Modeling of Cloud Microphysics: Can We Do Better?

Traditional cloud modeling methodologies apply a continuous approach
for all thermodynamic variables, not only for the temperature and
water vapor, but also for cloud condensate and precipitation.
Continuous in time and space Eulerian variables used to represent
cloud and precipitation particles are mass and sometimes number
mixing ratios in bulk schemes and mass and/or number spectral density
mixing ratios in bin schemes. Such a methodology has been the
workhorse of cloud-scale modeling from its early days. However,
there are challenges in applying such approaches due to numerical
diffusion in the physical space and in the particle mass (or size)
space for bin schemes, difficulty in representing aerosol processing
by clouds, and inability to properly represent unresolved spatial
scales that arguably play a significant role in the development of
the particle size/mass spectra. This presentation will discuss
problems with the Eulerian methodology and introduce a particle-based
Lagrangian approach that is gaining popularity in cloud-scale
modeling. Application of this approach to the problem of droplet
spectral broadening in warm shallow clouds will illustrate the key
advantage of the method. Prospects of applying the Lagrangian
particle-based methodology to more complex simulations involving
clouds will be discussed.