Date of Award
January 2020
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Atmospheric Sciences
First Advisor
Jianglong Zhang
Abstract
Using collocated NASA’s Cloud Physics Lidar (CPL) and Research Scanning Polarimeter (RSP) data from the SEAC4RS campaign, a new observational-based method was developed which uses a K-means clustering technique to classify ice crystal habit types into seven categories: column, plates, rosettes, spheroids and three different type of irregulars. Inter-compared with the collocated SPEC Inc. Cloud Particle Imager (CPI) data, the frequency of the detected ice crystal habits from the proposed method presented in the study agree within 5% of the CPI reported values for columns, irregulars, rosettes, and spheroids, with more disagreement for plates. This study suggests that a detailed ice crystal habit retrieval could be applied to combined space-based lidar and polarimeter observations such as CALIPSO and POLDER in addition to future missions such as the Aerosols, Clouds, Convection, and Precipitation (A-CCP).
Recommended Citation
Midzak, Natalie, "A Classification Of Ice Crystal Habits Using Combined CPL And RSP Observations During The Seac4rs Campaign" (2020). Theses and Dissertations. 3112.
https://commons.und.edu/theses/3112