidar thermodynamic profiles to refine NWP accuracy, particularly in representing boundary layer moisture and the processes critical to convective-scale forecasting.