A moment model for simulating raindrop scavenging of aerosols

Jung, Chang Hoon; Kim, Yong P.; Lee, K.W.

doi:10.1016/s0021-8502(03)00098-3

Cited by 48 publications

(38 citation statements)

References 14 publications

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“…The first type (hereafter referred to as Type I) calculates based on analytical formulas for raindropparticle collection efficiency, raindrop size distribution, and raindrop terminal velocity (e.g., Slinn, 1983;Mircea et al, 2000;Chate et al, 2003;Chate, 2005;Andronache, 2003;Andronache et al, 2006;Jung et al, 2003;Gong et al 2003;Loosmore and Cederwall, 2004;Park et al, 2005;Tost et al, 2006;Feng, 2007;Croft et al, 2009). Sections 2 and 3 dealt with this first type of size-resolved parameterization.…”

Section: Evaluation Of Existing Theoretical and Empirical Size-resolvmentioning

confidence: 99%

Uncertainty assessment of current size-resolved parameterizations for below-cloud particle scavenging by rain

Wang¹,

2010

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Abstract. Current theoretical and empirical size-resolved parameterizations of the scavenging coefficient ( ), a parameter commonly used in aerosol transport models to describe below-cloud particle scavenging by rain, have been reviewed in detail and compared with available field and laboratory measurements. Use of different formulations for raindropparticle collection efficiency can cause uncertainties in sizeresolved values of one to two orders of magnitude for particles in the 0.01-3 µm diameter range. Use of different formulations of raindrop number size distribution can cause values to vary by a factor of 3 to 5 for all particle sizes. The uncertainty in caused by the use of different droplet terminal velocity formulations is generally small than a factor of 2. The combined uncertainty due to the use of different formulations of raindrop-particle collection efficiency, raindrop size spectrum, and raindrop terminal velocity in the current theoretical framework is not sufficient to explain the one to two order of magnitude under-prediction of for the theoretical calculations relative to the majority of field measurements. These large discrepancies are likely caused by additional known physical processes (i.e, turbulent transport and mixing, cloud and aerosol microphysics) that influence field data but that are not considered in current theoretical parameterizations. The predicted size-resolved particle concentrations using different theoretical parameterization can differ by up to a factor of 2 for particles smaller than 0.01 µm and by a factor of >10 for particles larger than 3 µm after 2-5 mm of rain. The predicted bulk mass and number concentrations (integrated over the particle size distribution) can differ by a factor of 2 between theoretical and empirical parameterizations after 2-5 mm of moderate intensity rainfall.

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Section: Evaluation Of Existing Theoretical and Empirical Size-resolvmentioning

confidence: 99%

Uncertainty assessment of current size-resolved parameterizations for below-cloud particle scavenging by rain

Wang¹,

2010

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“…considering polydisperse AP and raindrops at each gridpoint of 3D domains 42 of simulation implies double integrations of the BCS rates over the two size 43 distributions (Mircea et al, 2000;Andronache, 2003;Jung et al, 2003). For 44 this reason and because of the vanishing part of the distributions, a special 45 attention needs to be drawn upon computation efficiency and accuracy, as 46 stressed by Sportisse (2007).…”

mentioning

confidence: 99%

Scavenging of aerosol particles by rain in a cloud resolving model

Berthet

Leriche

Pinty

et al. 2010

Atmospheric Research

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“…Moments (Binkowski and Shankar, 1995) 0, 3, 6 (Pirjola et al, 1999) 0, 2, 3 (Jung et al, 2003) 0, 2, 3 (Koziol and Leighton, 2007) 0, 1, 2 (Bae et al, 2009) 0, 3, 6 The recurrent usage of zeroth order moment brings substantial advantage, as it is equal to the total number concentration, and it is the order we will use as well. On the choice of the other moments authors differ.…”

Section: Referencementioning

confidence: 99%

“…Binkowski and Shankar, 1995;Pirjola et al, 1999;Jung et al, 2003). Adapting the deposition velocity models to the moment method framework is not straightforward, since the mathematical formulation of the moment method requires all terms in the equation to be in the form of the power law of the particle size.…”

Section: Introductionmentioning

confidence: 99%

Dry deposition model for a microscale aerosol dispersion solver based on the moment method

Šíp

Beneš

2017

Journal of Aerosol Science

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A dry deposition model suitable for use in the moment method has been developed. Contributions from five main processes driving the deposition -Brownian diffusion, interception, impaction, turbulent impaction, and sedimentation -are included in the model. The deposition model was employed in the moment method solver implemented in the OpenFOAM framework. Applicability of the developed expression and the moment method solver was tested on two example problems of particle dispersion in the presence of a vegetation on small scales: a flow through a tree patch in 2D and a flow through a hedgerow in 3D. Comparison with the sectional method showed that the moment method using the developed deposition model is able to reproduce the shape of the particle size distribution well. The relative difference in terms of the third moment of the distribution was below 10% in both tested cases, and decreased away from the vegetation. Main source of this difference is a known overprediction of the impaction efficiency. When tested on the 3D test case, the moment method achieved approximately eightfold acceleration compared to the sectional method using 41 bins.

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A moment model for simulating raindrop scavenging of aerosols

Cited by 48 publications

References 14 publications

Uncertainty assessment of current size-resolved parameterizations for below-cloud particle scavenging by rain

Uncertainty assessment of current size-resolved parameterizations for below-cloud particle scavenging by rain

Scavenging of aerosol particles by rain in a cloud resolving model

Dry deposition model for a microscale aerosol dispersion solver based on the moment method

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