Preprints
https://doi.org/10.5194/amt-2022-290
https://doi.org/10.5194/amt-2022-290
 
09 Nov 2022
09 Nov 2022
Status: this preprint is currently under review for the journal AMT.

Assessment of Severe Aerosol Events from NASA MODIS and VIIRS Aerosol Products for Data Assimilation and Climate Continuity

Amanda Gumber1, Jeffery S. Reid2, Robert E. Holz1, Thomas F. Eck3,4, N. Christina Hsu4, Robert C. Levy4, Jianglong Zhang5, and Paolo Veglio1 Amanda Gumber et al.
  • 1Space Science and Engineering Center, University of Wisconsin-Madison, Madison, WI 53706, USA
  • 2U.S. Naval Research Laboratory, Monterey, CA 93943, USA
  • 3Goddard Earth Sciences Technology and Research (GESTAR) II, University of Maryland Baltimore County, Baltimore, MD 21250, USA
  • 4NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
  • 5Department of Atmospheric Sciences, University of North Dakota, Grand Forks, ND 58202, USA

Abstract. While the use and data assimilation (DA) of operational Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol data is commonplace, MODIS is scheduled to sunset in the next year. For data continuity, focus has turned to the development of next generation aerosol products and sensors such as those associated with the Visible Infrared Imaging Radiometer Suite (VIIRS) on Suomi NPOESS Preparation Project (S-NPP) and NOAA-20. Like MODIS algorithms, products from these sensors require their own set of extensive error characterization and correction exercises. This is particularly true in the context of monitoring significant aerosol events that tax an algorithm’s ability to separate cloud from aerosol and account for multiple scattering related errors exacerbated by uncertainties in aerosol optical properties. To investigate the performance of polar orbiting satellite algorithms to monitor and characterize significant events a Level 3 (L3) product has been developed, using a consistent aggregation methodology, for four years of observations (2016–2019). Included in this product is AErosol RObotic NETwork (AERONET), MODIS Dark Target, Deep Blue, and Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithms. These MODIS “baseline algorithms” are compared to NASA’s recently released NASA Deep Blue algorithm for use with VIIRS. Using this new dataset, the relative performance of the algorithms for both land and ocean were investigated with a focus on the relative skill of detecting severe events and accuracy of the retrievals using AERONET. Maps of higher percentile AOD regions of the world by product, identified those with the highest measured AODs, and determined what is high by local standards. While patterns in AOD match across products and median to moderate AOD values match well, there are regionally correlated biases between products based on sampling, algorithm differences, and AOD range-in particular for higher AOD events. Most notable are differences in Boreal biomass burning and Saharan dust. Significant percentile biases that must be accounted for when data is used in trend studies, data assimilation, or inverse modeling. These biases vary by aerosol regime and are likely due to retrieval assumptions on lower boundary condition and aerosol optical models.

Amanda Gumber et al.

Status: open (until 31 Dec 2022)

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Amanda Gumber et al.

Amanda Gumber et al.

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Short summary
The purpose of the study is to create and evaluate a gridded dataset composed of multiple satellite instruments and algorithms to be used for data assimilation. An important part of aerosol data assimilation is having consistent measurements especially for severe aerosol events. This study evaluates 4 years of data from MODIS, VIIRS, and AERONET with a focus on aerosol severe event detection from a regional and global perspective.