Development and Evaluation of an Urbanized Land Data Assimilation System with Smoothing Spline 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 and the multi-layer urban model Building Energy Parameterization (BEP)-Building energy model (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 gridded meteorological dataset generated via thin-plate smoothing spline models. The structure of the coupled Noah-BEP-BEM is modified to enhance computational and storage efficiency, making RMAPS-LDAS operationally applicable for regional weather forecasts. The urban layer temperatures (including roof, wall and road) 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 RMSE and bias of simulated soil temperature by 47.8% and 92.3%, respectively, compared with ECMWF soil temperature forecasts. And the system reduces RMSE and bias in soil moisture simulations by 6.5% and 18.2%, respectively. Comparisons with observed road and building temperatures confirm that the integrated 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 (0.53%) was observed. Overall, the impact on wind speed predictions remains relatively minor.