A Physically Based Stochastic Boundary Layer Perturbation Scheme. Part II: Perturbation Growth within a Superensemble Framework

Abstract

Convection-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 behaviour. We compare the uncertainty due to initial and boundary condition (IBC) perturbations and boundary-layer turbulence using a super ensemble (SE) created to determine the influence of 12 IBC perturbations vs. 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 analysed 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 post-processing can be used instead of running larger ensembles. Use of these statistical post-processing techniques could lead to more reliable probabilistic forecasts of convective events and their associated hazards.

Flow‐ and scale‐dependent spatial predictability of convective precipitation combining different model uncertainty representations

“…"grand ensemble" (similar to the superensemble of Flack et al, 2021) containing IBC uncertainty and two different flavours of model uncertainty. The "grand ensemble" enables the inspection of the synergistic impact, but also facilitates an estimation of the individual impact of the PSP scheme and MPP by applying subsampling (as in Craig et al, 2022).…”

“…Our second objective in this work is to gauge the individual and synergistic impact of different formulations of model uncertainty, the PSP scheme and parameter perturbations in the microphysics scheme, both in the presence of operational IBC uncertainty. To achieve this goal we designed a “grand ensemble” (similar to the superensemble of Flack et al, 2021) containing IBC uncertainty and two different flavours of model uncertainty. The “grand ensemble” enables the inspection of the synergistic impact, but also facilitates an estimation of the individual impact of the PSP scheme and MPP by applying subsampling (as in Craig et al, 2022).…”

“…Independently, Clark et al (2021) proposed a similar stochastic boundary layer (SBL) scheme representing turbulent eddies as random events following a Poisson distribution inside a bulk model of the convective boundary layer. The variability in precipitation rates introduced by the SBL scheme is beneficial for detecting potential risk of flooding better, whereas the variability in spatial distribution “fills the gap” left by insufficient representation of uncertainty by perturbations in initial and lateral boundary conditions (Flack et al, 2021).…”

The sensitivity of probabilistic convective‐scale forecasts of an extratropical cyclone to atmosphere–ocean–wave coupling

“…Despite the many benefits of convective-scale EPSs compared to their medium-range counterparts described above, representation of model error at convective scale remains challenging because the smaller scales of motion simulated by convective-scale EPSs lead to a faster error growth (Clark et al, 2010;Baker et al, 2014;McCabe et al, 2016;Hagelin et al, 2017). Clark et al (2021), investigating a series of precipitation events simulated by a convective-scale EPS, demonstrated that model error impact is greatest when it arises from parametrization of boundary-layer structure. Moreover, Flack et al (2021) further demonstrated that uncertainty from stochastic perturbations applied to the model boundary-layer parametrization is statistically comparable to IC and LBC uncertainty.…”

“…Clark et al (2021), investigating a series of precipitation events simulated by a convective-scale EPS, demonstrated that model error impact is greatest when it arises from parametrization of boundary-layer structure. Moreover, Flack et al (2021) further demonstrated that uncertainty from stochastic perturbations applied to the model boundary-layer parametrization is statistically comparable to IC and LBC uncertainty. These findings clearly indicate that convective-scale EPSs need a more accurate representation of boundary-layer processes to reduce forecast error and better estimate model uncertainty of the meteorological variables that are sensitive to boundary-layer parametrizations.…”