An over-land aerosol optical depth data set for data assimilation by filtering, correction, and aggregation of MODIS Collection 5 optical depth retrievals

摘要

MODIS Collection 5 retrieved aerosol optical depth (AOD) over land(MOD04/MYD04) was evaluated using 4 years of matching AERONET observations,to assess its suitability for aerosol data assimilation in numerical weatherprediction models. Examination of errors revealed important sources ofvariation in random errors (e.g., atmospheric path length, scattering angle"hot spot"), and systematic biases (e.g., snow and cloud contamination,surface albedo bias). A set of quality assurance (QA) filters was developedto avoid conditions with potential for significant AOD error. An empiricalcorrection for surface boundary condition using the MODIS 16-day albedoproduct captured 25% of the variability in the site mean bias at low AOD.A correction for regional microphysical bias using the AERONET fine/coarsepartitioning information increased the global correlation between MODIS andAERONET from r2 = 0.62–0.65 to r2 = 0.71–0.73. Application of thesefilters and corrections improved the global fraction of MODIS AOD within(0.05 ± 20%) of AERONET to 77%, up from 67% using only built-inMODIS QA. The compliant fraction in individual regions was improved by asmuch as 20% (South America). An aggregated Level 3 product for use in adata assimilation system is described, along with a prognostic error modelto estimate uncertainties on a per-observation basis. The new filtered andcorrected Level 3 product has improved performance over built-in MODIS QAwith less than a 15% reduction in overall data available for dataassimilation.