Improving Regional Forecast by Assimilating Atmospheric InfraRed Sounder (AIRS) Profiles into WRF model

The use of state-of-the-art hyperspectral sensors--such as the Atmospheric InfraRed Sounder (AIRS) on NASA’s polar-orbiting Aqua satellite--to retrieve high vertical resolution thermodynamic profiles and their subsequent assimilation into forecast models holds promise in improving weather predictions. This improved vertical resolution over previous capabilities results from the use of thousands of channels in the retrieval process instead of 10-20 channels for previous instruments. Although these capabilities do not replace the robust vertical resolution provided by radiosondes, these retrieved soundings can have a significant impact on weather forecasts if properly assimilated into prediction models. Several recent studies have evaluated the performance of specific operational weather forecast models when AIRS data are included in the assimilation process. LeMarshall et al. (2006) concluded that the AIRS radiances significantly improved 500 hPa anomaly correlations in medium-range forecasts of the Global Forecast System (GFS) model at the Joint Center for Satellite Data Assimilation. McCarty et al. (2009) demonstrated similar forecast improvement in 0-48h forecasts in an off-line version of the operational NAM run by NCEP through the use of AIRS radiances at the regional scale. Reale et al. (2008) showed improvements to Northern Hemisphere 500 hPa height anomalies in NASA’s GEOS-5 global system with the inclusion of partly cloudy AIRS temperature profiles. Singh et al. (2008) assimilated AIRS temperature and moisture profiles into a regional modeling system for a study of a heavy rainfall event during the summer monsoon season in Mumbai, India.