Understanding tropical cyclone cloud-top microphysical relationships using CloudSat and A-Train data

Abstract:

Cloud top microphysical properties (specifically ice particles) have been estimated from satellite information for the past few decades. Previous studies have focused on these properties from a severe weather perspective examining how small ice particles are related to cloud base temperature, updraft velocity, vertical wind shear and temperature of the freezing level. This study will focus on cloud-top ice microphysical properties of tropical cyclones (TC’s) using data from CloudSat and A-Train dataset. The TC CloudSat and A-Train dataset contains storm specific TC information (maximum wind speed and vertical wind shear) and CloudSat ice properties.

Concentrating only on TC cases, we can look at understanding the relationship between ice particle size and TC behavior. Using the CloudSat TC dataset, we will examine ice particle parameters (effective radius and optical depth) and determine a statistical relationship between variations in effective particle size and optical depth as a function of environmental conditions (vertical wind shear and storm intensity) and reflectivity. Findings will concentrate on CloudSat overpasses within a 250km of storm center, with more than 1400 case studies available.