Transcript Use of Mesoscale and Ensemble Modeling for Predicting Heavy Rainfall Events Dave Ondrejik

Use of Mesoscale and Ensemble
Modeling for Predicting Heavy
Rainfall Events
Dave Ondrejik
Warning Coordination Meteorologist
[email protected]
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Weather Models
• Why use computer models to help forecast?
– Weather affects nearly everyone nearly every day
– Weather forecasts are issued:
• to save lives
• Reduce property damage
• reduce crop damage
• to let the general public know what to expect
– Forecasts are often utilized to make many important
decisions on a daily basis
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Computer Weather Forecasts:
The Basics
•Computers are used to solve mathematical equations.
•There are 7 equations of motion that govern how the
atmosphere “should” behave
•The basic input for computer models are…..
– OBSERVATIONS
•Must have good info going into the models or you get
bad info out
– GIGO – Garbage In Garbage Out!
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Computer Weather Forecasts:
The basics
• Seven Fundamental Variables:
– Temperature (T)
– Pressure (p)
– Specific humidity (q)
– Density (r)
– East/west wind component (u)
– South/north wind component (v)
– Vertical wind component (w)
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Equations of motion
East-west wind
North-south wind
Temperature
Humidity
Continuity of mass
Surface pressure
Slide courtesy of Tom Hamill, NOAA-CIRES Climate Diagnostics Center
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The First Step in Forecasting
• Model uses previous run’s forecast as “first guess”
– Today’s AM Model is initialized first with Last Night’s
12-hr forecast
• First guess gets modified by real observations
• Q: Why not go right with the real observations?
– Irregularly-spaced obs are ‘too rough’ to work
– Starting Data Fields need to be smooth and in
balance with wind and pressure patterns
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Model Initialization:
The 1st step
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Model Initialization:
The 1st step
•Why use Aircraft and Satellite observations?
•About 7/10 of the world is water (55% in the northern
Hemisphere).
•On land we can send up weather balloons, but over the
water it is much harder.
•There are thousands of miles of water west of
California… and the fish ain’t talking.
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The First Step in Forecasting
There are more point then shown…but they must be evenly spaced.
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Model Initialization
Observations are
not at points.
Wilkes-Barre and
Scranton do not
fall on grid points.
We cannot have an
observation at every
city, town or grid
point location.
Surface Data
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Model Initialization
Surface Data
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Model Initialization
This is reasonable based on
observations…however it is
not exact.
Surface Data
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A lot happens inside a grid box
Rocky Mountains
Approximate
size of one
grid box
Denver
Source: accessmaps.com
Slide courtesy of Tom Hamill, NOAA-CIRES Climate Diagnostics Center
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Model Calculations
•How many calculations do we make?
• There are 2500 grid points
• Calculate at 50 layers
• Every 6.5 minutes
• For all 7 equations of motion
• Out for over 10 days
• EQUALS over 250 Billion calculations
•That means each US citizen would have to do about 1000
calculations/day if we didn’t use computers.
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Model Calculations
• Are these model calculations exact???
• No – due to current computer power (and we do have some
of the most powerful computers in the world!), we must
round off the computations at 2 digits to the right of the
decimal
– i.e. 2.49 instead of 2.481359871048615793
• Huge errors are possible due to rounding alone!!
• After calculations are completed, you contour your results
and you have pretty maps!
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ETA FORECAST
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Why Ensemble?
• Creates a method used to improve medium and long-range
forecasts
• Produces a model forecast from the same model or a
couple models run many times
• The initial model conditions are slightly different for each
run to represent the "uncertainties" and "errors" inherent in
the observations used to initialize the model
– “Butterfly effect”
• If, at the end, all model runs produce a similar forecast,
then a forecaster can have greater confidence in the model
prediction.
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Why Ensemble?
• A type of consensus forecast as you can ensemble
different models to come up with a “best guess”.
• They help to identify those events that are possible.
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Why Ensemble?
• Ensembles are based on the 12 runs of a single model
• These runs include
– the operational run,
– a control version of the model (run at lower
resolution),
– and 5 pairs (positive and negative) of bred
perturbation runs.
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Why Ensemble?
• Output is similar to daily model outputs.
• Helps provide more confidence in the forecast
compared to a single model run.
– Model runs historically differ from one run to
another.
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Model Forecasts
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Ensemble Forecast for Dec 4, 2000
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Ensemble Example
a.
b.
Figure 2. Observed snowfall a) visible satellite image valid at 1545
UTC 4 December 2000 and b) the total analyzed snowfall (cm)
showing the region of heavy snowfall in eastern North Carolina and
extreme southeastern Virginia.
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And of course, there’s always that
tried and true method of forecasting!!
Flip a coin!
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Ensembles
• For more information on Ensembles and to see
ensemble output, see:
– http://eyewall.met.psu.edu/ensembles/
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