The NOAA Aerosol Reanalysis version 1.0 (NARA v1.0): Description of the Modeling System and its Evaluation

• Main Authors: Wei, Shih-Wei, Pagowski, Mariusz, da Silva, Arlindo, Lu, Cheng-Hsuan, Huang, Bo
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2023
• Subjects: article; Verlagsveröffentlichung; Merra; Aerosol Robotic Network
• Online Access: https://doi.org/10.5194/egusphere-2023-356; https://noa.gwlb.de/receive/cop_mods_00066845; https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00065315/egusphere-2023-356.pdf; https://egusphere.copernicus.org/preprints/2023/egusphere-2023-356/egusphere-2023-356.pdf

In this manuscript, we describe the first ever global aerosol reanalysis at the National Oceanic and Atmospheric Administration (NOAA), the NOAA Aerosol ReAnalysis version 1.0 (NARA v1.0) that was produced for the year 2016. In NARA v1.0, the forecast model is an early version of the operational Global Ensemble Forecast System-Aerosols (GEFS-Aerosols) model. The three-dimensional ensemble-variational (3D-EnVar) data assimilation (DA) system configuration is built using elements of the Joint Effort for Data assimilation Integration (JEDI) framework being developed at the Joint Center for Satellite Data Assimilation (JCSDA). The Neural Network Retrievals (NNR) of Aerosol Optical Depth (AOD) at 550 nm from the MODerate resolution Imaging Spectroradiometer (MODIS) instruments are assimilated to provide reanalysis of aerosol mass mixing ratios. We evaluate NARA v1.0 against a wide variety of Aerosol Robotic NETwork (AERONET) observations, against National Aeronautics and Space Administration’s (NASA) Modern-Era Retrospective analysis for Research and Applications 2 (MERRA-2; Gelaro et al., 2017; Randles et al., 2017; Buchard et al., 2017) and European Centre for Medium-Range Weather Forecasts’ (ECMWF) Copernicus Atmosphere Monitoring Service ReAnalysis (CAMSRA; Inness et al., 2019), and against measurements of surface concentrations of particulate matter 2.5 (PM2.5) and aerosol species. Overall, the 3D-EnVar DA system significantly improves AOD simulations compared to observations, but the assimilation has limited impact on chemical composition and size distributions of aerosols. This reveals limitations of assimilating AOD retrievals at a single wavelength. We also identify deficiencies in the model’s representations of aerosol chemistry and their optical properties elucidated from evaluation of NARA v1.0 against AERONET observations. A comparison of seasonal profiles of aerosol species from NARA v1.0 with the other two reanalyses exposes significant differences in climatologies. These differences reflect ...