Supercell Predictability Studies in Support of NOAA Warn-on-Forecast

1. Supercell Predictability Studies in Support of NOAA Warn-on-Forecast Corey K. Potvin1,2,3, Montgomery L. Flora3,Elisa M. Murillo3, and Louis J. Wicker2 1Cooperative Institute for Mesoscale Meteorological Studies, Norman, OK 2NOAA/OAR National Severe Storms Lab, Norman, OK 3School of Meteorology, University of Oklahoma, Norman, OK

2. Motivation: Warn-on-Forecast (WoF) • Goal: convection-allowing NWP ensembles that provide probabilistic guidance for thunderstorm hazard forecasts & warnings • Tornadoes, hail, wind, flash flooding • Challenge: real-time operation requires ensemble design compromises • Ensemble size, resolution, physics schemes • Question: how to optimize ensemble design?

3. Approach • Optimizing ensemble design requires knowledge of impacts from different forecast error sources • Construct sensitivity experiments that allow isolation and systematic exploration of errors • For some studies, combine idealized and real-data frameworks • Retain complete knowledge of “truth” but with full physics and observationally-constrained IC’s • Initial focus on supercells

4. Sensitivity to radar-to-storm geometry (Potvin and Wicker 2013, WAF) • Idealized OSSEs • 3 TRUTH supercells • Assimilate pseudo-radar data with EnKF, then ensemble forecast • Radar #1 > 100 km away • Radar #2 repositioned to vary radar-storm distance, cross-beam angles (CBAs)

5. Poor radar-to-storm geometry does not unduly degrade low-level rotation forecasts Poor Cross-Beam Angles Excellent Cross-Beam Angles CBA = 70-90° CBA = 20-30° Both radars > 100 km away CBA = 70-90° CBA = 0-30° Radar #2 much closer Neighborhood ensemble probability of strong low-level rotation; Red = TRUTH

6. Sensitivity to forecast grid spacing (Δx)Potvin and Flora (2015, MWR) • Idealized simulations with Δx = 333 m (TRUTH), 1-4 km • Range of environments (figure) • Compare to TRUTH and to TRUTH with scales < 2Δx filtered out • Identify errors arising from unresolved upscale effects

7. WK82 Snd • 4-km Δx too coarse • Delayed storm development • Premature demise • 3-km Δx much better Del City Snd El Reno Snd Time-height composites of ζ TRUTH Middle row: TRUTH, upscaled Bottom row: coarse sims 2-h forecasts of 1-km AGL dBZ • 1-km Δx needed to resolve low-level mesocyclone • Even finer Δx needed to forecasting its timing

8. Sensitivity to IC resolutionPotvin et al. (2017, JAS) • Select member of a full-physics, 3-km EnKF analysis of a real supercell and downscale to Δx ≈ 300 m • Integrate for 2 h (CNTL) and compare to simulations with spatially filtered ICs • Cutoff wavelengths = 2, 4, 8, or 16 km • Add noise to IC’s to generate ensembles • Since deterministic framework insufficient to reveal systemic impact of IC resolution (and some other types of error; see extra slide)

9. Surprising insensitivity to IC resolution! Probability-matched ensemble mean dBZ at t = 2 h, z = 2 km AGL

10. Largely since missing scales regenerate within 5-10 min of forecast w spectra at t = 5 min • Similar results for other cases & variables • In organized convection, larger scales (intra-storm and environment) strongly determine evolution w spectra at t = 0 min

11. Sensitivity to IC spread (Flora et al., in prep) • Evaluate practical & intrinsic predictability of supercells using feature-oriented framework • Downscale real-data 3-km ensemble analyses to 1-km, integrate for 3 h • Repeat with IC spread reduced to 50%, 25%, 10% of original spread • SEE POSTER 4 (session 2) Left: spread in forecast-maximum updraft helicity (UH) Right: neighborhood probability UH > 300 m2s-2

12. Conclusions • Typical (poor) radar-storm geometries do not preclude accurate supercell forecasts • Forecast ∆x must be ≤ 3 km to reliably predict general storm evolution • Major additional improvements require ∆x ≤ 1 km • However, reducing analysis ∆x below 3 km may be unnecessary • Finite computing resources are better spent on reducing forecast ∆x than analysis ∆x • Option 1: Perform coarse (e.g., 3-km) DA, then interpolate to fine forecast grid (e.g., 1 km) • Option 2: Mixed-resolution DA

13. Conclusions (cont.) • Intrinsic predictability limit < 3 h in some cases, but practical predictability limit longer • Improving supercell prediction at > 1-2 h lead times may require much better analyses of mesoscale environment • Real-world 1-km ensemble forecasts from 3-km EnKF analyses have severe biases (ongoing WoF work)  physics schemes remain a leading error source

14. Ongoing/Future Work • Real-world 1-km EnKF DA and/or forecasts • If these improve upon 3-km, then explore dual-resolution EnKF methods • Extend to mesoscale convective systems Acknowledgments • Warn-on-Forecast team: Thomas Jones, Kent Knopfmeier, Patrick Skinner, Dusty Wheatley, Nusrat Yussouf, Gerry Creager (and others) • National Weather Center REU program

15. Extra Slides

16. Why an ensemble framework? • If storm evolution is very sensitive to IC, then IC resolution impact could vary wildly between two nearly identical storms (or moment to moment in the same storm), making it impossible to use a single pair of sims to generalize the effect of IC resolution error IC resolution error >> IC perturbation error  deterministic framework is sufficient IC resolution error ≈ IC perturbation error  need ensemble framework!