Impact of ROMEX Radio Occultation bending angle assimilation on mesoscale weather prediction over the Indian Region

Data assimilation experiments were conducted for September 2022 at 9 km horizontal resolution using a cyclic three-dimensional variational (3D-Var) assimilation system. These experiments were designed to assess the impact of a large number of Global Navigation Satellite System (GNSS) radio occultation (RO) bending-angle observations on mesoscale weather prediction over the Indian region during the Indian summer monsoon. To enable this, a bending-angle observation operator was implemented within the WRF (Weather Research and Forecasting) data assimilation system.Two experiments were performed: a control experiment (CNTL), in which only conventional observations (surface observations from synoptic and METAR stations, upper-air observations from radiosondes, pilot balloons, wind profilers, aircraft measurements, winds derived from geostationary satellites and scatterometers, and marine observations from ships and buoys) 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 00:00 and 12:00 UTC analyses, 72 h forecasts were generated daily, resulting in approximately 60 forecast cases.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.Overall, the study demonstrated that assimilation of RO bending angle data significantly improved mesoscale weather forecasts over the Indian region.