Clouds and their constituents (mostly small particles) have relationships with climate change. Better understanding and characterization of cloud particles is essential for the creation of better climate models for better understanding of climate and its dynamics/changes.
The widely used cloud particle imager has resolution limitations. Two-dimensional light scattering patterns (2DLS patterns) of particles is an increasingly more promising approach for characterising cloud particles.
Existing methods for characterizing cloud particles based on their 2DLS patterns perform poorly especially for patterns with too little speckles or with numerous fringes.
In this talk, I will focus on the background of the project, and on how we used Zernike moments as features which are insensitive to image rotation to obtain ice particle size and aspect ratio.