Idealized numerical simulations of Mesoscale Convective Systems and their implications for forecast error

Abstract

Deep convective mass transfer in a rotating, stratified fluid is capable of generating characteristic potential vorticity features and, if of sufficient spatial scale, these may affect baroclinic wave development and hence weather predictability. In current global numerical prediction models, it is still necessary to include convection parametrization even though these algorithms are ill‐suited to representing singular, mesoscale convective events. Some stochastic representations of the non‐equilibrium convective process in ensemble prediction systems perturb the vertical component of vorticity in order to crudely represent the upscale impact of deep convection (i.e. kinetic energy backscatter schemes). These use ad hoc assumptions about the structure of random wind errors associated with convection. The idealized, explicit convection simulations described here form the basis of a study of these convectively generated vorticity perturbations, and their implications for stochastic convective forcing in ensemble prediction systems is considered. Cases with no initial flow and constant shear are examined with emphasis on the impact on vertical vorticity within the mesoscale cloud shield. The results are interpreted in the wider context of operational forecast error in the simulated infrared radiance for cases of intense convective activity over the North American Great Plains. A Met Office Unified Model forecast made with 2.2 km horizontal resolution during the Hazardous Weather Testbed project provides additional support for the idealized model findings. The Stochastic Convective Backscatter scheme proposed by the author is integrated forward in time using the simulated convective mass transport as a driver for the random forcing term. In its ensemble forecast model implementation, this term uses parametrized convective mass fluxes. The results suggest a retuning of the scheme's filter scales in order to obtain a representation of model wind error more consistent with the simulations.