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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/amt-2019-77
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2019-77
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 26 Mar 2019

Research article | 26 Mar 2019

Review status
This discussion paper is a preprint. A revision of the manuscript is under review for the journal Atmospheric Measurement Techniques (AMT).

Accuracy Assessment of MODIS Land Aerosol Optical Thickness Algorithms using AERONET Measurements

Hiren Jethva1,2, Omar Torres2, and Yasuko Yoshida3 Hiren Jethva et al.
  • 1Universities Space Research Association, 7178 Columbia Gateway Drive, Columbia, MD 21046, USA
  • 2NASA Goddard Space Flight Center, Earth Science Division, Code 614, Greenbelt, MD 20771, USA
  • 3Science Systems and Applications, Inc., 10210 Greenbelt Rd, Lanham, MD 20706 USA

Abstract. The planned simultaneous availability of visible and near-IR observations from the geostationary platforms of Tropospheric Emissions: Monitoring of Pollution (TEMPO) and GOES R/S Advanced Base Imager (ABI) will present the opportunity of deriving an accurate aerosol product taking advantage of both ABI's high spatial resolution in the visible and TEMPO's sensitivity to aerosol absorption in the near-UV. Because ABI's spectral coverage is similar to that of MODIS, currently used MODIS aerosol algorithms can be applied to ABI observations. In this work, we evaluate existing MODIS algorithms of that derive aerosol optical thickness (AOT) over land surfaces using visible and near-IR observations. The Dark Target (DT), Deep Blue (DB), and Multiangle Implementation of Atmospheric Correction (MAIAC) algorithms are all applied to Aqua-MODIS radiance measurements. We have carried out an independent evaluation of each algorithm by comparing the retrieved AOT to space-time collocated ground-based sunphotometer measurements of the same parameter at 171 sites of the Aerosol Robotic Network (AERONET) over North America (NA). A spatiotemporal scheme co-locating the satellite retrievals with the ground-based measurements was applied consistently to all three retrieval datasets. We find that while the statistical performance of all three algorithms is comparable over darker surfaces over eastern NA, the MAIAC algorithm provides relatively better comparison over western NA sites characterized by inhomogeneous elevation and bright surfaces. MAIAC's finer product resolution (1 km), allows a substantially larger number of matchups than DB 10-km and DT 10-km (DT 3-km) products by 108 % and 125 % (83 %) respectively over Eastern NA, and by 144 % and 220 % (195 %) over Western NA. The characterization of error in AOT for the three aerosol products as a function of MAIAC-retrieved bi-directional surface reflectance shows a systematic positive bias in DT retrievals over brighter surfaces, whereas DB and MAIAC retrievals showed no such bias throughout the wide range of surface brightness with MAIAC offering lowest spread in errors. The results reported here represent an objective, unbiased evaluation of existing over-land aerosol retrieval algorithms of MODIS. The detailed statistical evaluation of the performance of each of these three algorithms may be used as guidance in the development of inversion schemes to derive aerosol properties from ABI or other MODIS-like sensors.

Hiren Jethva et al.
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Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Hiren Jethva et al.
Hiren Jethva et al.
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