2-18 

### Figure 2-11. Plot of the 728 precipitation stations for the CALMET subregional domains. 200 400 600 800 1000 1200 1400 1600 1800 2000 LCC East (km) -1600 -1400 -1200 -1000

-800 -600 -400 -200 0 200 400LCC North (km)

LCC Origin: 40.0 N, 97.0 W Standard Parallels: 33.0 N, 45.0 N Datum: NWS-84 Elevation (meters)

Domain 1

Domain 2 Domain 3

Domain 4 Domain 5

GA AL TN

SC NC VA

FL KY

MS WV

-25 0.1 50 100 150 200 300 400 500 600 700 800 900 1000 1100 1200 1300 14002-19 

### Figure 2-12. Plot of 23 upper air stations for the CALMET subregional domains. 200 400 600 800 1000 1200 1400 1600 1800 2000 LCC East (km) -1600 -1400 -1200 -1000

-800 -600 -400 -200 0 200 400LCC North (km)

72201 72202 72206 72208

72210 72214 72215 72230

72233 72235

72240 72305 72317 72318

72327

72340 72402 72403 72426 72520 74455

74560

74794

LCC Origin: 40.0 N, 97.0 W Standard Parallels: 33.0 N, 45.0 N Datum: NWS-84 Elevation (meters)

Domain 1

Domain 2 Domain 3

Domain 4 Domain 5

GA AL TN

SC NC VA

FL KY

MS WV

-25 0.1 50 100 150 200 300 400 500 600 700 800 900 1000 1100 1200 1300 14002-20 

### Figure 2-13. Plot of 12 buoy stations for the CALMET subregional domains. 200 400 600 800 1000 1200 1400 1600 1800 2000 LCC East (km) -1600 -1400 -1200 -1000

-800 -600 -400 -200 0 200 400LCC North (km)

41004

41008

42003 42007

42036 42039 42040 41008 44009

44014

41009 41012

42001 LCC Origin: 40.0 N, 97.0 W Standard Parallels: 33.0 N, 45.0 N Datum: NWS-84 Elevation (meters)

Domain 1

Domain 2 Domain 3

Domain 4 Domain 5

GA AL TN

SC NC VA

FL KY

MS WV

-25 0.1 50 100 150 200 300 400 500 600 700 800 900 1000 1100 1200 1300 14002-21 

### Figure 2-14. Plot of MM5 12-km grid used in CALMET simulations for 2001 and 2002. 200 400 600 800 1000 1200 1400 1600 1800 2000 LCC East (km) -1600 -1400 -1200 -1000

-800 -600 -400 -200 0 200 400LCC North (km)

LCC Origin: 40.0 N, 97.0 W Standard Parallels: 33.0 N, 45.0 N Datum: NWS-84 Elevation (meters)

Domain 1

Domain 2 Domain 3

Domain 4 Domain 5

GA AL TN

SC NC VA

FL KY

MS WV

-25 0.1 50 100 150 200 300 400 500 600 700 800 900 1000 1100 1200 1300 14002-22 

### Figure 2-15. Plot of MM5 36 km grid used in CALMET simulations for 2003. 200 400 600 800 1000 1200 1400 1600 1800 2000 LCC East (km) -1600 -1400 -1200 -1000

-800 -600 -400 -200 0 200 400LCC North (km)

LCC Origin: 40.0 N, 97.0 W Standard Parallels: 33.0 N, 45.0 N Datum: NWS-84 Elevation (meters)

Domain 1

Domain 2 Domain 3

Domain 4 Domain 5

GA AL TN

SC NC VA

FL KY

MS WV

-25 0.1 50 100 150 200 300 400 500 600 700 800 900 1000 1100 1200 1300 14002-23 

### 2.4 Air Quality Monitoring Data

CALPUFF uses hourly ozone concentration measurem ents in the chemical transformation rates (SO 2 

### to SO 4 

### , NO x 

### to HNO 3 

### /NO 3 

### ). The ambient ozone measurements are used in determining SO 2 

### loss rates due to chemical transformation to sulfate and in determining NO x 

### loss rates to nitrate. Ambient ozone monitoring data from the U.S. EPA AIRS and CASTNet networks were used to develop the hourly ozone monitoring data file (OZONE.DAT).
All available ozone data within the VISTAS regional domain were assembled and processed. All ozone data were combined into OZONE.DAT fil es for each of the modeled years. These ozone data files were also part of the standard VISTAS data set available at http://www.src.com web page. Two types of runs were made for the pur poses of the BART analysis. For the screening runs, 12-km CALMET regional domain was u sed and CALPUFF computational subdomains were created on the 12-km grid. For most of the sources, these CALPUFF computational subdomains corresponded to the sizes and location of the five 4-km subregional domains. In the refined runs, 4-km CALMET/CALPUFF subregiona l domains were used. For each of the five CALPUFF subregional modeling domains, as well as for the CALPUFF computational subdomains within the VISTAS regional domain, smaller OZONE.DAT file s were extracted to include only those ozone monitoring stations that are located within the particular domain or subdomain and its vicinity. The extraction w as performed using the program SUBDOMN, a utility program run through the CALPUFF GUI. Figure 2-16 shows locations of all ozone stations used in the CALPUFF modeling on the five su bregional 4-km domains and computational subdomains on the 12-km grid. To tal of 497 ozone stations are sh own on this plot. The number of ozone stations (AIRS and CASTNet) used in each of the subregional domains are listed in Table A-2.
For the base case simulations, a constant ammoni a concentration of 0.5 parts per billion (ppb) was assumed as recommended by the Intera gency Workgroup on Air Quality Modeling (IWAQM, 1998) for forested Class I areas. In th is application of the model, the Ammonia Limiting Method (ALM) was not used.

2.5 Class I Areas

The Class I Areas within the VISTAS Region are: Breton Island, Brigantine, Cape Romain, Chassahowitzka, Cohutta, Dolly Sods, Everglades, Great Smoky Mountains, James River Face, Joyce Kilmer-Slickrock, Linville Gorge, Mammoth Cave, Mingo, Okefenokee, Otter Creek, Shenandoah, Shining Rock, Sipsey, St Marks, Sw anquarter, Wolf Island. The receptor locations along with receptor elevations were available from the National Park Service (NPS).2-24 

### Figure 2-16. Plot of the 497 ozone stations used for the CALPUFF simulations. 200 400 600 800 1000 1200 1400 1600 1800 2000 LCC East (km) -1600 -1400 -1200 -1000

-800 -600 -400 -200 0 200 400LCC North (km)

LCC Origin: 40.0 N, 97.0 W Standard Parallels: 33.0 N, 45.0 N Datum: NWS-84 Elevation (meters)

Domain 1

Domain 2 Domain 3

Domain 4 Domain 5

GA AL TN

SC NC VA

FL KY

MS WV

-25 0.1 50 100 150 200 300 400 500 600 700 800 900 1000 1100 1200 1300 14002-25 

### Table 2-4. Number of Ozone Stations used in the Modeling.

2001 2002 2003

Total AIRS CASTNet Total AIRS CASTNet Total AIRS CASTNet

Domain 1 63 62 1 65 64 1 63 62 1

Domain 2 79 76 3 79 76 3 81 78 3

Domain 3 276 250 26 273 246 27 271 244 27

Domain 4 234 218 16 233 216 17 231 214 17

Domain 5 299 271 28 297 269 28 297 269 282-26 

### 2.6 Model Configuration and Settings for Initial Analysis

VISTAS used CALMET Version 5.723 for the re gional domain and CALMET Version 5.720 for the subregional domains. For CALPUFF runs, VISTAS used CALPUFF Version 5.754. These versions contain enhancements funded by the Minerals Management Service (MMS) and VISTAS. They were developed by TRC and they are maintained on the CALPUFF website ( http://www.src.com ) for public access. This version includes CALMET, CALPUFF, CALPOST, CALSUM, and POSTUTIL as well as the CALVIEW graphical user interface.
The initial analysis for a specific source used a CALPUFF computational domain that included all Class I areas within 300 km of the source. These Class I areas are specified in the CALPUFF control file for analysis. This initial approach was designed to significantly reduce the CALPUFF simulation time by restricting the CALPUFF com putational domain size to include only areas where significant impacts are feasible rather than the entire regional domain. CALPUFF allows its computational domain to be specified as a subset of the CALMET meteorological domain by settings within the CALPUFF input file. The advantage of selecting a smaller CALPUFF computational domain in the regional CALPUFF simulations is that CALPUFF run time is proportional to the number and residence time of the puffs on the domain (and other factors such as the number of receptors and the internal time step computed by the model). A CALPUFF domain covering an area 300 km from a source in all directions would involve only 50 x 50 12-km grid cells, which would require modest computational resources.
CALMET output files for the VISTAS regional dom ain shown in Figure 2-2 were provided to VISTAS by TRC. These files are in CALPUFF-ready format, and as such, no CALMET user inputs are required. An option in CALMET allows finer grid CALMET input files to be calculated from the 12-km CALMET files.
The basic characteristics of the CALMET, CALPUFF and CALPOST configurations for the initial analyses are listed below.
2.7 CALMET Modeling Configuration ( 12-km Initial Exemption Modeling)

The CALMET model configuration for the regi onal CALMET simulations has been defined by TRC in collaboration with the VISTAS States. The basic model configuration followed the recommended IWAQM guidance (EPA, 1998; Pages A-1 through A-6), except as noted below.
The basic features of the modeling simulation are the following:

- Modeling period: 3 years (2001-2003)
- Meteorological inputs: MM5 data provide initial guess fields in CALMET
- CALMET grid resolution: 12-km (same Lambert Conformal coordinate system and grid cells

as the 12-km 2001/2002 MM5 simulations)
- CALMET vertical layers: 10 layers. Cell face hei ghts (meters): 0, 20, 40, 80, 160, 320, 640,

1200, 2000, 3000, 4000.2-27 

### - CALMET mode: No-Observations mode including option to read overwater data directly

from MM5.
- Diagnostic options: IWAQM default values, except as follows: diagnostic terrain blocking

and slope flow algorithms used for 2003 si mulations (using 36-km MM5 data), but no diagnostic terrain adjustments in 2001 a nd 2002 simulation (using 12-km MM5 data)
- CALMET options dealing with radius of influence parameters (R1, R2, RMAX1, RMAX2,

RMAX3), BIAS, ICALM parameters are not used in No-Observations mode.
- TERRAD (terrain scale) is required for runs with diagnostic terrain adjustments (i.e., the

2003 simulations). Value of 15 km was determ ined from testing and used in modeling.
- Land use defining water: JWAT1 = 55, JWAT2 = 55 (large bodies of water). This feature

allows the temperature field over large bodies of water such as the Atlantic Ocean and the Great lakes to be properly characterized by buoy observations.
- Mixing height averaging parameter for the regional simulations determined by TRC based on

sensitivity tests was MNMDAV=1. The purpose of the testing was to optimize the variable to allow spatial variability in the mixing height field, but without excessive noise.
- Geophysical data for regional runs: SRTM30 30 arc-seconds terrain data, Composite Theme

Grid (CTG) USGS 200m land use dataset. Refe rences for these and ot her CALMET datasets can be found on the CALPUFF data pa ge of the official CALPUFF site ( http://www.src.com ).
The CALMET input files with the complete set of model switch settings used for the regional simulations are provided on the VI STAS page of the CALPUFF web site.
2.8 CALMET Modeling Configuration (4-km Refined Exemption Modeling)

For the subregional CALMET simulations, the mode l configuration has also been defined by TRC in collaboration with the VISTAS States following the recommended IWAQM guidance (EPA, 1998; Pages A-1 through A-6), except as noted below.
The basic features of the modeling simulation are the following:

- Modeling period: 3 years (2001-2003)
- Meteorological inputs: MM5 data provide in itial guess fields in CALMET. Meteorological

observational data are used in the Step 2 calcu lations. Overwater (buoy) data were used in addition to the hourly surface meteorological observations, precipitation observations and twice-daily upper air sounding data.
- CALMET grid resolution: 4-km (same Lambert Conformal coordinate system as the 12-km

2001/2002 MM5 simulations)2-28 

### - CALMET vertical layers: 10 layers. Cell face hei ghts (meters): 0, 20, 40, 80, 160, 320, 640,

1200, 2000, 3000, 4000.

- CALMET mode: Refined mode with all available observational data included in the Step 2.
- Diagnostic options: IWAQM default values.
- CALMET options dealing with radius of in fluence parameters: R1 = 5 km, R2 = 5 km,

RMAX1 = 40 km, RMAX2 = 40 km, RMAX3 = 100 km, BIAS(NZ) = 10*0, ICALM = 0.
- TERRAD (terrain scale) is required for runs with diagnostic terrain adjustments which was

used for all years for the subregional domains. Value of 15 km was determined from testing and used in modeling.
- Land use defining water: JWAT1 = 55, JWAT2 = 55 (large bodies of water). This feature

allows the temperature field over large bodies of water such as the Atlantic Ocean and the Great lakes to be properly characterized by buoy observations.
- Mixing height averaging parameter for the subr egional simulations determined by TRC based

on sensitivity tests was MNMDAV=1. The purp ose of the testing was to optimize the variable to allow spatial variability in the mi xing height field, but without excessive noise.
- Geophysical data for regional runs: 3 arc- second USGS DEM terrain data set (SRTM30 data

also used as a backup in case when 3 arc-second data are missing), CTG USGS 200m land use dataset. References for these and other CA LMET datasets can be found on the CALPUFF data page of the official CALPUFF site ( http://www.src.com ).
The CALMET input files with the complete set of model switch settings used for the subregional simulations are provided on the VISTAS page of the CALPUFF web site.
2.9 CALPUFF Modeling Configuration (Initial Exemption Modeling)

The CALPUFF model configuration for the region al CALPUFF initial simulations followed the recommended IWAQM guidance (EPA, 1998; Pages B-1 through B-8), except as noted below:

- CALPUFF domain configured to include the s ource and all Class I areas within 300 km of

the source plus 50 km buffer zone in each direction. CALPUFF is recommended for all source-receptor distances to be considered in the BART analyses.

- Chemical mechanism: MESOPUFF II module
- Species modeled: SO 2 

### , SO 4 

### , NO x 

### , HNO 3 

### , NO 3 

### and particulate matter in size categories of

<0.625 μ m, 0.625-1.0 μ m, 1.0-1.25 μ m, 1.25-2.5 μ m, 2.5-6.0 μ m and 6-10 μ m aerodynamic diameters. As noted below, the particulat e matter emissions by size category have been combined into the appropriate species for the visibility analysis (i.e., elemental carbon (EC), fine PM or “soil” (< 2.5 μ m in diameter), coarse PM (between 2.5-10 μ m in diameter) and organics (called secondary organic aerosols (SOA) in the CALPOST postprocessor).2-29 

### - Emission rates for modeling based on EPA BART guidance, i.e., maximum 24-hour actual

emission rate with normal operations from th e highest emitting day of the meteorological period modeled (excluding days where star t-up, shutdown or malfunctions occurred sometime during the day.) Note that potential emi ssions were used to determine if a source is BART-eligible, but 24-hour average maximum emissions were used for modeling purposes (70 FR 39162). Pollutants considered include SO 2 

### , H 2 

### SO 4 

### , NO x 

### and PM 10 

### . Condensable emissions were considered as pr imary fine particulate matter and allocated equally to the two submicrometer-particle size classes. If actual source emissions data were not available, the modeling was based on permit limits. If source-specific size categories were not available, then AP-42 factors have been used for sources where AP-42 factors are available. For sources where AP-42 factors ar e not available, alternative approaches to speciation are given below.
Excluded from the modeling were pollutants with plant-wide emissions less than de minimis levels (40 tons per year for SO 2 

### and NO x 

### and 15 tons per year for PM 10 

### ). De minimis levels are plant wide for each visibility-impairing pollu tant, so individual units have been modeled even if they have emissions below de minimis if the plant total is greater than de minimis.
- Particulate emissions speciation: Break down, as appropriate, filterable and condensable

particulate matter into the following species cat egories: elemental carbon (soot), “soil” (fine PM < 2.5 μ m diameter), coarse particulate matter (2.5-10 μ m diameter) and organics. The process is sketched in Figure 2-17 and illustrated in more detail in Table 2-5. Table 2-5 is an image of an interactive spreadsheet where cells shown in blue are inputs that user can make depending on the level of information available. If source-specific speciated emissions factors were not available, AP-42 factors or speciati on information developed by the National Park Service (http://www2.nature.nps.gov/air/permits/ec t/index.cfm) was used to estimate the PM speciation for many source sectors.
Otherwise, assumptions were proposed by th e source, and reviewed and approved by the State. Possible acceptable alternative appro aches to estimating speciation include the following:
- Speciation profiles developed by the SMOKE emissions model for use in VISTAS’

CMAQ regional air quality modeling (available at http://www.vistassesarm.org/BART/calpuff.asp).
- The approach described in a memo available at http://www.vistassesarm.org/BART/calpuff.asp, which provid es reasonably conservative estimates in situations where data are incomplete.
- Class I receptors: used FLM Class I receptor list with receptor elevations provided (available

from the NPS).
- CALPUFF model options: used IWAQM (EPA, 1 998) default guidance, including PasquillGifford dispersion coefficients.2-30 

### - Ozone dataset – used obser ved ozone data for 2001-2003 from CASTNet and AIRS stations. Only non-urban ozone stations were used in the OZONE.DAT file. Monthly average ozone (backup) background values were computed based on daytime average ozone concentrations from the OZONE.DAT file (6am-6pm average ozone concentrations computed by month).
- Background ammonia concentration: In CALPUFF, used constant (0.5 ppb) value for

ammonia.
- Puff representation: integrated puff sampling methodology.
- Building downwash: Ignored building downwash unless source is within 50-km of a Class I

area and the relevant state agency instructed the source to specifically consider building downwash.
A sample CALPUFF input file with the complete set of recommended switch settings as used in the VISTAS BART modeling is provided on the VISTAS page of the CALPUFF web site ( http://www.src.com ).
2.10 CALPOST and POSTUTIL Configuration (Initial Exemption Modeling)

- Used Visibility Method 6 in CALPOST
- Species considered in visibility analysis: SO 4 

### , NO 3 

### , EC, SOA (i.e., condensable organic

emissions), soil, coarse PM
- Natural background light extinction: A monthl y average natural background as calculated by

CALPOST under Method 6, based on annual aver age default natural conditions component concentrations and monthly average f(RH) values for the centroid of the Class I area, from Table A-3 in the natural conditions guidance document,.
- Light extinction efficiencies: IMPROVE Methodology defined in FLAG (2000).
- Nitrate repartitioning in POSTUTIL: Not used for the initial modeling.
The initial run results were based on the highest change in light extinction (deciviews) from natural conditions over the three-year modeling pe riod for each Class I area considered. Predicted changes exceeding the “contribution” threshold (0 .5 deciviews) triggered a finer grid CALPUFF modeling analysis.2-31 

### Figure 2-17. Speciation of PM 10 

### emissions. The numbers in the bottom row are the extinction efficiencies applied in the standard IMPROVE extinction equations. PM 10

(including H 2 SO 4 )

Filterable Condensable

EC PMC PM 2.5 Inorganic PM

(including H 2 SO 4 ) Organic PM

SOIL

1 COARSE

0.6 EC

10 SO 4

3 x f(RH) SOIL (non-SO 4 inorganic) 1 OC

42-32Table 2-5. Example of Speciation of PM10emissions.inputs in unitsof (g/s)H2SO4PM10PM2.5PM10andH2SO41.63022.03116.520filterablecondensablecondensable %59%41%12.9989.033non H2SO4condensable7.403AP-42coarse coarse soil soilsoil soil EC EC EC EC H2SO4OC OC IC (soil) IC (soil)PM800 PM425 PM187 PM112 PM081 PM056 PM187 PM112 PM081 PM056 PM081 PM056 PM081 PM0566.00 -10.002.50-6.001.25-2.501.00-1.250.625-1.000.50-0.6251.25-2.501.00-1.250.625-1.000.50-0.6250.625-1.000.50-0.6250.625-1.000.50-0.62516.3% 26.1% 23.9% 6.5% 12.0% 15.2%23.9% 6.5% 12.0% 15.2% 50.0% 50.0% 50.0% 50.0%96.3%3.7%80%20%2.119 3.393 2.992 0.814 1.502 1.903 0.115 0.031 0.058 0.073 1.630 2.961 2.961 0.740 0.740EC % offilterable andinorganic % ofcondensableInputs to POSTUTIL:0.6 0.6 1 1 1 1 10 10 10 10 4 4Ext.coef.coarse coarse soil soil soil soil EC EC EC EC OC OCPM800 PM425 PM187 PM112 PM081 PM056 PM187 PM112 PM081 PM056 PM081 PM0562.119 3.393 2.992 0.814 2.242 2.643 0.115 0.031 0.058 0.073 2.961 2.961Note: This table is an image of an interactive spreadsheet wherevalues shown in blue are inputsthat user can make depending on the level of information available, while values shown inblack are computed in the spreadsheet.3-1 

## 3.0 MODELED FACILITIES FOR BART

3.1 Visibility Results

VISTAS provided a list of BART-eligible sources to TRC for the BART exemption modeling. Table 3-1 lists the VISTAS states and number of sources modeled in each of the states. In this table, the number of sources that can be exempt from BART cont rols is also provided. Figure 3-1 shows location of the modeled BART sources.
According to the VISTAS protocol, screening simulati on results below 0.5 dv for all three years indicate that facility is not subject to BART controls. For facilities failing the screen, or those located relatively close to one or more Class I areas (usually with in 50 km) the state has recommended immediately using the more refined 4-km CALMET meteorological grid. In the refined simulations, the 98th percentile value of prediction change in extinction (i.e., the 8th highest day) is compared to the 0.5 dv threshold. If the highest 98th percentile value during the three year simulation period is below the 0.5 dv threshold, the source is considered not subject to BART controls.
In Alabama, 24 facilities were modeled with the total of 169 point sources. All Alabama facilities were modeled using the 12-km Regional CALMET do main and CALPUFF computational subdomain. Maximum predicted extinction change for 16 of these sources at each of the Class I areas were all below the 0.5 dv threshold value. For the remaining 8 sour ces the threshold value was exceeded for some or all of the Class I areas within 300 km from the sour ce. For these 8 sources, refined modeling could be performed for further analysis, although due to the magnitude of the impacts, this was not done under VISTAS funding.
In Florida, three facilities were modeled. One of them was modeled on the VISTAS CALMET 12-km regional domain and for that facility, maximum predicted extinction change was below the threshold value of 0.5 dv. The other two facilities were mode led on the subregional VISTAS 4-km Domain 2. The results of the modeling for these two facilities show that maximum predicted extinction change was below the threshold value of 0.5 dv for Class I areas th at are not very close to these facilities. However, for each of these two facilities, for the Class I area th at are within 50 km from the facilities the 8th highest day exceeds the 0.5 dv threshold value.
In Georgia, seven facilities were modeled. One of the facilities was first modeled on the 12-km regional domain. Since the maximum predicted visibility imp act was above the threshold in one of the Class I areas, modeling was redone on the 4-km subregional Domain 4. The 98 th 

### percentile in that run was well below the 0.5 dv threshold value. Two more facilities were modeled on the 4-km subregional Domain 4. For both of these facilities, the 98 th 

### percentile values were below the 0.5 dv threshold. Remaining four facilities were modeled on the 12-km regional domain only. Three of these passed the screening with maximum predicted visibility impacts below the thre shold value. Predicted visibility impacts from one of those facilities were in exceedan ces of the threshold value of the 0.5 dv. For this facility, refined modeling could be performed for further analysis, but due to the magnitude of the impacts, this was not done under VISTAS funding.3-2 

### Table 3-1 List of BART Eligible Facilities (Sources) Modeled.

Number of sources modeled on a 12-km

VISTAS Regional CALMET domain 1 Number of sources with maximum change in deciviews < 0.5 dv (can be exempt from

BART controls) Number of sources modeled on a 4-km VISTAS CALMET domains 1 Number of sources with 8 th highest change in deciviews < 0.5 dv (can be exempt from

BART controls)

Alabama 24 (169) 16 (102) - -

Florida 1 (4) 1 (4) 2 (12) 0 (0)

Georgia 5 (19) 3 (9) 3 (10) 3 (10)

Kentucky 9 (119) 7 (91) 2 (28) 2 (28)

Mississippi 2 (6) 2 (6) - -

North Carolina - - 1 (38) 1 (38)

South Carolina 9 (43) 5 (16) 1 (5) 1 (5)

Tennessee 6 (39) 6 (39) - -

Virginia 3 (11) 1 (1) 1 (8) 1 (8)

West Virginia 12 (120) 11 (83) - -

Total 71 (530) 51 (351) 10 (101) 8 (89)

1 Values in brackets represent the number of individual point and line sources modeled.3-3 

### Figure 3-1. Locations of the modeled facilities in the VISTAS region. 200 400 600 800 1000 1200 1400 1600 1800 2000 LCC East (km) -1600 -1400 -1200 -1000 -800 -600 -400 -200 0 200 400LCC North (km)

LCC Origin: 40.0 N, 97.0 W Standard Parallels: 33.0 N, 45.0 N Datum: NWS-84 Elevation (meters)

GA AL TN

SC NC VA

FL KY

MS WV

Everglades Breton

Sipsey Cohutta

Mammoth Cave

Mingo

Cape Romain

Chassahowitzka Great Smoky Mountains
Joyce Kilmer-Slickrock Linville Gorge

Okefenokee

Shining Rock

St Marks Wolf Island James River Face

Swanquarter Shenandoah Dolly Sods Otter Creek Brigantine

-25 0.1 50 100 150 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400

Modeled BART Facility3-4 

### In Kentucky, nine facilities were modeled. As a screening run, all 9 facilities were modeled on the VISTAS CALMET 12-km regional domain. Maximum predicted extinction changes in each of the Class I areas for seven of these facilities were below th e threshold value of 0.5 dv. For the remaining two facilities the threshold value was exceeded in so me of the Class I areas. For these two facilities, refined modeling was performed on the 4-km s ubregional Domain 3 and Domain 5. The 98 th 

### percentile values in those runs were below the 0.5 dv threshold value.
In Mississippi, two facilities were modeled, both us ing the 12-km regional domain. Maximum predicted extinction changes in each of the Class I areas for bot h of these facilities were below the threshold value of 0.5 dv.
In North Carolina, one facility was mode led, using the 4-km Domain 5. The 8 th 

### highest values (98 th 

### percentile) of the extinction change predictions were below the 0.5 dv threshold for all years and for each of the Class I areas.
In South Carolina, 10 facilities were modeled. Nine of the facilities were modeled on the 12-km regional domain only. Five of these passed the screening with maximum predicted visibility impacts below the threshold value. Predicted visibility impacts from four of those facilities were in exceedances of the threshold value of the 0.5 dv. For these facilities, refined modeling could be performed for further analysis, but due to the magnitude of the impacts, this was not done under VISTAS funding. One of the facilities was modeled on the 4-km Domain 4. The 98 th 

### percentile in that run was well below the 0.5 dv threshold value.
In Tennessee, six facilities were modeled with the total of 39 point sources. All Tennessee facilities were modeled using the 12-km Regional CALM ET domain and CALPUFF computational subdomain. Maximum predicted extinction cha nge for all of the Tennessee sources at each of the Class I areas were below the 0.5 dv threshold value. Ammonia emissi ons were modeled in one of Tennessee sources.
In Virginia, three facilities were modeled. Initiall y, all three facilities were modeled on the VISTAS CALMET 12-km regional domain. For one of the fac ilities, maximum predicted extinction changes in each of the Class I areas were below the threshold va lue of 0.5 dv. For the remaining two facilities the threshold value was exceeded in one of the Class I areas for each facility. For one of these two facilities, modeling was redone on the 4-km Domain 5. The 98 th 

### percentile values in those runs were below the 0.5 dv threshold value.
In West Virginia, 12 facilities were modeled with the total of 120 point so urces. All West Virginia facilities were modeled using the 12-km regional CALMET domai n and CALPUFF computational subdomain. Maximum predicted extin ction change for eleven of the Tennessee sources at each of the Class I areas were below the 0.5 dv threshold value. Fo r one of the sources, the threshold value of 0.5 dv for extinction change was exceeded. For this facility, refined modeli ng could be performed for further analysis, although due to the magnitude of the im pacts, this was not done under VISTAS funding.
Overall, in all of the 10 VISTAS states 71 facilities were modeled on a 12 km VISTAS Regional CALMET domain (as shown in the last row of Tabl e 3-1). The results of those CALPUFF runs show that 51 facility or 72% of the total number of mode led facilities passed the screening, while 20 facilities did not pass the screening. For four of those 20 facilities, refined modeling was performed. The 98 th3-5 

### percentile values in those runs were all below the 0. 5 dv threshold value. In addition, refined modeling was performed for six more facilities (screening runs were not performed for these sources). Only two of these six sources did not pass. In total out of 10 facilities for which refined modeling was performed, eight facilities or 80% of the total number of modeled facilities passed.
It should be noted that in the screening approach , maximum visibility change is considered and compared against the 0.5 dv threshold value, not th e 98th percentile. This is a conservative approach. If maximum value is above the threshold, refined mode ling is performed to show compliance with BART. From the number of sources modeled in this study, it can be noted that screening approach provides very good estimate of the sources that could be scr eened out. Values with scree ning runs are not lower than with the refined runs.

3.2 NO x 

### Considerations in Respect to Visibility

The contribution of absorption by gases (b ag 

### ) has been examined for two hypothetical facilities with NO x 

### -dominated emission profiles placed in Florida and Georgia (see Figure 3-2). In the FLAG (2000) report, it is suggested that b ag 

### is dominated by nitrogen dioxide a nd is considered negligible at large source-receptor distances. This assumption is tested at three Class I areas for Source #1 and five Class I areas for Source #2. The absorption term was computed using the maximum predicted 24-hour NO x 

### concentration at each Class I area multiplied by extinction efficiency of 0.17, as recommended in FLAG (2002). It was conservatively assumed that all NO x 

### was in the form of NO 2 

### . Using a typical emissions profile it was found that in the near-field the contribution from NO x 

### is significant for the hypothetical sources examined, being 10-14% for the sources less than 25 km away from the Class I area (Table 3-2 and Table 3-3). For the Class I areas that are further away (200-290 km away), it was found that contribution was 2-5% which althoug h small is still not negligible (T able 3-2 and Table 3-3). For a reference, emissions assumed for Sources 1 and 2 are given in Table 3-4 and Table 3-5. Source 1 was modeled on VISTAS subregional Domain 2, while Source 2 was modeled on Domain 4.3-6 

### Figure 3-2. Locations of the two hypothetical NO x 

### -dominated facilities and Class I areas in the NO 2 

### absorption sensitivity tests. 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 LCC East (km) -1500 -1400 -1300 -1200 -1100 -1000 -900 -800 -700 -600 -500 -400LCC North (km)

S1 S2

LCC Origin: 40.0 N, 97.0 W Standard Parallels: 33.0 N, 45.0 N Datum: NWS-84 Elevation (meters)

GA AL SC NC

FL Cape Romain

Chassahowitzka

Okefenokee

St Marks Wolf Island

-25 0.1 50 100 150 200 300 400 500 600 700 800 900 1000 1100 1200 1300 14003-7 

### Table 3-2. Hypothetical Source 1 – Visibility Results, Maximum 24-hr Contribution.

Class I
Area Julian Day Dist. (km) Bext (Model) (Mm -1 ) Bext (BKG) (Mm -1 ) Bext (Total) (Mm -1 ) % Change of Bext F(RH) Bext SO 4 (Mm -1 ) Bext NO 3 (Mm -1 ) Bext OC (Mm -1 ) Bext EC (Mm -1 ) Bext PMC (Mm -1 ) Bext
PMF (Mm -1 ) Bext NO x (Mm -1 ) %Contribution of NO 2

CHAS 250 15 2.457 21.46 23.917 11.45 4.1 0.138 0.206 1.113 0.077 0.042 0.882 0.333 13.6%
OKEF 71 217 0.084 21.41 21.494 0.39 3.1 0.004 0.059 0.01 0.001 0.000 0.010 0.005 5.6%
STMA 29 219 0.282 21.54 21.822 1.31 3.7 0.008 0.239 0.017 0.001 0.001 0.016 0.007 2.5%



### Table 3-3. Hypothetical Source 2 – Visibility Results, Maximum 24-hr Contribution.

Class I
Area Julian Day Dist. (km) Bext (Model) (Mm -1 ) Bext (BKG) (Mm -1 ) Bext (Total) (Mm -1 ) % Change of Bext F(RH) Bext SO 4 (Mm -1 ) Bext NO 3 (Mm -1 ) Bext OC (Mm -1 ) Bext EC (Mm -1 ) Bext PMC (Mm -1 ) Bext
PMF (Mm -1 ) Bext NO x (Mm -1 ) %Contribution of NO 2

CARO 240 260 0.098 21.22 21.318 0.46 4.1 0.070 0.004 0.007 0.004 0.002 0.011 0.004 3.6%
CHAS 271 290 0.086 21.46 21.546 0.40 4.1 0.049 0.028 0.002 0.001 0.001 0.004 0.002 2.3%
OKEF 345 63 0.846 21.41 22.256 3.95 3.6 0.355 0.339 0.044 0.025 0.018 0.065 0.063 7.4%
STMA 322 263 0.132 21.54 21.672 0.61 3.7 0.044 0.066 0.006 0.004 0.002 0.011 0.003 2.6%
WOLF 241 26 1.419 21.33 22.749 6.65 4.1 0.649 0.201 0.170 0.092 0.067 0.240 0.145 10.2%3-8 

### Table 3-4. Hypothetical Source 1 – Stack Parameters and Emissions.

Stack ID # Stack Height Base Elevation Diam. Gas Exit Velocity Stack Gas Exit

Temp. SO 2 Emis. SO 4 Emis. NO x Emis. PM 10 Emis. PM 2.5 Emis. m m m m/s K g/s g/s g/s g/s g/s
1 22.86 45.72 0.91 7.19 302.59 0 0 0 0.108 0.0578
2 45.72 45.72 3.96 10.52 374.82 0.2604 0.1249 22.2138 0.9153 0.8624
3 15.24 45.72 3.05 9.06 444.26 0 0 0 0.6694 0.6246
4 21.34 45.72 0.79 14.35 366.48 0 0 0 3.8632 3.6051
5 45.72 45.72 0.61 24.26 302.59 0 0 0 0.0392 0.0365
6 65.84 45.72 0.61 24.26 302.59 0 0 0 0.2614 0.244
7 42.67 45.72 0.67 26.73 338.71 0 0 0 0.0074 0.0069
8 24.38 45.72 0.76 15.52 298.15 0 0 0 0.2276 0.1219
9 24.69 45.72 0.52 12.04 314.82 0 0 0 0.0635 0.034
10 9.75 45.72 0.61 24.26 294.26 0 0 0 0.272 0.1457



### Table 3-5. Hypothetical Source 2 – Stack Parameters and Emissions.

Stack ID # Stack Height Base Elevation Diam. Gas Exit Velocity Stack Gas Exit

Temp. SO 2 Emis. SO 4 Emis. NO x Emis. PM 10 Emis. PM 2.5 Emis. m m m m/s K g/s g/s g/s g/s g/s
1 82.9 1.5 3.95 8.2 474 3.88 0.2204 10.4 2.61 1.6
2 82.9 1.5 3.95 8.2 474 3.88 0.2204 10.4 2.61 1.6
3 19.8 1.5 3.66 14.33 339 0 0 0 0.111 0.111
4 80.5 1.5 1.04 18.6 350 0.16 0 0.35 2.1 1.93-9 

### 3.3 “Q over D” analysis

VISTAS States are using emissions (Q), expressed in units of tons per year, divided by distance from a Class I area boundary (D), expressed in units of k ilometers, as a presumptive indicator that a BARTeligible source is subject to BART. If Q/D for SO 2 

### is greater than 10 for 2002 actual annual emissions, then the State presumes that the source is subject to BART and no exemption modeling is performed using VISTAS funds. If the company agrees with this presumption, then the source can proceed to the BART determination using CALPU FF to evaluate impacts of control options and perform the engineering analyses. If the company disagrees, they ma y perform fine grid modeling to determine if its impact is <0.5 dv.
For sources with Q/D less than or equal to 10, T RC was requested by VISTAS to assist States with the initial CALPUFF exemption modeling. Each State prio ritized which sources were offered modeling by VISTAS. Note that VISTAS did not propose to use Q/D to exempt BART-eligible sources, but only to prioritize sources for modeling purposes. Thus this ap plication is consistent with EPA guidance not to use Q/D for exemption purposes.
Figure 3-3 and Figure 3-4 show “Q/D” plots. These plots were composed using the results for all modeled sources described in Section 3.1. In Figure 3-3, emissions comprise of SO 2 

### and NO x 

### , while in Figure 3-4 in addition to SO 2 

### and NO x 

### , PM 10 

### is also included. In analyzing the Q over D plots, the sum of SO 2 

### +NO x 

### emissions appears to be the overall b est predictor of the total extinction.3-10 

### Figure 3-3. Q/D plot where Q comprises of SO 2 

### and NO x 

### emissions.

Figure 3-4. Q/D plot where Q comprises of SO 2 

### , NO x 

### and PM 10 

### emissions. Q = SO2 + NOx

0.000 0.125 0.250 0.375 0.500 0.625 0.750 0.875 1.000 1.125 1.250 1.375 1.500 1.625

0.0 10.0 20.0 30.0 40.0 50.0 60.0
Q/Ddelta dv 2001 2002 2003

Q = SO2 + NOx + PM10

0.000 0.125 0.250 0.375 0.500 0.625 0.750 0.875 1.000 1.125 1.250 1.375 1.500 1.625

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0
Q/Ddelta dv 2001 2002 20033-11 

### 3.4 Use of CMAQ for ALM analysis

The use CMAQ output as background for nitrate pa rticulates prediction in CALPUFF using the Ammonia Limiting Method (ALM) wa s evaluated using a 2002 CMAQ output run. A summary of this study is attached in Appendix C.
First, CMAQ ammonia gas (NH 3 

### (g)) was compared to several sets of observations but the results show a significant CMAQ underprediction for NH 3 

### (g) at the sites evaluated. The first conclusion is that a direct use in CALPUFF of NH 3 

### (g) as predicted by CMAQ is not recommended because of this underprediction.
In a second part of the analysis, sulfate (SO 4 

### ), nitrate (NO 3 

### ), nitric acid (HNO 3 

### ) and total nitrate (HNO 3 

### + NO 3 

### ) from the CMAQ model were compared against the IMPROVE and CASTNet measurements of these same pollutants over the Southeastern part of the United States. Good agreements between CMAQ and observations for sulfate and total nitrate and a so mewhat overprediction of nitrate were encouraging results for the use of the CMAQ data to prov ide background pollutant concentrations. The NO 3 

### /HNO 3 

### equilibrium parameterization in CALPUFF chemistry was also tested against the equivalent CMAQ parameterization and very good agreement was found between the two as it is shown in Figure 3-5. The background NO 3 

### concentrations output of CALPUFF chemistr y track very closely when compared to observations as do the CMAQ NO 3 

### concentration predictions. This suggests that a repartitioning of HNO 3 

### and NO 3 

### within CALPUFF using the CMAQ data as bac kground is feasible and consistent with the CMAQ chemistry. Therefore, the use of hourly CMAQ output to determine background concentrations in the CALPUFF system was recomm ended for ALM applications. The combination of CMAQ outputs concentrations of sulfate (SO 4 

### ), total nitrate (NO 3 

### +HNO 3 

### ) and total ammonia (NH 3 

### (g) + NO 3 

### +2SO 4 

### ) represents a reliable estimate of background concentrations which will likely improve the quality of nitrate predictions in CALPUFF.
As an alternative to hourly background data for ALM application, 2002 CMAQ monthly average background was also tested and can be viewed as a compromise solution to the issue of background if an hourly CMAQ dataset is not available.
In addition, the substantial spa tial and temporal variability found in CMAQ ammonia predictions (see Appendix C) implies that varying ammonia better re flects background ammonia than the constant value throughout the year and for the whole domain as re commended by the Federal Land Manager guidance.Further information on the use of CMAQ data and ALM with CALPUFF can also be found in EscoffierCzaja and Scire (2007).3-12 

### Figure 3-5. Quantile-quantile plots of NO 3 

### predictions (CMAQ in pink and from CALPUFF chemistry in blue) as a function of NO 3 

### observations (CASTNet dataset at the top, weekly averages and IMPROVE dataset at the bottom, daily averages). 

### NO3 weekly average - Q-Q plot - year 2002

0 2 4 6 8 10 12

0 2 4 6 81 0 1 2 NO3 CASTNET observations (ug/m3)NO3 MODEL simulations (ug/m3)

CALPUFF Chemistry 'CMAQ'



### NO3 daily averaged - Q-Q plots - year 2002

0 2 4 6 8 10 12 14 16

0 2 4 6 8 10 12 14 16 NO3 IMPROVE observations (ug/m3)NO3 MODEL simulations (ug/m3)

CALPUFF Chemistry CMAQ4-1 

## 4.0 UPDATE OF CALPUFF MODEL CODES

4.1 Introduction

Prior to June 2007, the EPA-appr oved version of CALPUFF was based on model codes dated July 2004. Over the period from July 2004 through August 2006, a number of enhancemen ts and bug-fixes were made to the CALMET and CALPUFF models, culminati ng in the versions used by VISTAS for BART analyses: CALMET (v5.726) and CALPUFF (v5.756). Th e latest reviewed and approved update of the CALPUFF model codes by EPA was da ted June 15, 2006, when they approved the changes documented in Model Change Bulletin (MCB) A (released on July 16, 2004). However, because these codes did not include MCB-B (released on December 16, 2005) or later MCBs, the EPA-approved codes contained known bugs for which the code fixes were also known. As a result, it was proposed in BART modeling protocols, including the VISTAS protocol, that the later model codes referred to as the VISTAS codes would be used for BART analyses.
During the period from March through June 2007, working with EPA, TRC constructed a special version of the CALPUFF model, known as the “C” code (because it contained all bug fixes through MCB-C) starting with the July 2004 EPA-approve d codes. The EPA-approved versions of CALMET (v5.53a) and CALPUFF (v5.711a) were updated by applying both MCB-B and MCB-C changes to create CALMET (v5.53c) and CALPUFF (v5.711c), whic h were called the C versions. This allowed a separation of any changes due to model enhancemen ts from bug fixes to be identified and quantified. The result of this analysis was that all changes in the CALPUFF model from v5.711a to v5.756 were due to bug fixes, i.e., when the VISTAS version of CALPUFF was run with the same switch settings, input data and model options as the EPA-approved v5.7 11a code, the predicted concentrations with the VISTAS v5.756 code were the same. There were so me changes in model results due to CALMET enhancements, and the C version of CALMET allowed for tests of these effects separate from the bug fixes. During the testing period of March-June 2007 , EPA reviewed and approved the C code changes.
Application of the EPA CALPUFF Evaluation Tool to compare results from CALPUFF (v5.711c) / CALMET (v5.53c) and CALPUFF (v5.756) / CALMET (v5.726) shows that the two versions of CALPUFF produce no difference in concen trations when the latter is run with options compatible with the C version. Differences are found between CA LMET (v5.53c) and CALMET (v5.726) for the wind fields. The code associated with these differences has been isolated and applied to the CALMET (v5.726) code to create the C-equivalent versi on of CALMET referred to as EPA_CEQ. When run with options compatible with CALMET ( v5.53c), EPA_CEQ CALMET produces results that are the same as those from CALMET (v5.53c). Otherwise, the output is consistent with CA LPUFF (v5.756) / CALMET (v5.726).
Since August 2006, additional bug-fixes have been made to both CALMET (v5.726) and CALPUFF (v5.756), and those that apply to the EPA C-versions are documented in Model Change Bulletin D (MCB-D).
In collaboration with EPA, it was decided that the VISTAS codes served as the most appropriate starting point for the next version of the EPA-a pproved model codes (called v5.8). The v5.8 codes contain all known bug fixes and include many enha ncements in terms of data formats, convenience4-2 

### features (e.g., allowing multiple 3D.DAT files in a single run of CALMET) and additional quality assurance checks, as well as providing a framework for the evaluation of the new technical enhancements in the VISTAS code. EPA requested the introduction of a “regulatory option” in CALMET which checks for several switch settings:

- Mixing height method (IMIXH=-1) to use the Maul-Carson method over land and the OCD method over water;

- Overwater turbulence and flux calculations (ICOARE= 0) to use the OCD method for turbulence and fluxes over water;

- Sensible heat threshold variable (THRESHL=0.0) using a value of zero for the threshold sensible heat flux over land.

In addition, two other relevant changes were made at EPA’s request to the VISTAS CALMET code in developing the v5.8 code:

- The VISTAS CALMET code adopted a consistent set of similarity relations throughout the code. This affected some of the original model algorithms and was designed to make them consistent with the new COARE algorithms for the boundary layer added as an option enhancement. The VISTAS code was modified in v5.8 to use the original si milarity relations when the COARE algorithm is not used. Although this introduces inconsistencies in the way calculations are performed, it maintains the prior calculation methods associated with earlier versions of the model.

- Surface temperatures in the VISTAS code were com puted based on a spatial interpolation of the surface station measurements. In the v5.8 code, the previous “nearest-station” technique was reintroduced in order to be consistent with the pr ior regulatory-approved versions of the code. This results in the temperature from the nearest surface station being assigned to a grid point rather than a spatial interpolation being done.

In creating v5.8 of CALPUFF from the VISTAS code, a change was made at EPA’s request to make the default minimum σ v 

### values over water and land 0.5 m/s for all stabilities rather than 0.37 m/s over water and 0.5 m/s over land. Recent evaluation work with overw ater and coastal tracer datasets (Scire et al., 2005) suggested better model performance with th e 0.37 m/s values, but in order to maintain consistence with prior versions of CALPUFF, the de faults were changed to their original values of 0.5 m/s for both land and water. This is likely to result in greater dispersion over water than in the VISTAS code.
These versions, CALMET (v5.8) and CALPUFF (v5.8) also include updates that check control file inputs for consistency with the EPA-approved configura tion required to obtain the C-equivalent results and MCB-D changes. The v5.8 codes were officially approved by the EPA as the regulatory version of the models on June 29, 2007.4-3 

### The next section summarizes the results of the appli cation of the EPA Evaluation tool on two different versions of the model. The two versions of CALMET and CALPUFF used in the demonstration are referred to in the following way:

CALMET v5.53c EPA CALMET with MCB-B and MCB-C Updates

CALPUFF v5.711c EPA CALPUFF with MCB-B and MCB-C Updates

CALMET v5.8 CALMET with v5.53c-Equivalent modifications and MCB-D Updates

CALPUFF v5.8 CALPUFF with v5.711c-Equivalent modifications and MCB-D Updates

The results of these demonstrations show that the CALPUFF changes result in concentration changes for certain source configurations and model options, while no change in concentrations are found for other configurations. These results are expected due to the nature of the bugs that have been addressed.
Similarly, CALPUFF concentrations also change as a result of changes in CALMET, and these too are expected from the nature of the bugs that have been identified and addressed.

4.2 Results using the EPA Evaluation Tool

All 11 scenarios in the Evaluation Tool were processed to compare CALMET (v5.53c) / CALPUFF (v5.711c) to CALMET (v5.8) / CALPUFF (v5.8), and the results are presented in Table 4-1. No difference in concentrations was reported in Scen arios 03, 06, 07, 08, 09, 10, and 11. The secondary analysis feature of the Evaluation Tool was applie d to Scenarios 01, 02, 04, and 05 to determine if concentration differences arise from just CALMET, just CALPUFF, or both CALMET and CALPUFF. Differences in Scenarios 04 and 05 arise from the CALPUFF MCB-D changes, not from the CALMET MCB-D changes. Differences in Scenarios 01 a nd 02 arise from both the CALPUFF MCB-D changes and the CALMET MCB-D changes.4-4 

### Table 4-1 EPA CALPUFF Evaluation Tool Results for CALPUFF (v5.711c) with CALMET (v5.53c) compared to CALPUFF (v5.8) with CALMET (v5.8) Scenario Result 01 Difference due to CALMET and CALPUFF (point source only) 02 Difference due to CALMET and CALPUFF (point source only) 03 No Differences 04 Difference due to CALPUFF (point source only) 05 Difference due to CALPUFF (point source only) 06 No Differences 07 No Differences 08 No Differences 09 No Differences 10 No Differences 11 No Differences

Scenarios 01, 02, 04, and 05 include volume, point, and area sources. No chemical transformations and no removal mechanisms are simulated. Concentrations are extracted for averaging times of 1-hr, 3-hr, 24-hr, and run-length (673-hr for Scenario 02, 720-hr for the others) and the peak values are reported by rank, using ranks 1, 2, 4, and 8. Note that the run-length can only provide the largest concentration (rank 1), and in these tests the run length is far shor ter than that used in regulatory applications. Scenarios 01 and 02 use a large domain that is 960km on each side with an 80x80 grid of 12km cells. Sampling is done at the center of each grid cell excl uding the outermost 2 rows and columns, resulting in a sparse receptor grid with 12km spacing. Scen arios 04 and 05 use much smaller domains with finer grid resolutions of 2km and 0.25km, respectively. R eceptor spacing used in Scenarios 04 and 05 are also 2km and 0.25km, respectively.
Changes in concentration due to CALPUFF MCB-D updates (available from http://www.src.com ) are found for one or more of the point sources in these scenarios, and none of the other sources. None of these point sources is modeled with downwash, so the only MCB-D change that would lead to a difference should be the change associated with reset ting plume rise to final rise as soon as a puff enters a calm region. For confirmation, this one MCB-D ch ange was removed in a test version of CALPUFF (v5.8), and Scenarios 01, 02, 04, and 05 were rerun using CALMET (v5.53c) files to remove CALMET changes from this test. The result showed no diffe rences compared to CALPUFF (v5.711c), thereby confirming the source of the differences found.
Changes in concentration due to CALMET MCB-D updates are found in two scenarios. Scenario 01 uses no prognostic data and so it is expected to be influenced by few MCB-D changes. Given that it includes overwater cells, two model changes related to the overwater boundary layer apply. Both of4-5 

### these changes were removed in a test version of CALMET (v5.8), which was then applied to Scenario 01. The results confirmed that these (one or both) lead to the differences noted. Scenario 02 is the only one that uses the no-observations mode (NOO BS=2), so it relies on the use of prognostic data as input and is therefore subject to several of th e MCB-D changes. Because MM5 land use is not read, no cells are treated as water when computing MM5 pr ofiles near the surface. This scenario also uses ITPROG=2, which affects the temperature profile assigned to CALMET layers within the mixed layer.
The magnitude of the "High Values by Rank" concentr ation changes, expressed as the percent change from the base system CALMET (v5.53c) / CALPUFF ( v5.711c) to the beta system CALMET (v5.8) / CALPUFF (v5.8), for the MCB-D changes in Scenarios 01, 02, 04, and 05 range between:

Scenario 01 -0.077% to 0.34% Scenario 02 -10.4% to 1.56% Scenario 04 -0.00% to 0.53% Scenario 05 -0.003% to 0.004%

The range listed is obtained from the extreme valu es over all averaging times and ranks. These are all very small with the exception of Scenario 02. Becau se of the sparse 12km sampling grid used in this scenario, it is not surprising that changes in th e CALMET boundary layer and meteorological profiles can shift CALPUFF impact areas resolved by the sampling grid.

The results of these demonstrations show th at the CALMET and CALPUFF changes result in concentration changes for certain source configur ations and model options, while no change in concentrations are found for other configurations. Th ese results are expected du e to the nature of the bugs that have been addressed and the configuration of the test scenarios.
4.3 Analysis of Typical Class I Impacts

The EPA Evaluation Tool is useful for diagnostic tests of model differences. However, it does not measure the likely consequences in terms of regulatory compliance decisions because many of the tests involve non-regulatory model switches and the run lengt hs are short, varying from just 48 hours to one month. In addition, the standard EPA runs do no t involve chemical convers ion or the calculation of visibility or deposition. (Scenario 11 of the EPA set was added to include chemistry, but it does not include the calculation of deposition fluxes or visibility.)
An example of a more typical Class I impact anal ysis has been applied with a major source of SO 2 

### , NO x 

### and PM 10 

### , with a comparison of the effects on SO 2 

### ,NO x 

### and PM 10 

### concentrations, sulfur and nitrogen deposition fluxes and visibility using Method 6 as recommended for BART analyses. In these tests, full year simulations were conducted to allow a more accurate determination of the regulatory consequences of model changes.
In these tests, a realistic typical power plant source was placed in Virginia and the impacts at six Class I areas evaluated. The year 2001 was modeled for a source with emissions of 95 g/s (SO 2 

### ), 79 g/s (NO x 

### ), 4 g/s (H 2 

### SO 4 

### ) and 39 g/s (PM 10 

### ) emitted from a tall stack (152m). The results of a comparison of predicted concentrations for the year 2001 for SO 2 

### , NO x 

### and PM 10 

### concentrations is shown in Table 4-2.4-6 

### For the short-term concentrations (i.e., 3-hour and 24-hour averaging times), the concentrations are the highest second-high values predicted at each Class I area. The highest value for each version is highlighted in bold.
In general, the predicted impacts with the new EPA-approved v5.8 model codes are lower than those obtained with the VISTAS codes. The primary reason fo r this is not a code change, but a change in the preferred model setting requested by EPA to use an ove rland mixing height threshold of 0.0 rather than the 0.05 W/m 3 

### over land. This results in generally high er mixing heights with the v5.8 version of CALMET, which tends to produce more dilution ov er long range transport distances and therefore lower predicted impacts. Other factors can also play a role, so the impact vary, but with other things being equal, the tendency is for higher mi xing heights to produce lower impacts.
As shown in Tables 4-2 to 4-4, the changes in predicted SO 2 

### , PM 10 

### and NO x 

### concentrations are generally small, especially when comparing the overall highest predicted values over all Class I areas. For example, the highest predicted 3-hour average SO 2 

### impacts vary from 1.014 ug/m 3 

### (VISTAS) to 0.98 ug/m 3 

### (EPA). For 24-hour average SO 2 

### concentrations, the highest value are 0.252 ug/m 3 

### (VISTAS) vs. 0.249 ug/m 3 

### (EPA) and for annual averages the highest SO 2 

### concentrations are 0.016 ug/m 3 

### (VISTAS) and 0.015 ug/m 3 

### (EPA). Similar results are presented for PM 10 

### and NO x 

### in the tables.
For the annual sulfur and nitrogen deposition fluxes (Table 4-5 and Table 4-6), the highest values are unchanged in going from the VISTAS model codes to the v5.8 EPA-approved codes, although some of the lower deposition values change slightly at some Class I areas. Again the trend is that the VISTAS codes produce higher impacts than those predicted by the new EPA-approved codes.
The predicted visibility impacts (percent change in ex tinction) using Method 6 is presented in Table 4-7. The values listed are the highest predicted change in B ext 

### at each Class I area. The results from show lower impacts with the new EPA-approved v5.8 codes.
Although it is difficult to generalize, the results of this full-year analysis of concentration, deposition flux and visibility impacts at six Class I areas shows a tendency for the VISTAS c ode to be conservative relative to the v5.8 EPA-approved codes. In additi on, in most cases, the differences between model versions are relatively small.4-7Table 4-2. Comparison of Predicted SO2Concentrations (μg/m3) at Six Class I Areas for Different Model Versions.H2H 3-hour SO2H2H 24-hour SO2H1H Annual SO2Class I AreaVISTASMET/PUFFVer. CMET/PUFFVer. 5.8MET/PUFFVISTASMET/PUFFVer. CMET/PUFFVer. 5.8MET/PUFFVistasMET/PUFFVer. CMET/PUFFVer. 5.8MET/PUFFJames River 0.390 0.385 0.3870.138 0.130 0.130 0.009 0.008 0.008Shining Rock 0.445 0.453 0.4530.103 0.086 0.086 0.007 0.006 0.006Cohutta 0.293 0.282 0.281 0.074 0.066 0.066 0.003 0.003 0.003LinvilleGorge1.0140.972 0.9720.252 0.249 0.249 0.016 0.015 0.015Joyce Kilmer 0.539 0.528 0.5270.140 0.131 0.132 0.005 0.005 0.005Great Smoky 0.9790.980 0.9800.226 0.220 0.220 0.013 0.012 0.012Table 4-3. Comparison of Predicted PM10Concentrations (μg/m3) at Six Class I Areas for Different Model Versions.H2H 24-hour PM10H1H Annual PM10Class I AreaVISTASMET/PUFFVer. CMET/PUFFVer. 5.8MET/PUFFVISTASMET/PUFFVer. CMET/PUFFVer. 5.8MET/PUFFJames River 0.072 0.066 0.066 0.005 0.005 0.005Shining Rock 0.056 0.043 0.043 0.004 0.004 0.004Cohutta 0.046 0.038 0.038 0.002 0.002 0.002LinvilleGorge0.110 0.110 0.110 0.008 0.007 0.007Joyce Kilmer 0.071 0.064 0.064 0.003 0.003 0.003Great Smoky 0.105 0.100 0.100 0.007 0.006 0.0064-1 

### Table 4-4. Comparison of Predicted NO x 

### Concentrations ( μ g/m 3 

### ) for Different Model Versions.

H1H Annual NOx - 2001 Class I Area VISTAS MET/PUFF Ver. C MET/PUFF Ver. 5.8 MET/PUFF James River 0.0021 0.0020 0.0020 Shining Rock 0.0013 0.0012 0.0012 Cohutta 0.0003 0.0003 0.0003 Linville Gorge 0.0046 0.0044 0.0044 Joyce Kilmer 0.0011 0.0011 0.0011 Great Smoky 0.0038 0.0037 0.0037



### Table 4-5. Comparison of Predicted Sulfur Depositi on Fluxes (kg/ha/yr) at Six Class I Areas for Different Model Versions.

Table 4-6. Comparison of Predicted Nitrogen Depo sition Fluxes (kg/ha/yr) at Six Class I Areas for Different Model Versions. 2001 Sulfur VISTAS MET/PUFF Version C MET/PUFF Version 5.8 MET/PUFF James River 0.013 0.012 0.012 Shining Rock 0.008 0.007 0.007 Cohutta 0.003 0.003 0.003 Linville Gorge 0.020 0.020 0.020 Joyce Kilmer 0.005 0.005 0.005 Great Smoky 0.011 0.010 0.010

2001 Nitrogen VISTAS MET/PUFF Version C MET/PUFF Version 5.8 MET/PUFF James River 0.005 0.004 0.004 Shining Rock 0.002 0.002 0.002 Cohutta 0.001 0.001 0.001 Linville Gorge 0.007 0.007 0.007 Joyce Kilmer 0.001 0.001 0.001 Great Smoky 0.003 0.003 0.0034-2 

### Table 4-7. Comparison of Predicted Visibility Impa cts at Six Class I Areas for Different Model Versions. (Version C results are not available). The values shown are the highest percent change in extinction (B ext 

### ).

2001 Method 6 VISTAS MET/PUFF Version C MET/PUFF Version 5.8 MET/PUFF James River 7.4 NA 6.8 Shining Rock 4.9 NA 4.0 Cohutta 4.7 NA 3.8 Linville Gorge 8.3 NA 7.9 Joyce Kilmer 7.1 NA 6.8 Great Smoky 8.8 NA 8.15-1 

## 5.0 REFERENCES

Escoffier-Czaja C. and J.S. Scire, 2007: Use of Eu lerian Model Outputs as Background Concentrations for Nitrate Predictions in CALPUFF System, 11 th 

### International Conference on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes, Cambridge, England, 2-5 July, 2007 (http://www.harmo.org/Conferences/Proceedings /_Cambridge/publishedS ections/Op058-062.pdf)
FLAG, 2000. Federal Land Manager' Air Quality Related Values Workgroup (FLAG). Phase I Report. U.S. Forest Service, National Park Servi ce, U.S. Fish and Wildlife Service.

Scire, J.S., D.G. Strimaitis, and R.J. Yamartino, 2000a: A User’s Guide for the CALPUFF Dispersion Model (Version 5). Earth Tech, Inc., Concord, MA

Scire, J.S., F.R. Robe, M.E. Fernau, and R.J. Yamartino, 2000b: A User’s Guide for the CALMET Meteorological Model (Version 5). Earth Tech, Inc., Concord, MA.

Scire, J.S., D.G. Strimaitis and F. R. Robe, 2005: Evaluation of Enhancements to the CALPUFF Model for Offshore and Coastal Applications. Proceedings of the 10 th 

### International Conference on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes. Sissi (Malia), Crete, Greece. 17-20 October 2005.

VISTAS, 2005: Protocol for the Application of the CALPUFF Model for Analysis of Best Available Retrofit Technology (BART). Visibility Improvement State and Tribal Association of the Southeast (VISTAS). Revision 3.2, August 31, 2 006. Available from: http://www.vistassesarm.org/documents.
U.S. Environmental Protection Agency (EPA), 1998: Interagency Workgroup on Air Quality Modeling (IWAQM), Phase 2 Report: Summary Report and Recommendation for Modeling Long Range Transport and Impacts on Regional Visibility. EPA-454/R-98-019.
U.S. Environmental Protection Agency (EPA), 2003a: Revision to the Guideline on Air Quality Models: Adoption of a Preferred Long Range Transport Model and Other Revisions; Final Rule. 40 CRF 51. Federal Register, April 15, 2003, 68 , 72, pp 18440-18482.
U.S. Environmental Protection Agency (EPA), 2003b: Guidance for Estimating Natural Visibility Conditions Under the Regional Haze Program. EPA-454/B-03-005, September 2003.
U.S. Environmental Protection Agency (EPA), 2005: Regional Haze Regulations and Guidelines for Best Available Retrofit Technology (BART) Determina tions; Final Rule, 40 CFR 41. Federal Register, July 6, 2006, 70 , 128, pp 39104-39172.A-1 

## APPENDIX A – LAND USE AND ST ATION DATA FOR REGIONAL SUBDOMAINS

• Plots of Land Use Subregional Domains.

• Tables of Upper Air Stations and Buoys.A-2 

### Figure A-1. Land Use for the CALMET/ CALPUFF Domain 1. The Class I areas are also shown. 500 600 700 800 900 LCC East (km) -1100 -1000

-900 -800 -700 -600 -500LCC North (km)

LCC Origin: 40.0 N, 97.0 W Standard Parallels: 33.0 N, 45.0 N Datum: NWS-84 LA

AL TN

MS

Breton Sipsey



### 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

Urban/ built-up land Agricultural land Rangeland Forest land

Water Wetland Barren land

Tundra Snow / IceA-3 800 900 1000 1100 1200 1300 1400 1500 1600 1700 LCC East (km) -1500 -1400 -1300 -1200 -1100 -1000 -900 -800LCC North (km)

LCC Origin: 40.0 N, 97.0 W Standard Parallels: 33.0 N, 45.0 N Datum: NWS-84 FL GA AL MS

Everglades Chassahowitzka St Marks Okefenokee Wolf Island

Breton

10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

Urban/ built-up land Agricultural land Rangeland Forest land

Water Wetland Barren land

Tundra Snow / Ice



### Figure A-2. Land Use for the CALMET/ CALPUFF Domain 2. The Class I areas are also shown.A-4 

### Figure A-3. Land Use for the CALMET/ CALPUFF Domain 3. The Class I areas are also shown. 600 700 800 900 1000 1100 1200 1300 1400 1500 LCC East (km) -600 -500 -400 -300 -200 -100

0 100LCC North (km)

LCC Origin: 40.0 N, 97.0 W Standard Parallels: 33.0 N, 45.0 N Datum: NWS-84 TN KY WV

VA

NC

GA AL MS SC Mammoth Cave

Mingo

Great Smoky Mountains

Joyce Kilmer-Slickrock Linville Gorge

Shining Rock James River Face Dolly Sods
Otter Creek

Cohutta

Sipsey 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

Urban/ built-up land Agricultural land Rangeland Forest land

Water Wetland Barren land

Tundra Snow / IceA-5 

### Figure A-4. Land Use for the CALMET/ CALPUFF Domain 4. The Class I areas are also shown. 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 LCC East (km) -1200 -1100 -1000 -900 -800 -700 -600 -500 -400 -300 -200LCC North (km)

LCC Origin: 40.0 N, 97.0 W Standard Parallels: 33.0 N, 45.0 N Datum: NWS-84 FL GA TN

SC NC VA

KY

AL MS

Chassahowitzka St Marks Okefenokee Wolf Island Cape Romain Sipsey Cohutta Joyce Kilmer-Slickrok

Great Smoky Mountains Shining Rock Linville Gorge

Breton Mammoth Cave

10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

Urban/ built-up land Agricultural land Rangeland Forest land

Water Wetland Barren land

Tundra Snow / IceA-6 

### Figure A-5. Land Use for the CALMET/ CALPUFF Domain 5. The Class I areas are also shown. 1100 1200 1300 1400 1500 1600 1700 1800 1900 LCC East (km) -600 -500 -400 -300 -200 -100 0 100 200LCC North (km)

LCC Origin: 40.0 N, 97.0 W Standard Parallels: 33.0 N, 45.0 N Datum: NWS-84 GA SC NC TN VA WV

KY

Cape Romain Cohutta Joyce Kilmer-Slickrok Great Smoky Mountains Shining Rock Linville Gorge Swanquarter James River Face Shenandoah Dolly Sods Otter Creek Brigantine

10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

Urban/ built-up land Agricultural land Rangeland Forest land

Water Wetland Barren land

Tundra Snow / IceA-7 

### Table A-1 Upper Air Stations used in the Subregional Modeling

WMO INIT Station Name Latitude (deg.) Longitude (deg.) 72230 BMX Birmingham (Shelby Apt) 33.10 -86.70 72233 SIL Slidell 30.33 -89.82 72235 JAN Jackson/Thompson Fld 32.32 -90.07 72240 LCH Lake Charles 30.12 -93.22 72327 BNA Nashville 36.25 -86.57 Domain 1

72340 LZK N Little Rock 34.83 -92.27 72201 EYW Key West Int Ap 24.55 -81.75 72202 MFL Miami/Fl Intl Univ 25.75 -80.38 72206 JAX Jacksonville 30.43 -81.70 72210 TBW Tampa Bay/Ruskin 27.70 -82.40 72214 TLH Tallahasee Fl Us 30.38 -84.37 72215 FFC Peachtree City 33.35 -84.56 72230 BMX Birmingham (Shelby Apt) 33.10 -86.70 72233 SIL Slidell 30.33 -89.82 72235 JAN Jackson/Thompson Fld 32.32 -90.07 Domain 2

74794 XMR Cape Kennedy 28.48 -80.55 72208 CHS Charleston 32.90 -80.03 72215 FFC Peachtree City 33.35 -84.56 72230 BMX Birmingham (Shelby Apt) 33.1 -86.70 72235 JAN Jackson/Thompson Fld 32.32 -90.07 72317 GSO Greensboro 36.08 -79.95 72318 RNK Roanoke/Blacksburg 37.20 -80.41 72327 BNA Nashville 36.25 -86.57 72340 LZK N Little Rock 34.83 -92.27 72403 IAD Sterling(Wash Dulles) 38.98 -77.47 72426 ILN Wilmington 39.42 -83.82 72520 PIT Pittsburgh/Moon Township 40.53 -80.23 74455 DVN Davenport Municipal Ap 41.60 -90.57 Domain 3

74560 ILX Lincoln-Logan County Ap 40.15 -89.33 72206 JAX Jacksonville 30.43 -81.70 72208 CHS Charleston 32.90 -80.03 72210 TBW Tampa Bay/Ruskin 27.70 -82.40 72214 TLH Tallahasee Fl Us 30.38 -84.37 72215 FFC Peachtree City 33.35 -84.56 72221 VPS Valparaiso/Elgin Afb 30.52 -86.58 72230 BMX Birmingham (Shelby Apt) 33.10 -86.70 72233 SIL Slidell 30.33 -89.82 72235 JAN Jackson/Thompson Fld 32.32 -90.07 72305 MHX Morehead City/Newport 34.70 -76.80 72317 GSO Greensboro 36.08 -79.95 72318 RNK Roanoke/Blacksburg 37.20 -80.41 Domain 4

72327 BNA Nashville 36.25 -86.57 72208 CHS Charleston 32.90 -80.03 72215 FFC Peachtree City 33.35 -84.56 72305 MHX Morehead City/Newport 34.70 -76.80 72317 GSO Greensboro 36.08 -79.95 72318 RNK Roanoke/Blacksburg 37.20 -80.41 72402 WAL Wallops Island 37.93 -75.48 72403 IAD Sterling(Wash Dulles) 38.98 -77.47 72426 ILN Wilmington 39.42 -83.82 Domain 5

72520 PIT Pittsburgh/Moon Township 40.53 -80.23A-8 

### Table A-2 Buoys used in the Subregional Modeling

Station number Buoy name Latitude (deg.) Longitude (deg.)
42007 Biloxi 22 nm South-Southeast of Biloxi, MS 30.09 88.77 42039 Pensacola - 115NM East Southeast of Pensacola, FL 28.79 86.02 Domain 1 42040 Mobile South 64 nm South of Dauphin Island, AL 29.18 88.21 41008 Grays Reef - 40 NM Southeast of Savannah, GA 31.40 80.87 41009 Canaveral 20 NM East of Cape Canaveral, FL 28.50 80.17 41012 St. Augustine, FL 40NM ENE of St Augustine, FL 30.00 80.60 42001 Mid Gulf 180 nm South of Southwest Pass, LA. 25.90 89.67 42003 E Gulf 260 nm South of Panama City, FL 26.07 85.94 42007 Biloxi 22 nm South-Southeast of Biloxi, MS 30.09 88.77 42036 W. Tampa 106NM West Northwest of Tampa, FL 28.50 84.52 42039 Pensacola - 115NM East Southeast of Pensacola, FL 28.79 86.02 Domain 2

42040 Mobile South 64 nm South of Dauphin Island, AL 29.18 88.21

Domain 3 none 41004 Edisto - 41 NM Southeast of Charleston, SC 32.50 79.09 41008 Grays Reef - 40 NM Southeast of Savannah, GA 31.40 80.87 42007 Biloxi 22 nm South-Southeast of Biloxi, MS 30.09 88.77 42036 W. Tampa 106NM West Northwest of Tampa, FL 28.50 84.52 42039 Pensacola - 115NM East Southeast of Pensacola, FL 28.79 86.02 Domain 4

42040 Mobile South 64 nm South of Dauphin Island, AL 29.18 88.21 41008 Grays Reef - 40 NM Southeast of Savannah, GA 31.40 80.87 44009 Delaware Bay 26 NM Southeast of Cape May, NJ 38.46 74.70 Domain 5 44014 Virginia Beach 64 NM East of Virginia Beach, VA 36.61 74.84B-1 

## APPENDIX B – MEMO ON CALMET OBS/NO-OBS MODESB-2 

## Comments on Observations vs. No-Observations
Mode in CALMET

J. Scire Earth Tech, Inc. August 2, 2005

The CALMET meteorological model accepts observational data in the form of hourly surface observations of winds, temperature, pressure and cloud data, hourly precipitation observations, and twice-daily upper air sounding data of winds, pressure and temperature. In addition, CALMET allows three-dimensional gridded fields of data from prognostic numerical weather forecast models such as the Mesoscale Model Version 5 (MM5) and the Rapid Update Cycle (RUC) model. Th e three dimensional prognostic meteorological fields include winds, temperature, humidity and liquid water content data such as precipitation and cloud water. CALMET can be run in a mode using observations only (Obs-mode), prognostic data only (No-Obs mode) or both sets of data (hybrid mode). There are two forms of the hybrid mode – one where the prognostic data can replace certain types of observational data entirely (P artial No-obs mode) such as upper air observations, precipitation data or cloud data and a second form where the prognostic data is used to supplement observational data.
In the VISTAS BART modeling, Task 2 involv es running CALMET using a regional 12-km resolution CALMET domain (Figure 1) and Task 3 consists of developing sub-regiona l domains at a higher (4 km or better) grid resolution. The regional domain includes all of the southeastern United States. MM5 data at 36-km or 12-km grid spacing are available that covers the entire regional domain. Sub-regional domains will be defined for specific BART sources. In particular, higher resolution CALMET simulations are likely to be required for Class I areas located in complex terrain a nd possibly coastal regions. One issue is the CALMET mode to be used in the regional a nd sub-regional CALMET simulations. The inclusion of the observational data has both advantages and drawbacks. The considerations include the following:
(a) Time and space resolution of observational data . While all meteorological observations are useful in hybrid mode, the upper air wind observational da ta are normally the least resolved spatially and temporally. Upper air observational data is available at roughly 12-hourly inte rvals. The typical spacing between upper air soundings is over order several hundred kilometers. CALMET will pe rform a time interpolation of wind data between the sounding intervals. This linear time-interpolated wind data is likely to miss important patterns of wind variation at scales less than 12 hours. In Obs mode, the spatia l interpolation uses an objective analysis procedure to define an initial guess field, followed by diagnostic adjustments for terra in effects by CALMET. On the 12-km resolution regional domain, terrain effects seen by the model will be reduced due to the coarse grid resolution.
(b) Time and space resolution of 3-D prognostic data . The time and spatial resolution of the three-dimensional gridded MM5 meteorological fields is better than that from the surface and upper air observational network. Upper air soundings are available at 12-hr intervals and are widely distributed. Surface observations have hourly time resolution but are still more spatially distributed than the MM5 fields that are available at either 36- km or 12-km grid resolution.B-3 

### Figure 1. VISTAS 12 km regional CALMET modeling domain.

In addition, rather than just two soundings per day at irregularly spaced intervals separate by several hundred km as in the upper air observational dataset, the MM5 dataset essential provides hourly “soundings” over a regular grid at hourly intervals. In particular, the better time r esolution of the prognostic model fields is likely to better characterize the time variation of wind flows in the regi onal domain. However, on smaller scales, the MM5 data may miss important local flow features that are captured by the hourly surface observational network.
(c) Representativeness of observational data. On the regional domain, relatively large values of the radius of influence parameters that determine the relative weight of observations vs. gridded prognostic meteorological data are needed in order for the observational data to be relevant in the determination of the final wind fields. These weighting parameters in CALMET are applied thr oughout the modeling domain. In a large, regional domain such as anticipated for the Task 2 modeling, large values of the radius of influence parameters may not be appropriate because in the complex terrain areas (and al so possibly in coastal areas) of the domain, the spatial representativeness of the observations may be limited. Small values of the radius of influence parameters makes the weight given to the observations lim ited, and the final wind fields would be determined mainly by the initial guess field derived from the prognostic meteorological fields.B-4 

### (d) Discrepancies between prognostic winds and observations . The CALMET diagnostic wind field module resolves discrepancies between the prognostic model-derive d initial guess fields and the observations by giving greater weight to the observations in the “vicinity’ of th e observations (the “vicinity” is defined by the user using the radius of influence parameters) and greater we ight to the prognostic wind fields in portions of the domain away from the observations. Although the prognostic model simulation re flects observational data through the use of four-dimensional data assimilation of analysis fields in the MM5 simulations, there will sometimes be discrepancies between the observations (whi ch may reflect a very localized flow) and the 36-km or 12-km resolved MM5 winds. Sometimes the differences are due to poor time resolution of the observational data such as 12-hourly sounding data linear time-interpolat ed to hourly intervals. In particular, the blending of the MM5 data with the observations can lead to wi nd structures which may not be realistic under some conditions.
(e) Model biases . The MM5 model predictions of winds and temperatures may contain biases relative to the real atmosphere. In particular, MM5 sometimes under estimates the frequency of light wind and calm wind conditions and possibly underestimates the strength of the nocturnal surface temperature inversion. These biases may be most important for near surface sources when relative coarse grid resolution is used.
(f) Level of effort vs. benefit . The preparation of observational da tasets for CALMET on large regional domains involves a considerable effort due to the larg e number of surface, precipitation and upper air stations involved. The question is whether the effort associated with the preparation of this dataset is worthwhile given that the regional winds may be adequately or even bette r characterized by the 3-D prognostic model dataset than time- and space-interpolated observational data, especia lly above the surface where sounding data is fairly sparse spatially and poorly-resolved in time.

Recommendations
Given the considerations presented above, it is recommended that the regional VISTAS CALMET simulations be conducted in No-Observations mode using the highest resolution MM5 data available for each year modeled. The draft BART modeling protocol will address appropr iate applications for CALMET using a 12-km grid resolution and criteria to determine if 12-km or a finer gr id resolution is appropriate for modeling purposes. It is also recommended that sub-regional CALMET domains be modeled using the hybrid mode where the MM5 datasets are used to define the initial guess fields, but su rface and upper air observational data are used to refine the meteorological parameters locally in th e vicinity of the observational sites.
The regional wind patterns are likely to be adequate ly characterized by the MM5 simulations. Many observations, especially surface observations, may reflect local conditions on a scale smaller than the grid resolution of the regional domain. The introduction of the local observations into the regional modeling domain may extend the influence of the observational data beyo nd its true representativeness and result in internally inconsistent flow features. In particular the time in terpolation of the 12-hourly upper air sounding data may wash out structure in the MM5 fields that are appropria te to retain. Given that the regional modeling domain includes areas of complex terrain, coastal regions and rela tively flat terrain, a single set of representative weights that allows significant influence of the observations wh ere appropriate, will involve a considerable effort and substantial testing. The internally consistent MM5 fields are considered likely to be appropriate for the regional simulations, and the incremental benefit of adding the observational data into the regional CALMET simulations is not considered worthwhile.B-5 

### However, on the smaller sub-regional domains, with the higher CALMET grid resolution and the smaller domain size, more control over the region of influence of the meteorological observations can be achieved. It is easier for the diagnostic model to allow the local flow observations to have appropriate influence in the vicinityof the observation, but allow terrain-adjusted flow to dominate away from the observations. Given that the subregional domains will be used especially in complex flow situations (complex terrain and coastal regions), the relatively coarse-scale MM5 simulations are less likely to be adequate, and the introduc tion observational data is more likely to achieve improvements in the resulting meteorological fields.