12.6 Source Location Statistics

In the previous section, we configured the model to simultaneously calculate the dispersion from a regular array of source locations, keeping the results independent of each other. Given that measurement data are available, it is possible to compute the performance statistics of each source location to determine the location with the best performance. If not continuing from the previous section, load the saved files matrix_control.txt and matrix_setup.txt.

1. Start with the same model configuration as in the previous section, but instead of using a unit emission source term, set the rate back to a more realistic value so that the magnitude of the model predictions more closely match the measurements. We can use the known value of 67000 g/h. The issue here is to determine which release location gives the best fit with the measurements. Change the source term, save the changes, and then, as before, run the model from the Special Runs / Matrix menu tab.


2. When the run completes, press the Display / Source-Receptor / Stats menu tab to open the statistics menu. Define the measured data file (step 1) with the extracted 3-hour observations, set the units conversion factor to 1.0E+12. Edit the output file names if required, then press the Execute button of Step 4 to generate the statistics for each source location. This will take a little time as each source location is extracted from the binary output file and the results are accumulated in the statistical summary text file. When processing is completed, the termination message file will be displayed.


3. Select the Corr radiobutton to output the correlation coefficient as the primary statistic for viewing. Press the Execute of Step 5 and the map of the correlation coefficient shows the highest value is associated with the release location at N39.5 85.0W. See the output file sumstat.txt from Step 4 for all the statistical results associated with each release location. Other statistical metrics show a larger region of potential source locations.

In this example, we used the measurement data in conjunction with model simulations from multiple locations to determine which location provides the best fit with the measurements. In the next exercise, we will assume that we know the release location, but we will try to determine the time-varying emission rate.