Physics-Based Parameterizations for Cloud Microphysics and Entrainment--Mixing Processes: Addressing Knowledge Gaps
Yangang Liu
Brookhaven National Lab
Noon, 15 August in 2155
Abstract:
Despite decades of research, clouds and precipitation continue to
present one of the greatest challenges to accurately understand and
predict weather and climate systems. Significant knowledge gaps remain
in understanding cloud microphysics and thus in the parameterization
for large scale models. This seminar is focused on three closely
related topics. First, I will discuss the potentials of statistical
physics ideas in building a theoretical framework for developing cloud
microphysics parameterization in large scale models. Second, I will
discuss turbulent entrainment-mixing processes and their effects on
cloud microphysical properties. A unified parameterization is explored
to cover all the different types of entrainment-mixing processes.
Finally, I will introduce the particle-resolved direct numerical
simulation (P-DNS) model recently developed at BNL to address the
knowledge gaps at the fundamental level. The P-DNS is designed to track
individual particles in clouds and resolve the smallest turbulent
eddies in clouds, thus providing the bottom-up benchmark for evaluating
microphysical parameterizations as well.
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