Characteristics of Ice Clouds Over Mountain Regions Detected by CALIPSO and CloudSat Satellite Observations

Abstract

This study examines the characteristics of orographic ice clouds in steep mountain regions using 3 years of CloudSat and CALIPSO satellite products. A combination of radar and lidar cloud fraction data is used to identify ice cloud systems. Additionally, the retrieved ice water content (IWC) and ice number concentration (NI) are used to analyze the dominant ice cloud microphysics in convective‐ and cirrus‐type clouds. The analysis shows that temporally averaged values of the IWC and NI are larger in mountain regions than in land and ocean regions. For convective clouds over mountains, both the IWC and NI have larger values at atmospheric temperatures warmer than 250 K, suggesting a dominant role for the freezing of supercooled liquid water. For cirrus clouds over mountains, however, only the NI has larger values at atmospheric temperatures colder than 240 K, indicating the importance of homogeneous ice nucleation. These characteristics of ice‐phase clouds in terms of the IWC and NI in mountain regions are distinct, with a horizontal scale smaller than 300 km. This study suggests that it is useful to categorize ice clouds in mountain regions in addition to ocean and land regions to evaluate the microphysical properties (mass and number) of such ice clouds in atmospheric models.

Validation of Ice Cloud Microphysical Properties Retrieval Using a Markov Chain Monte Carlo Algorithm

“…Austin et al (2009) and Deng et al (2010) devised a modified algorithm for ice cloud properties calculation. The observation and retrieved data product of CloudSat CPR have been widely used to analyze the global occurrence frequency, horizontal and vertical structures of clouds (Mace et al, 2006;Sassen et al, 2008;Hong & Liu, 2015;Li et al, 2018;Xu et al, 2019;Seiki et al, 2019). Statistical comparisons of CloudSat data product with other measurements have been performed to evaluate retrieval techniques (Protat et al, 2010;Stein et al, 2011;Deng et al, 2013;Hu et al, 2020).…”