Logo Logo
Help
Contact
Switch Language to German
Flack, David L. A.; Gray, Suzanne L.; Plant, Robert S.; Lean, Humphrey W.; Craig, George C. (2018): Convective-Scale Perturbation Growth across the Spectrum of Convective Regimes. In: Monthly Weather Review, Vol. 146, No. 1: pp. 387-405
Full text not available from 'Open Access LMU'.

Abstract

Convection-permitting ensembles have led to improved forecasts of many atmospheric phenomena. However, to fully utilize these forecasts the dependence of predictability on synoptic conditions needs to be understood. In this study, convective regimes are diagnosed based on a convective time scale that identifies the degree to which convection is in equilibrium with the large-scale forcing. Six convective cases are examined in a convection-permitting ensemble constructed using the Met Office Unified Model. The ensemble members were generated using small-amplitude buoyancy perturbations added into the boundary layer, which can be considered to represent turbulent fluctuations close to the grid scale. Perturbation growth is shown to occur on different scales with an order of magnitude difference between the regimes [O(1) km for cases closer to nonequilibrium convection and O(10) km for cases closer to equilibrium convection]. This difference reflects the fact that cell locations are essentially random in the equilibrium events after the first 12 h of the forecast, indicating a more rapid upscale perturbation growth compared to the nonequilibrium events. Furthermore, large temporal variability is exhibited in all perturbation growth diagnostics for the nonequilibrium regime. Two boundary condition-driven cases are also considered and show similar characteristics to the nonequilibrium cases, implying that caution is needed to interpret the time scale when initiation is not within the domain. Further understanding of perturbation growth within the different regimes could lead to a better understanding of where ensemble design improvements can be made beyond increasing the model resolution and could improve interpretation of forecasts.