Physics-Based Parameterizations for Cloud Microphysics and Entrainment--Mixing Processes: Addressing Knowledge Gaps

Yangang Liu
Brookhaven National Lab
Noon, 15 August in 2155

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. .