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Total 550 nm aerosol optical depth, surface fine particulate matter (PM2.5), and meteorological observations were assimilated with continuously cycling three-dimensional variational (3DVAR), ensemble square root Kalman filter (EnSRF), and hybrid variational-ensemble data assimilation systems. The hybrid system's background error covariances (BECs) were a blend of those in 3DVAR and produced by the cycling EnSRF system, and the 3DVAR, EnSRF, and hybrid systems differed almost exclusively by their BECs. New analyses were produced every 6 h between 0000 UTC 1 June and 1800 UTC 14 July 2010 over a domain encompassing the contiguous United States (CONUS) and adjacent areas. Additionally, a control experiment that only assimilated meteorological observations was performed. Each 1800 UTC analysis initialized a 48 h Weather Research and Forecasting with Chemistry model forecast. These forecasts were evaluated with a focus on air quality prediction. The ensemble aerosol spread was generally insufficient, particularly over the western CONUS. However, despite the suboptimal ensemble spread, the hybrid system performed quite well and usually produced the best aerosol forecasts. Additionally, both the 3DVAR- and EnSRF-initialized forecasts typically outperformed the control. These results are encouraging and suggest the resiliency of the hybrid method. Improved aerosol ensembles should translate into even better future hybrid forecasts.

[1] The capability of assimilating surface PM10 (particulate matter with diameters less than 10 µm) observations has been developed within the National Centers for Environmental Prediction Gridpoint Statistical Interpolation three-dimensional variational (3DVAR) data assimilation (DA) system. It provides aerosol analyses for the Goddard Chemistry Aerosol Radiation and Transport aerosol scheme within the Weather Research and Forecasting/Chemistry model. Control and assimilation experiments were performed for June 2011 over China to explore in detail the impact of assimilating surface PM10. In the assimilation experiment, analyses were produced every 6 h to adjust the mass concentrations of different aerosol species. The statistical results from two parallel experiments demonstrate that the assimilation of surface PM10 observations can significantly reduce the uncertainty of initial aerosol fields and effectively improve the subsequent aerosol forecasts for at least 12 h. However, the benefit from the assimilation of PM10 diminishes rapidly with forecast range. Process analysis for PM10 formation indicates that the rapidly diminishing DA impact on aerosol forecasts, especially in early forecast hours, was dominated by vertical mixing with an additional contribution from advection. Both processes were mainly related to unbalanced aerosol fields in the horizontal and vertical after assimilating surface observations. These findings illustrate the importance of adjusting aerosol emission rates and the initial aerosol vertical profile.

[1] Total 550 nm aerosol optical depth (AOD) retrievals from Moderate Resolution Imaging Spectroradiometer (MODIS) sensors and surface fine particulate matter (PM2.5) observations were assimilated with the National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation (GSI) three-dimensional variational (3DVAR) data assimilation (DA) system. Parallel experiments assimilated AOD and surface PM2.5observations both individually and simultaneously. New 3DVAR aerosol analyses were produced every 6 h between 0000 UTC 01 June and 1800 UTC 14 July 2010 over a domain encompassing the continental United States. The analyses initialized Weather Research and Forecasting-Chemistry (WRF-Chem) model forecasts. Assimilating AOD, either alone or in conjunction with PM2.5 observations, produced better AOD forecasts than a control experiment that did not perform DA. Additionally, individual assimilation of both AOD and PM2.5 improved surface PM2.5 forecasts compared to when no DA occurred. However, the best PM2.5 forecasts were produced when both AOD and PM2.5 were assimilated. Considering the goodness of both AOD and PM2.5 forecasts, the results unequivocally show that concurrent DA of PM2.5 and AOD observations produced the best overall forecasts, illustrating how simultaneous DA of different aerosol observations can work synergistically to improve aerosol forecasts.

[1] Assimilation of the Moderate Resolution Imaging Spectroradiometer (MODIS) total aerosol optical depth (AOD) retrieval products (at 550 nm wavelength) from both Terra and Aqua satellites have been developed within the National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation (GSI) three-dimensional variational (3DVAR) data assimilation system. This newly developed algorithm allows, in a one-step procedure, the analysis of 3-D mass concentration of 14 aerosol variables from the Goddard Chemistry Aerosol Radiation and Transport (GOCART) module. The Community Radiative Transfer Model (CRTM) was extended to calculate AOD using GOCART aerosol variables as input. Both the AOD forward model and corresponding Jacobian model were developed within the CRTM and used in the 3DVAR minimization algorithm to compute the AOD cost function and its gradient with respect to 3-D aerosol mass concentration. The impact of MODIS AOD data assimilation was demonstrated by application to a dust storm from 17 to 24 March 2010 over East Asia. The aerosol analyses initialized Weather Research and Forecasting/Chemistry (WRF/Chem) model forecasts. Results indicate that assimilating MODIS AOD substantially improves aerosol analyses and subsequent forecasts when compared to MODIS AOD, independent AOD observations from the Aerosol Robotic Network (AERONET) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument, and surface PM10 (particulate matter with diameters less than 10 μm) observations. The newly developed AOD data assimilation system can serve as a tool to improve simulations of dust storms and general air quality analyses and forecasts.