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

• Published in: Journal of the atmospheric sciences 2021-03, Vol.78 (3), p.747-761
• Main Authors: Flack, D. L. A., Clark, P. A., Halliwell, C. E., Roberts, N. M., Gray, S. L., Plant, R. S., Lean, H. W.
• Format: Article
• Language: English
• Subjects: Atmospheric precipitations; Boundary conditions; Boundary layer turbulence; Boundary layers; Convection; Design; Experiments; Extreme weather; Flood forecasting; Flooding; Growth; Perturbation; Perturbations; Physics; Precipitation; Probability theory; Rain; Rainfall; Rainfall forecasting; Relocation; Statistical analysis; Turbulence; Uncertainty
• ISSN: 0022-4928; 1520-0469
• DOI: 10.1175/JAS-D-19-0292.1

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 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.