Satellite radiance assimilation using 3D-VAR and its impact on extreme weather event simulation

Severe thunderstorms with tremendous damage potential are the most dominant feature of the weather during the pre-monsoon months of April and May over east and northeast India and adjoining regions of Bangladesh. Convective systems such as sand storms, dust storms/ thunderstorms are prominent over northwestern India. Forecasting of local severe storms is a daunting challenge due to great complexity of the processes involved and interplay of many factors. Part of the problem is due to small time scales of these disturbances which enable only short lead times for forecasting. The initiation, intensification and propagation of thunderstorms are mostly governed by the synoptic situation and localized thermodynamic conditions of the atmosphere. The topography of the region also plays a significant role in initiation of convective activities over the region during the period. Prediction of these severe thunderstorms in advance is vital as it would minimize the damages associated with them. Over the last decade, high resolution mesoscale models with three dimensional variational techniques (3DVAR) are being increasingly applied for studying severe weather phenomena as these models possess the capability of generating reasonably good forecast of severe weather phenomena. However the numerical simulations are hampered by inappropriate representation of initial and boundary conditions used from a global model output of courser resolution, this issue is addressed by assimilating observations from various platforms into the model initial condition which would give way for better prediction of these events. The satellite radiance is an important data source for mesoscale/microscale weather analysis and forecasting. Currently, the variational techniques have received considerable attention for assimilation of satellite radiance. Satellite radiance assimilation has more impact on the moisture and temperature. It is observed that the model initial condition improved significantly after assimilation of satellite radiance observation as compared to the without assimilation experiment. The impact of the satellite radiance has witnessed in the dynamic and thermodynamic fields which gave way for reduction in temporal error in thunderstorm initiation and improved rainfall spatially and quantitatively. Assimilation of satellite radiance data into the model initial conditions has shown good improvement in the simulation of severe thunderstorms. Assimilation has improved the simulation results by capturing the surface meteorological variables like relative humidity, temperature and rainfall reasonably close to observation. Further, significant improvement is noticed in location and intensity of rainfall and reflectivity. Vertical profiles of relative humidity, vertical velocity and mixing ratio have improved significantly after assimilation of the satellite radiance data.