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CALIPSO lidar level 3 aerosol profile product: version 3 algorithm design.

Jason L Tackett1,2, David M Winker2, Brian J Getzewich1,2

  • 1Science Systems and Applications, Inc., Hampton, VA, USA.

Atmospheric Measurement Techniques
|January 29, 2021
PubMed
Summary
This summary is machine-generated.

Quality screening of Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) aerosol data is crucial. Filters significantly reduce biases, leading to more accurate monthly mean aerosol optical depth (AOD) products.

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Area of Science:

  • Atmospheric Science
  • Remote Sensing
  • Aerosol Science

Background:

  • The CALIPSO satellite provides aerosol extinction profiles globally.
  • Level 3 products aggregate monthly mean aerosol extinction profiles.
  • Quality control is essential for reliable aerosol data products.

Purpose of the Study:

  • To describe the quality screening and averaging methods for the CALIPSO Level 3 aerosol product (version 3).
  • To evaluate the impact of various quality filters on aerosol extinction profiles and aerosol optical depth (AOD).
  • To ensure the accuracy of globally gridded, monthly mean aerosol extinction profiles.

Main Methods:

  • Utilized CALIOP Level 2 aerosol extinction profiles and layer classification data.
  • Applied a series of quality filters to screen for layer detection, classification, retrieval errors, and surface signal anomalies.
  • Calculated monthly mean AOD by vertically integrating quality-screened extinction profiles.

Main Results:

  • The 'extinction QC flag' filter is most effective against high biases in mean extinction.
  • The 'misclassified cirrus fringe' filter aggressively removes misclassified cirrus clouds.
  • Quality screening reduced global ocean and land mean AOD by -24% and -31%, respectively.

Conclusions:

  • Rigorous quality screening is vital for generating accurate CALIPSO Level 3 aerosol products.
  • The implemented methods successfully mitigate various error sources, improving data reliability.
  • The quality filters significantly reduce biases in monthly mean AOD, enhancing its scientific utility.