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

Modeling SystemsModeling SystemsDavid SchultzDavid Schultz

NOAA/National Severe Storms LaboratoryNOAA/National Severe Storms Laboratory

Paul RoebberPaul RoebberUniversity of Wisconsin at MilwaukeeUniversity of Wisconsin at Milwaukee

Brian ColleBrian ColleState University of New York at Stony BrookState University of New York at Stony Brook

David StensrudDavid StensrudNOAA/National Severe Storms LaboratoryNOAA/National Severe Storms Laboratory

http://www.nssl.noaa.gov/~schultzhttp://www.nssl.noaa.gov/~schultz

Objectives of this TalkObjectives of this Talk Discuss issues for operational weather Discuss issues for operational weather

forecasting in going to higher-resolution NWP.forecasting in going to higher-resolution NWP. Briefly compare advantages and disadvantages Briefly compare advantages and disadvantages

of high-resolution simulations versus lower-of high-resolution simulations versus lower-resolution ensembles.resolution ensembles.

Example: 3 May 1999 Oklahoma tornado Example: 3 May 1999 Oklahoma tornado outbreak.outbreak.

Discuss unresolved scientific issues that will Discuss unresolved scientific issues that will lead to improving predictability for operational lead to improving predictability for operational forecasters.forecasters.

High-Resolution NWPHigh-Resolution NWP High resolution (< 6 km) is now possible in High resolution (< 6 km) is now possible in

real time due to increasing computer power real time due to increasing computer power and real-time distribution of data from National and real-time distribution of data from National and International Modeling Centres. and International Modeling Centres.

Many groups have demonstrated high-Many groups have demonstrated high-resolution real-time NWP (Mass and Kuo resolution real-time NWP (Mass and Kuo 1998).1998).

Small-scale weather features are able to be Small-scale weather features are able to be reproduced by high-resolution models (e.g., reproduced by high-resolution models (e.g., sea breezes, orographic precipitation, frontal sea breezes, orographic precipitation, frontal circulations, convection).circulations, convection).

But, . . .But, . . . The use of models to study physical processes The use of models to study physical processes

and to make weather forecasts are two and to make weather forecasts are two distinctly different applications of the same tool.distinctly different applications of the same tool.

No guarantee that a high-resolution model will No guarantee that a high-resolution model will be more useful to forecasters than a model with be more useful to forecasters than a model with larger grid spacing.larger grid spacing.

Model errors may increase with increasing Model errors may increase with increasing resolution, as high-resolution models have resolution, as high-resolution models have more degrees of freedom.more degrees of freedom.

High-resolution models may produce High-resolution models may produce wonderfully detailed, wonderfully detailed, but inaccuratebut inaccurate, forecasts., forecasts.

Ensemble Modeling SystemsEnsemble Modeling Systems Ensembles of lower-resolution models can Ensembles of lower-resolution models can

have greater skill than a single higher-have greater skill than a single higher-resolution forecast (e.g., Wandishin et al. resolution forecast (e.g., Wandishin et al. 2001; Grimit and Mass 2001).2001; Grimit and Mass 2001).

Ensemble forecasts directly express Ensemble forecasts directly express uncertainty through their inherently uncertainty through their inherently probabilistic nature.probabilistic nature.

But, what is the minimum resolution needed But, what is the minimum resolution needed for “accurate” simulations?for “accurate” simulations?

How to best construct an ensemble?How to best construct an ensemble?

The Forecast ProcessThe Forecast Process Hypothesis FormationHypothesis Formation

– Forecaster develops a conceptual understanding of Forecaster develops a conceptual understanding of the forecast scenario (“problem of the day”)the forecast scenario (“problem of the day”)

Hypothesis TestingHypothesis Testing – Forecaster seeks “evidence” that will confirm or Forecaster seeks “evidence” that will confirm or

refute hypothesis refute hypothesis – observations, NWP output, conceptual modelsobservations, NWP output, conceptual models– Continuous processContinuous process

PredictionPrediction– Forecaster conceptual model of forecast scenario(s)Forecaster conceptual model of forecast scenario(s)

(e.g., Doswell 1986; Doswell and Maddox 1986; Hoffman 1991; Pliske et al. 2003)

Intuitive ForecastersIntuitive Forecasters Defined by Pliske et al. (2003) as those who Defined by Pliske et al. (2003) as those who

construct conceptual understanding of their construct conceptual understanding of their forecasts on the basis of dynamic, visual forecasts on the basis of dynamic, visual images (as opposed to “rules of thumb”).images (as opposed to “rules of thumb”).

Such forecasters would benefit from both Such forecasters would benefit from both high-resolution forecasts and ensembles.high-resolution forecasts and ensembles.– Show detailed structures/evolutions not possible Show detailed structures/evolutions not possible

in lower-resolution modelsin lower-resolution models– Developing alternate scenarios from ensemblesDeveloping alternate scenarios from ensembles– Construct probabilistic forecastsConstruct probabilistic forecasts

3 May 1999 Oklahoma Outbreak3 May 1999 Oklahoma Outbreak

(Jarboe)(Jarboe)

66 tornadoes, produced by 10 long-lived and 66 tornadoes, produced by 10 long-lived and violent supercell thunderstormsviolent supercell thunderstorms

45 fatalities, 645 injuries in Oklahoma45 fatalities, 645 injuries in Oklahoma ~2300 homes destroyed; 7400 damaged~2300 homes destroyed; 7400 damaged Over $1 billion in damage, the nation’s most Over $1 billion in damage, the nation’s most

expensive tornado outbreakexpensive tornado outbreak

((Daily OklahomanDaily Oklahoman))(Schultz)(Schultz)

0131 UTC0131 UTC

0221 UTC0221 UTC 0200 UTC0200 UTC

0100 UTC0100 UTC

Observed radar imagery (courtesy of (courtesy of Travis Smith, NSSL)Travis Smith, NSSL)

2-km MM5 simulationinitialized 25 hours earlier (no data assimilation)

pink: 1.5-km w (> 0.5 m/s)

blue: 9-km cloud-icemixing ratio (>0.1 g/kg)

MooreMoore••

MooreMoore••

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

••MooreMoore

Modeled Storms as SupercellsModeled Storms as Supercells

Identify updrafts(> 5 m/s) Identify updrafts(> 5 m/s) correlated correlated with vertically with vertically coherent relative vorticity coherent relative vorticity

for at least 60 minutesfor at least 60 minutes

22 supercells,22 supercells, 11 of which are on11 of which are on OK–TX OK–TX borderborder

Observed vs Modeled SupercellsObserved vs Modeled Supercells

OBSERVED MODELED

LIFETIMES(minutes)

120–450minutes for 10supercells

60–170 minutesfor 11 supercellsnear OK–TXborder

MEDIAN LIFESPAN(minutes)

203 90

SIMULTANEOUSSTORMS

7 5

LONGEST TRACK(km)

250 160

Ensembles (Stensrud and Weiss)Ensembles (Stensrud and Weiss)

36-km MM5 simulations initialized 24 h 36-km MM5 simulations initialized 24 h aheadahead

Six members with varying model physics Six members with varying model physics packages: 3 convective schemes (Kain–packages: 3 convective schemes (Kain–Fritsch, Betts–Miller–Janjic, Grell) and 2 PBL Fritsch, Betts–Miller–Janjic, Grell) and 2 PBL schemes (Blackadar, Burke–Thompson)schemes (Blackadar, Burke–Thompson)

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

Convective Available Potential Energy (J/kg)

ensemblemean

ensembleminimum

ensemblemaximum

ensemblespread

2000 J/kg

2000 J/kg1000 J/kg

750 J/kg

Storm-Relative Helicity (m2 s–2)

ensemblemean

ensembleminimum

ensemblemaximum

ensemblespread

200

200

75

200

Bulk Richardson Number Shear (m2 s–2)

ensemblemean

ensembleminimum

ensemblemaximum

ensemblespread

40 20

4040

ComparisonComparison Both the high-resolution forecast and the Both the high-resolution forecast and the

ensemble forecasts did not put the bulk of ensemble forecasts did not put the bulk of the precipitation in the right place in central the precipitation in the right place in central Oklahoma.Oklahoma.

Both models indicated the potential for Both models indicated the potential for supercell thunderstorms with tornadoes in supercell thunderstorms with tornadoes in the Oklahoma–Texas region.the Oklahoma–Texas region.

Both models were sensitive to the choice of Both models were sensitive to the choice of parameterization schemes (e.g., PBL).parameterization schemes (e.g., PBL).

Remaining Scientific IssuesRemaining Scientific Issues When should forecasters believe the model forecast When should forecasters believe the model forecast

as a literal forecast?as a literal forecast? What is the role of model formulation in predictability?What is the role of model formulation in predictability? What is the value of mesoscale data assimilation in What is the value of mesoscale data assimilation in

the initial conditions?the initial conditions? What constitutes an appropriate measure of What constitutes an appropriate measure of

mesoscale predictability?mesoscale predictability? What is the appropriate role of postprocessing model What is the appropriate role of postprocessing model

data (e.g., neural networks, bias-correction data (e.g., neural networks, bias-correction techniques)?techniques)?

Other examples and further discussion will be found in Other examples and further discussion will be found in a manuscript, currently in preparation.a manuscript, currently in preparation.