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.

The runoff calculation scheme in the Weather Research and Forecasting (WRF) model is based on an infiltration-excess surface runoff scheme, which likely leads to an overestimation of soil moisture and an underestimation of surface runoff when heavy rainfall occurs in areas with complex terrain. To overcome this defect, we considered the overland flow process on the grid scale of the WRF model for the first time by coupling a two-dimensional diffusion wave equation into the WRF Noah land surface model (LSM), called the WRF_Overland Flow (WRF_OLF) model. The new WRF model was then utilized to simulate the extreme rainfall that occurred during 19–22 July 2021 near the city of Zhengzhou in central China, which led to an extreme flood event. The results showed that the new WRF model simulated well the convergence and accumulation of overland flow in low-lying areas, changing the distributions of surface runoff and soil moisture and thereby influencing the exchanges of heat and water vapor between the surface and the atmosphere. The local change in non-adiabatic heating at the surface contributed to a decrease in surface pressure and then affected the development of the weather systems associated with the heavy rainfall event. Relative to a remarkable underestimation of rainfall in the original WRF simulation, the maximum rainfall intensity and the cumulative rainfall in the simulation with the new WRF configuration increased by 54.7% and 49.5%, respectively, bringing them closer to their observations. Concurrently, the new WRF model increased the skill for flood prediction. The results of this study provide new insights into the mechanisms of interaction between the land surface and the atmosphere and their roles in helping to predict heavy rainfall and associated flooding in areas of complex topography.

Urbanization-related precipitation and surface runoff changes have been widely investigated, but few studies have directly quantified these changes and their link to urbanization in the hydrological cycle. A two-way dynamically coupled atmospheric–hydrological modeling system, Weather Research and Forecasting (WRF)-Hydro, has been applied in this study to perform the quantification. The offline WRF-Hydro was first calibrated and validated for several flooding events against gauge observed streamflow data, with the Nash–Sutcliffe efficiency reaching 0.9. Compared to the WRF model, WRF-Hydro resolves more detailed rainfall pattern features and reproduces the gauge rainfall with a correlation coefficient of 0.8. Then, the impact of urbanization on hydrometeorological processes was investigated with coupled WRF-Hydro sensitivity simulations over the Qinhuai River basin of China during 2 June–31 July 2015. The results indicate that urbanization enhances regional precipitation, resulting in an indirect increase in surface runoff, overland flow, and streamflow by 16.7, 93.5, and 111.2 mm, respectively; however, the impervious area results in higher surface runoff, overland flow, and streamflow. Moreover, changes in main hydrometeorological processes further impact the atmospheric–terrestrial water budget, resulting in a decrease in terrestrial water storage and an increase (a decrease) in precipitable water storage in the middle (lower) parts of the lower troposphere. These changes are likely associated with the warmer urban environment than rural areas. Increased water vapor and strengthened convective conditions in the middle part of the lower troposphere due to urban warming are advantageous to the formation of precipitation in urban areas, which in turn increases surface runoff, thereby facilitating the water cycle and altering the atmospheric–terrestrial water budget.

This paper describes a fire forecast system—Weather Research and Forecasting-Fire (WRF-Fire)—that is employed to simulate a real wildfire case in Xichang, Sichuan Province, Southwest China on 30 March 2020 at a 100-m resolution over the fire area, in order to provide a fine representation of the terrain and fuel heterogeneities and explicitly resolve the atmospheric turbulence. Four sensitivity experiments were conducted to analyze the impacts of atmospheric model grid spacing and fire–atmosphere interaction on simulated meteorological fields and fire behavior. The results indicate that finer horizontal grid spacing in the atmospheric model improves the accuracy of wind, temperature, and moisture simulations in the near surface layer. Especially, it can better describe local wind field characteristics, capture microscale wind speed fluctuations, and produce more significant effect from fire–atmosphere interaction. The mass and energy released by the fire model and its feedback to the atmospheric model exhibit enhanced heterogeneous characteristics. The simulated fire area aligns well with the observation, with KAPPA coefficient (KC) of 0.56–0.59 and spatial correlation coefficient (SC) of 0.52–0.59. For this real case, the influence of heterogeneous land surface on the fire behavior is much greater than the atmosphere–fire interaction. The study suggests that WRF-Fire holds high potential as a real wildfire simulation tool, offering a new and feasible approach for fire prediction.

This study investigated how the Taihang Mountains and the Yanshan Mountains affect low-level jets (LLJs) in the Beijing area, based on conventional radiosonde observations from Nanjiao Observatory (2016–2017) and high-resolution Weather Research and Forecasting–Advanced Research WRF (WRF-ARW) model simulations. Analysis of radiosonde observations indicated that LLJs in the study area are mainly from the southwest and northwest directions, with occurrence frequency of 44.6% and 33.0%, respectively. Southwest (northwest) LLJs are aligned parallel (perpendicular) to the orientation of the Taihang Mountain Range. Terrain sensitivity experiments using the WRF-ARW model were then conducted to examine the effects of terrain forcing on the northwest and southwest LLJs, with adopted terrain heights of 100% and 50%. The results showed that for northwest LLJs, reduction in the elevation of the Taihang Mountain Range led to weakening of jet intensity by approximately 20% and reduction in jet maximum height by approximately 250 m; lowering the Yanshan Mountain Range had minor influence on the northwest LLJs, with only a 5.2% reduction in intensity and no substantial change in jet maximum height. For southwest LLJs, reduction in the elevation of both the Taihang and Yanshan Mountain ranges resulted in minor changes in the intensity and height of the jets. Further analysis revealed that the topography in the Beijing area could modulate the height and intensity of the stable layer by altering the inversion structure within the boundary layer. The LLJs can develop rapidly within the stable layer, and both the location and the scale of the jet core exhibited reasonable agreement with the extent of the stable layer.