# 5-1 5.0 DATA BASES FOR CA LPUFF MODELING

To support BART modeling by the states and source operators, both meteorological and aerometric data sets are required. Regional meteor ological data sets generated by the CALMET model suitable for direct input to the CALPUFF modeling sy stem have been developed and archived. These data sets cover calendar years 2001, 2002, and 2003 fo r three sub-regional grid domains shown in Figures 5-1 through 5-4. The proc edures used in developing the CALMET data sets generally follow the IWAQM recommendations (EPA, 1998), ex cept for a few notable refinements. The processed CALMET files, in CALPUFF-ready input format, ar e available from CENRAP on hard disk drives to

interested states and stakeholders.

This chapter describes how these meteorological modeling sets were deve loped and evaluated. The basic CALMET model configuration used to generate the three years of CALPUFF-ready meteorology is described in detail so that users of this information have a clear understanding of the data sets and their appli cability. In addition, for those states or source operators who elect to conduct more source-specific CALMET/CALPU FF modeling, the information in th is chapter may be helpful in guiding specification of revised CA LMET model inputs and generation of revised CALMET data sets.

Also included in Sectio n 5 .2 is a discussion of routinely available air quality monitoring data sets available to the states and source operators in support of screening and source-specific BART modeling exercises.

5.1 Development of CALMET Meteorological Files

5.1.1 MM5 Data Sets

Alpine Geophysics developed a consistent se t of CALMET regional meteorological modeling data sets for use by the CENRAP States, BART elig ible sources within the region and others. These meteorological modeling data sets were constructe d through the joint use of the CALMET processor and results from existing annual three-dimensional MM5 meteorologi cal simulations. The specific annual prognostic model simulations availa ble for CENRAP BART modeling included:

> 2001 MM5 data set at 36/12 km resolu tion developed for EPA by Alpine Geophysics (McNally and Tesche, 2002; McNally 2003);

> 2002 MM5 data set at 36 km resolution developed for CENRAP by Iowa DNR (Johnson, 2003a,b),

> 2003 MM5 data set at 36 km resolution developed for the Midwest RPO (Baker, 2005; Baker et al., 2004; Kembell-Cook et al., 2005)

Each of these studies included a performance eval uation of the MM5 genera ted data sets against surface meteorological observat ions and the results of these evaluati ons are contained in the reports or presentations cited above. While there exists a set of annual 12 km MM5 meteorology for 2002, this data set was developed by four i ndependent CENRAP modeling centers and these data sets have not

been concatenated into one master data base. Mo re importantly, there has b een no systematic, rigorous model performance evaluation performed on the CENR AP 2002 12 km MM5 data yet. Accordingly,

until such time as the 2002 12 km data set has b een evaluated and shown to be of comparable reliability as the aforementioned MM5 data sets, it’s use is contraindicated.

# 5-2 5.1.2 CALMET Model Configuration

The CALMET modeling procedures used to cons truct meteorological i nputs to CALPUFF for visibility screening of BART eligible sources generally follows the IWAQM recommendations (EPA, 1998), except as noted below.

CALMET Model Options

. The CALMET model has a number of user-selected options, parameter settings, and ‘switches’ that must be defined prior to exercising the processing system. These options and settings are well-d escribed in the CALMET User’s Guid e (Scire et al., 2000a) and in the CALMET input file to the executabl e code. Appendix A of this pr otocol summarizes the CALMET configurations used in developing the processed 6 km meteorological fields over the three CENRAP BART modeling domains. Also included in the tables in Appendix A ar e the default CALMET options and parameter settings recommended in the IWAQM Phase 2 Report (EPA, 1998).

CALMET Domain. Three slightly overla pping modeling domains were defined by CENRAP to support BART modeling. These domains are shown in Figures 5-1 through 5-4 and Table 5-1. The processors used to generate the domain, land use, and elevation data for the CALMET/CALPUFF system include TERREL, CTGPROC, and MAKEGEO, as described below.

> TERREL is the terrain pre-processor that aver ages terrain features to the modeling grid resolution; TERREL constructs the basic pr operties of the gridded domain and defines the coordinates upon which meteorological da ta are stored. Key parameters include specification of grid type, location, resolution and terrain elevation.

> CTGPROC computes the fractional land use fo r the modeling grid resolution. Land use characteristics for each grid cell are assigned using CT GPROC. The primary variable adjustment associated with CTGRPOC is sel ection of an appropriate land use database.

Version 2.0 of the North American Land Cove r Characteristics database is used.

> MAKEGEO is the final pre-processor that co mbines the terrain and land use data for input to CALMET. Generating the appropria te MAKEGEO.INP control file requires only minimal alteration of the default assignments. Key modifications include specifying domain attributes and ensuri ng input files are correctly referenced.

Terrain . CALMET requires both terrain height and la nd use/land cover for the application region. These are generated using the CALMET CTGP ROC, TERREL and MAKEGEO processors. The

terrain data were created usi ng the TERREL (version 3.311 , level 030709) processor and the Shuttle Radar Topography Mission (SRTM)-GTOPO 30 second (~1 km) re solution dataset.

Land Use . The landuse data set was created using th e Composite Theme Grid CTGROC processor (version 2.42, level 030709) and the United States Geological Survey (USGS) Global Land Cover Characterization (GLCC) version 2. 0 database. The GLCC database is available at 30 second (~1km)

resolution. References for these and ot her modeling datasets can be found at www.src.com

.

Vertical Layer Structure. The vertical layer structure for the CALMET/CALPUFF screening applications is more refined than the general sugg estions of IWAQM. The CE NRAP vertical structure was designed to reduce the need for vertical inte rpolation while simultane ously improving vertical resolution within the planetary boundary layer (PBL). Table 5-2 identifies the 11 layer interfaces required to define the 10 layer vertical CALMET grid structure. The top interface in the CALMET

simulation is 4000 meters.

# 5-3 Use of Observations

. Based on considerable discussions with State and Federal managers and agency personnel, CENRAP has elected to use the No-Obs mode in CALMET for constructing the 6 km meteorological fields for CALPUFF screening exer cises. The three annual MM5 simulations (2001, 2002, and 2003) will be used as the sole source fo r meteorological data within CALMET. Blending

observational data with the MM5 data within CALM ET (i.e., use of the “OBS” option is essentially a redundant use of the same data. Substantial improveme nt in the MM5 initializat ion data and in the use of four dimensional data assimilation (FDDA) has been achieved in recent years using observational data. The ETA analysis data used in initial a nd boundary conditions estimates as well as within the FDDA fields derive from 3-hourly, 40 km objective analysis fields computed using an extensive supply of observational data (National Weather Se rvice surface and upper air data, GOES satellite precipitable water; VAD wind profile s from NEXRAD; ACARS aircraft temperature data; SSM/I oceanic surface winds; daily NESDIS snow cover a nd sea-ice analysis data; RAOB balloon drift; GOES and TOVS-1B radiance data; 2D-VAR SST from NCEP Ocean Modeling Branch; radar estimated rainfall; and surface rainfall). The complexi ty, resolution, and accuracy of the ETA data that is used to initialize and ‘nudge’ th e MM5 forecasts is extensive indee d. Particularly at the 12-36 km horizontal grid scales over the fl at to modestly rolling topography of the CENRAP domain, there is no need to introduce local meteorologi cal observations in order to retrieve local terrain effects, for example. Thus, mesoscale wind patterns are likel y to be adequately ch aracterized by the MM5

simulations.

Many observations, especially surface observations , reflect local conditions on a scale smaller

than the 6 km CENRAP CALMET fiel ds. The introduction of the local observations into the regional modeling domain may extend the infl uence of the observational data beyond its true re presentativeness and result in internally inconsistent flow features. In particular the time interpolation of the 12-hourly upper air sounding data may wash out st ructure in the MM5 fields that ar e appropriate to retain. Given

that the CENRAP domain as a whole includes areas of moderately rolling terrain, coastal regions and relatively flat terrain, a single set of representative weights 1 

# that allows significant influence of the observations where appropriate, wi ll 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 observa tional data into the regi onal CALMET simulations is

not considered worthwhile.

However, on the smaller domains likely to be considered in source-specific modeling (e.g., 1-4 km in scale) with the higher CALMET grid resoluti on and the smaller domain size, more control over the region of influence of the me teorological observations can be achieved. It is easier for the diagnostic model to allow the local flow observations to have appropriate influence in the vicinity of the observation, but allow terrain-adjusted flow to dominate away from the observations. Given that the fine scale source-specific domains will be used especially in irregular and/or meteorologically complex settings, the relatively coarser-scale MM5 simu lations are less likely to be fully adequate, and the introduction observational data into CALMET is more likely to achieve improvements in the resulting meteorological fields.

Diagnostic Model Settings

A number of diagnostic model settings must be selected for CALMET to properly process representative diagnostic meteorologi cal data sets. These are summari zed in Appendix A, compared to the default CALMET settings, a nd discussed in the following:

1 

## Weights are assigned in CALMET to control the ‘blending’ of observations and MM5 predictions

.

# 5-4 > CALMET options dealing with radius of influence parameters (R1, R2, RMAX1, RMAX2, RMAX3), BIAS, ICALM parameters are not used in No-Observations mode;

> Gridded cloud data were inferred from the MM5 relative humidity fields (ICLOUD=3);

> Given that all state variables are MM 5-derived (IPROG=14; IT PROG=2), surface layer winds were not extrapolated to the upper layers (IEXTRP = -1);

> The IWAQM recommendation for disabling th e computation of kinematic effects in the wind field options and parameters was selected. This was selected in light of the very

modest elevated terra in in the CENRAP domain, relativ e to the mountainous regions in the U.S. and Alps where the kinematic parame terizations were originally developed. Thus, the option for computing kinematic effects was disabled (IKINE = 0).

> The BIAS array was set to 0. in the CALM ET control file because surface and upper air data were not used (NOOBS = 2);

> Because the MM5 wind fields supply CALMET with the initial guess fields to the diagnostic wind model (IWFCOD =1, IPROG = 14) and observational data are not reintroduced, the following variables were set to nominal values:

 The minimum distance for which extrapol ation of surface winds should occur was set to -1 (RMIN2 = -1.).

 RMIN was left at the IWAQM recommendation of 0.1 km.

 RMAX1 and RMAX2 were each assigne d a value of 30 km. RMAX3 was assigned a value of 50 km.

 R1 and R2 were each assigned the value of 1.0.

 ISURFT and IUPT were assigned placeholder values of 4 and 2, respectively.

> The radius of influence regarding terrain f eatures is comparable to the resolution of the processed terrain data: 12 km.

> The radius of influence for temperatur e interpolation is set to 36 km (TRADKM), a value considered appropriate given th e 6 km CALMET domain and 36/12 km MM5

domain.

> The beginning/ending land use categories fo r temperature interpolation over water are assigned category 55: (JWAT1 = JWAT2 = 55).

> SIGMAP was set to 50 km, while the IW AQM recommendation is 100 km, but with no supporting documentation. Because precipitatio n rates are explicitly incorporated from the MM5 data, a lower radius of in fluence was deemed appropriate.

> Diagnostic options: IWAQM default values were used (see Appendix A);

> TERRAD (terrain scale) is required for runs with diagnostic terra in adjustments (i.e.,
the 2003 simulations). Values of ~10-20 km were tested, and an appropriate value

determined.

# 5-5 > Land use defining water: JWAT1 = 55, JW AT2 = 55 (large bodies of water). This feature allows the temperature field over la rge bodies of water such as the Gulf of Mexico and the Great lakes to be pr operly characterized by buoy observations.

> Mixing height averaging parameter (MNMDAV) we re determined sensitivity tests. The purpose of the testing is to optimize the variable to allow spatial variability in the mixing height field, but without excessive noise.

Obviously, there are some instances where more a dvanced and/or recently developed procedures for constructing the CALMET fields have been used compared with the IWAQ M (1998) guidance. For example, one agency expressed concern about th e choice to employ prognostic model-derived gridded cloud cover data in CALMET (ICLOUD = 3). While this is admittedly a ‘non-guideline’ option, in our view it represents the best sc ience option currently available. In particular, the EPA CAIR and CAMR rulemaking modeling and the CAMx/CMAQ modeling being performed by the RPOs for regional haze all utilize the gridde d moisture fields in the MM5 mode l as a basis for estimating cloud. Presumably, if the method is suitable for such advanced visibility modeling, it is adequate for CALPUFF modeling. Of course, in the protocol negotiation, th e States, source operators, and regulatory agencies have an opportunity to re-exami ne the CALMET diagnostic model settings used in creating the CENRAP gridded fields and modify them if warranted.

In summary, the development of the regional CALMET meteorological fields from MM5 data was conducted in No-Observations (“No-Obs”) m ode. CALMET’s boundary layer modules were used to compute mixing heights, turbulence coefficients and other meteorological parameters required as input to CALPUFF.

5.1.3 MM5/CALMET Processing

Construction of the CALPUFF-rea dy meteorological fields entails a two-step process. First, the MM5 prognostic model outpu t fields are extracted and processed for input to CALMET. This step entails running various extraction software routin es followed by the CALMM5 code. Then, CALMET is exercised for the full three year peri od over each sub-regional CENRAP domain.

CALMM5. Previous applications of the prognostic Me soscale Meteorological model version 5 (MM5) served as the source of the gridded meteorologi cal fields for calendar years 2001, 2002, and 2003. The actual CALMM5 configuration enta iled modification of a few user-s pecified variables. However, two setting are of primary importance:

> All vertical layers from MM5 were extracted, providing CALMET configuration flexibility, and

> Vertical velocity, relative humidity, cl oud/rain fields, and ice/snow fields were extracted. (Graupel was extracted for 2001, the only year where the data were available in the MM5 datasets.)

CALMET. CALMET (v5.53a, lev 040716) was applied co nsistent with CENRAP’s recommendation that the 6 km be generated using the ‘No-Obs’ op tion. The specific options used have been discussed above and are summarized in Appendix A.

# 5-6 5.1.4 Evaluation of the CALPUFF-Re ady Meteorological Data Sets

In typical applications the ad equacy of the CALMET fields is seldom evaluated using independent measurements. Often, onl y cursory visual examination of wind vector plots or time series is considered. This evaluation is important becau se the CALMET performance analysis gives direct insight into the adequacy of the mo del-processed fields on a subregional basis. It also serves as an independent quality assurance tool . Alpine’s MAPS evaluation softwa re to perform an independent evaluation of the processed CALMET data bases. MAPS was used in conjunction with the NCAR

DS472 TDL data sets to evaluate the surface winds and temperat ures for 2001-2003 across all three domains. Since only a small portion of the meteorologi cal content of these data were ingested in the MM5 data assimilation routines (see Johnson, 2003a), th ese data sets are essentially an independent, quantitative means for evaluating the adequacy of the meteorological fiel ds input to CALPUFF.

CALMET Evaluation Methodology

Several statistical measures were calculated as part of the CALMET meteorological evaluation using established procedures (e.g., Tesche et al ., 1990; Emery et al., 2001). Additional plots and graphs are used to present these statistics on bot h hourly and daily time frames over the full annual cycle. For this study, evaluation meas ures were calculated for wind, temp erature, and relative humidity because these parameters are the principal meteor ological inputs to CALPU FF. The full set of

CALMET evaluation statistics an d graphical displays generated with the AG-MAPS software (McNally and Tesche,1994) are contained on a DVD available from CENRAP.

The statistics used to evaluate the meteorological fields for 2001-2003 are generated in both absolute terms (e.g., wind speed error in m/s), and re lative terms (percent erro r) as is commonly done for air quality assessments. Obviously, a very differe nt significance is associated with a given relative error for different meteorological parameters. For example, a 10% error for wind speed measured at

10 m/s is an absolute error of 1 m/s, a minor error. Yet a 10% error for temperature at 300 K is an absolute error of 30 K, a ridiculous ly large error. On the other hand, pollutant concentration errors of 10% at 1 ppb or 10 ppm carry prac tically the same significance.

Three key meteorological metrics include the bi as, error, and index of agreement (IOA) for wind speed, temperature and relative humidity. These measures are defined as follows:

Bias (B) : Calculated as the mean difference in prediction- observation pairings with valid data within a given analysis region and for a gi ven time period (hourly or daily):

Error (E) : Calculated as the mean absolute difference in prediction-observation pairings with valid data within a given analysis region and fo r a given time period (hourly or daily).

Note that the bias and gross error for winds are cal culated from the predicted-observed residuals in speed and direction (not from vector components u and v ). The direction erro r for a given predictionobservation pairing is limited to range from 0 to ± 180 °

. 

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# 5-7 Index of Agreement (IOA) : calculated following the approach of Willmont (1981). This metric

condenses all the differences betwee n model estimates and observations within a given analysis region and for a given time period (hourly and daily) into one statistical quantity. It is the ratio of the total

RMSE to the sum of two differences – between each prediction and the observed mean, and each

observation and the observed mean:

Viewed from another perspective, the index of agreement is a measure of the match between the departure of each prediction from the observed mean and the departure of each observation from the observed mean. Thus, the correspondence between pr edicted and observed values across the domain at a given time may be quantified in a single metric and displayed as a time series. The index of agreement has a theoretical range of 0 to 1, the latter score suggesti ng perfect agreement.

CALMET Evaluation Results

Table 5-5 summarizes the statistical measures, av eraged over the month, for temperature, wind speed, and relative humidity fo r all three years. The CALM ET evaluation DVD contains a full compilation of the statistical and graphical results . Figures 5-7 through 5-31 present a variety of graphical displays of processed and observed su rface temperature, relative humidity, and wind across the three CENRAP subdomains for the three-y ear period 2001-2003. Figures 5-28 through 5-31 provide convenient summaries of the bias and erro r in the relative humidit y, temperature, and wind speed fields across the conti nuous 36 month period by subdomain.

Thorough discussion of the performance findings is beyond the scope of these guidelines. However, a few key findings of th e evaluation are worth noting here. From Table 5-5, the wind speed index of agreement, a general measure of corre lation between measured and observed winds, is systematically greater than a value of 0.8 for virtually every mont h. These values are typically better than those generally achieved in urban- and regional-scale model a pplications for ozone SIPs. For example, the statistical benchmar k for IOA suggested by Emery et al ., (2001) is IOA > 0.6. Thus, the wind speed agreement for all three domains and all three years appear s quite good relative to other MM5/RAMS model applications. From Figure 5-11, th e wind speed root mean square error for the Central domain for 2002 is generally below 2.0 m/s, the performance goal for this parameter. From Figure 5-29 (as well as in Table 5-5), the temperatur e bias results for the 36 month are generally quite

close to the + 0.5 deg C performance goal. As shown in Fi gure 5-30 the temperature error results are slightly poorer than the 2 deg C performance goal for 2001 and 2003, but are below the 2.0 deg C

threshold for 2002. Note that the benchmarks were developed not to provide a pass/fail standard to which all modeling results should be held, but rather to put the result s into an historical context.

In summary, we find that:

 Relative Humidity

 Bias over three-year peri od near zero all domains

 For some months over- and under-prediction (up to 10% or more) is evident – no

discernable trend ⎥ ⎥ ⎥ ⎥

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# 5-8  Errors typically diminish from 2001 thr ough 2003, and are genera lly < 12% after 1st quarter of 2001.

 Surface Temperatures

 Monthly averaged temperatures are system atically biased low (cooler) by 0.25 to 1.25 deg C.

 The errors in monthly averaged temperatures typically range betw een 1.8 and 2.6 deg C

 Average error over all mont hs is about 2.2 deg C.

 Surface Wind Speeds

 IOA typically between 0.8 0-0.9

 Seasonally variable

 Central subdomain gives best correlation

 Results from MM5/CALMET evaluation provide pot entially useful information for diagnosing BART visibility modeling analyses

 MM5/CALMET fields exhibit good statistical ag reement with observations, in part because observations figure prominently in the constr uction of the interpolated CALMET fields.

 MM5/CALMET fields for the three CENRAP s ubdomains are quite sufficient for use in CALPUFF modeling.

5.1.5 Meteorological Data Archive and Distribution

All models, scripts and CALMET data (excepti ng MM5 outputs) are ava ilable from CENRAP on appropriate external combin ation Firewire/USB drives.

5.2 Aerometric Monitoring Networks

Data from ambient monitoring networks for bot h gas-phase and aerosol species are available

for use in CENRAP BART modeli ng analyses. Table 5-4 summari zes ambient monitoring networks. Data for 2002 have been compiled for all networks covering the CENRAP domain with the exception of the PAMS and PM Supersites. These data sets may be obtained from CE NRAP. Figures 5-5 and 5- 6 display the locations of monitoring s ites in and near the CENRAP States.

# 5-9 Table 5-1. CENRAP Lambert Conic Conformal Modeling Domain Specifications (40.97 degree projection origin; 33 and 45 degree matching parallels).

Domain Southwest Coordinate (km) Number

of X grid cells Number

of Y grid cells Horizontal

Resolution

CALMET South -1008, -1620 306 246 6 km Central -1008, -864 388 234 6 km North -1008, 0 300 193 6 km

Table 5-2. Vertical Layer Structure in CALMET Fields. (Heights are in meters.)

L AYER

NUMBER 

# L AYER


# H EIGHT 

# L AYER

NUMBER 

# L AYER


# H EIGHT


# 0 0. 6 640.

1 20. 7 1200.

2 40. 8 2000.

3 80. 9 3000.

4 160. 10 4000.

5 320.

Table 5-3. Meteorological Model File Sizes for CENRAP BART Modeling.

Domain Monthly Annual 3 Years Domain Grid 3 years

North 4.6 55.2 165.6 2001 12 km 1370

Central 6.6 79.2 237.6 2002 36 km 430

South 6.0 72.0 216.0 2003 36 km 430

total 17.2 206.4 619.2 total 2230 CALMET 6 km File Sizes, (Gbytes) MM5 File Sizes, (Gbytes)

# 5-10 Table 5-4. Statistical Evaluation of th e CALMET Meteorological Fields for 2001-2003.

Jan Feb Mar Apr May Jun Jul A ug Sep Oct Nov Dec Mean RH Bias (%)

North 4.54 3.19 0.17 -14.55 -12.09 -4.35 -0.62 1.17 -2.07 -7.98 -6.62 -4.22 -3.62

Central -2.60 -7.28 -11.38 -10.69 -8.62 -2.90 0.66 1.07 -1.44 -5.46 -6.16 -7.78 -5.21

South -10.23 -11.53 -13.78 -4.24 -2.08 0.99 4.12 3.16 -0.12 -2.12 -3.44 -9.76 -4.09 RH Error (%)

North 10.06 10.31 14.03 18.77 16.28 12.39 11.82 11.76 13.26 15.54 13.53 12.89 13.39

Central 13.32 15.86 17.45 17.05 14.50 11.67 11.52 11.32 12.26 15.52 14.79 14.95 14.18

South 16.22 18.37 18.17 13.26 12.15 11.51 12.09 12.40 11.82 14.85 14.73 16.19 14.31
Temp Bias ( 0 C)

North -1.63 -1.23 -1.23 -0.24 0.08 -0.29 -0.23 -0.54 -0.55 -0.09 -0.40 -1.27 -0.64

Central -0.99 -0.65 -0.54 -0.16 0.13 -0.23 -0.43 -0.54 -0.36 -0.34 -0.30 -0.74 -0.43

South -0.47 -0.42 0.03 -0.31 -0.33 -0.63 -0.99 -0.85 -0.52 -0.36 -0.19 -0.21 -0.44
Temp Error ( 0 C)

North 3.10 2.88 2.54 2.49 2.44 2.43 2.42 2.49 2.58 2.48 2.89 2.55 2.61

Central 2.38 2.25 1.99 2.18 1.99 2.01 2.07 2.11 2.21 2.52 2.61 2.42 2.23

South 2.31 2.28 1.92 2.13 2.01 2.17 2.19 2.21 2.19 2.70 2.49 2.50 2.26 Wind Speed IO A

North 0.79 0.83 0.83 0.87 0.86 0.85 0.81 0.84 0.84 0.82 0.81 0.79 0.83

Central 0.85 0.87 0.88 0.88 0.89 0.86 0.84 0.86 0.87 0.86 0.85 0.84 0.86

South 0.81 0.80 0.85 0.79 0.83 0.83 0.78 0.80 0.82 0.82 0.80 0.82 0.81

Jan Feb Mar Apr May Jun Jul A ug Sep Oct Nov Dec Mean RH Bias (%)

North 8.33 9.52 6.63 0.95 -2.42 1.25 2.43 1.60 0.57 0.47 4.47 7.73 3.46

Central 7.43 5.13 4.60 1.65 -1.02 1.52 2.50 1.88 -0.27 -1.40 -0.01 4.35 2.20

South 3.08 -1.19 2.53 2.32 1.26 1.98 2.51 2.62 -0.80 -2.42 -4.45 -1.03 0.53 RH Error (%)

North 11.85 13.18 11.61 11.13 11.90 10.04 9.54 9.08 10.26 10.26 11.55 11.61 11.00

Central 12.21 12.43 11.26 10.58 10.72 9.89 9.55 9.54 10.22 10.25 11.42 11.26 10.78

South 11.24 11.76 10.34 8.95 9.30 9.49 9.46 9.61 9.68 9.33 11.63 10.95 10.14
Temp Bias ( 0 C)

North -0.70 -0.82 -0.96 -0.52 -0.25 -0.36 -0.53 -0.49 -0.44 -0.67 -0.76 -0.69 -0.60

Central -0.57 -0.65 -0.79 -0.62 -0.41 -0.68 -0.81 -0.74 -0.49 -0.54 -0.55 -0.52 -0.61

South -0.23 -0.13 -0.52 -0.61 -0.61 -0.94 -0.94 -1.07 -0.65 -0.47 0.04 -0.13 -0.52
Temp Error ( 0 C)

North 2.15 2.07 2.04 1.89 1.86 1.83 1.86 1.80 1.95 1.78 1.99 2.15 1.95

Central 2.12 2.05 2.14 1.95 1.91 1.93 1.93 1.92 2.02 1.77 2.00 2.00 1.98

South 2.18 2.05 2.17 1.83 1.89 1.91 1.88 2.00 1.92 1.68 2.06 1.93 1.96 Wind Speed IO A

North 0.82 0.84 0.86 0.88 0.86 0.85 0.85 0.83 0.85 0.85 0.81 0.78 0.84

Central 0.87 0.88 0.90 0.90 0.88 0.87 0.84 0.84 0.87 0.88 0.85 0.85 0.87

South 0.86 0.86 0.85 0.85 0.84 0.82 0.79 0.80 0.83 0.83 0.83 0.82 0.83

Jan Feb Mar Apr May Jun Jul A ug Sep Oct Nov Dec Mean RH Bias (%)

North 10.15 7.40 6.01 0.93 -3.76 -0.38 1.38 2.04 -1.66 -1.99 2.96 7.68 2.56

Central 6.94 4.76 4.15 0.42 -2.18 0.17 2.08 2.13 -2.05 -4.13 0.00 5.47 1.48

South 0.00 0.00 0.47 -1.10 -0.37 0.54 1.77 2.89 -3.31 -6.01 -3.66 -0.33 -0.76 RH Error (%)

North 13.30 11.21 12.32 11.70 11.65 10.03 9.70 9.57 11.13 12.68 11.53 11.85 11.39

Central 12.77 10.95 11.61 11.18 10.33 9.91 9.49 9.50 10.70 12.69 12.10 12.43 11.14

South 11.18 10.00 9.85 10.17 9.20 9.54 8.90 9.91 10.21 12.12 12.15 12.39 10.47
Temp Bias ( 0 C)

North -1.24 -0.99 -0.63 -0.29 -0.11 -0.10 -0.22 -0.49 -0.34 0.29 -0.85 -1.34 -0.53

Central -0.84 -0.80 -0.64 -0.47 -0.27 -0.36 -0.60 -0.66 -0.32 0.30 -0.54 -0.89 -0.51

South -0.17 -0.27 -0.36 -0.43 -0.46 -0.62 -0.91 -0.98 -0.28 0.53 0.00 -0.03 -0.33
Temp Error ( 0 C)

North 2.31 2.15 2.14 2.02 1.81 1.77 1.91 1.98 2.25 2.57 2.30 2.67 2.16

Central 2.14 2.03 2.15 2.13 1.80 1.81 1.96 1.99 2.16 2.54 2.31 2.45 2.12

South 2.10 1.90 2.00 2.08 1.84 1.81 1.88 2.06 1.94 2.40 2.28 2.48 2.06 Wind Speed IO A

North 0.79 0.81 0.83 0.86 0.87 0.85 0.86 0.87 0.84 0.82 0.80 0.82 0.83

Central 0.85 0.88 0.87 0.89 0.90 0.87 0.87 0.86 0.87 0.87 0.86 0.86 0.87

South 0.83 0.83 0.85 0.83 0.85 0.81 0.83 0.82 0.85 0.84 0.82 0.82 0.83 CALMET Model Evaluation Statistics for 2001.

CALMET Model Evaluation Statistics for 2002.

CALMET Model Evaluation Statistics for 2003.5-11Table 5-5. Overview of Ambient Data Monitoring Networks Covering the CENRAP Domain.Monitoring NetworkChemical Species MeasuredSamplingPeriodData Availability/SourceThe Interagency Monitoring ofProtected Visual Environments(IMPROVE)Speciated PM25 and PM10 (seespecies mappings)1 in 3 days; 24hr averagehttp://vista.cira.colostate.edu/improve/Data/IMPROVE/improve_data.htmClean Air Status and TrendsNetwork (CASTNET)Speciated PM25, Ozone (seespecies mappings)Approximately 1-week averagehttp://www.epa.gov/castnet/data.htmlNational Atmospheric DepositionProgram (NADP)Wet deposition (hydrogen (acidity aspH), sulfate, nitrate, ammonium,chloride, and base cations (such ascalcium, magnesium, potassium andsodium)), Mercury1-week averagehttp://nadp.sws.uiuc.edu/Air Quality System (AQS) AkaAerometric Information RetrievalSystem (AIRS)CO, NO2, O3, SO2, PM25, PM10,PbTypically hourlyaveragehttp://www.epa.gov/air/data/Speciation Trends Network (STN) Speciated PM 24-hour averagehttp://www.epa.gov/ttn/amtic/amticpm.htmlSoutheastern Aerosol Researchand Characterization (SEARCH)(Southeastern US only)24-hr PM25 (FRM Mass, OC, BC,SO4, NO3, NH4, Elem.); 24-hr PMcoarse (SO4, NO3, NH4, elements);Hourly PM2.5 (Mass, SO4, NO3,NH4, EC, TC); Hourly gases (O3,NO, NO2, NOy, HNO3, SO2, CO)Hourly or 24-hour average,depending onparameter.Electric Power Research Institute (EPRI),Southern Company, and other companies.http://www.atmospheric-research.comEPA Particulate Matter Supersites(Includes St. Louis in the CENRAPregion)Speciated PM25http://www.epa.gov/ttn/amtic/supersites.htmlPhotochemical AssessmentMonitoring Stations (PAMS)Varies for each of 4 station types.http://www.epa.gov/ttn/amtic/pamsmain.htmlNational Park Service GaseousPollutant Monitoring NetworkAcid deposition (Dry; SO4, NO3,HNO3, NH4, SO2), O3,meteorological dataHourlyhttp://www2.nature.nps.gov/ard/gas/netdata1.htm

# 5-12 Figure 5-1. CENRAP North, Central, a nd South 6 km Meteorological Domains.

# 5-13 Figure 5-2. CENRAP South Domain.

# 5-14 Figure 5-3. CENRAP Central Domain.

# 5-15 Figure 5-4. CENRAP North Domain.

# 5-16 Figure 5-5. Locations of IMPROVE, CASTN et, SEARCH, STN and NADP Monitoring Sites

in and Near the CENRAP States. 

# -1000 -750 -500 -250 0 250 500 750 1000 -1500 -1250 -1000

-750 -500 -250 0 250 500 750 1000 1250

IMPROVE

CASTNet

SEARCH

STN

NADP

AQS

# 5-17 Figure 5-6. Locations of AQS Monitoring Sites in and Near the CENRAP States. 

# -1000 -750 -500 -250 0 250 500 750 1000 -1500 -1250 -1000

-750 -500 -250 0 250 500 750 1000 1250

IMPROVE

CASTNet

SEARCH

STN

NADP

AQS

# 5-18 Figure 5-7. Spatial Mean Relative Humidi ty (%) over the Central Domain: July 2002.

Figure 5-8. Spatial Mean Surface Temperature (deg C) over the Central Domain: July 2002.

# 5-19 Figure 5-9. Wind Speed Index of Agreemen t over the Central Domain: July 2002.

Figure 5-10. Standard Deviation in Wind Sp eed (m/s) over the Central Domain: July 2002.

# 5-20 Figure 5-11. Root Mean Square Error in Wind Speed (m/s) over the Central Domain: July 2002.

Figure 5-12. Scalar Mean Wind Speed (m/ s) over the Central Domain: July 2002.

# 5-21 Figure 5-13. Vector Mean Wind Speed (m/ s) over the Central Domain: July 2002.

Figure 5-14. Normalized Bias in Relative Hu midity (%) over the Central Domain: July 2002.

# 5-22 Figure 5-15. Normalized Error in Relative Humidity (%) over the Central Domain: July 2002.

Figure 5-16. Relative Humidity (%) at Kenosha, WI: July 2002.

# 5-23 Figure 5-17. Relative Humidity (%) at Topeka, KS: July 2002.

Figure 5-18. Normalized Bias in Surface Te mperature (%) over the Central Domain: July 2002.

# 5-24 Figure 5-19. Normalized Error in Surface Te mperature over the Central Domain: July 2002.

Figure 5-20. Surface Temperature (deg C) at Kenosha, WI: July 2002.

# 5-25 Figure 5-21. Surface Temperatu re at Topeka, KS: July 2002.

# 5-26 Figure 5-22. MM5/CALMET Relative Humidity Bi as (%) by Month for Three BART Modeling Years (2001, 2003, and 2003). Monthly Average Mean Bias in Relative Humidity for 2001

-20.0 -15.0 -10.0

-5.0 0.0 5.0 10.0

J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c M e a nRelative Humidity Bias,%

North Central South

Monthly Average Mean Bias in Relative Humidity for 2002

-20.0 -15.0 -10.0

-5.0 0.0 5.0 10.0

J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c M e a nRelative Humidity Bias,%

North Central South



### Monthly Average Mean Bias in Relative Humidity for 2003

-20.0 -15.0 -10.0

-5.0 0.0 5.0 10.0

J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c M e a nRelative Humidity Bias,%

North Central South

# 5-27 Figure 5-23. MM5/CALMET Relative Humidi ty Error (%) by Month for Three BART Modeling Years (2001, 2003, and 2003). Monthly Average Mean Errror in Relative Humidity for 2002

0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0

J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c M e a nRelative Humidity Error,%

North Central South Monthly Average Mean Errror in Relative Humidity for 2001

0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0

J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c M e a nRelative Humidity Error,%

North Central South



### Monthly Average Mean Errror in Relative Humidity for 2003

0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0

J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c M e a nRelative Humidity Error,%

North Central South

# 5-28 Figure 5-24. MM5/CALMET Temperature Bias (deg C) by Month for Three BART Modeling Years (2001, 2003, and 2003).

Monthly Average Temperature Bias for 2001 (deg C)

-2.00 -1.75 -1.50 -1.25 -1.00 -0.75 -0.50 -0.25 0.00 0.25 0.50

J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c M e a nTemperature Bias, deg C

North Central South

Monthly Average Temperature Bias for 2002 (deg C)

-2.00 -1.75 -1.50 -1.25 -1.00 -0.75 -0.50 -0.25 0.00 0.25 0.50

J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c M e a nTemperature Bias, degC

North Central South



### Monthly Average Temperature Bias for 2003 (deg C)

-2.00 -1.75 -1.50 -1.25 -1.00 -0.75 -0.50 -0.25 0.00 0.25 0.50

J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c M e a nTemperature Bias, deg C

North Central South

# 5-29 Figure 5-25. MM5/CALMET Temperature Error (deg C) by Month for Three BART Modeling Years (2001, 2003, and 2003). Monthly Average Temperature Errror for 2001 (deg C)

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0

J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c M e a nTemperature Error, degC

North Central South

Monthly Average Temperature Errror for 2002 (deg C)

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0

J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c M e a nTemperature Error, degC

North Central South



### Monthly Average Temperature Errror for 2003 (deg C)

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0

J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c M e a nTemperature Error, degC

North Central South

# 5-30 Figure 5-26. MM5/CALMET Wind Speed Index of Agreement by Month for Three BART Modeling Years (2001, 2003, and 2003). Monthly Average Wind Speed Index of Agreement for 2001

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c M e a nWind Speed Index of Agreement

North Central South



### Monthly Average Wind Speed Index of Agreement for 2002

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c M e a nWind Speed Index of Agreement

North Central South



### Monthly Average Wind Speed Index of Agreement for 2003

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c M e a nWind Speed Index of Agreement

North Central South5-31Figure 5-27. MM5/CALMET Relative Humidity Bias(%) over Three Years in All CENRAP Domains.Monthly Average Relative Humidity Bias, %-15.0-10.0-5.00.05.010.0 J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c M e a n

Relative Humidity Bias, %NorthCentralSouth5-32Figure 5-28. MM5/CALMET Relative Humidity Error(%) over Three Years inAll CENRAP Domains.Monthly Average Relative Humidity Error, %0.02.55.07.510.012.515.017.520.0 J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c M e a n

Relative Humidity Error, %NorthCentralSouth5-33Figure 5-29. MM5/CALMET Surface Temperature Bias (degC) over Three Years in All CENRAP Domains.Monthly Average Temperature Bias, deg C-2.00-1.75-1.50-1.25-1.00-0.75-0.50-0.250.000.250.50 J a n M a r M a y J u l S e p N o v J a n M a r M a y J u l S e p N o v J a n M a r M a y J u l S e p N o v

Temperature Bias, deg CNorthCentralSouth5-34Figure 5-30. MM5/CALMET Surface Temperature Error (degC) over Three Years in All CENRAP Domains.Monthly Average Temperature Errror, deg C0.00.51.01.52.02.53.03.54.0 J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c M e a n

Temperature Error, deg CNorthCentralSouth5-35Figure 5-31. MM5/CALMET Wind Speed Index of Agreement over Three Years in All CENRAP Domains.Monthly Average Wind Speed Index of Agreement0.600.650.700.750.800.850.900.951.00 J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c

M e a n

Wind Speed Index of AgreementNorthCentralSouth