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atmospheric sciences. On the other hand, because of the low axial resolution of inline holography, the investigation of the three-dimensional surface of atmospheric objects, for example, is usually not possible. This note presents observations indicating another potentially useful exploitation of the illuminating light’s coherence: gaining information on the shape or the structure of the surface of large ice crystals via investigation of interference patterns. Figure 1 shows examples of reconstructed
atmospheric sciences. On the other hand, because of the low axial resolution of inline holography, the investigation of the three-dimensional surface of atmospheric objects, for example, is usually not possible. This note presents observations indicating another potentially useful exploitation of the illuminating light’s coherence: gaining information on the shape or the structure of the surface of large ice crystals via investigation of interference patterns. Figure 1 shows examples of reconstructed
et al. 2011 ). Heterogeneous freezing occurs at lower supersaturation and higher temperatures T than homogeneous freezing. Secondary ice formation processes can occur through fracture of ice crystals exposed to dry air layers or collision of preexisting ice crystal ( Vardiman 1978 ; Oraltay and Hallett 1989 ); fragmentation of large individual cloud drops during freezing in free fall, thus playing a role in multiplying the number of ice particles in clouds ( Hobbs and Alkezweeny 1968 ); and
et al. 2011 ). Heterogeneous freezing occurs at lower supersaturation and higher temperatures T than homogeneous freezing. Secondary ice formation processes can occur through fracture of ice crystals exposed to dry air layers or collision of preexisting ice crystal ( Vardiman 1978 ; Oraltay and Hallett 1989 ); fragmentation of large individual cloud drops during freezing in free fall, thus playing a role in multiplying the number of ice particles in clouds ( Hobbs and Alkezweeny 1968 ); and
aerosols has been used to estimate aerosol optical properties ( Xiong and Friedlander 2001 ). Fractal particles generally have a random appearance, although the average structural properties can be estimated using D f . Figure 1 shows ice crystal aggregates imaged by the Cloud Particle Imager (CPI) probe in convective clouds during the Cirrus Regional Study of Tropical Anvils and Cirrus Layers–Florida Area Cirrus Experiment (CRYSTAL-FACE) in July 2002. The crystals have a highly irregular structure
aerosols has been used to estimate aerosol optical properties ( Xiong and Friedlander 2001 ). Fractal particles generally have a random appearance, although the average structural properties can be estimated using D f . Figure 1 shows ice crystal aggregates imaged by the Cloud Particle Imager (CPI) probe in convective clouds during the Cirrus Regional Study of Tropical Anvils and Cirrus Layers–Florida Area Cirrus Experiment (CRYSTAL-FACE) in July 2002. The crystals have a highly irregular structure
1. Introduction Consider a tiny, fragile ice crystal falling before you, drifting gently toward the surface of Earth. This is simply one of many beautiful, everyday examples of time-dependent fluid dynamics at low to intermediate Reynolds numbers. When envisioning the fall trajectory of a snow or ice crystal, firsthand experience, or perhaps a bit of imagination, tells us to expect complex translational and rotational motions, such as side-to-side oscillations (fluttering), sideways drifting
1. Introduction Consider a tiny, fragile ice crystal falling before you, drifting gently toward the surface of Earth. This is simply one of many beautiful, everyday examples of time-dependent fluid dynamics at low to intermediate Reynolds numbers. When envisioning the fall trajectory of a snow or ice crystal, firsthand experience, or perhaps a bit of imagination, tells us to expect complex translational and rotational motions, such as side-to-side oscillations (fluttering), sideways drifting
1. Introduction As ice crystals collide in a turbulent cloud, they may stick to each other and coalesce to form larger aggregates ( Pruppacher and Klett 1997 ). This process is an important ingredient in the growth of ice crystals and the formation of snow aggregates ( Lo and Passarelli 1982 ; Mitchell 1988 ). Detailed field measurements have shown that the resulting aggregates have a characteristic exponential size distribution ( Field and Heymsfield 2003 ), and are shaped approximately
1. Introduction As ice crystals collide in a turbulent cloud, they may stick to each other and coalesce to form larger aggregates ( Pruppacher and Klett 1997 ). This process is an important ingredient in the growth of ice crystals and the formation of snow aggregates ( Lo and Passarelli 1982 ; Mitchell 1988 ). Detailed field measurements have shown that the resulting aggregates have a characteristic exponential size distribution ( Field and Heymsfield 2003 ), and are shaped approximately
SW cooling and LW warming impacts are almost balanced; the global net cloud forcing by high, optically thin clouds is estimated to be only about 2 W m −2 ( Hartmann et al. 1992 ). Compared to warm clouds composed of liquid water droplets, ice clouds are more challenging to model because of the more complex ice-phase microphysics and the nonspherical shapes of the ice crystals. The latter means that the ice content is not unambiguously connected to the optical cross section, and the optical
SW cooling and LW warming impacts are almost balanced; the global net cloud forcing by high, optically thin clouds is estimated to be only about 2 W m −2 ( Hartmann et al. 1992 ). Compared to warm clouds composed of liquid water droplets, ice clouds are more challenging to model because of the more complex ice-phase microphysics and the nonspherical shapes of the ice crystals. The latter means that the ice content is not unambiguously connected to the optical cross section, and the optical
1. Introduction A substantial uncertainty in properly representing ice clouds and consequently their climate feedbacks is characterizing their radiative properties ( Stephens et al. 1990 ; Stackhouse and Stephens 1991 ; Schlimme et al. 2005 ; Fu 2007 ). The fundamental radiative properties of atmospheric ice crystals for atmospheric models are the ice extinction cross section, single-scattering albedo, and the first moment of the scattering phase function, commonly referred to as the
1. Introduction A substantial uncertainty in properly representing ice clouds and consequently their climate feedbacks is characterizing their radiative properties ( Stephens et al. 1990 ; Stackhouse and Stephens 1991 ; Schlimme et al. 2005 ; Fu 2007 ). The fundamental radiative properties of atmospheric ice crystals for atmospheric models are the ice extinction cross section, single-scattering albedo, and the first moment of the scattering phase function, commonly referred to as the
1. Introduction and motivation The shape, or habit, of ice crystals growing by vapor deposition has long been known to depend on such ambient characteristics as temperature and supersaturation (e.g., Nakaya 1954 ; Hallett and Mason 1958 ). The primary habits of ice crystals can be defined in terms of the aspect ratio ϕ ≡ c / a ( ϕ < 1 for plates; ϕ > 1 for columns), where c is the crystal semidimension along the c axis (normal to the basal faces) and a is the semidimension along
1. Introduction and motivation The shape, or habit, of ice crystals growing by vapor deposition has long been known to depend on such ambient characteristics as temperature and supersaturation (e.g., Nakaya 1954 ; Hallett and Mason 1958 ). The primary habits of ice crystals can be defined in terms of the aspect ratio ϕ ≡ c / a ( ϕ < 1 for plates; ϕ > 1 for columns), where c is the crystal semidimension along the c axis (normal to the basal faces) and a is the semidimension along
. 2013 ), thereby determining the change in volume during the time step. This deposition density roughly approximates the sparse distribution of branches and subbranches during branched planar crystal growth; ρ at any stage of growth is simply the mass of the particle divided by its volume. The first component of a radar forward model maps the physical properties of a given ice particle to scattering properties. These scattering properties are calculated using two general methods: detailed
. 2013 ), thereby determining the change in volume during the time step. This deposition density roughly approximates the sparse distribution of branches and subbranches during branched planar crystal growth; ρ at any stage of growth is simply the mass of the particle divided by its volume. The first component of a radar forward model maps the physical properties of a given ice particle to scattering properties. These scattering properties are calculated using two general methods: detailed
1. Introduction Quantifying the vapor growth rate of ice crystals is challenging because it is governed by two connected processes: gas-phase diffusion and surface attachment kinetics ( Markov 2003 ). During mass growth, water vapor molecules diffuse through the surrounding gas to reach the crystal. In the classical model of faceted growth, water molecules must then adsorb onto the crystal, though some molecules may be reflected from the surface. The fraction of molecules that adsorb onto the
1. Introduction Quantifying the vapor growth rate of ice crystals is challenging because it is governed by two connected processes: gas-phase diffusion and surface attachment kinetics ( Markov 2003 ). During mass growth, water vapor molecules diffuse through the surrounding gas to reach the crystal. In the classical model of faceted growth, water molecules must then adsorb onto the crystal, though some molecules may be reflected from the surface. The fraction of molecules that adsorb onto the
