Precipitation Uncertainty Due to Variations in Precipitation Particle Parameters within a Simple Microphysics Scheme

By M. Gilmore, Jerry M. Straka, E. Rasmussen. 2004

302 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/73eb5151fd15257f96611bf0bfd1f81f45081eed

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Citations in the corpus

The Impact of Hail Size on Simulated Supercell Storms (2004, background): “Experiments designed to investigate the sensitivity to including the ice phase in mesoscale models have demonstrated significant impacts on both the dynamic and precipitation characteristics of simulated deep convective storms (e.g., Chen and Cotton 1988; Jewett et al. 1990; Johnson et al. 1993; Tar”
Effects of Upper-Level Shear on the Structure and Maintenance of Strong Quasi-Linear Mesoscale Convective Systems (2006, background): “In addition to liquid water processes, ice processes are represented with a three-class (ice, snow, graupel/hail) scheme (see Gilmore et al. 2004a).”
Comparison of Two-Moment Bulk Microphysics Schemes in Idealized Supercell Thunderstorm Simulations (2011, methodology): “The larger surface precipitation rates in the hail-only runs are broadly consistent with the results of Gilmore et al. (2004) using a single rimed-ice category one-moment BMS, where N0 and the bulk particle density were varied.”
Ensemble Kalman Filter Assimilation of Radar Observations of the 8 May 2003 Oklahoma City Supercell: Influences of Reflectivity Observations on Storm-Scale Analyses (2011, methodology): “We use the default Lin et al. (1983) and Gilmore et al. (2004a) parameters: rh 5 900 kg m 23 and n0h 5 4 3 10 4 m24.”
Evaluation of cloud‐resolving and limited area model intercomparison simulations using TWP‐ICE observations: 1. Deep convective updraft properties (2014, result): “…have the smallest rimed ice mixing ratios (Figure 11d) and greatest snow-mixing ratios (Figure 11e) in the upper troposphere, which is consistent with results in several other studies [Gilmore et al., 2004; van den Heever and Cotton, 2004; Morrison andMilbrandt, 2011; VanWeverberg et al., 2012].”
Parameterization of Cloud Microphysics Based on the Prediction of Bulk Ice Particle Properties. Part I: Scheme Description and Idealized Tests (2015, methodology): “…versus graupel-like characteristics can have large impacts on storm structure and precipitation associated with deep convection (e.g., McCumber et al. 1991; Gilmore et al. 2004; Cohen andMcCaul 2006; Morrison andMilbrandt 2011; Bryan and Morrison 2012; Van Weverberg 2013; Adams-Selin et al. 2013).”
Sensitivity of a Simulated Squall Line to Horizontal Resolution and Parameterization of Microphysics (2012, unknown): “One key uncertainty inparameterizing icemicrophysics is the number and type of ice species (e.g., cloud ice, snow, graupel, hail, etc.) (Fovell and Ogura 1988; McCumber et al. 1991; Ferrier et al. 1995; Gilmore et al. 2004b; VanWeverberg et al. 2010; Morrison and Milbrandt 2011).”
Simulations of Polarimetric Radar Signatures of a Supercell Storm Using a Two-Moment Bulk Microphysics Scheme (2010, background): “Researchers reported that N0 can vary significantly among precipitation systems and even within the same system (e.g., Waldvogel 1974; Gilmore et al. 2004).”
A Novel Approach for Representing Ice Microphysics in Models: Description and Tests Using a Kinematic Framework (2007, background): “The representation of ice microphysics in models has a significant impact on quantitative precipitation forecasts (e.g., Rutledge and Hobbs 1984; Gilmore et al. 2004; Thompson et al. 2004), prediction of supercooled liquid water (e.g., Reisner et al. 1998; Thompson et al. 2004; Morrison and Pinto…”
Comparison of Evaporation and Cold Pool Development between Single-Moment and Multimoment Bulk Microphysics Schemes in Idealized Simulations of Tornadic Thunderstorms (2010, methodology): “It is known that variations in the hail category have a strong impact on storm simulations (Gilmore et al. 2004; van den Heever and Cotton 2004).”
A Description of the Advanced Research WRF Model Version 4 (2019, background): “(2010) ranging from single-moment with graupel properties specified (Gilmore et al., 2004) or predicted with hail added to double-moment of all species with or without hail and also optionally adding CCNs.”
Some issues in uncertainty quantification and parameter tuning: a case study of convective parameterization scheme in the WRF regional climate model (2011, background): “Larger number or ranges of input parameters usually result in higher uncertainties in climate simulations because of nonlinear interactions and compensating errors of parameters (Gilmore et al., 2004; Molders, 2005; Min et al., 2007; Murphy et al., 2007).”
Uncertainty quantification and parameter tuning in the CAM5 Zhang‐McFarlane convection scheme and impact of improved convection on the global circulation and climate (2013, background): “…current situations may break down as the climate evolves or because compensating errors that exist among the parameterization schemes for different physical processes no longer cancel out in the future [Gilmore et al., 2004;Molders, 2005;Golaz et al., 2007; Min et al., 2007; Murphy et al., 2007].”
CONVECTIVE-SCALE WARN-ON-FORECAST SYSTEM: A vision for 2020 (2009, background): “…convection-resolving models, however, indicate that rapidly evolving convective events and tornado predictions are highly sensitive to both environmental conditions (Elmore et al. 2002; Martin and Xue 2006) and internal storm processes (Gilmore et al. 2004; Tong and Xue 2008; Snook and Xue 2008).”