13.4 Multiple Meteorological Data


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Another obvious ensemble 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. For this example, we will run the full simulation. Start by retrieving the previously saved captex_control.txt and captex_setup.txt settings into the GUI menu.

  1. Before running the first simulation, 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.

  2. To speed up the computations, open the Advanced / Configuration Setup / Concentration / Menu #4 and change the particle release per cycle from 50000 to 10000. We can easily run with more particles than the previous simulations because only 5 members will be included in this ensemble. Save the changes and run the model.

  3. Once the simulation has completed, open the Concentration / Utilities / Convert to DATEM menu and as before select the measured data file captex2_meas.txt, convert the model output to the DATEM format, and finally Compute Statistics. Then rename the statistical results file, statA.txt, to a unique file name.

  4. Now repeat the same process with each of the other four meteorological data files, giving each output file and resulting statistical file a unique name:

      hysplit2.001captex2_narr.binstatA_narr.txt
      hysplit2.002captex2_era40.binstatA_era40.txt
      hysplit2.003captex2_gblr.binstatA_gblr.txt
      hysplit2.004captex2_wrf27.binstatA_wrf27.txt
      hysplit2.005captex2_wrf09.binstatA_wrf09.txt

    Remember the Convert to DATEM step must be performed with each new simulation, otherwise the previous file will be used for the statistical analysis.

  5. 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.

  6. Although there are a variety of possible metrics, the complete statistics for each of the simulations compared with the measured data can be extracted from the statA.txt file. The correlation coefficient and the rank (0-4) are shown below. The higher the rank, the better the overall performance. The rank consists of the sum of the normalized correlation, fractional bias, figure-of-merit, and Kolmogorov-Smirnov parameter.

    • 0.57 (2.82) NARR
    • 0.79 (2.82) ERA40
    • 0.61 (2.67) GBL
    • 0.65 (2.89) WRF27
    • 0.66 (3.00) WRF09

    The results suggest that the best performance, with respect to the correlation, is when using the ECMWF meteorological data fields, but the simulation using the WRF data at 9 km resolution shows the highest rank. 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.

  7. Open the Concentration / Setup Run / Grid menu and change the base name from hysplit2 to cmean. Then open the Utility / Convert to DATEM menu and run the statistical analysis for the ensemble mean, which shows a correlation of 0.80 and a rank of 3.26, 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.