A Large-Droplet Mode and Prognostic Number Concentration of Cloud Droplets in the Colorado State University Regional Atmospheric Modeling System (RAMS). Part I: Module Descriptions and Supercell…

By S. Saleeby, William Cotton. 2004

300 citations recorded by the source corpus.

Subfields: Atmospheric Science, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/fcd3817adfb37cb697ef710944e5f99705479edf

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Citations in the corpus

Aerosol Indirect Effects on Tropical Convection Characteristics under Conditions of Radiative-Convective Equilibrium (2011, methodology): “1997) Aerosol scheme (Saleeby and Cotton 2004) Vapor, cloud water, rain, pristine ice, snow, aggregates, graupel, and hail activated Convective initiation No cumulus parameterization; convection resolved Boundary conditions 1) Periodic lateral boundaries 2) Fixed lower oceanic boundary (SST 5 300 K)”
Comparison of Evaporation and Cold Pool Development between Single-Moment and Multimoment Bulk Microphysics Schemes in Idealized Simulations of Tornadic Thunderstorms (2010, methodology): “For completeness, we point out that some BMPs try to emulate a bin model for specific processes through the use of lookup tables (Saleeby and Cotton 2004).”
Developments in the CSU-RAMS Aerosol Model: Emissions, Nucleation, Regeneration, Deposition, and Radiation (2013, unknown): “(4) A distribution width of s g 5 1.8 was assumed for the aerosol spectra used in the creation of the nucleation lookup tables (similar to Saleeby and Cotton 2004a).”
Representation of microphysical processes in cloud‐resolving models: Spectral (bin) microphysics versus bulk parameterization (2015, methodology): “As described above, a Lagrangian parcel model is initially used to perform offline bin calculations, and the critical CCN radius and the size of the nucleated drops are determined using the Köhler theory [Saleeby and Cotton, 2004].”
Modeling Aerosol Impacts on Convective Storms in Different Environments (2009, background): “The number of CCN that are activated to form cloud droplets is determined by an activation function that depends on vertical velocity, temperature, water supersaturation, and aerosol properties (Saleeby and Cotton 2004).”
Modeling Supersaturation and Subgrid-Scale Mixing with Two-Moment Bulk Warm Microphysics (2008, methodology): “, Khairoutdinov and Kogan 2000; MG07) or parameterize the number of nucleated droplets as a function of vertical velocity and other aerosol and thermodynamic parameters (e.g., Twomey 1959; Cohard et al. 1998; Saleeby and Cotton 2004).”
A Novel Approach for Representing Ice Microphysics in Models: Description and Tests Using a Kinematic Framework (2007, background): “More detailed two-moment schemes were subsequently developed that predict both mixing ratio and number concentration of cloud droplets and drizzle/rain (e.g., Khairoutdinov and Kogan 2000; Seifert and Beheng 2001; Saleeby and Cotton 2004; Morrison et al. 2005).”
Aerosol Effects on Intensity of Landfalling Hurricanes as Seen from Simulations with the WRF Model with Spectral Bin Microphysics (2010, methodology): “Recent versions of the scheme are described in detail by Saleeby and Cotton (2004, 2008). important in maritime clouds where supersaturation is high and often increases with height because of a decrease in droplet concentration and increase in vertical velocity with height.”
Nucleation processes in deep convection simulated by a cloud-system-resolving model with double-moment bulk microphysics (2007, background): “…schemes do apply a doublemoment representation to both of these species (e.g., Levkov et al. 1992; Wang and Chang 1993; Girard and Curry 2001; Cotton et al. 2003; Saleeby and Cotton 2004; Morrison et al. 2005; Seifert and Beheng 2005), but the present scheme differs from these in ways noted above.”
A modeling study of the response of tropical deep convection to the increase of cloud condensation nuclei concentration: 1. Dynamics and microphysics (2005, background): “At the cloud base, the overall conversion efficiency from CCN to cloud droplets, defined as a ratio between the p mean of CDNC and CCN0, is shown to decrease with CCN0 (Figure 1; see Saleeby and Cotton [2004] ).”
The dependence of aerosol effects on clouds and precipitation on cloud‐system organization, shear and stability (2008, background): “…issues related to application of bin schemes to cloud-system resolving models (CSRMs) with coarser spatial resolutions used for mesoscale studies (e.g., droplet nucleation [cf. Saleeby and Cotton, 2004], and the impact of entrainment and mixing on cloud droplet spectra [cf. Grabowski, 2006]).”