Generic Guidance and Optimum Model Settings for the CALPUFF ...

scatter type plot containing a color coded symbol reflecting the concentration for a particular
pollutant and the scale consists of concentric rings of distance, where the distance is the travel
time during one hour for the wind speed measured for the same time period as the concentration,
and the direction of the data point relative to north is plotted based on the simultaneous measured
wind direction.
TRC’s pollutant rose module supports the following meteorological data sets, 3D.DAT,
SURF.DAT, CALMET.DAT and, it supports all pollutant species captured in the
CALPUFF.DAT binary file.
The pollution wind rose plot gives very detailed information about the direction from which high
pollution events are coming.
Use the Pollutant rose plotter along with a terrain map to show multiple pollution wind roses at
specific locations which will show the overall spatial distribution and high pollution events.
4.3.2 Statistical Evaluation
TRC’s Meteorological Evaluation Module performs quantitative statistical comparisons of two
meteorological datasets comprised of time series of meteorological parameters at a number of
locations.
This package performs analysis of various types of modeled meteorological data such as
CALMET, MM5 and WRF and observed data. It is especially useful for model-to-model
comparisons, model to observation comparisons and observation to observation comparisons. An
example of observation to observation comparisons is the ‘evaluation of co-located instruments or
different types of instrumentation (e.g. a tower and SODAR/RASS system).
Examples of the suite of statistical performance measures include scalar and vector mean wind
speeds, standard deviations in measured and observed winds, RMSE errors (total plus systematic
and unsystematic components), two model skill measures, the Index of Agreement, as well as the
mean and standard deviations in modeled and observed wind speeds.
The Statistical measures include
- mean value (e.g. mean observation and mean prediction)
- bias error (average difference e.g. Predicted – Observation)
- Gross or Absolute error (average of the absolute value of the |P-O| values)
- Root-mean square error (RMSE), including its systematic (RMSEs) and
unsystematic (RMSEu) components
- Index of Agreement (IOA)
The bias and gross errors for wind speed and wind direction are computed from the wind speed
and wind direction values, not the U. V components of the winds.
46