A158-07
Using remotely sensed cloud top properties to look at drizzle formation

Monday, 14 December 2020: 07:20
Virtual
Brian Cairns1, Johnathan W Hair2, Mikhail D Alexandrov3, Kenneth Sinclair4, Bastiaan van Diedenhoven5, Andrzej P Wasilewski6, Ewan Crosbie7, Chris A Hostetler2, Yongxiang Hu2, Richard Moore2, Amy Jo Scarino8, Taylor J Shingler9, Michael Shook2, Luke D Ziemba2 and Armin Sorooshian10, (1)NASA Goddard Institute for Space Studies, New York, NY, United States, (2)NASA Langley Research Center, Hampton, VA, United States, (3)Columbia Univ, New York, NY, United States, (4)CCSR / NASA GISS, New York, NY, United States, (5)Columbia University, New York, United States, (6)Trinnovim LLC, New York, NY, United States, (7)University of Arizona, Tucson, AZ, United States, (8)Science Systems and Applications Inc., Hampton, VA, United States, (9)Science Systems and Applications, Inc., Hampton, VA, United States, (10)University of Arizona, Department of Chemical and Environmental Engineering, Tucson, AZ, United States
Recorded Presentation
Abstract:
In this study we examine processes related to the formation of drizzle that can be obtained from collocated remote sensing estimates of cloud droplet number concentrations (CDNCs) and droplet size distributions (DSD) at cloud top. The CDNC combined with the DSD allows for the direct identification of drizzle (droplets above a threshold size) and its concentration at cloud top. In addition, the CDNC and DSD are used to evaluate the rates of drizzle formation using the stochastic collection equation. While cloud top properties do not control the intensity of precipitation they are important for the initiation of precipitation and can be used to evaluate the bulk parameterizations of auto-conversion that are the primary control on the frequency of warm precipitation in general circulation models (GCMs). The remote sensing techniques used to obtain cloud top CDNC are high vertical resolution lidar profiles of attenuation from which cloud top extinction is derived and polarimeter observations of the cloud bow from which the DSD is retrieved using a rainbow Fourier transform. The cloud top liquid water content (LWC) is also derived from the CDNC and DSDs. It is important to note that these remote sensing products make no assumptions about the profile of liquid water content. In order to evaluate the remote sensing products in situ cloud probe data are compared against the retrieval products both for case study flights and also statistically over the entire available data sets. The data sets that we use are from aircraft flights conducted during the three North Atlantic Aerosol and Marine Ecosystems Study (NAAMES) campaigns during November 2015, May 2016, and September 2017 and the first Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) field campaign in February and March 2020. These campaigns have provided an extensive dataset sampling low-level water clouds over a wide range of synoptic and thermodynamic conditions. The study finishes with a statistical summary of the prevalence of drizzle at cloud top and drizzle formation rates and their consistency with predictions from the bulk parameterizations of auto-conversion that are used in GCMs.
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