The preliminary steps of assimilating AIRS radiance data into a mesoscale model are presented. First, a stand-alone 1D-Var
driver is developed in order to retrieve temperature and specific humidity profiles from AIRS data using background
profiles obtained from a mesoscale model. Vertical background error covariance matrices are calculated for both
temperature and specific humidity. The inverses of the background error covariance matrices are estimated using a
singular value decomposition procedure, in which the small singular values and associated small-scale structures in the
background error covariances are removed. By comparing with two available collocated radiosonde data, it is then
shown that AIRS radiance-derived vertical profiles of temperature and specific humidity are more consistent to
radiosonde observations than the background profiles. Finally, a multi-profile retrieval is performed which produced
largest analysis increments of temperature and moisture in the region of a mid- and upper-level moisture gradient
associated with a cold front.
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