Title:  Novel methods for effectively leveraging polarimetric radar observations to 
improve understanding and modeling of clouds and precipitation

Presenter: Marcus van Lier-Walqui

Abstract: As models of clouds and precipitation increase in complexity, it becomes 
increasingly difficult to use observations to effect improvements in their representation 
of physical processes. Improved observational analysis of poorly understood processes, 
such as deep convective microphysics, can help. Here I present work on tracking 
individual updraft cells using polarimetric radar and lightning observations, in order to 
make effective comparison to updrafts tracked from model simulations. A deeper 
problem is that the very structure of most cloud microphysical parameterization 
schemes limits our ability to improve them using observations. To some extent it is 
possible to tune free parameters of these schemes, but it is extremely difficult to 
systematically improve the structural assumptions of parameterization schemes using 
observations. I will present work on development of a new scheme, the Bayesian 
Observationally-constrained Statistical-physical microphysics Scheme (BOSS) that 
eschews the ad hoc and poorly constrained assumptions of most current schemes, and 
is instead guided by available observations of clouds and precipitation, such as 
polarimetric radar data. Additional benefits of this approach include the ability to fully 
quantify scheme uncertainty, and also the ability to add or subtract scheme complexity 
as required by the observations.