Exploring new radar constraints on cloud microphysical uncertainty 
 
Marcus van Lier-Walqui. 
 
Cloud microphysical processes remain a large source of uncertainty and error in numerical 
simulations of weather and climate. This is due to both a lack of theoretical understanding, as well as 
the approximations employed to simulate these processes with computational tractability. Radar 
observations, in particular polarimetric radar and vertically profiling radar Doppler spectra, are an 
ideal source of information capable of constraining cloud microphysical uncertainties. Results are 
presented from investigation of the polarimetric radar signals associated with deep convection for 
comparison with detailed microphysical models. Additionally a Bayesian parameter estimation 
experiment was performed where S-band radar Doppler spectra are used to probabilistically 
constrain ice particle sticking efficiencies using a Markov chain Monte Carlo (MCMC) sampler.