The combined impact of model uncertainty on flow-dependent spatial predictability of convective precipitation

Accurate precipitation forecasts at kilometre scales are still a key challenge for convective scale ensemble prediction systems. We assess the spatial forecast skill-spread relationship for summer convection in 2021 and address the impact of considering model uncertainties from two physics parametrisations -- microphysics and planetary boundary layer turbulence -- together with initial and lateral boundary conditions uncertainties. To investigate their flow dependence all analyses are done conditionally to strong and weak synoptic convective forcing cases.
It is found that the spatial skill-spread relationship is highly dependent on synoptic forcing and the current operational ensemble forecasts are spatially underdispersive especially during weak synoptic control, whereas a good agreement is found during strong synoptic control. Case studies during weak synoptic control demonstrate that perturbations in the planetary boundary layer contribute to improving forecast skill and increase spread at small scales while microphysical perturbations contribute to spread increase across all scales. Overall, the combination of both perturbations seems to combine their individual impacts and thus benefits the spatial skill-spread relationship at most times and scales.