A Study of Multi-Scale Initial Condition Perturbation Methods for Convection-Permitting Ensemble Forecasts

The impacts of multi-scale flow-dependent initial condition (IC) perturbations for storm scale ensemble forecasts of mid-latitude convection are investigated using perfect model Observation System Simulation Experiments.  Several diverse cases are used to quantitatively and qualitatively understand the impacts of different IC perturbations on ensemble forecast skill.  Scale-dependence of the results is assessed by evaluating two-hour storm-scale reflectivity forecasts separately from hourly accumulated mesoscale precipitation forecasts. 

Forecasts are initialized with different IC ensembles, including an ensemble of multi-scale perturbations produced by a multi-scale data assimilation system, mesoscale perturbations produced at a coarser resolution, and filtered multi-scale perturbations.  Mesoscale precipitation forecasts initialized with the multi-scale perturbations are more skillful than the forecasts initialized with the mesoscale perturbations at several lead times.  This multi-scale advantage is due to greater consistency between the IC perturbations and IC uncertainty.  This advantage also affects the short term, smaller scale forecasts. Reflectivity forecasts on very small scales and very short lead times are more skillful with the multi-scale perturbations as a direct result of the smaller scale IC perturbation energy.  The small scale IC perturbations also contribute to some improvements to the mesoscale precipitation forecasts after the ~5 h lead time.  Altogether, these results suggest that the multi-scale IC perturbations provided by ensemble data assimilation on the convection-permitting grid can improve storm scale ensemble forecasts by improving the sampling of IC uncertainty, compared to downscaling of IC perturbations from a coarser resolution ensemble.