A two-moment cloud microphysics parameterization for mixed-phase clouds. Part 1: Model description

By A. Seifert, Klaus D. Beheng. 2006

776 citations recorded by the source corpus.

Subfields: Atmospheric Science, Earth-Surface Processes, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/70c458fda6f6b5b0b4686b257b594e9d061d71b5

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Citations in the corpus

Aerosol-cloud-precipitation effects over Germany as simulated by a convective-scale numerical weather prediction model (2011, methodology): “Seifert, A., Khain, A., Pokrovsky, A., and Beheng, K. D.: A comparison of spectral bin and two-moment bulk mixed-phase microphysics, Atmos.”
The Formation of Wider and Deeper Clouds as a Result of Cold-Pool Dynamics (2014, methodology): “A full two-moment ice microphysics scheme including ice, snow, graupel, and hail as cold species as developed by Seifert and Beheng (2006) is used.”
An improved representation of the raindrop size distribution for single‐moment microphysics schemes (2012, background): “…is typically represented with either a gamma distribution (Ferrier, 1994; Morrison et al., 2005) or an exponential distribution (Lin et al., 1983; Rutledge and Hobbs, 1983; Fowler et al., 1996; Wilson and Ballard, 1999; Seifert and Beheng, 2006; Morrison and Gettelman, 2008; Thompson et al., 2008).”
The Non-hydrostatic Icosahedral Atmospheric Model: description and development (2014, methodology): “Growth equations of hydrometeor particles with similar radii are solved together and the particle size distribution (PSD) f (r), which is the number density sorted by the radius of the hydrometeors, is then predicted.”
Overview of the PALM model system 6.0 (2019, methodology): “In PALM 4.0, the bulk liquid-phase (i.e., no ice) two-moment microphysics scheme of Seifert and Beheng (2001, 2006) was implemented, which only predicts the rain droplet number concentration (nr) and rainwater mixing ratio (qr).”
Representation of microphysical processes in cloud‐resolving models: Spectral (bin) microphysics versus bulk parameterization (2015, methodology): “…time evolution of the moments of PSDs of collecting particles M kð Þx , collected particles M kð Þ y , and resulting hydrometeor M kð Þ z can be written as [Seifert and Beheng, 2006a] ∂M kð Þx ∂t xþy >z ¼ ∫ ∞ 0 ∫ ∞ 0 Kxyf x mxð Þf y my mkxdmxdmy (18) ∂M kð Þy ∂t ! xþy >z ¼ ∫ ∞ 0 ∫ ∞ 0 Kxyf x mxð…”
Comparison of Evaporation and Cold Pool Development between Single-Moment and Multimoment Bulk Microphysics Schemes in Idealized Simulations of Tornadic Thunderstorms (2010, background): “Recently, MM schemes have enjoyed increasing popularity in cloud and storm modeling (Ferrier et al. 1995; Meyers et al. 1997; Reisner et al. 1998; Seifert and Beheng 2001; Seifert and Beheng 2006; Seifert et al. 2006; Mansell 2008; Morrison et al. 2009).”
A large‐eddy model for cirrus clouds with explicit aerosol and ice microphysics and Lagrangian ice particle tracking (2010, methodology): “More recently, two-moment schemes based on ice water content and total ice crystal number density have been developed and are being used in LESs or mesoscale models (Levkov et al., 1992; Seifert and Beheng, 2006; Morrison and Grabowski, 2008; Spichtinger and Gierens, 2009).”
Can deep learning beat numerical weather prediction? (2021, background): “…diurnal cycle of convection (e.g. [36,37]), the turbulent transports in the planetary boundary layer (e.g. [38]) and in simulating the bulk properties of hydrometeors (e.g. [39]), i.e. clouds and precipitation, are only a small sample of recent progress in tuning physical parameterization schemes.”