Evaluating Stochastic Parameter Perturbations in Convection-Permitting Ensemble Forecasts of Lake-Effect Snow

Abstract Lake-effect snowstorms can produce large snowfall accumulations that are challenging to simulate and forecast. One source of forecast uncertainty for these events is the uncertain parameterization of subgrid processes, such as planetary boundary layer and surface layer (PBL/SL) turbulence and cloud and precipitation microphysics (MP), in numerical weather prediction models. One way to quantify this uncertainty is to design ensembles that use stochastic parameter perturbations (SPPs) to vary individual uncertain parameters within physics schemes. This research aims to evaluate and improve the utility of SPP for convection-permitting ensemble forecasts of lake-effect snow, with a focus on PBL/SL and MP parameterizations. We focus on a snowfall event observed during the Ontario Winter Lake-effect Systems (OWLeS) field campaign, which is simulated with 1-km horizontal grid spacing using the Weather Research and Forecasting Model. A suite of 20-member ensemble simulations are run, including ensembles where SPP is applied only to PBL/SL or MP, where SPP is applied to multiple schemes concurrently, where perturbations to initial and boundary conditions (ICs/BCs) are applied instead of SPP, and where SPP and IC/BC perturbations are applied together. SPPs produce substantial spread in simulated precipitation, despite having only modest impacts on the synoptic-scale flow. They accomplish this by modulating lake–atmosphere fluxes, boundary layer characteristics, precipitation growth processes, and hydrometeor terminal fall speeds. The spread and skill of simulated precipitation from an ensemble using SPP alone is comparable to that from ensemble that uses IC/BC perturbations alone. The physical pathways whereby SPPs generate spread are examined and discussed.