Articles | Volume 4, issue 3
Atmos. Meas. Tech., 4, 379–408, 2011

Special issue: The 2009 WE-Heraeus-Seminar on satellite remote sensing of...

Atmos. Meas. Tech., 4, 379–408, 2011

Research article 01 Mar 2011

Research article | 01 Mar 2011

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

E. J. Hyer1, J. S. Reid1, and J. Zhang2 E. J. Hyer et al.
  • 1Naval Research Laboratory, 7 Grace Hopper Avenue, Monterey, CA 93943, USA
  • 2University of North Dakota, 4149 University Avenue Stop 9006, Grand Forks, ND 58202, USA

Abstract. 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 weather prediction models. Examination of errors revealed important sources of variation 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 developed to avoid conditions with potential for significant AOD error. An empirical correction for surface boundary condition using the MODIS 16-day albedo product captured 25% of the variability in the site mean bias at low AOD. A correction for regional microphysical bias using the AERONET fine/coarse partitioning information increased the global correlation between MODIS and AERONET from r2 = 0.62–0.65 to r2 = 0.71–0.73. Application of these filters and corrections improved the global fraction of MODIS AOD within (0.05 ± 20%) of AERONET to 77%, up from 67% using only built-in MODIS QA. The compliant fraction in individual regions was improved by as much as 20% (South America). An aggregated Level 3 product for use in a data assimilation system is described, along with a prognostic error model to estimate uncertainties on a per-observation basis. The new filtered and corrected Level 3 product has improved performance over built-in MODIS QA with less than a 15% reduction in overall data available for data assimilation.