A Comparison of Precipitation Forecast Skill between Small Convection-Allowing and Large Convection-Parameterizing Ensembles

Abstract

2An experiment is designed to evaluate and compare precipitation forecasts from a 5-member, 4-km grid-spacing (ENS4) and a 15-member, 20-km grid-spacing (ENS20) Weather Research and Forecasting (WRF) model ensemble, which cover a similar domain over the central United States.The ensemble forecasts are initialized at 2100 UTC on 23 different dates and cover forecast lead times up to 33 hours. Previous work has demonstrated that simulations using convection-allowing resolution (CAR; dx ~ 4-km) have a better representation of the spatial and temporal statistical properties of convective precipitation than coarser models using convective parameterizations. In addition, higher resolution should lead to greater ensemble spread as smaller scales of motion are resolved. Thus, CAR ensembles should provide more accurate and reliable probabilistic forecasts than parameterized-convection resolution (PCR) ensembles.Computation of various precipitation skill metrics for probabilistic and deterministic forecasts reveals that ENS4 generally provides more accurate precipitation forecasts than ENS20, with the differences tending to be statistically significant for precipitation thresholds above 0.25 inches at forecast lead times of 9 to 21 hours (0600 -1800 UTC) for all accumulation intervals analyzed (1-, 3-, and 6-hr). In addition, an analysis of rank histograms and statistical consistency reveals that faster error growth in ENS4 eventually leads to more reliable precipitation forecasts in ENS4 than in ENS20. For the cases examined, these results imply that the skill gained by increasing to CAR outweighs the skill lost by decreasing the ensemble size. Thus, when computational capabilities become available, it will be highly desirable to increase the ensemble resolution from PCR to CAR, even if the size of the ensemble has to be reduced.

High‐resolution simulation over central Europe: assimilation experiments during COPS IOP 9c

“…Vegetation and land-use classifications, are provided by the new IGBP-MODIS dataset containing 20 different land-use categories with a resolution of 30 . Although we operate the model with a horizontal resolution of 3.6 km, we did not use a convection parametrization, since a resolution of ∼4 km was shown to reasonably represent precipitation patterns without a convection scheme (Kain et al, 2008;Weisman et al, 2008;Clark et al, 2009).…”

On the forecast skill of a convection-permitting ensemble

“…They mainly focus on the investigation of the impact of CP ensemble configurations, for example, the generation of IC perturbation, representation of the model error, uncertainties from the lateral boundary conditions (LBCs), ensemble size, and spatial scale (Kong et al, 2006;Clark et al, 2009Clark et al, , 2011Bouttier et al, 2012;Ben Bouallègue et al, 2013;Kühn-lein et al, 2014;Schwartz et al, 2015;Schumacher and Clark, 2014;Romine et al, 2014;Tennant, 2015). There are few comprehensive studies on the evaluation of CP EPS, in particular, in comparison with the mesoscale regional EPS.…”

“…There are few comprehensive studies on the evaluation of CP EPS, in particular, in comparison with the mesoscale regional EPS. Clark et al (2009) compared a 5-member 4 km grid spacing convection-permitting ensemble with a 15-member 20 km grid spacing regional ensemble. Their case studies revealed that the convection-permitting ensemble generally provided more accurate precipitation forecasts than the coarser-resolution regional EPS.…”

Ensemble forecasts of a flood‐producing storm: comparison of the influence of model‐state perturbations and parameter modifications

“…Both approaches address aspects of model uncertainty and the former approach is in common use (e.g. Clark et al, 2009;Yussouf and Stensrud, 2011;Gebhardt et al, 2011). The main goal of this study is to compare the two approaches.…”