Development and Evaluation of an Urbanized Land Data Assimilation System with Enhanced Meteorological Forcing and Multi-Layer Urban Model for North China

To enhance the precision of short-term regional weather forecasts with the Rapid-refresh Multi-scale Analysis and Prediction System-Urban subsystem (RMAPS-Urban), an urbanized land data assimilation system RMAPS-LDAS with a spatial resolution of 1 km is developed by coupling the Noah land surface model with a multi-layer urban model, namely, the building effect parameterization and building energy model (BEP–BEM), enabling improved representation of land surface elements for both natural and urban underlying surfaces. RMAPS-LDAS runs offline utilizing merged meteorological data from high-density in-situ observations and a gridded meteorological dataset generated via thin-plate smoothing spline models. The structure of the coupled Noah and BEP–BEM model is modified to enhance computational and storage efficiency, making RMAPS-LDAS operationally applicable for regional weather forecasts. The urban layer (roof, wall, and road) temperatures require a spin-up period of no more than seven days, significantly shorter than that needed for soil states. Evaluation over the Beijing–Tianjin–Hebei region show that RMAPS-LDAS reduces the root-mean-square error (RMSE) and bias of simulated soil temperature by 47.8% and 92.3%, respectively, compared with ECMWF soil temperature forecasts. The system reduces RMSE and absolute bias in soil moisture simulations by 6.5% and 18.2%, respectively. Comparisons with observed road and building temperatures confirm that the BEP–BEM model realistically captures the thermal evolution of urban surfaces. Coupling RMAPS-LDAS with RMAPS-Urban led to a 9.6% reduction in RMSE for 2-m specific humidity forecasts, although a slight increase in air temperature RMSE (0.54%) was observed. However, for urban areas, the coupling reduced air temperature RMSE by 4.9%. Overall, the impact on wind speed prediction remains relatively minor.