Meteorological Grid Ensemble

The uncertainty of the concentration calculation can be addressed in similar way in which we earlier addressed trajectory uncertainty, by slightly offsetting the meteorological data to test the sensitivity of the advection calculation to the gradients in the meteorological data fields. Start by retrieving the previously saved captex_control.txt and captex_setup.txt settings into the GUI menu. Note that these files may now be in your archived working directory as well as the \Tutorial\files directory.

1. Recall from the trajectory discussion that the internal grid offset is in the horizontal and vertical. Therefore, to take full advantage of the ensemble, the release height should be changed to 200 m from the Setup Run / Starting Locations menu. Also to reduce the number of output frames, open the grid menu and change the averaging time from 03 to 06 hours and the sampling start time from 18 to 21 to line up the model output times with the majority of the measurement data.


2. Open the Advanced / Configuration Setup / Menu #4. Because the GUI script will cycle through 27 simulations, we can speed up the calculation by reducing the particle release number from 50000 to 25000 (perhaps even 10000 for an even faster calculation). Then save to close all menus.


3. To start the model simulation (this calculation may take some time: ~1 hr), press the Special Runs / Ensemble / Meteorology menu tab and accept the prompts to continue and when finished, close the ensemble complete menu. The calculation results are saved to the \working directory and the base output file name, hysplit2.bin is appended with a simulation sequence number from .001 to .027.


4. To analyze the simulation results, a special pre-processing program needs to be run. Press the Display / Ensemble / View map menu tab. This opens up the ensemble display menu. The upper part of the menu is used to change how the input data files are processed. In the middle section, change the output selection option Concentration at Percentile from its default 95% to 50%. This means that the output maps will show the median concentration of all the ensemble members. There is one graphic for each time period. If you set the aggregation period from 1 to 10, then all the time periods are included in the statistic and you only get one plot covering all times. If no plot is produced, your binary output files may not contain all 10 output time periods. Otherwise, reset the aggregation period back to 1 when finished.


5. The View Map processing of the ensemble output files leaves a variety of different files (prob??, cmax??, cmean) in the working directory. These files were previously displayed as maps (using concplot) or they can be displayed as box plots. Start from the Display / Ensemble / box plot tab. This opens a simple menu where you can set the location of the data extraction point. Use the same location, sampler 510 (Little Valley, NY), that we examined in the concentration utility tutorial by entering 42.25 -78.80, then Execute Display.


6. The resulting box plot shows the ensemble concentration predictions at Little Valley for each 6-hr averaging period during the simulation. The box bottom and top shows the 25th and 75th percentile concentrations, the single lines the 10th and 90th percentiles, and the circles the 5th and 95th percentile concentrations. The plus symbol and middle line show the mean and median concentrations. Note there are no unit conversion options for any of these programs so the results are all shown as g m^-3. An additional graphic with the same axis labels is also created that shows the individual member concentrations indicated by member number.


7. To relate the box plot with the original graphic of median concentrations, the median box plot concentration on the 26th between 15 and 21 is perhaps 7x10^-10. Examining the graphic at 42.25 -78.80 shows the location to be in the blue (10^-10) region very close to the yellow (10^-9) contour.


8. To view how the measured data fall within the ensemble estimates, we can overlay the data points on the boxplot. In the concentration utility tutorial we created a file with just the measurements from station 510 for display purposes. Unfortunately, the box plot program has no overlay capability, but we can manually (using paint after converting the image to BMP) add the measurements to the box plot graphic after converting the numbers from pg back to g. Two of the three highest measured concentrations correspond with the smallest uncertainty range. Note that 10^-11 is approximately the detection threshold for the measured concentrations. Values lower than this are effectively zero. The ensemble also provides an estimate of the potential maximum concentration.

There are many causes of concentration uncertainty, such as how the well the meteorological data represent the true flow field, errors in the parameterization of physical processes in the model, and even the effects of atmospheric turbulence not accounted for by the model. In this case we looked at the data issue. In the subsequent sections we will explore the other factors.