Assimilation of AIRS radiance observations into a mesoscale model: Adjoint development, quality control, and assimilation results

Radiance data obtained from NASA's Advanced Infrared Sounder (AIRS) is used in an attempt to improve the mesoscale prediction of temperature and moisture using one- and four-dimensional variational data assimilation (1D/4D-Var). The joint National Center for Atmospheric Research and Pennsylvania State University fifth-generation mesoscale model (MM5) along with the Stand-alone AIRS Radiative Transfer Algorithm (SARTA) is selected for this project. This work aims to utilize AIRS "clear-channel" radiances to enhance the first-guess analysis regarding the temperature and moisture content as a precursor to improving short-term precipitation forecasts.The adjoint operator for SARTA has been derived and linked to the MM5 adjoint modeling system a "clear-channel" identification scheme, which is compatible with SARTA, has been developed and verified and a set of one-dimensional variational data assimilation (1D-Var) experiments have been done in order to determine the impact of AIRS channels on the vertical profiles of temperature and moisture. Lastly, a preliminary 4D-Var experiment is carried out to determine the impact of a limited number of clear-channel AIRS radiances on the prediction of temperature and moisture. An adjoint-sensitivity based forecast verification technique is used to compare the 4D-Var forecast results to a control forecast.