An Update to the Stochastically Perturbed Parameterization Scheme of HarmonEPS

High-resolution, limited-area forecasting is strongly affected by errors in the initial atmospheric state, lateral boundary conditions (LBCs), and physical parameterizations used by numerical weather prediction (NWP) models. These errors need to be accounted for through the introduction of uncertainty in an ensemble prediction system, EPS. One approach to account for model error is to use a stochastically perturbed parameterizations (SPPs) scheme. A first version of the SPP scheme of HARMONIE EPS (HarmonEPS) has been tested, with promising improvements in ensemble spread. However, it introduced systematic biases and deteriorated skill scores for some variables. Here, we investigate the performance of an updated version of the HarmonEPS SPP scheme, which includes (i) the use of uniform distributions, (ii) the correlation of stochastic patterns between key SPP parameters, and (iii) the introduction of four additional parameters, in the microphysics and mass-flux schemes. Two five-parameter SPP-based setups are compared against initial and LBC perturbations setups for five forecast periods: (i) 22–28 March 2019, (ii) 6–12 July 2020, (iii) 20–26 February 2021, (iv) 13–26 January 2021, and (v) 20 May–2 June 2021. We find that SPP-based experiments show better probabilistic metrics for near-surface and cloud-related variables than the non-SPP experiments. The SPP-based ensembles show increased spatial spread (as indicated by dFSS), while maintaining similar spatial skill (as indicated by eFSS) with the non-SPP experiment. In addition, the systematic bias in the ensemble members of the previous SPP iteration has been alleviated with the use of uniform distributions. Finally, the use of microphysical and mass-flux perturbations improves the ensemble scores for cloud-related variables, precipitation, and visibility.