Abstract
AbstractConvection-permitting forecasts have improved the forecasts of flooding from intense rainfall. However, probabilistic forecasts, generally based upon ensemble methods, are essential to quantify forecast uncertainty. This leads to a need to understand how different aspects of the model system affect forecast behavior. We compare the uncertainty due to initial and boundary condition (IBC) perturbations and boundary layer turbulence using a superensemble (SE) created to determine the influence of 12 IBC perturbations versus 12 stochastic boundary layer (SBL) perturbations constructed using a physically based SBL scheme. We consider two mesoscale extreme precipitation events. For each, we run a 144-member SE. The SEs are analyzed to consider the growth of differences between the simulations, and the spatial structure and scales of those differences. The SBL perturbations rapidly spin up, typically within 12 h of precipitation commencing. The SBL perturbations eventually produce spread that is not statistically different from the spread produced by the IBC perturbations, though in one case there is initially increased spread from the IBC perturbations. Spatially, the growth from IBC occurs on larger scales than that produced by the SBL perturbations (typically by an order of magnitude). However, analysis across multiple scales shows that the SBL scheme produces a random relocation of precipitation up to the scale at which the ensemble members agree with each other. This implies that statistical postprocessing can be used instead of running larger ensembles. Use of these statistical postprocessing techniques could lead to more reliable probabilistic forecasts of convective events and their associated hazards.
Highlights
Forecasting of convective events has had a ‘‘step change’’ in ability since the advent of convection-permitting models (e.g., Lean et al 2008; Clark et al 2016)
The magnitude analysis has revealed that for the Kent case, independent of the type of perturbation, the common points are precipitating at a similar rate, whereas for the Coverack case the precipitation rate is being altered by both types of perturbations, with the initial and boundary condition (IBC) having a stronger impact than the perturbations from the stochastic boundary layer (SBL) scheme
Convective-scale ensembles are enabling better probabilistic forecasts of severe weather associated with convective events
Summary
Forecasting of convective events has had a ‘‘step change’’ in ability since the advent of convection-permitting models (e.g., Lean et al 2008; Clark et al 2016). These studies showed that the total (area-averaged) precipitation had reduced spread between ensemble members in strong synoptically forced compared to weakly forced cases These results were developed by Keil et al (2014) and Kühnlein et al (2014) to consider the response of convection to different perturbation strategies. This result was found by Durran and Gingrich (2014) and Weyn and Durran (2017), though the latter study notes that there is no upscale/downscale growth within their idealized simulations and the errors grow up-amplitude on all scales simultaneously These discrepancies show that further work needs to go into these practical predictability experiments as this will help indicate where forecasts can be improved further, for example through better specification of initial conditions or better representation of unresolved processes such as turbulent eddies.
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