Liquid Phase Cloud Microphysical Property Estimates From CALIPSO Measurements

• Main Authors: Yongxiang Hu, Xiaomei Lu, Peng-Wang Zhai, Chris A. Hostetler, Johnathan W. Hair, Brian Cairns, Wenbo Sun, Snorre Stamnes, Ali Omar, Rosemary Baize, Gorden Videen, Jay Mace, Daniel T. McCoy, Isabel L. McCoy, Robert Wood
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2021-09-01
• Series: Frontiers in Remote Sensing
• Subjects: CALIPSO; water cloud; microphysics; number concentration; water content
• Online Access: https://www.frontiersin.org/articles/10.3389/frsen.2021.724615/full
• ISSN: 2673-6187

A neural-network algorithm that uses CALIPSO lidar measurements to infer droplet effective radius, extinction coefficient, liquid-water content, and droplet number concentration for water clouds is described and assessed. These results are verified against values inferred from High-Spectral-Resolution Lidar (HSRL) and Research Scanning Polarimeter (RSP) measurements made on an aircraft that flew under CALIPSO. The global cloud microphysical properties are derived from 14+ years of CALIPSO lidar measurements, and the droplet sizes are compared to corresponding values inferred from MODIS passive imagery. This new product will provide constraints to improve modeling of Earth’s water cycle and cloud-climate interactions.