Modeling Ice Crystal Aspect Ratio Evolution during Riming: A Single-Particle Growth Model

Jensen, Anders A.; Harrington, Jerry Y.

doi:10.1175/jas-d-14-0297.1

Cited by 58 publications

(75 citation statements)

References 52 publications

(83 reference statements)

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“…In the generation functions and number balances above, gravitational collection kernels are used to describe all processes:

\begin{matrix} K_{br} (t) & = π (ξ_{G} a_{G} {(t)}^{2} + ξ_{g} a_{g} {(t)}^{2}) (v_{t, G} - v_{t, g}) \\ K_{RS, g} (t) & = π (r_{R} {(t)}^{2} + ξ_{g} a_{g} {(t)}^{2}) (v_{t, g} - v_{t, R}) {RS}_{T} \\ K_{RS, G} (t) & = π (r_{R} {(t)}^{2} + ξ_{G} a_{G} {(t)}^{2}) (v_{t, G} - v_{t, R}) {RS}_{T} \\ K_{agg} (t) & = π (ξ_{g} a_{g} {(t)}^{2} + r_{i}^{2}) (v_{t, g} - v_{t, i}) \\ K_{coal} (t) & = π (r_{r}^{2} + r_{d}^{2}) v_{t, r}, \end{matrix}

where ξ is the ratio of actual cross section to that of a circumscribed circle as in Jensen and Harrington [], a is a spheroidal major axis, r is a radius, and v t is the hyd...…”

Section: Modelmentioning

confidence: 99%

Investigating the contribution of secondary ice production to in‐cloud ice crystal numbers

2017

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In‐cloud measurements of ice crystal number concentration can be orders of magnitude higher than the precloud ice nucleating particle number concentration. This disparity may be explained with secondary ice production processes. Several such processes have been proposed, but their relative importance and even the exact physics are not well known. In this work, a six‐hydrometeor‐class parcel model is developed to investigate the ice crystal number enhancement, both its bounds and its value for different cloud states, from rime splintering and breakup upon graupel‐graupel collision. The model also includes ice aggregation and droplet coalescence, ice hydrometeor nonsphericity, and a time delay formulation for hydrometeor growth. Conditions to maximize the breakup contribution, as well as the effects of nonsphericity and turbulence, are discussed. We find that the largest enhancement of ice crystal number occurs for “intermediate” conditions, characterized by moderate updrafts and activation and nucleation rates. In this case, vertical motion is strong enough, and new hydrometeor formation limited enough, to sustain supersaturation as hydrometeors grow to larger sizes. After these larger hydrometeors form at sufficient number concentrations, the ice crystal number can be enhanced by a factor of 104 in some cases relative to the number generated by primary ice nucleation alone. Excluding ice hydrometeor nonsphericity limits secondary production significantly, and the parcel updraft can modulate it by about an order of magnitude.

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“…In the generation functions and number balances above, gravitational collection kernels are used to describe all processes:

\begin{matrix} K_{br} (t) & = π (ξ_{G} a_{G} {(t)}^{2} + ξ_{g} a_{g} {(t)}^{2}) (v_{t, G} - v_{t, g}) \\ K_{RS, g} (t) & = π (r_{R} {(t)}^{2} + ξ_{g} a_{g} {(t)}^{2}) (v_{t, g} - v_{t, R}) {RS}_{T} \\ K_{RS, G} (t) & = π (r_{R} {(t)}^{2} + ξ_{G} a_{G} {(t)}^{2}) (v_{t, G} - v_{t, R}) {RS}_{T} \\ K_{agg} (t) & = π (ξ_{g} a_{g} {(t)}^{2} + r_{i}^{2}) (v_{t, g} - v_{t, i}) \\ K_{coal} (t) & = π (r_{r}^{2} + r_{d}^{2}) v_{t, r}, \end{matrix}

where ξ is the ratio of actual cross section to that of a circumscribed circle as in Jensen and Harrington [], a is a spheroidal major axis, r is a radius, and v t is the hyd...…”

Section: Modelmentioning

confidence: 99%

Investigating the contribution of secondary ice production to in‐cloud ice crystal numbers

2017

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“…Different options of change of particle aspect ratio accompanying the growth by riming in the model use the rime density calculated from empirical formulas (Macklin, 1962;Pflaum and Pruppacher, 1979;Heymsfield and Pflaum, 1985). The following options for the evolution of the area ratio for a given increase of the rimed fraction can be selected in the model: (i) using one of the empirical relations of mass-density-area ratio (by choosing an appropriate relation from the table in Szyrmer et al, 2012 or others), or (ii) obtained by interpolation based on rimed fraction between the values associated with the unrimed particle and graupel (as in Lin and Colle, 2011), or (iii) calculated from the assumed relation of particle geometry between area ratio and aspect ratio (e.g., Avramov et al, 2011;Jensen and Harrington, 2015).…”

Section: Appendix A: Parameterizations Of Riming Efficiency and Physicsmentioning

confidence: 99%

Fingerprints of a riming event on cloud radar Doppler spectra: observations and modeling

Kalesse¹,

Szyrmer²,

Kneifel³

et al. 2016

Atmos. Chem. Phys.

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Abstract. Radar Doppler spectra measurements are exploited to study a riming event when precipitating ice from a seeder cloud sediment through a supercooled liquid water (SLW) layer. The focus is on the "golden sample" case study for this type of analysis based on observations collected during the deployment of the Atmospheric Radiation Measurement Program's (ARM) mobile facility AMF2 at Hyytiälä, Finland, during the Biogenic Aerosols -Effects on Clouds and Climate (BAECC) field campaign. The presented analysis of the height evolution of the radar Doppler spectra is a state-of-the-art retrieval with profiling cloud radars in SLW layers beyond the traditional use of spectral moments. Dynamical effects are considered by following the particle population evolution along slanted tracks that are caused by horizontal advection of the cloud under wind shear conditions. In the SLW layer, the identified liquid peak is used as an air motion tracer to correct the Doppler spectra for vertical air motion and the ice peak is used to study the radar profiles of rimed particles. A 1-D steady-state bin microphysical model is constrained using the SLW and air motion profiles and cloud top radar observations. The observed radar moment profiles of the rimed snow can be simulated reasonably well by the model, but not without making several assumptions about the ice particle concentration and the relative role of deposition and aggregation. This suggests that in situ observations of key ice properties are needed to complement the profiling radar observations before process-oriented studies can effectively evaluate ice microphysical parameterizations.

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“…The increase in dimension due to riming initiates once the ice particle obtains a spherical shape. This method was employed by several models to represent riming (Morrison and Grabowski, 2008;hereafter MG08;Morrison and Grabowski, 2010;Jensen and Harrington, 2015; hereafter JH15; Morrison and Milbrandt, 2015).…”

Section: Characteristics Of Rimingmentioning

confidence: 99%

Growth of ice particle mass and projected area during riming

Erfani

Mitchell

2017

Atmos. Chem. Phys.

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Abstract. There is a long-standing challenge in cloud and climate models to simulate the process of ice particle riming realistically, partly due to the unrealistic parameterization of the growth of ice particle mass (m) and projected area (A) during riming. This study addresses this problem, utilizing ground-based measurements of m and ice particle maximum dimension (D) as well as theory to formulate simple expressions describing the dependence of m and A on riming. It was observed that β in the m − D power law m = α D β appears independent of riming during the phase 1 (before the formation of graupel), with α accounting for the ice particle mass increase due to riming. This semi-empirical approach accounts for the degree of riming and renders a gradual and smooth ice particle growth process from unrimed ice particles to graupel, and thus avoids discontinuities in m and A during accretional growth. Once the graupel with quasispherical shape forms, D increases with an increase in m and A (phase 2 of riming). The treatment for riming is explicit, and includes the parameterization of the ice crystal-cloud droplet collision efficiency (E c ) for hexagonal columns and plates using hydrodynamic theory. In particular, E c for cloud droplet diameters less than 10 µm are estimated, and under some conditions observed in mixed-phase clouds, these droplets can account for roughly half of the mass growth rate from riming. These physically meaningful yet simple methods can be used in models to improve the riming process.

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Modeling Ice Crystal Aspect Ratio Evolution during Riming: A Single-Particle Growth Model

Cited by 58 publications

References 52 publications

Investigating the contribution of secondary ice production to in‐cloud ice crystal numbers

Investigating the contribution of secondary ice production to in‐cloud ice crystal numbers

Fingerprints of a riming event on cloud radar Doppler spectra: observations and modeling

Growth of ice particle mass and projected area during riming

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