Multiresolution Ensemble Forecasts of an Observed Tornadic Thunderstorm System. Part I: Comparsion of Coarse- and Fine-Grid Experiments

Abstract

Using a nonhydrostatic numerical model with horizontal grid spacing of 24 km and nested grids of 6- and 3-km spacing, the authors employ the scaled lagged average forecasting (SLAF) technique, developed originally for global and synoptic-scale prediction, to generate ensemble forecasts of a tornadic thunderstorm complex that occurred in north-central Texas on 28–29 March 2000. This is the first attempt, to their knowledge, in applying ensemble techniques to a cloud-resolving model using radar and other observations assimilated within nonhorizontally uniform initial conditions and full model physics. The principal goal of this study is to investigate the viability of ensemble forecasting in the context of explicitly resolved deep convective storms, with particular emphasis on the potential value added by fine grid spacing and probabilistic versus deterministic forecasts. Further, the authors focus on the structure and growth of errors as well as the application of suitable quantitative metrics to assess forecast skill for highly intermittent phenomena at fine scales. Because numerous strategies exist for linking multiple nested grids in an ensemble framework with none obviously superior, several are examined, particularly in light of how they impact the structure and growth of perturbations. Not surprisingly, forecast results are sensitive to the strategy chosen, and owing to the rapid growth of errors on the convective scale, the traditional SLAF methodology of age-based scaling is replaced by scaling predicated solely upon error magnitude. This modification improves forecast spread and skill, though the authors believe errors grow more slowly than is desirable. For all three horizontal grid spacings utilized, ensembles show both qualitative and quantitative improvement relative to their respective deterministic control forecasts. Nonetheless, the evolution of convection at 24- and 6-km spacings is vastly different from, and arguably inferior to, that at 3 km because at 24-km spacing, the model cannot explicitly resolve deep convection while at 6 km, the deep convection closure problem is ill posed and clouds are neither implicitly nor explicitly represented (even at 3-km spacing, updrafts and downdrafts only are marginally resolved). Despite their greater spatial fidelity, the 3-km grid spacing experiments are limited in that the ensemble mean reflectivity tends to be much weaker in intensity, and much broader in aerial extent, than that of any single 3-km spacing forecast owing to amplitude reduction and spatial smearing that occur when averaging is applied to spatially intermittent phenomena. The ensemble means of accumulated precipitation, on the other hand, preserve peak intensity quite well. Although a single case study obviously does not provide sufficient information with which to draw general conclusions, the results presented here, as well as those in Part II (which focuses solely on 3-km grid spacing experiments), suggest that even a small ensemble of cloud-resolving forecasts may provide greater skill, and greater practical value, than a single deterministic forecast using either the same or coarser grid spacing.

Convection-Allowing and Convection-Parameterizing Ensemble Forecasts of a Mesoscale Convective Vortex and Associated Severe Weather Environment

“…These results are consistent with more recent studies documenting rapid error growth at convective scales in convection-allowing models (e.g., Kong et al 2006Kong et al , 2007aZhang et al 2006;Hohenegger and Schä r 2007), and relatively poor warm season quantitative precipitation forecasting (QPF) over much of the United States (e.g., Fritsch and Carbone 2004), when the majority of rainfall is contributed by convective systems (e.g., Fritsch et al 1986;Schumacher and Johnson 2006). For a more thorough review of predictability at convective scales, see Lilly (1990) and Wandishin et al (2008).…”

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

“…Although increasing to CAR may not necessarily increase forecast skill for deterministic forecasts as measured by traditional "grid-based" metrics [e.g., Equitable Threat Score (Schaefer 1990) and bias] because of small displacement errors in small scale features leading to large errors (Baldwin et al 2001;Davis et al 2006a), it is possible that significant improvements in probabilistic precipitation forecasts may be obtained from an ensemble using CAR because of superior spatial/temporal representation of statistical properties of convective precipitation in the CAR members (e.g., Fritsch and Carbone 2004;Kong et al 2006 and. Also, because error growth occurs more rapidly at smaller scales, ensembles using CAR may have a better representation of forecast uncertainty.…”

The 26 July 2005 heavy rainfall event over Mumbai: numerical modeling aspects

“…Since the model simulations are known to be affected by the details of the ensemble methodology employed (e.g., Kong et al 2006), the third set of experiments (hereafter referred to as EXP3) uses a relatively new technique of ensemble construction known as the Scaled Lagged Average Forecasting or SLAF (Kalnay 2003;Kong et al 2006). The model setup is same as EXP2, except for the construction of ensembles, and the finest horizontal grid spacing being 2 km (Fig.…”