Aerosol Impacts on Satellite Radiance Assimilation

The goal of this study is to improve operational weather forecast accuracies through more accurate use particularly of hyperspectral IR data assimilation. We seek to quantify the impact from dust and sea salt aerosol on hyperspectral infrared (such as CrIS, AIRS and IASI) brightness temperature approximated using aerosol loading from the Navy Aerosol Analysis Prediction System (NAAPS). These impacts are tested in the NAVy Global Environmental Model (NAVGEM) and its associated 4D-Var, the Naval Research Laboratory (NRL) Atmospheric Variational Data Assimilation System Accelerated Representer (NAVDAS-AR). The NAVDAS-AR data assimilation system uses the JCSDA Community Radiative Transfer Model (CRTM), and we have tested the default CRTM dust and sea-salt coefficient file along with one which has been adjust to better fit the Navy aerosol model (NAAPS) aerosol profiles. In these aerosol loading simulations, the effective diameter for each species have been fixed. However, different fixed effective diameters were used between the default and NAAPS-tuned aerosol coefficient. The one parameter which varies pixel by pixel is the aerosol number concentration, which is derived from the NAAPS field. We have focused our testing over the boreal summer, and in particular on 2017 which had numerous tropical storms extending into boreal autumn. We have seen some expected improvements in the simulation of the longwave window channels in the infrared; however, see some unexpected degradation in channels peaking in the stratosphere. Overall, the experiments show neutral impact against the control, and show some promising ability to use modern aerosol models to account for contamination in the infrared radiances. Further, improvements to both the aerosol modeling, and the CRTM, through the ability to tune the aerosol coefficient for the specific aerosol models should continue to build upon this early result and subsequently increase the significance of the impact on the NWP analysis and forecast.