Documentation of the Evaluation of CALPUFF and Other Long ...

centered upon the use of the same observed meteorological data and similar modeling
domains. To the extent practical, default model options were selected for all models in their
evaluation. Reflecting that evaluation paradigm, a major focus of the LRT model evaluation
using the ETEX database in this study was to provide a common source of meteorological fields
to each of the dispersion models evaluated.
Five different LRT dispersion models were evaluated using the ETEX database. Each of the LRT
models in this exercise requires three‐dimensional meteorological fields as input to the model.
For the majority of these models, meteorological fields from prognostic meteorological models
are the primary source of the meteorological inputs. However, CALPUFF and SCIPUFF typically
rely upon their own diagnostic meteorological models to provide three‐dimensional
meteorological fields to the dispersion model. In cases where prognostic meteorological model
data are ingested to set the initial conditions within the diagnostic meteorological model, much
of the original prognostic meteorological data is not preserved, and key parameters are
rediagnosed. This compromises a key component of the evaluation paradigm of Chang et al.
(2003) that we have adopted for the ETEX evaluation, namely a common meteorological
database. The Mesoscale Model Interface (MMIF) software program (Emery and Brashers,
2009) was developed to facilitate direct ingestion of prognostic meteorological model data by
the LRT dispersion model, bypassing the diagnostic meteorological model component and
rediagnosing algorithms effectively overcoming the challenge to this evaluation paradigm.
6.2.2 Meteorological Inputs
During the original ATMES‐II project, participating agencies during ETEX were required to
calculate concentration fields for their respective models using analysis fields from the
European Center for Medium‐Range Weather Forecasts (ECMWF). ECMWF analysis fields were
available at 6‐hour intervals and a horizontal resolution of 0.5° (~50 km) latitude‐longitude
(D’Amours, 1998). Participating agencies could also submit results obtained using different
meteorological analyses. Van Dop et al. (1998) and Nasstrom et al. (1998) found that increasing
the resolution of the input meteorological fields enhanced the performance of the dispersion
models evaluated in the ATMES‐II study. Similarly, Deng et al. (2004) found that SCIPUFF model
performance for the Cross‐Appalachian Tracer Experiment (CAPTEX) improved by increasing
meteorological model horizontal and vertical resolution, use of four dimensional data
assimilation (FDDA), and more advanced meteorological model physics. However, they also
noted that use of the more advanced physics options were responsible for more improvement
in model performance than merely increasing horizontal grid resolution.
For the LRT model evaluation exercise using the ETEX database presented in this report,
meteorological inputs were generated using a limited‐area mesoscale meteorological model to
produce higher temporally and spatially resolved meteorological data than used in the ATMES‐II
project. By producing more accurate meteorological fields, it should be possible to maximize
performance of the LRT models under evaluation in this study. Furthermore, by using a
common source of meteorological data between each of the five modeling systems, it reduces
the potential contribution of differences in meteorological data on dispersion model
performance and facilitates a more direct intercomparison of dispersion model results.
Hourly meteorological fields were derived from the PSU/NCAR Mesoscale Meteorological
Model (MM5) Version 3.74 (Grell et al., 1995). MM5 was initialized with National Center for
Environmental Prediction (NCEP) reanalysis data (NCAR, 2008). NCEP reanalysis fields are
available every 6 hours on a 2.5° x 2.5° (~275 km) grid. The MM5 horizontal grid resolution was
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