Revision and clarification of “A general hydrodynamic theory for mixed-phase microphysics”

Böhm, Johannes Peter

doi:10.1016/s0169-8095(99)00033-2

Cited by 26 publications

(19 citation statements)

References 7 publications

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“…Here,

E_{coll}

is the collision efficiency, which we adopt from a series of papers by Böhm (, 1992b, 1992c, ) and scale it with a sticking efficiency

S_{eff}

of ice depending solely on the temperature of the surrounding air (see Pruppacher & Klett, , section 16.2):

S_{eff} = true{\begin{array}{l} left & 0.10, & T \in [- 6, - 9] \cup [- 4, 0]^{°} C; \\ left & 0.40, & T \in [- 20, - 17] \cup [- 12.5, - 9]^{°} C; \\ left & 0.60, & T \in [- 9, - 6]^{°} C; \\ left & 1.00, & T \in [- 17, - 12.5]^{°} C; \\ left & 0.25, & T \in [- 60, - 20]^{°} C . \end{array}

…”

Section: Model Description and Notion Of A Super Particlementioning

confidence: 99%

McSnow: A Monte‐Carlo Particle Model for Riming and Aggregation of Ice Particles in a Multidimensional Microphysical Phase Space

Brdar

Seifert

2018

J Adv Model Earth Syst

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We present a novel Monte‐Carlo ice microphysics model, McSnow, to simulate the evolution of ice particles due to deposition, aggregation, riming, and sedimentation. The model is an application and extension of the super‐droplet method of Shima et al. (2009) to the more complex problem of rimed ice particles and aggregates. For each individual super‐particle, the ice mass, rime mass, rime volume, and the number of monomers are predicted establishing a four‐dimensional particle‐size distribution. The sensitivity of the model to various assumptions is discussed based on box model and one‐dimensional simulations. We show that the Monte‐Carlo method provides a feasible approach to tackle this high‐dimensional problem. The largest uncertainty seems to be related to the treatment of the riming processes. This calls for additional field and laboratory measurements of partially rimed snowflakes.

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“…Here,

E_{coll}

is the collision efficiency, which we adopt from a series of papers by Böhm (, 1992b, 1992c, ) and scale it with a sticking efficiency

S_{eff}

of ice depending solely on the temperature of the surrounding air (see Pruppacher & Klett, , section 16.2):

S_{eff} = true{\begin{array}{l} left & 0.10, & T \in [- 6, - 9] \cup [- 4, 0]^{°} C; \\ left & 0.40, & T \in [- 20, - 17] \cup [- 12.5, - 9]^{°} C; \\ left & 0.60, & T \in [- 9, - 6]^{°} C; \\ left & 1.00, & T \in [- 17, - 12.5]^{°} C; \\ left & 0.25, & T \in [- 60, - 20]^{°} C . \end{array}

…”

Section: Model Description and Notion Of A Super Particlementioning

confidence: 99%

McSnow: A Monte‐Carlo Particle Model for Riming and Aggregation of Ice Particles in a Multidimensional Microphysical Phase Space

Brdar

Seifert

2018

J Adv Model Earth Syst

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“…DHARMA uses an integrated treatment of cloud particle fall speeds and gravitational collection rates [ Böhm 1989, 1992a, 1992b, 1992c, 1994, 1999, 2004]. In this treatment, fall speeds and collection rates of particles with arbitrary shapes are based on four parameters: particle mass, maximum dimension, maximum projected area and aspect ratio.…”

Section: Model Descriptionmentioning

confidence: 99%

“…Collision efficiencies for all combinations (liquid‐liquid, liquid‐ice, ice‐ice) are calculated following Böhm [1999]. We use size‐dependent coalescence efficiencies for water droplets [ Beard and Ochs , 1984].…”

Section: Model Descriptionmentioning

confidence: 99%

Toward ice formation closure in Arctic mixed-phase boundary layer clouds during ISDAC

et al. 2011

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[1] A modeling study of a low-lying mixed-phase cloud layer observed on 8 April 2008 during the Indirect and Semi-Direct Aerosol Campaign is presented. Large-eddy simulations with size-resolved microphysics were used to test the hypothesis that heterogeneous ice nucleus (IN) concentrations measured above cloud top can account for observed ice concentrations, while also matching ice size distributions, radar reflectivities, and mean Doppler velocities. The conditions for the case are favorable for the hypothesis: springtime IN concentrations are high in the Arctic, the predominant ice habit falls slowly, and overlying IN concentrations were greater than ice particle number concentrations. Based on particle imagery, we considered two dendrite types, broad armed (high density) and stellar (low density), in addition to high and low density aggregates. Two simulations with low-density aggregates reproduced observations best overall: one in which IN concentrations aloft were increased fourfold (as could have been present above water saturation) and another in which initial IN concentrations were vertically uniform. A key aspect of the latter was an IN reservoir under the well-mixed cloud layer: as the simulations progressed, the reservoir IN slowly mixed upward, helping to maintain ice concentrations close to those observed. Given the uncertainties of the measurements and parameterizations of the microphysical processes embedded in the model, we found agreement between simulated and measured ice number concentrations in most of the simulations, in contrast with previous modeling studies of Arctic mixed-phase clouds, which typically show a large discrepancy when IN are treated prognostically and constrained by measurements.

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“…DHARMA consists of a large‐eddy simulation (LES) dynamical core [ Stevens et al , 2002] with size‐resolved bin microphysics [ Ackerman et al , 1995; Fridlind et al , 2007] and a two‐stream, 44 wavelength radiative transfer model [ Toon et al , 1989]. The latest addition to the model, which makes it very suitable for mixed‐phase cloud simulations, is an integrated treatment of cloud particle fall speeds and gravitational collection rates [ Böhm 1989, 1992a, 1992b, 1992c, 1994, 1999, 2004]. The simulations analyzed here are described in more detail by Avramov et al [2011, section 5].…”

Section: Description Of Radar Measurements and Cloud Modelmentioning

confidence: 99%

“…The various microphysical processes transfer mass between bins at a specified grid‐volume, while advection and sedimentation of hydrometeors move mass from grid‐volume to grid‐volume. The hydrometeor sedimentation velocity depends on the particle's mass, maximum dimension, cross‐sectional area, and aspect ratio [ Böhm , 1992a, 1999]. The characteristic cross‐sectional area for each mass bin is diagnosed from a specified area‐dimensional relationship, where the (maximum) dimension is determined from a mass‐dimensional relationship.…”

Section: Description Of Radar Measurements and Cloud Modelmentioning

confidence: 99%

Millimeter wave scattering from ice crystals and their aggregates: Comparing cloud model simulations with X- and Ka-band radar measurements

et al. 2011

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[1] Arctic clouds are often mixed-phase, such that the radiative properties of the clouds are a strong function of the relative amounts of cloud liquid and ice. Modeling studies have shown that the poorly understood ice phase processes are the regulators of the liquid water fraction. However, evaluating the fidelity of the model ice parameterizations has proven to be a difficult task. This study evaluates results of different ice microphysics representations in a cloud resolving model (CRM) using cloud radar measurements. An algorithm is presented to generate realistic ice crystals and their aggregates from which radar backscattering cross sections may be calculated using a generalized solution for a cluster of spheres. The aggregate is composed of a collection of ice crystals, each of which is constructed from a cluster of tiny ice spheres. Each aggregate satisfies the constraints set by the component crystal type and the mass-dimensional relationship used in the cloud resolving model, but is free to adjust its aspect ratio. This model for calculating radar backscattering is compared to two spherical and two spheroidal (bulk model) representations for ice hydrometeors. It was found that a refined model for representing the ice hydrometeors, both pristine crystals and their aggregates, is required in order to obtain good comparisons between the CRM calculations and the radar measurements. The addition of the radar-CRM comparisons to CRM-in situ measurements comparisons allowed conclusions about the appropriateness of different CRM ice microphysics parameterizations.

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Revision and clarification of “A general hydrodynamic theory for mixed-phase microphysics”

Cited by 26 publications

References 7 publications

McSnow: A Monte‐Carlo Particle Model for Riming and Aggregation of Ice Particles in a Multidimensional Microphysical Phase Space

McSnow: A Monte‐Carlo Particle Model for Riming and Aggregation of Ice Particles in a Multidimensional Microphysical Phase Space

Toward ice formation closure in Arctic mixed-phase boundary layer clouds during ISDAC

Millimeter wave scattering from ice crystals and their aggregates: Comparing cloud model simulations with X- and Ka-band radar measurements

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