1. Simple Analysis and Display of Variability in MM5 Outputs
Mark Kruger
Applied Physics Laboratory
21 May 2002

2. Past Experiences at APL
Observations from numerous GUI’s
developed at the APL:
Simple analysis tools work.
2D displays give better analytical results, 3D displays generate better mental model results.
End user included in the development process yields better software.
Build test build
Decide on who the end user is and talk to them
Get them involved early in the process
It’s easier for them to tell you what’s wrong than what is right
Simple analysis tools
HSW’s variability display relies on very simple analysis tools
Establishes a baseline to determine the need for more work, and how much improvement the follow on work provides
Good enough is the enemy of better
Display types
3D displays can pack more information onto the screen
Monitors, paper are 2D, so 3D displays are at best, a projection
The projection makes getting “numbers” difficult

3. High Seas Warning (HSW)
Developed using CEROS funding to help with High Seas Analysis and Forecasting
Plot of sea height for the western pacific between North America and Hawaii
Buoy observation data
Model data
Variation displayed by highlighting Buoy observation data that does not agree with the model data at that point within a certain, user defined, percentage.
Model data interpolated to buoy position using linear interpolation
Lets the human decide which data to believe

4. SENNEV Signal Excess Neural Net Environmental Visualization
Developed as part of a Navy funded research project
Displays Signal Excess (Sonar parameter) for a given point and ocean state.
2D view of a “depth slice”
Very easy to determine what the signal excess is at a given point on that depth slice
Very hard to get a feeling for what is going on overall
3D view of a iso-surface of Signal Excess
General effects due to sound speed profile and bathymetry become visible
Hard to determine exact extent of a feature

5. Current Work Focus on TAF tools for NAS Whidbey Is. Getting data
Well defined user group, close at hand
“Simple” problems
Getting data
MM5 output as well as MM5 ensemble
Analyzing model data specific for NAS Whidbey Is.

6. NASWI Temperatures From Ensemble Models
Temperature versus time from forecast for 6 different ensemble members and the centroid.
Ensemble members created by taking various global models and using them as initial parameters for the mm5 mesoscale model along with station observations.
Centroid created by averaging the global models and using that average as the initial parameters for the mm5 mesoscale model.
From a small set of other plots, these trends can be noticed:
Nogaps (Navy) model starts out cold
Taiwanese model gets warm

7. Analysis of Temperatures
Temperature versus time from forecast.
Mean, median, centroid, minimum, and maximum values plotted.
Mean, median, and centroid values are very consistent
Minimum and maximum values plotted instead of other averaging schemes because of the lack of data points.
Less spread when the temperature is changing rapidly than when a peak appears.
Some idea of the variability of the forecasted temperature can be gained by looking at the spread.
Less effort needs to spent when there is low spread.
More effort and other data is required when there is a large spread.

8. NASWI Temp., Same Model, Different Run Times
Temperature versus time from first forecast for 5 consecutive runs of the main mm5 model (which is run every 12 hours).
Good, but not perfect fit in the overlapping areas.
Note the rapid change at the start of each forecast.
Some idea of the variability of a model can be gleaned from how the same model changes over time.
Large spread in model predictions at a given point in time imply large variability and sensitivity to initial conditions
Small spread in model predictions at a given time imply the opposite.

9. d(Model) / dt
Plot of a particular time on the previous plot. At the given time, 5 temperature values are available, one for each of the model runs.
These values can be plotted, not only against temperature, but when the value was forecast (the newer values are to the right, the values taken from forecasts farther in the past are on the left).
Forecast variability becomes obvious and easier to quantify.
Model trends may also be derived.

10. The Future Closer work with the TAF producers Multidimensional views
Spend some time with the AG’s, showing them displays and getting direct feedback
Multidimensional views
VisAD or XIS as a toolkit
More advanced analysis