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

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

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

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

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.

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

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.

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.

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.

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