The Risks and Rewards of High-Resolution and Ensemble Modeling Systems

1. The Risks and Rewards of High-Resolution and Ensemble Modeling Systems David Schultz NOAA/National Severe Storms Laboratory Paul Roebber University of Wisconsin at Milwaukee Brian Colle State University of New York at Stony Brook David Stensrud NOAA/National Severe Storms Laboratory http://www.nssl.noaa.gov/~schultz

2. Objectives of this Talk • Discuss issues for operational weather forecasting in going to higher-resolution NWP. • Briefly compare advantages and disadvantages of high-resolution simulations versus lower-resolution ensembles. • Example: 3 May 1999 Oklahoma tornado outbreak. • Discuss unresolved scientific issues that will lead to improving predictability for operational forecasters.

3. High-Resolution NWP • High resolution (< 6 km) is now possible in real time due to increasing computer power and real-time distribution of data from National and International Modeling Centres. • Many groups have demonstrated high-resolution real-time NWP (Mass and Kuo 1998). • Small-scale weather features are able to be reproduced by high-resolution models (e.g., sea breezes, orographic precipitation, frontal circulations, convection).

4. But, . . . • The use of models to study physical processes and to make weather forecasts are two distinctly different applications of the same tool. • No guarantee that a high-resolution model will be more useful to forecasters than a model with larger grid spacing. • Model errors may increase with increasing resolution, as high-resolution models have more degrees of freedom. • High-resolution models may produce wonderfully detailed, but inaccurate, forecasts.

5. Ensemble Modeling Systems • Ensembles of lower-resolution models can have greater skill than a single higher-resolution forecast (e.g., Wandishin et al. 2001; Grimit and Mass 2001). • Ensemble forecasts directly express uncertainty through their inherently probabilistic nature. • But, what is the minimum resolution needed for “accurate” simulations? • How to best construct an ensemble?

6. The Forecast Process • Hypothesis Formation • Forecaster develops a conceptual understanding of the forecast scenario (“problem of the day”) • Hypothesis Testing • Forecaster seeks “evidence” that will confirm or refute hypothesis • observations, NWP output, conceptual models • Continuous process • Prediction • Forecaster conceptual model of forecast scenario(s) (e.g., Doswell 1986; Doswell and Maddox 1986; Hoffman 1991; Pliske et al. 2003)

7. Intuitive Forecasters • Defined by Pliske et al. (2003) as those who construct conceptual understanding of their forecasts on the basis of dynamic, visual images (as opposed to “rules of thumb”). • Such forecasters would benefit from both high-resolution forecasts and ensembles. • Show detailed structures/evolutions not possible in lower-resolution models • Developing alternate scenarios from ensembles • Construct probabilistic forecasts

8. 3 May 1999 Oklahoma Outbreak • 66 tornadoes, produced by 10 long-lived and violent supercell thunderstorms • 45 fatalities, 645 injuries in Oklahoma • ~2300 homes destroyed; 7400 damaged • Over $1 billion in damage, the nation’s most expensive tornado outbreak (Jarboe) (Schultz) (Daily Oklahoman)

9. Moore• Observed radar imagery (courtesy of Travis Smith, NSSL) 2-km MM5 simulation initialized 25 hours earlier (no data assimilation) pink: 1.5-km w (> 0.5 m/s) blue: 9-km cloud-ice mixing ratio (>0.1 g/kg) Moore• 0131 UTC 0100 UTC 0221 UTC 0200 UTC

10. •Moore Stage IV Radar/Gauge Precip. Analysis (Baldwin and Mitchell 1997)

11. Modeled Storms as Supercells • Identify updrafts(> 5 m/s) correlated with vertically coherent relative vorticity for at least 60 minutes • 22 supercells, 11 of which are on OK–TX border

12. Observed vs Modeled Supercells

13. Ensembles (Stensrud and Weiss) • 36-km MM5 simulations initialized 24 h ahead • Six members with varying model physics packages: 3 convective schemes (Kain–Fritsch, Betts–Miller–Janjic, Grell) and 2 PBL schemes (Blackadar, Burke–Thompson)

14. Ensemble mean convective precipitation: 2300 UTC 3 May to 0000 UTC 4 May (every 0.1 mm)

15. ensemble mean ensemble spread 2000 J/kg 750 J/kg ensemble maximum ensemble minimum 2000 J/kg 1000 J/kg Convective Available Potential Energy (J/kg)

16. ensemble mean ensemble spread 75 200 ensemble maximum ensemble minimum 200 200 Storm-Relative Helicity (m2 s–2)

17. ensemble mean ensemble spread 40 20 ensemble minimum ensemble maximum 40 40 Bulk Richardson Number Shear (m2 s–2)

18. Comparison • Both the high-resolution forecast and the ensemble forecasts did not put the bulk of the precipitation in the right place in central Oklahoma. • Both models indicated the potential for supercell thunderstorms with tornadoes in the Oklahoma–Texas region. • Both models were sensitive to the choice of parameterization schemes (e.g., PBL).

19. Remaining Scientific Issues • When should forecasters believe the model forecast as a literal forecast? • What is the role of model formulation in predictability? • What is the value of mesoscale data assimilation in the initial conditions? • What constitutes an appropriate measure of mesoscale predictability? • What is the appropriate role of postprocessing model data (e.g., neural networks, bias-correction techniques)? • Other examples and further discussion will be found in a manuscript, currently in preparation.