Model Resolution

Prof. David Schultz

University of Helsinki, Finnish Meteorological Institute, and University

of Manchester

What Resolution Means to You

Will affect the types of weather phenomena you can forecast using model guidance

Will affect the conclusions you can draw from your research

Will affect your choice of parameterizations or how you set up the model

Will affect your computer resources and how fast the model runs

Goals for this LectureGoals for this Lecture

Distinguish between resolution and grid spacing.

What does resolution mean?

Why you should care

Why higher resolution is not necessarily better

Resolution is the ability of a NWP model to represent a feature adequately.

Grid spacing is the distance between grid points (usually referring to the horizontal).

Resolution is the ability of a NWP model to represent a feature adequately.

Grid spacing is the distance between grid points (usually referring to the horizontal).

Resolution ≠ grid spacing!

Resolution does not equal grid spacing!Resolution does not equal grid spacing!

Five grid points are needed to “resolve” the wave.

How many points are needed to resolve a feature will depend upon the requirements of the user and the type of feature.

Mass et al. (2002)

(Baldwin and Wandishin 2002)

Just because a model advertises a certain grid spacing does not mean that the model is actually “resolving” features of that scale.

schematic

(Baldwin and Wandishin 2002)

Just because a model advertises a certain grid spacing does not mean that the model is actually “resolving” features of that scale.

Effective resolution 10 km

Effective resolution 25 km

schematic

(Skamarock 2004)

real models

(Skamarock 2004)

Effective resolution 22-km WRF

Effective resolution 4-km WRF

real models

20 km

110 km

Because the resolution of a model is ambiguous, use the term grid spacing instead.

How do you select the grid spacing of a model?

No Man’s Land

(Joe Klemp)

No Man’s Land

(Joe Klemp)

What is meant by “resolved convection”?

People now refer to “convection-permitting” or “convection-allowing” models, indicating that organized mesoscale convective storms can be simulated and resolved, but the individual convective cells are not fully resolved.

Although grid spacings of 1–4 km may produce realistic-looking mesoscale convective systems, the turbulent eddies of the convection are not resolved.

(Axel Seifert)

(Bryan et al. 2003)

across-line along-line

equivalent potential temperature 3-D idealized squall line simulations

1 km

125 m

Richard Rotunno and Yongsheng Chen

Hurricane Eyewall Simulations

10-m wind speed

Physical processes may not be adequately represented in the model because of large grid spacing.

Example 1:

Spurious convection ahead of squall lines

Spurious Convection

Near Squall Lines

2-km WRF

George Bryan (2005, Monthly Weather Review)

Spurious Convection

Near Squall Lines

A result of moist absolutely unstable layers

(MAULs)

Physical processes that were unimportant at large grid spacing become important at small grid spacing.

Example 2:

Bias in location of orographic precipitation

Mass et al. (2002)

Two years of model runs

Over-prediction downwind of mountains:Improved riming parameterization(Colle, in preparation)

Verification of high-resolution output is problematic.

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

Increasing resolutionIncreasing resolutionrequires a new requires a new

forecasting approach.forecasting approach.

(Sami Niemelä)

7.5-km HIRLAM 2.5-km AROME radar

precipitation

Increasing resolutionIncreasing resolutionrequires a new requires a new

forecasting approach.forecasting approach.

(Sami Niemelä)

7.5-km HIRLAM 2.5-km AROME radar

precipitation

Increasing resolutionIncreasing resolutionrequires a new requires a new

forecasting approach.forecasting approach.

(Sami Niemelä)

7.5-km HIRLAM 2.5-km AROME radar

precipitation

?

3 May 1999 Oklahoma Outbreak

(Jarboe)(Jarboe)

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 U.S.’s most

expensive 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 Supercells

Identify updrafts(> 5 m/s) correlated with

vertically coherent relative vorticity for at least 60 minutes

22 supercells,11 of which are onOK–TX border

Observed 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

Summary of Oklahoma Outbreak

The high-resolution forecast did not put the precipitation in the right place in central Oklahoma.

The model indicated the potential for supercell thunderstorms with tornadoes in the Oklahoma–Texas region.

Some Remaining 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)?

What tools can be developed to help forecasters view, use, and diagnose high-resolution model output?

For Further Reading Some practical considerations regarding

horizontal resolution in the first generation of operational convection-allowing NWP – Kain et al. (2008)

Toward improved prediction: High-resolution and ensemble modeling systems in operations – Roebber et al. (2004)

Does increasing horizontal resolution produce more skillful forecasts? – Mass et al. (2002)