A three-dimensional ensemble-variational global aerosol data assimilation system based on the Joint Effort for Data assimilation Integration (JEDI) was developed for the Global Ensemble Forecast System-Aerosols (GEFS-Aerosols) coupled with the Goddard Chemistry Aerosol Radiation and Transport (GOCART) model at the National Centers for Environmental Prediction. Aerosol mass mixing ratios in GEFS-Aerosols were selected as control or analysis variables and were adjusted by assimilating 550 nm Aerosol Optical Depth (AOD) retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS) instruments onboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite produced by the National Environmental Satellite, Data, and Information Service (NESDIS) at National Oceanic and Atmospheric Administration (NOAA). The original NOAA/NESDIS S-NPP VIIRS Level 2.0 550 nm AOD retrievals were converted to JEDI Interface for Observation Data Access format. AOD forward operator and its tangent-linear and adjoint models were implemented based on GOCART in JEDI Unified Forward Operator. A stochastically perturbed emission (SPE) approach was developed in the Common Community Physics Package-based GEFS-Aerosols to account for aerosol emission uncertainty. One-month retrospective and three-month near-real-time experiments consistently showed improved GEFS-Aerosols analyses and forecasts from assimilating VIIRS 550 nm AOD retrievals against independent NASA Aqua and Terra Moderate Resolution Imaging Spectroradiometer AOD retrievals, Aerosol Robotic Network AOD, and independent AOD and aerosol analyses from NASA and European Centre for Medium-Range Weather Forecasts. Through scaling and perturbing aerosol emissions, SPE enhanced ensemble error-spread consistency and further improved AOD assimilation. The valid-time-shifting ensemble approach in a cost-effective manner of populating background ensembles showed positive impacts on AOD assimilation. Key Points A Joint Effort for Data assimilation Integration-based three-dimensional ensemble-variational global aerosol data assimilation system was developed for Global Ensemble Forecast System-Aerosols (GEFS-Aerosols) at NCEP A stochastically perturbed emission approach was developed in GEFS-Aerosols to account for aerosol emission uncertainty Assimilation of Visible Infrared Imaging Radiometer Suite aerosol optical depth (AOD) retrievals improves GEFS-Aerosols analyses and forecasts against independent AOD and aerosol analyses Plain Language Summary Accurate representation of atmospheric aerosols is becoming increasingly important for weather, climate, and air quality. Data assimilation (DA) that “optimally” combines information from various sources has been widely applied to generate initial conditions or analyses for atmospheric numerical models. In this study, an ensemble-based global aerosol DA capability was developed within the Joint Effort for Data assimilation Integration to synthesize information from aerosol model forecasts and aerosol observations to improve initial conditions and subsequent forecasts in the global aerosol model at National Centers for Environmental Prediction. In an ensemble-based DA system where model uncertainty needs to be accurately estimated, two approaches were explored to better account for aerosol model uncertainty by scaling and stochastically perturbing aerosol emissions and leveraging background ensembles valid at different times to inexpensively populate background ensembles at the analysis time. Robustness of this newly developed global aerosol DA system and positive impacts of these two approaches were demonstrated in the retrospective and near-real-time experiments against independent aerosol observations and analyses.
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