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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)
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