Impact of Low-altitude Meteorological Drone Data Assimilation on Convective-Scale Short-Term Rainfall Forecasts: An Observing System Simulation Study

Abstract. This work leverages observing system simulation experiments (OSSEs) to quantify the utility of low-altitude meteorological drone (MD) measurements for convective-scale analyses and short-term rainfall forecasts over the Beijing-Tianjin-Hebei region. Synthetic MD observations of temperature, specific humidity, and horizontal wind are generated from a free-running truth simulation and assimilated into the Weather Research and Forecasting (WRF) model using the National Severe Storms Laboratory three-dimensional variational data assimilation (DA) system. Five sets of sensitivity experiments are conducted to evaluate the impacts of horizontal resolution, observation height, spatial distribution, joint assimilation of thermodynamic and wind observations, and observation errors of MD data, respectively. The results show that assimilation of MD temperature and humidity observations improves both thermodynamic analyses and precipitation forecasts, with the magnitude of benefit strongly dependent on MD network design. Denser MD networks more effectively reduce thermodynamic analysis and forecast errors, leading to better rainband placement, rainfall intensity, and higher quantitative precipitation skill. Among the tested configurations, the 5- and 10-km networks provide the most robust and consistent forecast benefits. Multi-level MD data yield the most balanced improvement, while observations extending to higher levels within the planetary boundary layer are generally more beneficial than those confined to the lowest level alone. Restricting observations to the plain area degrades forecast performance, highlighting the importance of upstream mountainous observations where convection is initiated. In addition, joint assimilation of thermodynamic and wind observations further improves quantitative precipitation forecasts by substantially reducing lower-tropospheric wind errors. Short-term forecast skill is also sensitive to the observation error standard deviations, with inflated wind observation error producing a larger degradation than inflated thermodynamic errors. Overall, it is demonstrated that MD observations have considerable potential to improve convective-scale numerical weather prediction, particularly when the observing network is sufficiently dense, vertically resolved, and capable of constraining both thermodynamic and dynamical structures within the planetary boundary layer.