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

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  • 1 Department of Meteorology, University of Reading, Reading, UK
  • 2 Department of Meteorology, University of Reading, Reading, UK
  • 3 Met Office, MetOffice@Reading, Department of Meteorology, University of Reading, Reading, UK
  • 4 Department of Meteorology, University of Reading, Reading, UK
  • 5 Met Office, MetOffice@Reading, Department of Meteorology, University of Reading, Reading, UK
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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.

Denotes content that is immediately available upon publication as open access.

Corresponding author address: P.A. Clark, Department of Meteorology, University of Reading, Earley Gate, P.O. Box 243, Reading, RG6 6BB, UK. E-mail: p.clark@reading.ac.uk

Current affiliation:Met Office, Exeter, UK.

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.

Denotes content that is immediately available upon publication as open access.

Corresponding author address: P.A. Clark, Department of Meteorology, University of Reading, Earley Gate, P.O. Box 243, Reading, RG6 6BB, UK. E-mail: p.clark@reading.ac.uk

Current affiliation:Met Office, Exeter, UK.

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