Growth of Spread in Convection-Allowing and Convection-Parameterizing Ensembles

Abstract

Abstract During the 2007 NOAA Hazardous Weather Testbed Spring Experiment, a 10-member 4-km grid-spacing Storm-Scale Ensemble Forecast (SSEF) system was run in real time to provide experimental severe weather forecasting guidance. Five SSEF system members used perturbed initial and lateral boundary conditions (ICs and LBCs) and mixed physics (ENS4), and five members used only mixed physics (ENS4phys). This ensemble configuration facilitates a comparison of ensemble spread generated by a combination of perturbed ICs/LBCs and mixed physics to that generated by only mixed physics, which is examined herein. In addition, spread growth and spread-error metrics for the two SSEF system configurations are compared to similarly configured 20-km grid-spacing convection-parameterizing ensembles (ENS20 and ENS20phys). Twelve forecast fields are examined for 20 cases. For most fields, ENS4 mean spread growth rates are higher than ENS20 for ensemble configurations with both sets of perturbations, which is expected as smaller scales of motion are resolved at higher resolution. However, when ensembles with only mixed physics are compared, mass-related fields (i.e., geopotential height and mean sea level pressure) in ENS20phys have slightly higher spread growth rates than ENS4phys, likely resulting from the additional physics uncertainty in ENS20phys from varied cumulus parameterizations that were not used at 4-km grid spacing. For 4- and 20-km configurations, the proportion of spread generated by mixed physics in ENS4 and ENS20 increased with increasing forecast lead time. In addition, low-level fields (e.g., 2-m temperature) had a higher proportion of spread generated by mixed physics than mass-related fields. Spread-error analyses revealed that ensemble variance from the current uncalibrated ensemble systems was not a reliable indicator of forecast uncertainty. Furthermore, ENS4 had better statistical consistency than ENS20 for some mass-related fields, wind-related fields, precipitation, and most unstable convective available potential energy (MUCAPE) with no noticeable differences for low-level temperature and dewpoint fields. The variety of results obtained for the different types of fields examined suggests that future ensemble design should give careful consideration to the specific types of forecasts desired by the user.