Data assimilation experiments were conducted for September 2022 at 9-km horizontal resolution using a cyclic three-dimensional variational (3D-Var) assimilation system to assess the impact of a large number of Global Navigation Satellite System (GNSS) radio occultation (RO) bending angle data on mesoscale weather prediction over the Indian region. To enable this, a bending angle observation operator was implemented within the WRF (Weather Research and Forecasting) data assimilation system.</p> <p>Two experiments were performed: a control experiment (CNTL), in which only conventional observations were assimilated, and a second experiment (RMX-BA), in which RO bending angle observations from the Radio Occultation Modelling Experiment (ROMEX) were assimilated along with conventional data. A 6-h assimilation cycle was performed throughout September 2022. From the 00 and 12 UTC analyses, 72-h forecasts were generated daily, resulting in approximately 60 forecast cases.</p> <p>Forecasts from both experiments were verified against ERA5 reanalysis for water vapor, temperature, and wind, while rainfall forecasts were compared against Integrated Multi-satellite Retrievals for GPM (IMERG) rainfall estimates. The results show that assimilating RO data improves both the analyses and forecasts of specific humidity, temperature, wind, and rainfall compared to the CNTL experiment. The rainfall forecast skill improved significantly, mainly due to more accurate water vapor in the model's initial conditions. The moist total energy norm (TE), which accounts for forecast errors in water vapor, wind, temperature, and pressure, was reduced by about 20% at the analysis time and by approximately 8.5% in the 72-h forecast.</p> <p>Overall, the study demonstrated that assimilation of RO bending angle data significantly improved mesoscale weather forecasts over the Indian region.