NASA's Atmospheric Infrared Sounder (AIRS) has the ability to provide atmospheric temperature and water vapor information at higher resolution and accuracy than previous systems, which may be very beneficial for improving forecasts of high impact weather, cloud and precipitation systems. Accordingly, we are conducting a series of tests to evaluate the impact of assimilating AIRS single field-of-view (SFOV) retrieved temperature and water vapor profiles, which are created by the Cooperative Institute for Meteorological Satellite Studies (CIMSS) at the University of Wisconsin-Madison, into the NOAA Rapid Refresh (RR) mesoscale model system. The RR is a high frequency (1-hour) cycling assimilation and prediction system that will replace the Rapid Update Cycle (RUC) at the National Centers for Environmental Prediction (NCEP) in the fall of 2011. Like the RUC, the RR provides hourly updated short-term weather forecasts, that are used for aviation, severe weather, and general weather forecasting guidance. The RR utilizes the Gridpoint Statistical Interpolation (GSI) for the analysis component and the Advanced Research WRF (ARW) for the model component.

The SFOV retrieval profiles have high vertical resolution and accuracy with good horizontal resolution of 15 km at nadir. Prior to assimilation of the SFOV data, an assessment of the retrieval profiles was performed by comparing them with collocated radiosonde profiles. From this analysis, biases of the SFOV data relative to the radiosonde data were computed. Next, a 9-day 3-hourly cycled RR retrospective control run and rawinsonde denial experiment were completed for an 8-16 May 2010 period. Results confirmed the robustness and accuracy of the retrospective assimilation and prediction system. Then, a series of tests incorporating the SFOV data have been performed to examine the impact of the AIRS data on subsequent weather forecasts (as measured by upper-air and precipitation verification). A variety of different data thinning and observation error specification strategies were considered, yielding considerably improved results compared to the initial SFOV assimilation experiment. Assimilation of a new set of SFOV retrievals, created using a refined algorithm by CIMSS, produced the greatest forecast improvement, yielding results with near neutral or slightly positive impacts at most levels.

Work is ongoing, focused on tests with a new SFOV retrieval set with improved coverage over the Pacific Ocean and improved quality control and application of a simple bias correction scheme. As a complement, comparisons of assimilating AIRS SFOV profiles with the 3X3 retrieval profiles from the AIRS Science Team have also been examined. Three-hourly cycled work will be followed up 1-h cycle RR retrospective runs, including the partial cycle.