Ensemble Multiple Meteorological Data

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The last ensemble that can be created is to run the dispersion simulation with different meteorological data files. There is no one-click option through the GUI. Each simulation must be configured manually, but the GUI can be used to analyze the products. As in all the previous ensemble sections, start by retrieving the previously saved captex_control.txt and captex_setup.txt settings into the GUI menu.

  1. Before running a simulation, we should delete all the ensemble files left over from the previous sections. There is a Special Runs / Ensemble / cleanup tab that opens the menu. There are no options, just press Execute Script and all ensemble related files will be deleted from the working directory. Only the model output concentration files with the root name set in the Grid menu with the .000 suffix will be deleted.

  2. Before starting the base simulation using NARR, open the Setup Run / Grid Menu and change the output file name from hysplit2.bin to hysplit2.001. The suffix will be manually incremented with each new simulation, following the same procedure used in the automated ensemble scripts. Save the changes and run the model. Once the simulation has completed, open the Concentration / Utilities / Convert to DATEM menu and as before select the measured data file, create the DATEM file, and finally Compute Statistics. Then rename the statistical results file, statA.txt, to a unique file name.

  3. Now repeat the same process with each of the other three meteorological data file, giving the output file and resulting statistical file a unique names:


  4. When all the simulations have been completed, open the Setup Run / Grid Menu and rename the output file from hysplit2.00? to just hysplit2. The base name will then be passed through the GUI to the ensemble scripts, where the programs automatically search for the 3-digit suffix. Save the changes, run the Display / Ensemble / View Map to generate the probability files, then create the boxplot at Little Valley, NY (42.2N 78.8W). With only four members, it is not surprising that except for one time period, the concentration variations are larger than in the previous ensemble examples for this location. A view of the member plot shows that simulation 002 (the ERA40) gave the highest concentrations.

  5. Although there are a variety of possible metrics, the correlation coefficients for each of the simulations

    • 0.51 NARR
    • 0.79 ERA
    • 0.62 GBL
    • 0.68 WRF

    suggests that the best performance is when using ECMWF meteorological data fields. Now in most modeling situations, we usually don't know the correct answer. One approach is to use the ensemble mean concentration for the simulation. This field has already been computed in the View Map step.

  6. Then open the Utilities / Setup Run / Grid menu and change the base name from hysplit2 to cmean. The open the Utility / Convert to DATEM menu and run the statistical analysis for the ensemble mean, which shows a correlation of 0.81, a better performance than any of the other ensemble members. A similar, but more qualitative comparison, can be made for the scatter plots, with the ensemble mean plot also showing excellent results.

The results shown here suggest that a practical application for ensemble products may be simply to compute the ensemble mean and use that as one would a single deterministic simulation. It might be an interesting exercise to go back to some of the previous ensemble examples and perform a similar analysis.