Liquid Phase Cloud Microphysical Property Estimates From CALIPSO Measurements





Citation of Original Publication

Hu Y, Lu X, Zhai P-W, Hostetler CA, Hair JW, Cairns B, Sun W, Stamnes S, Omar A, Baize R, Videen G, Mace J, McCoy DT, McCoy IL and Wood R (2021) Liquid Phase Cloud Microphysical Property Estimates From CALIPSO Measurements. Front. Remote Sens. 2:724615. doi: 10.3389/frsen.2021.724615


This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
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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.