SUPERCELL PREDICTABILITY: Exploring the Sensitivity of Ensemble Forecasts to IC Uncertainty Montgomery L. Flora1,2,3, Corey Potvin1,2,3, Lou Wicker3, Dustan Wheatley2,3, Kent Knopfmeier2,3, Patrick Skinner2,3 1School of Meteorology, University of Oklahoma, Norman, OK; 2Cooperative Institute for Mesoscale Meteorological Studies, Norman, OK 3NOAA/OAR/National Severe Storms Laboratory, Norman, OK BACKGROUND RESULTS With convection-allowing ensembles running operationally, increasing our understanding of storm-scale predictability is critical. Especially true for the NOAA Warn-on-Forecast (WoF) project, a promising effort to improve warning lead times by providing rapid-update probabilistic guidance to human forecasters (Stensrud et al. 2009, 2013). We focused on supercell predictability since they produce disproportionate amount of storm hazards Largely neglected in past studies: 1) fully flow-dependent IC errors, and 2) feature-oriented metrics 9 May 16 May 30 50 70 90 0.0 0.1 0.2 0.3 0.4 24 May 0.0 0.3 0.6 0.9 1.2 30 50 70 90 Spread convergence! 0.0 0.3 0.6 0.9 1.2 Original 50% 25% 10% 30 50 70 90 OBJECTIVES Develop a suitable full-physics/real-data NWP framework for evaluating storm-scale predictability Assess the intrinsic and practical predictability of supercell features: mid- and low-level mesocyclone, up- and downdraft, rain, and severe surface winds Explore sensitivity of supercell evolution to perturbations inside versus outside of the storm Fig. 2 Left Columns: (a-d) Normalized updraft helicity (UH) forecast spread swaths (filled contours) with probability-matched mean contoured (black lines; thick line = 350 m2 s-2 with a contour interval of 250 m2 s-2). Yellow lines show approx. forecast timing. Right columns: (e-h) Probability of UH > 300 m2s-2 (filled contour) within a 3 X 3 neighborhood. 9 May 16 May 24 May METHODS 20 15 10 5 Distance (km) Spread convergence! Choose control member close to ensemble mean. Generate perturbations by subtracting control member from each ensemble member over the entire 3D domain for all state variables Reduce perturbation magnitude (50% , 25%, and 10% of original magnitude) Add reduced perturbations onto control member to generate new ensembles with reduced IC spread 3 events used to explore case-dependence of supercell predictability (9,16,24 May 2016; Fig. 1) To explore relative impact of IC errors inside vs. outside the storm, in 2nd set of experiments, perturbations were reduced to zero outside or inside a sub-domain centered on the storm (Fig. 1) Original 50% 25% 10% Original ENV STRM Fig. 3 Time series of the average inter-member difference in storm location (based on max UH). 0.2 0.6 1.0 1.4 200 600 1000 1400 1800 Updraft Helicity (m2s-2) Norm. Forecast Spread Spread convergence! 9 May 16 May 24 May t = 0 min t = 60 min t = 120 min 9 May 16 May 24 May Fig. 1 Control member reflectivity at 1.5 km AGL (dBZ). Gray translucent box denotes sub-domain position used in second set of experiments. Original 50% 25% 10% CNTL Fig. 4 Top row: ensemble standard deviation in domain-max UH normalized by ensemble average domain-max UH. Bottom row: ensemble average domain-max UH. Control member domain-max UH in dashed magenta. CONCLUSIONS Supercell predictability, intrinsic and practical, is case-dependent. In two of the three cases, the intrinsic predictability limits for the remaining features were greater than 3 h, similar to mid-level mesocyclone By reducing IC spread by 50%, mid-level UH predictability was increased, on average, by 40 mins. Similar to tropical cyclones, location predictability was far greater than intensity predictability (cf. Fig 3 and 4 for 24 May) 0-2 h forecasts of storm location would benefit more from reducing IC uncertainty inside vs. outside the storm (See Fig. 3). WoF lead-times will not be limited by intrinsic predictability limits, and ensemble forecasts of supercells can be substantially improved by a 50% reduction in current IC spread. Funding provided by NOAA–University of Oklahoma Cooperative Agreement NA11OAR4320072, U.S. Department of Commerce.