5-1

5.0 DATA BASES FOR CALPUFF MODELING 

To support BART modeling by the states and source operators, both meteorological and 

aerometric data sets are required. Regional meteorological data sets generated by the CALMET model 

suitable for direct input to the CALPUFF modeling system have been developed and archived. These 

data sets cover calendar years 2001, 2002, and 2003 for three sub-regional grid domains shown in 

Figures 5-1 through 5-4. The procedures used in developing the CALMET data sets generally follow 

the IWAQM recommendations (EPA, 1998), except for a few notable refinements. The processed 

CALMET files, in CALPUFF-ready input format, are available from CENRAP on hard disk drives to 

interested states and stakeholders. 

This chapter describes how these meteorological modeling sets were developed 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 applicability. In addition, for those states or source operators who elect to conduct 

more source-specific CALMET/CALPUFF modeling, the information in this chapter may be helpful in 

guiding specification of revised CALMET model inputs and generation of revised CALMET data sets. 

Also included in Section 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 set of CALMET regional meteorological modeling 

data sets for use by the CENRAP States, BART eligible sources within the region and others. These 

meteorological modeling data sets were constructed through the joint use of the CALMET processor 

and results from existing annual three-dimensional MM5 meteorological simulations. The specific 

annual prognostic model simulations available for CENRAP BART modeling included: 

> 2001 MM5 data set at 36/12 km resolution 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 evaluation of the MM5 generated data sets against 

surface meteorological observations and the results of these evaluations 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 independent CENRAP modeling centers and these data sets have not 

been concatenated into one master data base. More importantly, there has been no systematic, rigorous 

model performance evaluation performed on the CENRAP 2002 12 km MM5 data yet. Accordingly, 

until such time as the 2002 12 km data set has been 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 construct meteorological inputs 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-described in the CALMET User’s Guide (Scire et al., 2000a) and in the 

CALMET input file to the executable code. Appendix A of this protocol 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 are the default CALMET 

options and parameter settings recommended in the IWAQM Phase 2 Report (EPA, 1998). 

 

CALMET Domain. Three slightly overlapping 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 averages terrain features to the modeling grid 

resolution; TERREL constructs the basic properties of the gridded domain and defines 

the coordinates upon which meteorological data are stored. Key parameters include 

specification of grid type, location, resolution and terrain elevation. 

> CTGPROC computes the fractional land use for the modeling grid resolution. Land use 

characteristics for each grid cell are assigned using CTGPROC. The primary variable 

adjustment associated with CTGRPOC is selection of an appropriate land use database. 

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

> MAKEGEO is the final pre-processor that combines the terrain and land use data for 

input to CALMET. Generating the appropriate MAKEGEO.INP control file requires 

only minimal alteration of the default assignments. Key modifications include 

specifying domain attributes and ensuring input files are correctly referenced. 

Terrain. CALMET requires both terrain height and land use/land cover for the application region. 

These are generated using the CALMET CTGPROC, TERREL and MAKEGEO processors. The 

terrain data were created using the TERREL (version 3.311, level 030709) processor and the Shuttle 

Radar Topography Mission (SRTM)-GTOPO 30 second (~1 km) resolution dataset. 

Land Use. The landuse data set was created using the 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 other 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 suggestions of IWAQM. The CENRAP vertical structure 

was designed to reduce the need for vertical interpolation while simultaneously 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 exercises. The three annual MM5 simulations (2001, 

2002, and 2003) will be used as the sole source for meteorological data within CALMET. Blending 

observational data with the MM5 data within CALMET (i.e., use of the “OBS” option is essentially a 

redundant use of the same data. Substantial improvement in the MM5 initialization 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 and 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 Service surface and upper air data, GOES satellite 

precipitable water; VAD wind profiles from NEXRAD; ACARS aircraft temperature data; SSM/I 

oceanic surface winds; daily NESDIS snow cover and 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 complexity, resolution, and accuracy of the ETA data that 

is used to initialize and ‘nudge’ the MM5 forecasts is extensive indeed. Particularly at the 12-36 km 

horizontal grid scales over the flat to modestly rolling topography of the CENRAP domain, there is no 

need to introduce local meteorological observations in order to retrieve local terrain effects, for 

example. Thus, mesoscale wind patterns are likely to be adequately characterized by the MM5 

simulations. 

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

than the 6 km CENRAP CALMET fields. The introduction of the local observations into the regional 

modeling domain may extend the influence of the observational data beyond its true representativeness 

and result in internally inconsistent flow features. In particular the time interpolation of the 12-hourly 

upper air sounding data may wash out structure in the MM5 fields that are 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 weights1

 that allows significant influence of the 

observations where 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. 

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 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 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 simulations 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 meteorological data sets. These are summarized in Appendix A, compared to 

the default CALMET settings, and 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 MM5-derived (IPROG=14; ITPROG=2), surface layer 

winds were not extrapolated to the upper layers (IEXTRP = -1); 

 > The IWAQM recommendation for disabling the computation of kinematic effects in the 

wind field options and parameters was selected. This was selected in light of the very 

modest elevated terrain in the CENRAP domain, relative to the mountainous regions in 

the U.S. and Alps where the kinematic parameterizations were originally developed. 

Thus, the option for computing kinematic effects was disabled (IKINE = 0). 

 > The BIAS array was set to 0. in the CALMET 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 extrapolation 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 assigned 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 features is comparable to the resolution of the 

processed terrain data: 12 km. 

 > The radius of influence for temperature interpolation is set to 36 km (TRADKM), a 

value considered appropriate given the 6 km CALMET domain and 36/12 km MM5 

domain. 

 > The beginning/ending land use categories for temperature interpolation over water are 

assigned category 55: (JWAT1 = JWAT2 = 55). 

 > SIGMAP was set to 50 km, while the IWAQM recommendation is 100 km, but with no 

supporting documentation. Because precipitation rates are explicitly incorporated from 

the MM5 data, a lower radius of influence was deemed appropriate. 

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

> TERRAD (terrain scale) is required for runs with diagnostic terrain 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, JWAT2 = 55 (large bodies of water). This 

feature allows the temperature field over large bodies of water such as the Gulf of 

Mexico and the Great lakes to be properly characterized by buoy observations.

> Mixing height averaging parameter (MNMDAV) were 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 advanced and/or recently developed procedures for 

constructing the CALMET fields have been used compared with the IWAQM (1998) guidance. For 

example, one agency expressed concern about the 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 science 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 gridded moisture fields in the MM5 model 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, the States, source operators, and 

regulatory agencies have an opportunity to re-examine 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”) mode. 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-ready meteorological fields entails a two-step process. First, 

the MM5 prognostic model output fields are extracted and processed for input to CALMET. This step 

entails running various extraction software routines followed by the CALMM5 code. Then, CALMET 

is exercised for the full three year period over each sub-regional CENRAP domain. 

CALMM5. Previous applications of the prognostic Mesoscale Meteorological model version 5 (MM5) 

served as the source of the gridded meteorological fields for calendar years 2001, 2002, and 2003. 

The actual CALMM5 configuration entailed modification of a few user-specified variables. However, 

two setting are of primary importance: 

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

flexibility, and 

> Vertical velocity, relative humidity, cloud/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 consistent with CENRAP’s recommendation 

that the 6 km be generated using the ‘No-Obs’ option. The specific options used have been discussed 

above and are summarized in Appendix A. 

 
 

5-6

 5.1.4 Evaluation of the CALPUFF-Ready Meteorological Data Sets 

In typical applications the adequacy of the CALMET fields is seldom evaluated using 

independent measurements. Often, only cursory visual examination of wind vector plots or time series 

is considered. This evaluation is important because the CALMET performance analysis gives direct 

insight into the adequacy of the model-processed fields on a subregional basis. It also serves as an 

independent quality assurance tool. Alpine’s MAPS evaluation software 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 temperatures for 2001-2003 across all three 

domains. Since only a small portion of the meteorological content of these data were ingested in the 

MM5 data assimilation routines (see Johnson, 2003a), these data sets are essentially an independent, 

quantitative means for evaluating the adequacy of the meteorological fields 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 both hourly and daily time frames over the full annual 

cycle. For this study, evaluation measures were calculated for wind, temperature, and relative humidity 

because these parameters are the principal meteorological inputs to CALPUFF. The full set of 

CALMET evaluation statistics and 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 relative terms (percent error) as is commonly done 

for air quality assessments. Obviously, a very different 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 ridiculously large error. On the other hand, pollutant concentration errors of 

10% at 1 ppb or 10 ppm carry practically the same significance. 

Three key meteorological metrics include the bias, 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 given 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 for a given time period (hourly or daily). 

Note that the bias and gross error for winds are calculated from the predicted-observed residuals in 

speed and direction (not from vector components u and v). The direction error for a given predictionobservation pairing is limited to range from 0 to ±180°. 

∑∑( ) = =

= −

J

j

I

i

i

j

i Pj O

IJ

B

1 1

1

∑∑= =

= −

J

j

I

i

i

j

i Pj O

IJ

E

1 1

1
 

5-7

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

condenses all the differences between 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 predicted 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 suggesting perfect agreement. 

CALMET Evaluation Results 

Table 5-5 summarizes the statistical measures, averaged over the month, for temperature, wind 

speed, and relative humidity for all three years. The CALMET 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 surface temperature, relative humidity, and wind across 

the three CENRAP subdomains for the three-year period 2001-2003. Figures 5-28 through 5-31 

provide convenient summaries of the bias and error in the relative humidity, temperature, and wind 

speed fields across the continuous 36 month period by subdomain. 

Thorough discussion of the performance findings is beyond the scope of these guidelines. 

However, a few key findings of the evaluation are worth noting here. From Table 5-5, the wind speed 

index of agreement, a general measure of correlation between measured and observed winds, is 

systematically greater than a value of 0.8 for virtually every month. These values are typically better 

than those generally achieved in urban- and regional-scale model applications for ozone SIPs. For 

example, the statistical benchmark 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 appears quite good relative to other 

MM5/RAMS model applications. From Figure 5-11, the 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 temperature bias results for the 36 month are generally quite 

close to the + 0.5 deg C performance goal. As shown in Figure 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 results into an historical context. 

In summary, we find that: 

 Relative Humidity 

 Bias over three-year period near zero all domains 

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

discernable trend 

⎥

⎥

⎥

⎥

⎦

⎤

⎢

⎢

⎢

⎢

⎣

⎡

− + −

⋅ = −

∑∑= =

J

j

I

i

o

i

o j

i Pj M O M

IJ RMSE IOA

1 1

2

1
 

5-8

 Errors typically diminish from 2001 through 2003, and are generally < 12% after 1st 

quarter of 2001. 

 Surface Temperatures 

 Monthly averaged temperatures are systematically biased low (cooler) by 0.25 to 1.25 

deg C. 

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

 Average error over all months 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 potentially useful information for diagnosing 

BART visibility modeling analyses 

 MM5/CALMET fields exhibit good statistical agreement with observations, in part because 

observations figure prominently in the construction of the interpolated CALMET fields. 

 MM5/CALMET fields for the three CENRAP subdomains are quite sufficient for use in 

CALPUFF modeling. 

 5.1.5 Meteorological Data Archive and Distribution 

All models, scripts and CALMET data (excepting MM5 outputs) are available from CENRAP 

on appropriate external combination Firewire/USB drives. 

5.2 Aerometric Monitoring Networks 

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

for use in CENRAP BART modeling analyses. Table 5-4 summarizes 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 CENRAP. Figures 5-5 and 5-

6 display the locations of monitoring sites 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.) 

LAYER 

NUMBER

LAYER 

HEIGHT

LAYER 

NUMBER

LAYER 

HEIGHT

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 the CALMET Meteorological Fields for 2001-2003. 

Jan Feb Mar Apr May Jun Jul Aug 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 IOA

 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 Aug 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 IOA

 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 Aug 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 IOA

 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. 
5-11

Table 5-5. Overview of Ambient Data Monitoring Networks Covering the CENRAP Domain. 

Monitoring Network Chemical Species Measured Sampling Period Data Availab

The Interagency Monitoring of Protected Visual Environments (IMPROVE) 

Speciated PM25 and PM10 (see species mappings) 

1 in 3 days; 24 

hr average 

http://vista.cira.colostate.

VE/improve_data.htm

Clean Air Status and Trends Network (CASTNET) 

Speciated PM25, Ozone (see species mappings) 

Approximately 1-

week average 

http://www.epa.gov/castn

National Atmospheric Deposition Program (NADP) 

Wet deposition (hydrogen (acidity as pH), sulfate, nitrate, ammonium, chloride, and base cations (such as calcium, magnesium, potassium and sodium)), Mercury 

1-week average http://nadp.sws.uiuc.edu/

Air Quality System (AQS) Aka 

Aerometric Information Retrieval System (AIRS) 

CO, NO2, O3, SO2, PM25, PM10, Pb 

Typically hourly 

average 

http://www.epa.gov/air/da

Speciation Trends Network (STN) Speciated PM 24-hour average http://www.epa.gov/ttn/amSoutheastern Aerosol Research and Characterization (SEARCH) (Southeastern US only) 

24-hr PM25 (FRM Mass, OC, BC, SO4, NO3, NH4, Elem.); 24-hr PM coarse (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 on 

parameter. 

Electric Power Research Southern Company, and http://www.atmospheric-r

EPA Particulate Matter Supersites (Includes St. Louis in the CENRAP region) 

Speciated PM25 http://www.epa.gov/ttn/am

Photochemical Assessment Monitoring Stations (PAMS) 

Varies for each of 4 station types. http://www.epa.gov/ttn/am

National Park Service Gaseous Pollutant Monitoring Network 

Acid deposition (Dry; SO4, NO3, HNO3, NH4, SO2), O3, meteorological data 

Hourly http://www2.nature.nps.g
5-12

Figure 5-1. CENRAP North, Central, and 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, CASTNet, 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 Humidity (%) 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 Agreement over the Central Domain: July 2002. 

Figure 5-10. Standard Deviation in Wind Speed (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 Humidity (%) 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 Temperature (%) over the Central Domain: July 2002. 
5-24

Figure 5-19. Normalized Error in Surface Temperature over the Central Domain: July 2002. 

Figure 5-20. Surface Temperature (deg C) at Kenosha, WI: July 2002. 
5-25

Figure 5-21. Surface Temperature at Topeka, KS: July 2002. 
5-26

Figure 5-22. MM5/CALMET Relative Humidity Bias (%) 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

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Mean

Relative 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

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Mean

Relative 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

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Mean

Relative Humidity Bias, 

%

North Central South
5-27

Figure 5-23. MM5/CALMET Relative Humidity 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

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Mean

Relative 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

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Mean

Relative 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

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Mean

Relative 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

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Mean

Temperature 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

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Mean

Temperature Bias, deg 

C

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

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Mean

Temperature 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

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Mean

Temperature Error, deg 

C

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

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Mean

Temperature Error, deg 

C

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

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Mean

Temperature Error, deg 

C

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

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Mean

Wind 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

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Mean

Wind 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

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Mean

Wind Speed Index of Agreement

North Central South
5-31

Figure 5-27. MM5/CALMET Relative Humidity Bias (%) over Three Years in All CENRAP Domains. 

Monthly Average Relative Humidity Bias, %

-15.0-10.0

-5.0

0.05.0

10.0

JanFebMarAprMayJunJul AugSepOctNovDecJanFebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJul AugSepOctNovD

Relative Humidity Bias, %

North Central South
5-32

Figure 5-28. MM5/CALMET Relative Humidity Error (%) over Three Years in All CENRAP Domains. 

Monthly Average Relative Humidity Error, %

0.02.55.07.5

10.012.515.017.520.0

JanFebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOctNovDec

Relative Humidity Error, %

North Central South
5-33

Figure 5-29. MM5/CALMET Surface Temperature Bias (deg C) over Three Years in All CENRAP Domains. 

Monthly Average Temperature Bias, deg C

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

0.000.250.50

JanMarMayJulSepNovJanMarMayJulSepNovJanMarMayJulSepNov

Temperature Bias, deg C

North Central South
5-34

Figure 5-30. MM5/CALMET Surface Temperature Error (deg C) over Three Years in All CENRAP Domains. 

Monthly Average Temperature Errror, deg C

0.00.51.01.52.02.53.03.54.0

JanFebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOctNovDe

Temperature Error, deg C

North Central South
5-35

Figure 5-31. MM5/CALMET Wind Speed Index of Agreement over Three Years in All CENRAP Domains. 

Monthly Average Wind Speed Index of Agreement

0.600.650.700.750.800.850.900.951.00

JanFebMarApr MayJunJul AugSepOct NovDecJanFebMarApr MayJunJul AugSepOct NovDecJanFebMarApr MayJunJul AugSepOct NovDecMea

Wind Speed Index of AgreementNorth Central South