Abstract
Abstract. The super-droplet method (SDM) is a particle-based numerical scheme
that enables accurate cloud microphysics simulation with lower
computational demand than multi-dimensional bin schemes.
Using SDM, a detailed numerical model of
mixed-phase clouds is developed in which ice
morphologies are explicitly predicted without assuming ice categories
or mass–dimension relationships. Ice particles are approximated using
porous spheroids. The elementary cloud microphysics processes considered are advection and sedimentation; immersion/condensation
and homogeneous freezing; melting; condensation and evaporation
including cloud condensation nuclei activation and deactivation;
deposition and sublimation; and coalescence, riming, and aggregation.
To evaluate the model's performance, a 2-D large-eddy simulation of a
cumulonimbus was conducted, and the life cycle of a cumulonimbus typically
observed in nature was successfully reproduced. The mass–dimension and
velocity–dimension relationships the model predicted show a
reasonable agreement with existing formulas. Numerical
convergence is achieved at a super-particle number concentration as
low as 128 per cell, which consumes 30 times more
computational time than a two-moment bulk model. Although the model still has room
for improvement, these results strongly support the efficacy of
the particle-based modeling methodology to simulate
mixed-phase clouds.
Cited by
27 articles.
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