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

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Presentation transcript:

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

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?

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

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)

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

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

Time-height composites of ζ 2-h forecasts of 1-km AGL dBZ 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

Sensitivity to IC resolution Potvin 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)

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

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

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

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

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

Ongoing/Future Work Acknowledgments 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

Extra Slides

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!