Satellite retrieval of aerosol combined with assimilated forecast
2021
Abstract. We developed a new aerosol satellite retrieval algorithm
combining a numerical aerosol forecast. In the retrieval algorithm, the
short-term forecast from an aerosol data assimilation system was used as an a priori estimate instead of spatially and temporally constant values. This
method was demonstrated using observation of the Advanced Himawari Imager
onboard the Japan Meteorological Agency's geostationary satellite
Himawari-8. Overall, the retrieval results incorporated strengths of the
observation and the model and complemented their respective weaknesses,
showing 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 an a priori
model forecast by adding satellite information. Further, the satellite
retrieval accuracy was improved by introducing the model forecast instead of
the constant a priori estimates. By using the assimilated forecast for an a priori estimate, information from previous observations can be propagated to
future retrievals, leading to better retrieval accuracy. Observational
information from the satellite and aerosol transport by the model are
incorporated cyclically to effectively estimate the optimum field of
aerosol.
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