Retrieval of Aerosol Combined with Assimilated Forecast

Abstract. We developed a new aerosol retrieval algorithm combining a numerical aerosol forecast. In the retrieval algorithm, the short-term forecast from an aerosol data assimilation system was used for a priori estimate instead of spatially and temporally constant values. This method was demonstrated using the Advanced Himawari Imager onboard the Japan Meteorological Agency’s geostationary satellite Himawari-8, and the results showed spatially finer distributions than the model forecast and less noisy distributions than the original algorithm. We validated the new algorithm using ground observation data and found that the aerosol parameters detectable by satellite sensors were retrieved more accurately than a priori model forecast by adding satellite information. Moreover, the retrieval accuracy was improved by using the model forecast as compared with using constant a priori estimates. By using the assimilated forecast for a priori estimate, information from previous observations can be propagated to future retrievals, thereby leading to better retrieval accuracy. Observational information from the satellite and aerosol transport by the model is incorporated cyclically to effectively estimate the optimum field of aerosol.