Assimilating synthetic hyperspectral sounder temperature and humidity retrievals to improve severe weather forecasts

This study uses an Observation System Simulation Experiment (OSSE) approach to simulate temperature and humidity profiles from a hypothetical geostationary-based sounder from a nature run of a high impact weather event on 20 May 2013. The simulated observations are then assimilated using an ensemble adjustment Kalman filter approach, testing both hourly and 15 minute cycling to determine their relative effectiveness at improving the near storm environment. Results indicate that assimilating both temperature and humidity profiles reduced mid-tropospheric both mean and standard deviation of analysis and forecast errors compared to assimilating conventional observations alone. The 15 minute cycling generally produced the lowest errors while also generating the best 2-4 hour updraft helicity forecasts of ongoing convection. This study indicates the potential for significant improvement in short-term forecasting of severe storms from the assimilation of hyperspectral geostationary satellite data. However, more studies are required using improved OSSE designs encompassing multiple storm environments and additional observation types such as radar reflectivity to fully define the effectiveness of assimilating geostationary hyperspectral observations for high impact weather forecasting applications.