A generalized approach to parameterizing convection combining ensemble and data assimilation techniques

TLDR

The study introduces a generalized convective parameterization that incorporates a wide range of prior assumptions and demonstrates two ensemble‑based methods for selecting the optimal parameter value to feed back into a larger‑scale model. The authors generate a broad solution spread by selecting assumptions, then apply simple statistical estimation and Bayesian data assimilation—using the ensemble probability density as a prior—to assimilate meteorological observations directly into the model fields. The results show that, when supplied with appropriate observations, this approach can be extended beyond convective parameterizations to other parameterization schemes.

Abstract

A new convective parameterization is introduced that can make use of a large variety of assumptions previously introduced in earlier formulations. The assumptions are chosen so that they will generate a large spread in the solution. We then show two methods in which ensemble and data assimilation techniques may be used to find the best value to feed back to the larger scale model. First, we can use simple statistical methods to find the most probable solution. Second, the ensemble probability density function can be considered as an appropriate “prior” (a'priori density) for Bayesian data assimilation. Using this “prior”, and information about observation likelihood, measured meteorological or climatological data can be directly assimilated into model fields. Given proper observations, the application of this technique is not restricted to convective parameterizations, but may be applied to other parameterizations as well.