Vapor Diffusional Growth of Free-Falling Snow Crystals between -3 and -23&amp;deg;C

Takahashi, Tomoki; Endoh, Tatsuo; Wakahama, Gorow; Fukuta, N.

doi:10.2151/jmsj1965.69.1_15

Cited by 144 publications

(151 citation statements)

References 22 publications

(31 reference statements)

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“…The same subfreezing temperature at low latitudes corresponds to a higher height above mean sea level and therefore a lower atmospheric pressure level than mid-and high latitudes. Takahashi et al (1991) show that the mass growth rate at 860 mb is approximately 30 % larger than at 1010 mb due to the impact of pressure difference on the diffusivity of water vapor in air. It is also noted that, from Fig.…”

Section: Resultsmentioning

confidence: 91%

Ice particle production in mid-level stratiform mixed-phase clouds observed with collocated A-Train measurements

et al. 2018

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Abstract. Collocated A-Train CloudSat radar and CALIPSO lidar measurements between 2006 and 2010 are analyzed to study primary ice particle production characteristics in midlevel stratiform mixed-phase clouds on a global scale. For similar clouds in terms of cloud top temperature and liquid water path, Northern Hemisphere latitude bands have layer-maximum radar reflectivity (ZL) that is ∼ 1 to 8 dBZ larger than their counterparts in the Southern Hemisphere. The systematically larger ZL under similar cloud conditions suggests larger ice number concentrations in mid-level stratiform mixed-phase clouds over the Northern Hemisphere, which is possibly related to higher background aerosol loadings. Furthermore, we show that springtime northern midand high latitudes have ZL that is larger by up to 6 dBZ (a factor of 4 higher ice number concentration) than other seasons, which might be related to more dust events that provide effective ice nucleating particles. Our study suggests that aerosol-dependent ice number concentration parameterizations are required in climate models to improve mixedphase cloud simulations, especially over the Northern Hemisphere.

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Section: Resultsmentioning

confidence: 91%

Ice particle production in mid-level stratiform mixed-phase clouds observed with collocated A-Train measurements

et al. 2018

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“…The type of ice crystals is determined depending on temperature range where the particles arise (Takahashi et al, 1991). Secondary ice generation is taken into account during riming (Hallett and Mossop, 1974).…”

Section: Model Descriptionmentioning

confidence: 99%

Application of a new scheme of cloud base droplet nucleation in a spectral (bin) microphysics cloud model: sensitivity to aerosol size distribution

Ilotoviz

Хаин

2016

Atmos. Chem. Phys.

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Abstract. A new scheme of droplet nucleation at cloud base is implemented into the Hebrew University Cloud Model (HUCM) with spectral (bin) microphysics. In this scheme, supersaturation maximum S max near cloud base is calculated using theoretical results according to which, where w is the vertical velocity at cloud base and N d is droplet concentration. Microphysical cloud structure obtained in the simulations of a midlatitude hail storm using the new scheme is compared with that obtained in the standard approach, in which droplet nucleation is calculated using supersaturation calculated in grid points. The simulations were performed with different concentrations of cloud condensational nuclei (CCN) and with different shapes of CCN size spectra. It is shown that the new nucleation scheme substantially improves the vertical profile of droplet concentration shifting the concentration maximum to cloud base. It is shown that the effect of the CCN size distribution shape on cloud microphysics is not less important than the effect of the total CCN concentration. It is shown that the smallest CCN with diameters less than about 0.015 µm have a substantial effect on mixed-phase and ice microphysics of deep convective clouds. Such CCN are not measured by standard CCN probes, which hinders understanding of cold microphysical processes.

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“…At higher temperatures, might differ significantly from unity and strongly impact the evolution of the aspect ratio (ϕ = c/a) of an ice particle that develops from the initial geometry, prescribed by , towards a geometry as predicted at a given temperature. Such more extreme habits result in highly nonlinear growth rates of ice particles in different temperature regimes since non-spherical particles grow faster than spheres of the same mass (Mason, 1953;Takahashi et al, 1991). Atmospheric observations reveal that ice is sometimes formed at habit-prone temperatures and model studies have shown the need of the detailed description of ice growth rates as they significantly affect the phase partitioning in mixed-phase clouds (Avramov and Harrington, 2010;.…”

Section: Temperature Dependence Of Ice Habit Formationmentioning

confidence: 99%

On the representation of immersion and condensation freezing in cloud models using different nucleation schemes

Ervens

Feingold

2012

Atmos. Chem. Phys.

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Abstract. Ice nucleation in clouds is often observed at temperatures > 235 K, pointing to heterogeneous freezing as a predominant mechanism. Many models deterministically predict the number concentration of ice particles as a function of temperature and/or supersaturation. Several laboratory experiments, at constant temperature and/or supersaturation, report heterogeneous freezing as a stochastic, timedependent process that follows classical nucleation theory; this might appear to contradict deterministic models that predict singular freezing behavior.We explore the extent to which the choice of nucleation scheme (deterministic/stochastic, single/multiple contact angles θ) affects the prediction of the fraction of frozen ice nuclei (IN) and cloud evolution for a predetermined maximum IN concentration. A box model with constant temperature and supersaturation is used to mimic published laboratory experiments of immersion freezing of monodisperse (800 nm) kaolinite particles (∼ 243 K), and the fitness of different nucleation schemes. Sensitivity studies show that agreement of all five schemes is restricted to the narrow parameter range (time, temperature, IN diameter) in the original laboratory studies, and that model results diverge for a wider range of conditions.The schemes are implemented in an adiabatic parcel model that includes feedbacks of the formation and growth of drops and ice particles on supersaturation during ascent. Model results for the monodisperse IN population (800 nm) show that these feedbacks limit ice nucleation events, often leading to smaller differences in number concentration of ice particles and ice water content (IWC) between stochastic and deterministic approaches than expected from the box model studies. However, because the different parameterizations of θ distributions and time-dependencies are highly sensitive to IN size, simulations using polydisperse IN result in great differences in predicted ice number concentrations and IWC between the different schemes. The differences in IWC are mostly due to the different temperatures of the onset of freezing in the nucleation schemes that affect the temporal evolution of the ice number concentration. The growth rates of ice particles are not affected by the choice of the nucleation scheme, which leads to very similar particle sizes. Finally, since the choice of nucleation scheme determines the temperature range over which ice nucleation occurs, at habit-prone temperatures (∼ 253 K), there is the potential for variability in the initial inherent growth ratio of ice particles, which can cause amplification or reduction in differences in predicted IWC.

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Vapor Diffusional Growth of Free-Falling Snow Crystals between -3 and -23°C

Cited by 144 publications

References 22 publications

Ice particle production in mid-level stratiform mixed-phase clouds observed with collocated A-Train measurements

Ice particle production in mid-level stratiform mixed-phase clouds observed with collocated A-Train measurements

Application of a new scheme of cloud base droplet nucleation in a spectral (bin) microphysics cloud model: sensitivity to aerosol size distribution

On the representation of immersion and condensation freezing in cloud models using different nucleation schemes

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