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Abstract
Abstract. During the 2012 Deep Convective Clouds and Chemistry (DC3) experiment the National Science Foundation/National Center for Atmospheric Research Gulfstream V (GV) aircraft sampled the upper anvils of two storms that developed in eastern Colorado on 6 June 2012. A cloud particle imager (CPI) mounted on the GV aircraft recorded images of ice crystals at altitudes of 12.0 to 12.4 km and temperatures (T) from −61 to −55 ∘C. A total of 22 393 CPI crystal images were analyzed, all with maximum dimension (Dmax)<433 µm and with an average Dmax of 80.7±45.4 µm. The occurrence of well-defined pristine crystals (e.g., columns and plates) was less than 0.04 % by number. Single frozen droplets and frozen droplet aggregates (FDAs) were the dominant habits with fractions of 73.0 % (by number) and 46.3 % (by projected area), respectively. The relative frequency of occurrence of single frozen droplets and FDAs depended on temperature and position within the anvil cloud. A new algorithm that uses the circle Hough transform technique was developed to automatically identify the number, size, and relative position of element frozen droplets within FDAs. Of the FDAs, 42.0 % had two element frozen droplets with an average of 4.7±5.0 element frozen droplets. The frequency of occurrence gradually decreased with the number of element frozen droplets. Based on the number, size, and relative position of the element frozen droplets within the FDAs, possible three-dimensional (3-D) realizations of FDAs were generated and characterized by two different shape parameters, the aggregation index (AI) and the fractal dimension (Df), that describe 3-D shapes and link to scattering properties with an assumption of spherical shape of element frozen droplets. The AI of FDAs decreased with an increase in the number of element frozen droplets, with larger FDAs with more element frozen droplets having more compact shapes. The Df of FDAs was about 1.20–1.43 smaller than that of black carbon (BC) aggregates (1.53–1.85) determined in previous studies. Such a smaller Df of FDAs indicates that FDAs have more linear chain-like branched shapes than the compact shapes of BC aggregates. Determined morphological characteristics of FDAs along with the proposed reconstructed 3-D representations of FDAs in this study have important implications for improving the calculations of the microphysical (e.g., fall velocity) and radiative (e.g., asymmetry parameter) properties of ice crystals in upper anvil clouds.
Highlights
Single frozen droplets represented the dominant habit by number, whereas frozen droplet aggregates (FDAs) were dominant by projected area
The fraction of well-defined pristine ice crystals, such as plates and columns, was less than 0.04 % by number and 0.12 % by area for all time periods, whereas unclassified crystals represented 6.1 % (3.9 %; 5.3 %; 7.5 %) by number for all periods and 13.5 % (6.5 %; 10.9 %; 16.9 %) by area. These fractions of unclassified crystals were lower than those obtained from anvil cloud in the tropics (Um and McFarquhar, 2009) that showed more than 22 % and 37 % contributions by number and area, respectively
During the 2012 Deep Convective Clouds and Chemistry (DC3) experiment the National Science Foundation/National Center for Atmospheric Research Gulfstream V (GV) aircraft sampled the upper anvils of two storms that developed in eastern Colorado on 6 June 2012
Summary
Deep convective systems, such as thunderstorms and mesoscale convective systems (MCSs), play an important role in Earth’s climate system, for example, by conveying ice crystals to the upper troposphere and lower stratosphere, redistributing latent heat, controlling precipitation, and regulating the Earth’s radiation budget (Jensen et al, 1996; Stephens, 2005; de Reus et al, 2009; Frey et al, 2011; Feng et al, 2011, 2012; Gayet et al, 2012; Taylor et al, 2016). Vigorous turrets associated with deep convection generate intense precipitation that influences the hydrological cycle and large anvil shields that modulate radiation due to their extensive spatial and temporal coverage (Feng et al, 2011, 2012; Wang et al, 2015). The relationships between the spatial and temporal coverage of convectively generated clouds and their radiative impact are still not well understood and affect the representation of cloud feedbacks in numerical models (Bony et al, 2015, 2016; Hartmann, 2016; Hartmann and Berry, 2017)
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