Impacts of Combining Various Types of Perturbation Methods on Convection-Permitting Ensemble Forecasting of Precipitation during SCMREX

This study investigates the impacts of different types of perturbation methods and their combination on the convection-permitting ensemble forecasts of precipitation over Southern China during the Southern China Monsoon Rainfall Experiment (SCMREX) in May 2014. The analysis presented here is based on 32 12-h forecasts and the heavy-rainfall cases during SCMREX are discriminated between the strongly and weakly forced events in terms of synoptic-scale forcing. The Initial Condition (IC) perturbation methods include the downscaling, ensemble of data assimilation (EDA), and time-lagging, while the MOdel physics (MO) perturbation methods include the stochastically perturbed parameterization tendency (SPPT), multi-physics, and perturbed parameters. Different perturbation methods for IC or MO show different multiscale characteristics in forecast perturbations. Combinations of such different IC or MO perturbation methods increase ensemble dispersions to alleviate the underestimations of forecast errors in individual perturbation methods especially at meso-α scales and thus improve forecast performances. Adding MO perturbations to IC perturbations may cause dispersion reduction, which decrease forecast perturbations and is sensitive to both perturbation-method design and synoptic-scale forcing. The dispersion reduction due to combining various IC and MO perturbation methods improves the probabilistic forecasts of precipitation, with more evident improvements in the cases with weaker forcing. To improve precipitation forecasting, it is instructive to apply dispersion reduction to the design of perturbation methods for CPEPSs, where forecast errors have been overestimated by IC perturbations especially at smaller scales.