Impact of direct and indirect assimilation of DWR reflectivity on the simulation of heavy rainfall events over Jeddah, Saudi Arabia

Abstract

To improve the understanding and prediction of convective storms over the Kingdom of Saudi Arabia (KSA), the National Center for Meteorology (NCM) established a network of Doppler weather radars (DWRs) across the country in 2009. Despite this advancement, assimilation of DWR observations into numerical weather prediction models has not yet been explored in KSA. In this study, we investigate, for the first time, the impact of assimilating DWR observations on the simulation of the heavy rainfall event that affected Jeddah on 25 November 2009 using a high-resolution (1-km) configuration of the Weather Research and Forecasting (WRF) model. Three radar reflectivity assimilation strategies are evaluated: (i) direct assimilation of radar reflectivity, (ii) indirect assimilation through relative humidity (RH) retrieved from radar reflectivity, and (iii) indirect assimilation through hydrometeor mixing ratios derived from radar reflectivity. The results demonstrate a clear positive impact of DWR data assimilation on the representation of the initiation, intensity, and duration of the heavy rainfall event. These improvements are primarily associated with corrections to the low-level wind field and moisture distribution, which enhance moisture convergence and lead to more realistic rainfall patterns. While all assimilation experiments capture the location and large-scale characteristics of the storm, the indirect assimilation of hydrometeor mixing ratios derived from DWR reflectivity provides the most realistic representation of the storm’s thermodynamic structure, small-scale convective cells, and quasi-stationary behavior over Jeddah, highlighting its potential to improve the prediction of heavy rainfall events.