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Prediction of graupel density in a bulk microphysics scheme

  • Université Laval and Environment and Climate Change Canada

Research output: Contribution to journalArticlepeer-review

63 Scopus citations

Abstract

Amethod to predict the bulk density of graupel rg has been added to the two-moment Milbrandt-Yau bulk microphysics scheme. The simulation of graupel using the modified scheme is illustrated through idealized simulations of a mesoscale convective system using a 2D kinematic model with a prescribed flow field and different peak updraft speeds. To examine the relative impact of the various approaches to represent rimed ice, simulations were run for various graupel-only and graupel-plus-hail configurations. Because of the direct feedback of rg to terminal fall speeds, the modified scheme produces a much different spatial distribution of graupel, with more mass concentrated in the convective region resulting in changes to the surface precipitation at all locations. With a strong updraft, the model can now produce solid precipitation at the surface in the convective region without a separate hail category. It is shown that a single rimed-ice category is capable of representing a realistically wide range of graupel characteristics in various atmospheric conditions without the need for a priori parameter settings. Sensitivity tests were conducted to examine various aspects of the scheme that affect the simulated rg. Specific parameterizations pertaining to other hydrometeor categories now have a direct impact on the simulation of graupel, including the assumed aerosol distribution for droplet nucleation, which affects the drop sizes of both cloud and rain, and the mass-size relation for snow, which affects its density and hence the embryo density of graupel converted from snow due to riming.

Original languageEnglish
Pages (from-to)410-429
Number of pages20
JournalJournal of the Atmospheric Sciences
Volume70
Issue number2
DOIs
StatePublished - Feb 2013

Keywords

  • Cloud parameterizations
  • Cloud resolving models
  • Parameterization

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