Satellite Radiance Data Impacts within a Prototype RRFS

Satellite radiance data comprise a majority of all available observations assimilated in numerical weather prediction models. Both geostationary and polar orbiting satellites provide nearly full coverage of the Earth on a daily basis, providing observations of temperature, winds, and moisture in clear and cloudy conditions that are invaluable at the convective-scale. Simultaneously, the next-generation regional convection allowing Rapid Refresh Forecast System (RRFS) model is being developed by the Global Systems Laboratory (GSL) and the Environmental Modeling Center (EMC) of the National Centers for Environmental Prediction (NCEP). We explore the impact and ultimately the value of microwave and infrared radiance observations from polar, low-earth orbiter (LEO) satellites in a 13-km horizontal grid spacing RRFS domain covering North America. Data denial experiments, which involve removing microwave and infrared radiance observations in different experiments, are conducted jointly and separately using the Gridpoint Statistical Interpolation (GSI) hybrid variational/Ensemble Kalman Filter (EnKF) data assimilation system. We evaluate the impact of both types of radiances using upper-level, surface, and ceiling verification. We also evaluate the value of Cross-track Infrared Sounder Full-Spectral-Resolution (CrIS-FSR) observations and examine inter-channel correlations present within different sets of channels and conduct similar verification.