A Multimoment Bulk Microphysics Parameterization. Part II: A Proposed Three-Moment Closure and Scheme Description

By Jason A. Milbrandt, M. Yau. 2005

558 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/2da38a47aa7e984a7251087e8ba2914c7ddf143c

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Citations in the corpus

Parameterization of Cloud Microphysics Based on the Prediction of Bulk Ice Particle Properties. Part III: Introduction of Multiple Free Categories (2016, background): “…microphysics away from the continued addition of predefined categories and toward providing more predictive information of the particle properties of the existing (Hashino and Tripoli 2007; Grabowski and Morrison 2008; Mansell et al. 2010; Milbrandt and Morrison 2013; Harrington et al. 2013).”
Comparison of Evaporation and Cold Pool Development between Single-Moment and Multimoment Bulk Microphysics Schemes in Idealized Simulations of Tornadic Thunderstorms (2010, methodology): “The distribution has also been applied to other types of hydrometeors, including cloud and various ice categories (Walko et al. 1995; Meyers et al. 1997; Milbrandt and Yau 2005a, hereafter MY05a; Milbrandt and Yau 2005b, hereafter MY05b).”
Simulated Electrification of a Small Thunderstorm with Two-Moment Bulk Microphysics (2010, methodology): “In previous two-moment schemes (e.g., Ziegler 1985; Ferrier et al. 1995), number concentration fallout was calculated with the mass-weighted fall speed because of problems that can arise when mass falls faster than number concentration (Wacker and Seifert 2001; Milbrandt and Yau 2005a).”
A New Visibility Parameterization for Warm-Fog Applications in Numerical Weather Prediction Models (2006, background): “In the near future, a new three-moment bulk microphysical scheme developed by Milbrandt and Yau (2005), which includes independent predictive equations for the number concentration, mass content, and spectral dispersion for six different hydrometeor categories, will be tested in the GEM model…”
Representation of microphysical processes in cloud‐resolving models: Spectral (bin) microphysics versus bulk parameterization (2015, methodology): “These parameterizations differ in regard to their assumptions concerning the initial DSD shape and in the use of different definitions of the characteristic time scale of the formation of the first raindrops [Cohard and Pinty, 2000; Milbrandt and Yau, 2005a, 2005b].”
On the Parameterization of Evaporation of Raindrops as Simulated by a One-Dimensional Rainshaft Model (2008, background): “Khairoutdinov and Kogan (2000), Milbrandt and Yau (2005b), and Morrison and Grabowski (2007) assume that 1; that is, that the mean volume diameter does not change during evaporation.”
Sensitivity of a Simulated Squall Line to Horizontal Resolution and Parameterization of Microphysics (2012, unknown): “In terms of organized deep convection, key processes that are impacted by using two-moment versus one-moment schemes include particle size sorting and rain evaporation (Milbrandt and Yau 2005b; Morrison et al. 2009; Luo et al. 2010).”
Parameterization of Cloud Microphysics Based on the Prediction of Bulk Ice Particle Properties. Part II: Case Study Comparisons with Observations and Other Schemes (2015, methodology): “…retrieved and modeled precipitation rates for MOR-H, P3, and the sensitivity tests using these schemes with modiﬁed fall speed, MOR-H-F and P3-F, respectively. which are known to result in excessive size sorting (Wacker and Seifert 2001; Milbrandt and Yau 2005a; Milbrandt and McTaggart-Cowan 2010).”
The Non-hydrostatic Icosahedral Atmospheric Model: description and development (2014, methodology): “The triple-moment bulk method, which was recently developed and used (Milbrandt and Yau 2005a, b; Seifert 2008; Shipway and Hill 2012), predicts or diagnoses the radar reflectivity factor in addition to the aforementioned two moments and is applied to the regional simulations of severe storms…”
Comparison of Two-Moment Bulk Microphysics Schemes in Idealized Supercell Thunderstorm Simulations (2011, background): “…and total number concentration of at least one species was predicted (e.g., Ziegler 1985; Cotton et al. 1986; Murakami 1990; Ferrier 1994; Meyers et al. 1997; Reisner et al. 1998; Cohard and Pinty 2000; Seifert and Beheng 2001; Milbrandt and Yau 2005b; Morrison et al. 2005; Phillips et al. 2007).”
Polarimetric Radar Characteristics of Melting Hail. Part I: Theoretical Simulations Using Spectral Microphysical Modeling (2013, methodology): “We parameterized the shed raindrop size distribution (DSD) as a gamma function, N(D)5N0,shD m exp(2LshD) , (4) with spectral shape parameter m 5 2.0 [as in Milbrandt and Yau (2005)] and the slope parameter Lsh 5 2.0mm21.”
Confronting the Challenge of Modeling Cloud and Precipitation Microphysics (2020, background): “More recently, this idea was extended to three‐moment bulk schemes that predict three quantities, typically number, mass, and radar reflectivity factor (e.g., Loftus et al., 2014; Milbrandt & Yau, 2005b; Naumann & Seifert, 2016; Paukert et al., 2018; Shipway & Hill, 2012).”
Parameterization of Cloud Microphysics Based on the Prediction of Bulk Ice Particle Properties. Part I: Scheme Description and Idealized Tests (2015, background): “The simulation of microphysical processes and sedimentation for a given category is generally improved with the two-moment approach (Ferrier 1994; Milbrandt and McTaggart-Cowan 2010; Dawson et al. 2010).”
A New Bulk Microphysical Scheme That Includes Riming Intensity and Temperature-Dependent Ice Characteristics (2011, background): “Recent work by Milbrandt et al. (2010) showed that a single-moment version of the multimoment scheme of Milbrandt and Yau (2005) reduced the snow aloft compared with the two-moment version for the 13–14 December IMPROVE-2 event (Garvert et al. 2005a,b; Colle et al. 2005).”
Intercomparison of large‐eddy simulations of Arctic mixed‐phase clouds: Importance of ice size distribution assumptions (2014, unknown): “A robust solution to the problem is through the development of a method to predict or diagnose a measure of the relative width of the ice spectrum using a combination of in situ and remote sensing observations and modeling [Milbrandt and Yau, 2005].”