Evaluation of stochastic parameter representation of microphysics parameterization uncertainty for convective scale ensemble data assimilation and prediction

It has long been known that microphysics parameterizations are among leading sources of model uncertainty in storm and convective scale weather prediction.  The uncertainty results from combination of imperfect knowledge of the microphysics processes, inability to explicitly resolve them at computationally feasible spatial and phase-space resolutions, as well as from inherent limited predictability of micro to turbulent scale processes.   Representing these in the context of improving probabilistic prediction skill using ensembles has been the subject of many studies, but remains an outstanding problem.  The problem is especially acute in storm and convective scale ensemble prediction, where there may be strong coupling of errors between ensemble data assimilation and forecasting. 

Over the last decade, the inclusion of stochastic representation of model uncertainty associated with physical parameterizations has emerged as a viable approach for representing the intrinsic uncertainties of the microphysical parameterizations.  This study examines sensitivity of storm scale ensemble simulations to representation of microphysics parameterization uncertainties using a cloud resolving model.  We compare several stochastic parameter (SP) perturbation methods, including various parameter distributions and parameter covariance models, applied to physical parameters in a bulk microphysics parameterization.  The study follows a prior study, in which a 1D column version of the 3D cloud resolving model was used to test non-stochastic and several SP perturbation methods for which the parameter perturbation statistical distributions were based on Markov Chain Monte Carlo (MCMC) inversions with synthetic observations. That study indicated that SP schemes produce significantly more ensemble variance of microphysics states than non-stochastic, and that inclusion of parameter covariances, and specifically those that vary with the state of the system, improve their performance.

The current study investigates impacts of SP scheme configurations on microphysics with dynamical feedbacks in 3D ensemble simulations.  The statistical parameter distributions used for the SP scheme are obtained as in the 1D study using MCMC inversions with synthetic observations. The results are evaluated in terms of changes to the ensemble mean and variance of microphysical and dynamical states and the simulated column integral microphysics-sensitive satellite-based observable quantities. We discuss the results and note the implications for convective scale ensemble data assimilation and forecasting.