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Accurate nowcasting of short-lived extreme weather events is essential for saving millions of lives and property. Traditional methods of nowcasting are majorly focused on extrapolation of precipitation derived from radar reflectivity data, which often fail to capture the initiation and decay of weather systems. Earlier studies have shown the ability of high-resolution Numerical Weather Prediction (NWP) models to better capture the structure and lifecycle of storms compared to data-driven methods. However, the initial value problem of NWP makes it more challenging to be implemented for nowcasting applications. To handle such uncertainty from initial conditions, we have designed an NWP nowcasting system based on variational approach using WRF model. One of the major challenges of the variational methods in the nowcasting system is the choice of control variables used for generating background error statistics. Thus, we have investigated the impact of control variable options on improving the skill of variational-based NWP nowcasting system. The proposed nowcasting system was tested for a heavy rainfall event that occurred over the Chennai city, India, on 1 December 2015, by assimilating Doppler Weather Radar data using different control variable options in Weather Research and Forecast-three-dimensional (3DVAR)- and four-dimensional variational data assimilation (4DVAR)-based nowcasting system. Results show that control variables choices have a positive impact on 4DVAR analysis, particularly on radial velocity. Our results also indicate that assimilation of Doppler Weather Radar data with zonal and meridional momentum control variable in a 4DVAR system shows more than 30% improvement in precipitation forecast skill compared to the 3DVAR system.