Documentation of the Evaluation of CALPUFF and Other Long ...
Documentation of the Evaluation of CALPUFF and Other Long ... 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 103
36 kilometers and the vertical structure contained 43 vertical layers. Physics options were not optimized for northern European operations, but were based upon more advanced physics options available in MM5, reflecting the findings of Deng et al. (2004). Key MM5 options included: • ETA Planetary Boundary Layer (PBL) scheme; • Kain‐Fritsch II cumulus parameterization (Kain, 2004); • Rapid Radiative Transfer Model (RRTM) radiation scheme (Mlawer et al. 1997); • NOAH land surface model (LSM) (Chen et al. 2001); and • Dudhia Simple Ice microphysics scheme (Dudhia, 1989). Four dimensional data assimilation (FDDA) (Stauffer et al. 1990, 1991) was employed for this study. “Analysis nudging” based upon the NCEP reanalysis fields were used with default values for nudging strengths. 6.2.3 LRT Model Configuration and Inputs Three distinct classes of LRT dispersion models were included as part of the ETEX tracer evaluation including four Lagrangian models and one Eulerian model. CALPUFF Version 5.8 (Scire et al. 2000b) and SCIPUFF Version 2.303 (Sykes et al., 1998) are Lagrangian Gaussian puff models. HYSPLIT Version 4.8 (Draxler 1997) and FLEXPART Version 6.2 (Siebert 2006) are Lagrangian particle models. CAMx Version 5.2 (ENVIRON, 2010) is an Eulerian grid model. The respective user’s guides provide a complete description of the technical formulations of each of these models. Both CALPUFF and SCIPUFF are based upon Gaussian puff formulation. The two puff models have the advantage of more robust capabilities for source characterization, having the ability to treat dispersion for point, area, or line sources. Furthermore, these models can more accurately characterize dynamic releases of pollutants by accounting for initial plume rise of the pollutant. Conversely, the two particle models are very limited in their capability to characterize sources, having no direct ability to account for variations in source configurations or consider plume rise. The CAMx grid model is limited in its ability to simulate “plumes” by the grid resolution specified. CAMx includes a subgrid‐scale Plume‐in‐Grid (PiG) module to treat the early evolution, transport and dispersion of point source plumes whose effect on model performance was investigated using sensitivity tests. Since plume rise varies from hour‐to‐hour as a function of ambient temperature, wind speed and stability it is not possible to define a release height which would reflect this variation. Therefore, a constant release height of 10 meters was assigned for the two particle models in this study. This limitation of the particle models is problematic when comparing against models such as CALPUFF, SCIPUFF and CAMx that can simulate dynamic releases of emissions and calculate hour‐specific plume rise using hourly meteorological data. Iwasaki et al. (1998) found that the initial release height assigned to the Japan Meteorological Agency (JMA) particle model had a large impact on the predicted ground level concentrations. Investigation of initial release height sensitivity of the two particle models was beyond the scope of this evaluation. However, this limitation should be noted when considering the uncertainty of concentration estimates from the two particle models. Each of the four models requires gridded meteorological fields for dispersion calculations. CALPUFF normally uses output from the CALMET diagnostic wind field model (Scire et al., 104
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36 kilometers <strong>and</strong> <strong>the</strong> vertical structure contained 43 vertical layers. Physics options were not<br />
optimized for nor<strong>the</strong>rn European operations, but were based upon more advanced physics<br />
options available in MM5, reflecting <strong>the</strong> findings <strong>of</strong> Deng et al. (2004). Key MM5 options<br />
included:<br />
• ETA Planetary Boundary Layer (PBL) scheme;<br />
• Kain‐Fritsch II cumulus parameterization (Kain, 2004);<br />
• Rapid Radiative Transfer Model (RRTM) radiation scheme (Mlawer et al. 1997);<br />
• NOAH l<strong>and</strong> surface model (LSM) (Chen et al. 2001); <strong>and</strong><br />
• Dudhia Simple Ice microphysics scheme (Dudhia, 1989).<br />
Four dimensional data assimilation (FDDA) (Stauffer et al. 1990, 1991) was employed for this<br />
study. “Analysis nudging” based upon <strong>the</strong> NCEP reanalysis fields were used with default values<br />
for nudging strengths.<br />
6.2.3 LRT Model Configuration <strong>and</strong> Inputs<br />
Three distinct classes <strong>of</strong> LRT dispersion models were included as part <strong>of</strong> <strong>the</strong> ETEX tracer<br />
evaluation including four Lagrangian models <strong>and</strong> one Eulerian model. <strong>CALPUFF</strong> Version 5.8<br />
(Scire et al. 2000b) <strong>and</strong> SCIPUFF Version 2.303 (Sykes et al., 1998) are Lagrangian Gaussian puff<br />
models. HYSPLIT Version 4.8 (Draxler 1997) <strong>and</strong> FLEXPART Version 6.2 (Siebert 2006) are<br />
Lagrangian particle models. CAMx Version 5.2 (ENVIRON, 2010) is an Eulerian grid model. The<br />
respective user’s guides provide a complete description <strong>of</strong> <strong>the</strong> technical formulations <strong>of</strong> each <strong>of</strong><br />
<strong>the</strong>se models.<br />
Both <strong>CALPUFF</strong> <strong>and</strong> SCIPUFF are based upon Gaussian puff formulation. The two puff models<br />
have <strong>the</strong> advantage <strong>of</strong> more robust capabilities for source characterization, having <strong>the</strong> ability to<br />
treat dispersion for point, area, or line sources. Fur<strong>the</strong>rmore, <strong>the</strong>se models can more<br />
accurately characterize dynamic releases <strong>of</strong> pollutants by accounting for initial plume rise <strong>of</strong> <strong>the</strong><br />
pollutant. Conversely, <strong>the</strong> two particle models are very limited in <strong>the</strong>ir capability to<br />
characterize sources, having no direct ability to account for variations in source configurations<br />
or consider plume rise. The CAMx grid model is limited in its ability to simulate “plumes” by <strong>the</strong><br />
grid resolution specified. CAMx includes a subgrid‐scale Plume‐in‐Grid (PiG) module to treat<br />
<strong>the</strong> early evolution, transport <strong>and</strong> dispersion <strong>of</strong> point source plumes whose effect on model<br />
performance was investigated using sensitivity tests.<br />
Since plume rise varies from hour‐to‐hour as a function <strong>of</strong> ambient temperature, wind speed<br />
<strong>and</strong> stability it is not possible to define a release height which would reflect this variation.<br />
Therefore, a constant release height <strong>of</strong> 10 meters was assigned for <strong>the</strong> two particle models in<br />
this study. This limitation <strong>of</strong> <strong>the</strong> particle models is problematic when comparing against models<br />
such as <strong>CALPUFF</strong>, SCIPUFF <strong>and</strong> CAMx that can simulate dynamic releases <strong>of</strong> emissions <strong>and</strong><br />
calculate hour‐specific plume rise using hourly meteorological data. Iwasaki et al. (1998) found<br />
that <strong>the</strong> initial release height assigned to <strong>the</strong> Japan Meteorological Agency (JMA) particle model<br />
had a large impact on <strong>the</strong> predicted ground level concentrations. Investigation <strong>of</strong> initial release<br />
height sensitivity <strong>of</strong> <strong>the</strong> two particle models was beyond <strong>the</strong> scope <strong>of</strong> this evaluation. However,<br />
this limitation should be noted when considering <strong>the</strong> uncertainty <strong>of</strong> concentration estimates<br />
from <strong>the</strong> two particle models.<br />
Each <strong>of</strong> <strong>the</strong> four models requires gridded meteorological fields for dispersion calculations.<br />
<strong>CALPUFF</strong> normally uses output from <strong>the</strong> CALMET diagnostic wind field model (Scire et al.,<br />
104