Development of Algorithm for Discriminating Hydrometeor Particle Types with a Synergistic Use of CloudSat and CALIPSO

Area of Science:

• Atmospheric Science
• Remote Sensing
• Cloud Physics

Background:

• Accurate classification of hydrometeor types is crucial for understanding cloud processes and improving climate models.
• Previous methods often lacked the ability to fully characterize vertical cloud structures and phases.

Purpose of the Study:

• To develop a novel algorithm for classifying hydrometeor particle types using synergistic satellite data.
• To investigate the global relationships between cloud phase, ice crystal habit, radar reflectivity, and temperature.
• To provide a more comprehensive, vertically resolved hydrometeor classification.

Main Methods:

• Synergistic use of CloudSat/Cloud Profiling Radar (CPR) and CALIPSO/CALIOP satellite observations.
• Investigated global relationships between CALIOP-characterized cloud properties and CPR reflectivity/temperature.
• Developed a CPR-alone algorithm, then combined it with CALIPSO data for synergy classification.

Main Results:

• Developed an algorithm yielding thirteen distinct hydrometeor types.
• Identified randomly-oriented ice crystal (3D-ice) as the most frequent type globally (53.8%).
• Other significant types include supercooled water (14.3%) and horizontally-oriented plate (2D-plate) ice (9.2%).

Conclusions:

• The CPR-CALIOP synergy classification offers a more complete picture of vertically resolved hydrometeor types.
• This classification provides valuable observation-based data for climate model diagnostics.
• The findings enhance the representation of cloud phase and microphysical characteristics in climate models.