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

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...

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Main Authors: E. J. Hyer, J. S. Reid, J. Zhang
Format: Article
Language:English
Published: Copernicus Publications 2011-03-01
Series:Atmospheric Measurement Techniques
Online Access:http://www.atmos-meas-tech.net/4/379/2011/amt-4-379-2011.pdf
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author E. J. Hyer
J. S. Reid
J. Zhang
author_facet E. J. Hyer
J. S. Reid
J. Zhang
author_sort E. J. Hyer
collection DOAJ
description 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 <i>r</i><sup>2</sup> = 0.62–0.65 to <i>r</i><sup>2</sup> = 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.
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spelling doaj.art-4042b7472d9b4bd4a283c1ee2dbec5b92022-12-21T20:08:42ZengCopernicus PublicationsAtmospheric Measurement Techniques1867-13811867-85482011-03-014337940810.5194/amt-4-379-2011An over-land aerosol optical depth data set for data assimilation by filtering, correction, and aggregation of MODIS Collection 5 optical depth retrievalsE. J. HyerJ. S. ReidJ. ZhangMODIS 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 <i>r</i><sup>2</sup> = 0.62–0.65 to <i>r</i><sup>2</sup> = 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.http://www.atmos-meas-tech.net/4/379/2011/amt-4-379-2011.pdf
spellingShingle E. J. Hyer
J. S. Reid
J. Zhang
An over-land aerosol optical depth data set for data assimilation by filtering, correction, and aggregation of MODIS Collection 5 optical depth retrievals
Atmospheric Measurement Techniques
title An over-land aerosol optical depth data set for data assimilation by filtering, correction, and aggregation of MODIS Collection 5 optical depth retrievals
title_full An over-land aerosol optical depth data set for data assimilation by filtering, correction, and aggregation of MODIS Collection 5 optical depth retrievals
title_fullStr An over-land aerosol optical depth data set for data assimilation by filtering, correction, and aggregation of MODIS Collection 5 optical depth retrievals
title_full_unstemmed An over-land aerosol optical depth data set for data assimilation by filtering, correction, and aggregation of MODIS Collection 5 optical depth retrievals
title_short An over-land aerosol optical depth data set for data assimilation by filtering, correction, and aggregation of MODIS Collection 5 optical depth retrievals
title_sort over land aerosol optical depth data set for data assimilation by filtering correction and aggregation of modis collection 5 optical depth retrievals
url http://www.atmos-meas-tech.net/4/379/2011/amt-4-379-2011.pdf
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