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1. Introduction Observations regularly show a myriad of ice particle shapes within an ice cloud, even when only particles within a narrow size range are considered. The ice microphysical research community has adopted a one-dimensional unit of measure (length, or maximum dimension) to categorize atmospheric ice particles. This leads to significant, generally uncharacterized, uncertainty given that atmospheric ice particles are three-dimensional while measurements are generally two
1. Introduction Observations regularly show a myriad of ice particle shapes within an ice cloud, even when only particles within a narrow size range are considered. The ice microphysical research community has adopted a one-dimensional unit of measure (length, or maximum dimension) to categorize atmospheric ice particles. This leads to significant, generally uncharacterized, uncertainty given that atmospheric ice particles are three-dimensional while measurements are generally two
conclusion that the most naturally occurring ice particles are of an irregular shape seems to contradict our experience that most snow crystals (either single crystals or the component crystals of snow aggregates) are readily identified as regular types within established crystal classification systems, based on careful stereo microscope observations of snow particles at the ground during the Improvement of Microphysical Parameterization through Observational Verification Experiment from November to
conclusion that the most naturally occurring ice particles are of an irregular shape seems to contradict our experience that most snow crystals (either single crystals or the component crystals of snow aggregates) are readily identified as regular types within established crystal classification systems, based on careful stereo microscope observations of snow particles at the ground during the Improvement of Microphysical Parameterization through Observational Verification Experiment from November to
1. Introduction Ice microphysical processes play important roles in both cloud and precipitation processes (e.g., Koenig and Murray 1976 ; Lin et al. 1983 ; Lord et al. 1984 ; Rutledge and Hobbs 1984 ; Dudhia 1989 ) and climate (e.g., Liou 1976 , 1986 ; Hartmann and Short 1980 ; Ramanathan et al. 1989 ; Ramanathan and Collins 1991 ; Chen et al. 2000 ; Vavrus 2004 ; Waliser et al. 2009 ). As ice particles grow by vapor depositional growth and/or collisions with other particles
1. Introduction Ice microphysical processes play important roles in both cloud and precipitation processes (e.g., Koenig and Murray 1976 ; Lin et al. 1983 ; Lord et al. 1984 ; Rutledge and Hobbs 1984 ; Dudhia 1989 ) and climate (e.g., Liou 1976 , 1986 ; Hartmann and Short 1980 ; Ramanathan et al. 1989 ; Ramanathan and Collins 1991 ; Chen et al. 2000 ; Vavrus 2004 ; Waliser et al. 2009 ). As ice particles grow by vapor depositional growth and/or collisions with other particles
1. Introduction This study addresses the relationship between the optical effective radius r eff and the volume mean radius r vol of ice particles in upper tropospheric ice clouds (cirrus) and aircraft condensation trails (contrails). The effective particle radius is defined such that the extinction coefficient (optical depth) is proportional to the ice water content (IWC) [ice water path (IWP)] divided by the effective radius ( Hansen and Travis 1974 ; Garrett et al. 2003 ). While the
1. Introduction This study addresses the relationship between the optical effective radius r eff and the volume mean radius r vol of ice particles in upper tropospheric ice clouds (cirrus) and aircraft condensation trails (contrails). The effective particle radius is defined such that the extinction coefficient (optical depth) is proportional to the ice water content (IWC) [ice water path (IWP)] divided by the effective radius ( Hansen and Travis 1974 ; Garrett et al. 2003 ). While the
in fall speed led to significant changes in the net global flux divergence. For V m , using the expected range of 25 to 75 cm s −1 , the net flux varied over 8 W m −2 . Iacobellis et al. (2003) found in a 1D-column model that, as particle fall speed increases, the overall cloud fraction, cloud height, and grid-averaged ice water path decreased. The outgoing longwave radiation differed by up to 4 W m −2 over the range of fall speeds they examined. Given that 1 W m −2 converts roughly to 0
in fall speed led to significant changes in the net global flux divergence. For V m , using the expected range of 25 to 75 cm s −1 , the net flux varied over 8 W m −2 . Iacobellis et al. (2003) found in a 1D-column model that, as particle fall speed increases, the overall cloud fraction, cloud height, and grid-averaged ice water path decreased. The outgoing longwave radiation differed by up to 4 W m −2 over the range of fall speeds they examined. Given that 1 W m −2 converts roughly to 0
, respectively) derived a version of the kinetic equation of stochastic condensation for the small-size droplet fraction in liquid clouds. This equation accounts for the nonconservativeness of supersaturation fluctuations and has a solution in the form of a simple gamma distribution for particles with negligible fall velocities. In this paper, the kinetic equation from KC99a is extended to treat both small- and large-size fractions, and conditions relevant to ice clouds. The equation accounts for the
, respectively) derived a version of the kinetic equation of stochastic condensation for the small-size droplet fraction in liquid clouds. This equation accounts for the nonconservativeness of supersaturation fluctuations and has a solution in the form of a simple gamma distribution for particles with negligible fall velocities. In this paper, the kinetic equation from KC99a is extended to treat both small- and large-size fractions, and conditions relevant to ice clouds. The equation accounts for the
improve the models. A key component of ice microphysical modules used for such studies is the assumed ice particle bulk density ρ i or a related quantity, the ice particle mass m . The mass is usually expressed in the form m = aD b , where D is taken to be a representation of particle size and a and b are empirically derived terms; ρ i = m /[( π /6) D 3 ]. It is often assumed for simplicity and computational considerations that ρ i is a constant value: a is constant and b = 3 (e
improve the models. A key component of ice microphysical modules used for such studies is the assumed ice particle bulk density ρ i or a related quantity, the ice particle mass m . The mass is usually expressed in the form m = aD b , where D is taken to be a representation of particle size and a and b are empirically derived terms; ρ i = m /[( π /6) D 3 ]. It is often assumed for simplicity and computational considerations that ρ i is a constant value: a is constant and b = 3 (e
, including ICE-L. Testing this parameterization also provides a means to examine whether ignoring the chemical speciation of ice nuclei introduces large errors. Airborne aerosol, microphysical, and thermodynamic measurements for one particular wave cloud case during ICE-L are used in our study. Here we examine the general equivalence of IN number concentrations, both as measured entering the clouds and from evaporated cloud particles selected by a counterflow virtual impactor (CVI), with ice crystal
, including ICE-L. Testing this parameterization also provides a means to examine whether ignoring the chemical speciation of ice nuclei introduces large errors. Airborne aerosol, microphysical, and thermodynamic measurements for one particular wave cloud case during ICE-L are used in our study. Here we examine the general equivalence of IN number concentrations, both as measured entering the clouds and from evaporated cloud particles selected by a counterflow virtual impactor (CVI), with ice crystal
1. Introduction The sedimentation rates of ice crystals and snowflakes remain poorly characterized at present. Analytical solutions for the terminal velocity υ t of natural ice particles as they fall through the air have not been forthcoming because of their nonspherical shape and the range of flow regimes that they span. Empirical formulas based on experimental data and approximate theory are therefore required to predict how fast an ice particle with a given shape, size, and mass will fall
1. Introduction The sedimentation rates of ice crystals and snowflakes remain poorly characterized at present. Analytical solutions for the terminal velocity υ t of natural ice particles as they fall through the air have not been forthcoming because of their nonspherical shape and the range of flow regimes that they span. Empirical formulas based on experimental data and approximate theory are therefore required to predict how fast an ice particle with a given shape, size, and mass will fall
measure one polarimetric variable [viz., depolarization ratio (DR)], thus limiting a number of potentially retrievable parameters describing particle shapes. It has been shown experimentally with nearly collocated cloud radar and in situ ice particle observations that different types of Ka- and W-band DR values and their elevation angle trends can be used to differentiate among columnar and planar types of crystals and their aggregates (e.g., Matrosov et al. 2001 , 2012 ; Reinking et al. 2002
measure one polarimetric variable [viz., depolarization ratio (DR)], thus limiting a number of potentially retrievable parameters describing particle shapes. It has been shown experimentally with nearly collocated cloud radar and in situ ice particle observations that different types of Ka- and W-band DR values and their elevation angle trends can be used to differentiate among columnar and planar types of crystals and their aggregates (e.g., Matrosov et al. 2001 , 2012 ; Reinking et al. 2002
