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MODIS Collection 6 shortwave-derived cloud phase classification algorithm and comparisons with CALIOP.

Benjamin Marchant1,2, Steven Platnick2, Kerry Meyer1,2

  • 11) USRA Universities Space Research Association, Columbia, Maryland, USA.

Atmospheric Measurement Techniques
|August 21, 2020
PubMed
Summary
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The new MODIS Collection 6 cloud phase algorithm significantly improves cloud thermodynamic phase (ice, liquid, undetermined) classification. This enhanced accuracy is crucial for reliable cloud property retrievals and understanding climate impacts.

Area of Science:

  • Atmospheric Science
  • Remote Sensing
  • Cloud Physics

Background:

  • Accurate cloud thermodynamic phase (ice, liquid, undetermined) classification is essential for passive sensor cloud retrievals.
  • Incorrect phase identification leads to significant errors in cloud optical and microphysical properties.
  • Monitoring cloud phase distribution is vital due to differing impacts of ice and liquid clouds on Earth's energy budget and water cycle.

Purpose of the Study:

  • To introduce and evaluate the improved MODIS Collection 6 (C6) shortwave-derived cloud thermodynamic phase algorithm.
  • To enhance the phase discrimination skill across diverse cloudy conditions and surface types.

Main Methods:

  • Rewriting the shortwave-derived cloud thermodynamic phase algorithm for MODIS Collection 6.

Related Experiment Videos

  • Conducting extensive granule-level and global comparisons of the C6 algorithm against the previous C5 algorithm.
  • Validating C6 performance using data from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIOP) instrument.

    • Main Results:

    • The MODIS C6 cloud phase algorithm demonstrates wholesale improvement compared to the C5 algorithm.
    • Enhanced phase discrimination skill is observed for various challenging cloudy scenes, including thin/thick clouds over diverse surfaces (ocean, land, desert, snow, ice).

    Conclusions:

    • The MODIS C6 cloud phase algorithm represents a significant advancement in cloud detection and characterization.
    • Improved cloud phase classification leads to more accurate cloud optical and microphysical property retrievals, benefiting climate research.