Practical Predictability of Supercells: Exploring Ensemble Forecast Sensitivity to Initial Condition Spread

Abstract

As convection-allowing ensembles are routinely used to forecast the evolution of severe thunderstorms, developing an understanding of storm-scale predictability is critical. Using a full-physics numerical weather prediction (NWP) framework, the sensitivity of ensemble forecasts of supercells to initial condition (IC) uncertainty is investigated using a perfect model assumption. Three cases are used from the real-time NSSL Experimental Warn-on-Forecast System for Ensembles (NEWS-e) from the 2016 NOAA Hazardous Weather Testbed Spring Forecasting Experiment. The forecast sensitivity to IC uncertainty is assessed by repeating the simulations with the initial ensemble perturbations reduced to 50% and 25% of their original magnitudes. The object-oriented analysis focuses on significant supercell features, including the mid- and low-level mesocyclone, and rainfall. For a comprehensive analysis, supercell location and amplitude predictability of the aforementioned features are evaluated separately. For all examined features and cases, forecast spread is greatly reduced by halving the IC spread. By reducing the IC spread from 50% to 25% of the original magnitude, forecast spread is still substantially reduced in two of the three cases. The practical predictability limit (PPL), or the lead time beyond which the forecast spread exceeds some prechosen threshold, is case and feature dependent. Comparing to past studies reveals that practical predictability of supercells is substantially improved by initializing once storms are well established in the ensemble analysis.

Sensitivity of Hail Precipitation to Ensembles of Uncertainties of Representative Initial Environmental Conditions From ECMWF

“…However, the intrinsic predictability of hail precipitation remained unclear, so it was necessary to investigate if reducing the magnitude of these initial condition perturbations would linearly reduce the uncertainty of the hail precipitation rate. Previously, Flora et al (2018) found a nonlinear response when reducing initial condition uncertainty in an ensemble of supercell simulations. The same three sets of simulations described above were repeated, but with the initial ensemble perturbations reduced to 10% of their original magnitudes to investigate the intrinsic predictability limit of hail precipitation.…”

“…In Zhang et al (2016), a tornadic supercell's intrinsic predictability limit was found to be 3-6 hr using an ensemble generated from small-magnitude perturbations. Substantial reductions in the forecast spread of the supercell structure were found in sensitivity simulations by Flora et al (2018) that reduced the magnitude of spread of their original initial conditions. However, relatively few studies have paid attention to the uncertainties in forecasts of high-impact hazards produced by supercells, including hail, and reduced initial condition errors have not yet been imposed on simulations of a hailstorm to examine the predictability of hail and compare it to the predictability of total precipitation.…”

“…In recently published papers, the predictability and various causes of the loss of predictability of other hazards related to severe storms, including tornadoes, heavy rainfall, severe convective winds, and tropical cyclones, have been quantified using ensemble simulations (Durran & Weyn, ; Emanuel & Zhang, ; Fernández‐González et al, ; Flora et al, ; Miglietta et al, , ; Munsell et al, ; Nielsen & Schumacher, ; Weyn & Durran, , ; Zhang et al, , ; Zhang & Tao, ). For example, the practical and intrinsic predictability of a tornadic supercell have been explored from the perspective of convection initiation time, which was strongly modulated by the evolution of the planetary boundary layer, and local topography (Zhang et al, , ).…”

“…The difficulty of convective storm predictability in numerical weather forecasts is becoming increasingly problematic as the damage caused by hailstorms intensifies across the world (e.g., Allen & Tippett, 2015;Guan et al, 2015;Punge & Kunz, 2016). Considerable efforts have been made to explore the predictability of severe storms (e.g., Flora et al, 2018;Weyn & Durran, 2017). Ensemble forecasting is now an important component of operational forecasting and is increasingly used at convection-permitting or even higher spatial resolutions (Miltenberger et al, 2018).…”

“…Ensemble forecasting is now an important component of operational forecasting and is increasingly used at convection-permitting or even higher spatial resolutions (Miltenberger et al, 2018). Past studies have demonstrated the ability of storm-scale ensemble analyses to simulate severe storms (Flora et al, 2018;Potvin et al, 2017;Zhang et al, 2016). However, the critical initial perturbations contributing to variance in the simulated hail precipitation as well as the upper limit of practical predictability of hail remains particularly challenging (Snook et al, 2016), especially in ensemble simulations of hailstorms.…”