February 24, 2006 

Final Report for the Western Regional Air 

Partnership (WRAP) 2002 Visibility Model 

Performance Evaluation 

WGA Contract Number: 30203 

Prepared for: 

Western Regional Air Partnership 

Western Governors’ Association 

1515 Cleveland Place, Suite 200 

Denver, CO 80202 

Prepared by: 

Gail Tonnesen, Zion Wang, Mohammad Omary, Chao-Jung Chien, and Yingqun Wang

Bourns College of Engineering Center for Environmental Research and 

Technology (CE-CERT), University of California, Riverside 

1084 Columbia Avenue, Riverside, CA 92507 

http://pah.cert.ucr.edu/aqm/308/

Ralph Morris, Sue Kemball-Cook, Yiqin Jia, Steven Lau, and Bonyoung Koo 

ENVIRON International Corporation 

101 Rowland Way, Suite 220, Novato, CA 94945 

http://www.environcorp.com 

Zac Adelman, Andy Holland, and James Wallace 

UNC-Chapel Hill, Carolina Environmental Program 

Bank of America Plaza, CB# 6116, 137 E. Franklin Street, Chapel Hill, NC 27599-6116 

http://cf.unc.edu/cep/empd
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 ii 

Executive Summary 

The Clean Air Act (CAA) establishes special goals for visibility in many national parks, 

wilderness areas, and international parks. Through the 1977 amendments to the Clean Air Act, 

Congress set a national goal for visibility as “the prevention of any future, and the remedying of 

any existing, impairment of visibility in mandatory Class I Federal areas which impairment 

results from manmade air pollution” (40 CFR 51.300). States are required to develop State 

Implementation Plans (SIPs) to attain visibility standards; also, tribes may opt to assume 

responsibility for visibility programs under 40 CFR Part 49 by developing Tribal Implementation 

Plans (TIPs). The goal of the U.S. Environmental Protection Agency (EPA) Regional Haze Rule 

(RHR) is to achieve natural visibility conditions at 156 Federally mandated Class I areas by 

2064. To achieve this goal, the RHR has set up milestone years of 2018, 2028, 2038, 2048, 2058, 

and 2064 to monitor progress toward natural visibility conditions. CAA Section 308 (§308) 

requires that the first visibility SIP/TIP be submitted to EPA by 2007-2008 to demonstrate 

progress toward natural visibility conditions in 2018 using the 2000-2004 five-year baseline. 

The Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC) is responsible 

for performing regional air quality modeling simulations for the contiguous WRAP region; the 

WRAP states and tribes then use the analytical results to develop SIPS or TIPs under the RHR. 

The contiguous WRAP region includes Arizona, California, Colorado, Idaho, Montana, New 

Mexico, North Dakota, Oregon, South Dakota, Utah, Washington, and Wyoming.*

 The air 

quality and visibility modeling results for the contiguous WRAP region will be used by the states 

and tribes in their technical support documents (TSDs) demonstrating progress toward attaining 

visibility goals. Key RMC visibility modeling work elements include the following: 

1. Evaluation of the visibility model for a historical episode—in this case, for calendar year 

2002. Output from the model simulation is compared with ambient air quality data for the 

historical episode as part of a model performance evaluation (MPE). 

2. Development of visibility planning scenarios for the regional haze baseline period of 

2000-04 and for the initial regional haze future projection period, calendar year 2018. 

3. Modeling a variety of emissions sensitivity, emissions source apportionment, and emissions control strategies to assess whether planned future regional emissions reductions 

will be sufficient to demonstrate reasonable progress toward achieving visibility goals. 

This document is a final report that discusses the activities the WRAP RMC performed in 

support of work element 1 above. We used MPEs to assess the suitability of two modeling 

systems for simulating air quality and visibility for the 2002 calendar year, so that the models 

could be used for subsequent planning, sensitivity, and emissions control strategy modeling. The 

two models are EPA’s Community Multiscale Air Quality (CMAQ) modeling system and 

ENVIRON’s Comprehensive Air quality Model with extensions (CAMx). This report also 

 

*

Modeling analyses and results for the Class I areas in the state of Alaska (which is also included in the WRAP 

region) and any analyses for native villages in Alaska are described in a separate report(s). 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 iii 

discusses some model intercomparison work that we performed for the modeling systems. We 

use all this information as the basis for recommending the selection of CMAQ and/or CAMx to 

complete work elements 2 and 3 above. 

The visibility modeling work included developing emissions inventories for 2002, developing 

meteorology data for 2002 using a meteorology model, operating the air quality and visibility 

models, and developing tools and procedures for comparing the model results to ambient monitoring data as part of the model evaluation exercise. Each of these efforts is summarized below. 

Emissions Modeling 

For the emissions modeling work conducted in support of work element 1 above (details are in 

Section 2), we used improved 2002 emissions data for the United States, Mexico, and Canada to 

create a final base 2002 annual emissions database that was used in the CMAQ and CAMx 

model performance evaluations. Sources for emissions inventory and ancillary modeling data 

included WRAP emissions inventory contractors, other Regional Planning Organizations 

(RPOs), and EPA. Building from the WRAP preliminary 2002 modeling cases completed earlier 

(Tonnesen et al., 2005), we integrated several updates to the inventories and ancillary data to 

create final 2002 emissions input files; this simulation is referred to as 2002 base case A, or 

Base02a. We used the Sparse Matrix Operator Kernel Emissions (SMOKE) version 2.1 

processing system (CEP, 2004a) to prepare the inventories for input to the air quality modeling 

systems. The SMOKE simulations documented in this report include emissions generated for 

(1) annual CMAQ and CAMx simulations at a 36-km model grid resolution and (2) a short-term 

CMAQ test simulation at a 12-km model grid resolution. We performed the modeling and quality assurance (QA) work based on the WRAP RMC emissions QA protocol (Adelman, 2004). 

Simulation Base02a represents a significant improvement to the preliminary 2002 emissions 

modeling previously performed by the RMC. While the preliminary 2002 modeling served its 

purpose to develop the infrastructure at the RMC for modeling large emissions data sets and 

producing annual emissions simulations, much of the input data (both as inventories and 

ancillary data) were placeholders for actual 2002 data that were being prepared through calendar 

year 2005. As these actual 2002 data sets became available to us, we integrated them into the 

SMOKE modeling and QA system that we had developed during the preliminary modeling, to 

produce a high-quality emissions data set for use in the CMAQ and CAMx MPEs. The addition 

of entirely new inventory categories, like marine shipping and oil and gas production sectors, 

added complexity to the Base02a modeling. By the end of the data collection phase for 

simulation Base02a, we had identified 22 different emissions categories to compose this data set. 

MM5 Modeling 

Meteorology data are key input data required for running any air quality model. These data 

include information on wind speed and direction, atmospheric stability and vertical motion in the 

atmosphere, sunlight intensity, clouds and precipitation, and vertical mixing. For photochemical 

grid models, such as CMAQ and CAMx, meteorology data are typically developed by operating 

a prognostic numerical simulation model that solves the fundamental equations governing 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

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conservation of mass, energy, and momentum. For the WRAP modeling we applied the FifthGeneration Pennsylvania State University/National Center for Atmospheric Research 

(PSU/NCAR) Mesoscale Model (MM5) (Grell et al., 1994) for both a 36-km continental domain 

and a fine-resolution, nested 12-km domain in the western U.S. This work, completed in 2004, is 

summarized in Section 3 of this report, and also described in a final report on the RMC web site: 

http://pah.cert.ucr.edu/aqm/308/mm5.shtml

In our original 36-km MM5 simulation, surface humidity and temperature fields fell within the 

benchmarks for much of the year for most subdomains, but the model had a marked cold, wet 

bias, especially during the summer, in the western subdomains; also, the surface wind fields 

failed to meet the performance benchmark for the entire year in several model subdomains. We 

performed extensive MM5 sensitivity testing with alternate science configurations to improve 

the MM5 performance in the WRAP regions. The final 36-km and 12-km MM5 runs show 

improvement in the modeled precipitation fields, particularly in the summer in the Southwest. 

With a reduction in the overprediction of summer convective rainfall in both the 36- and 12-km 

runs, wet biases in the surface humidity and cold biases in the surface temperature are now 

smaller in the North and desert Southwest subdomains in the summer months. Although the final 

WRAP run was optimized for summer performance over the WRAP region, the winter 

performance in the western U.S. did not deteriorate significantly. 

Based on the upper-air soundings, one of the most serious problems is the difficulty MM5 has in 

establishing the observed planetary boundary layer (PBL) structure. The model has trouble 

getting the PBL depth correct, particularly in the stable nocturnal case. Also, MM5’s difficulty in 

simulating the observed fine structure of the dew point temperature profile and the overall level 

of saturation in the lower troposphere is cause for concern. It is important that the model produce 

cloud decks at the correct height. Errors in humidity and cloud prediction have a negative impact 

on the accuracy of downwelling solar radiation, cause errors in the temperature profile and the 

surface fluxes, affect the atmospheric chemistry, and make it difficult for the particulate matter 

(PM) model to perform properly. 

We concluded that the final 36-km and 12-km WRAP MM5 runs exhibited reasonably good 

performance and were within the bounds of other meteorological databases used for prior air 

quality modeling efforts, and that it was therefore reasonable to proceed with their use as inputs 

for the RMC visibility modeling. 

Visibility Modeling 

Visibility impairment occurs when fine particulate matter (PM2.5) in the atmosphere scatters and 

absorbs light, thereby creating haze. PM2.5 can be emitted into the atmosphere directly as primary 

particulates, or it can be produced in the atmosphere from photochemical reactions of gas-phase 

precursors and subsequent condensation to form secondary particulates. Examples of primary 

PM2.5 include crustal materials and elemental carbon; examples of secondary PM include ammonium nitrate, ammonium sulfates, and secondary organic aerosols (SOA). Secondary PM2.5 is 

generally smaller than primary PM2.5, and because the ability of PM2.5 to scatter light depends on 

particle size—with light scattering for fine particles being greater than for coarse particles—
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 v 

secondary PM2.5 plays an especially important role in visibility impairment. Moreover, the smaller secondary PM2.5 can remain suspended in the atmosphere for longer periods and transported 

long distances, thereby contributing to regional-scale impacts of pollutant emissions on visibility. 

The sources of PM2.5 are difficult to quantify because of the complex nature of their formation, 

transport, and removal from the atmosphere. This makes it difficult to simply use emissions data 

to determine which pollutants should be controlled to most effectively improve visibility. Photochemical air quality models provide a better understanding of the sources of PM2.5 by simulating 

the emissions of pollutants and the formation, transport, and deposition of PM2.5. If an air quality 

model performs well for a historical episode, the model may then be useful for identifying the 

sources of PM2.5 and helping to select the most effective emissions reduction strategies for attaining future visibility goals. Although several types of air quality modeling systems are available, 

the gridded, three-dimensional, Eulerian models provide the most complete spatial representation 

and the most comprehensive representation of processes affecting PM2.5, especially for situations 

in which multiple pollutant sources interact to form PM2.5. For less complex situations in which a 

few large point sources of emissions are the dominant source of PM2.5, trajectory models (such as 

the California Puff Model [CALPUFF]) may also be useful for simulating PM2.5. 

For this study, we compared two Eulerian air quality models, CMAQ and CAMx. Both models 

were operated for calendar year 2002 for the RPO Unified Continental 36-km Modeling Grid 

domain, shown in Figure ES-1. In addition, we compared CMAQ results from the 36-km model 

domain with those from a high resolution, nested 12-km domain in the WRAP region. Details on 

all of this work are provided in Section 4. 

For each of these model simulations, we performed extensive comparisons of the modelsimulated PM2.5 with measured PM2.5 from several ambient monitoring networks. The ambient 

data and the networks are described in Section 4.2. Speciated PM2.5 data from the IMPROVE, 

CASTNet, NADP/NTN, and STN monitoring networks are included, as well as gas-phase data 

from the AQS network. 

Section 4.3 describes the approach used to compare model data with ambient data. Because there 

is no final guidance from EPA on how to perform a model evaluation for visibility modeling, we 

experimented with a variety of approaches for presenting the model-to-data comparisons. These 

range from very detailed comparisons at individual monitoring sites to very general summaries 

showing monthly average bias and error for each monitoring network for all sites in the WRAP 

region. The project web site includes tens of thousands of plots resulting from the model-to-data 

comparisons (see http://pah.cert.ucr.edu/aqm/308/cmaq.shtml); staff at states and tribes can use 

subsets of these to focus in detail on locations of specific interest. The project web site also 

includes animations of model and data results that are best reviewed on the web site because they 

cannot easily be presented in this report. The report includes a series of appendices (which, for 

size reasons, must be downloaded separately from the main body of the report) that contain 

examples of the displays of model performance that we found to be most useful for comparing 

the model data to the ambient data: stacked-bar time-series plots for each of the model 

performance evaluations, and stacked-bar plots showing the model performance for the average 

of the best-20% and the worst-20% visibility days at each IMPROVE site. The body of this 

report contains only the more general summary plots that are presented in Section 4, with 

additional displays of model performance available on the RMC web site listed above. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

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Figure ES-1. Modeling domains used in this study. The red grid is the RPO 

Unified 36-km domain; the blue grid is the 12-km nested domain used for 

high-resolution modeling in the WRAP region. 

The comparison of the 36-km and 12-km CMAQ simulations is presented in Section 4.6. A key 

finding is that model performance does not appear to benefit significantly from using the finerresolution grid for modeling the lower concentrations of PM2.5 that typically occur in the Class I 

areas. These results are consistent with previous WRAP model comparisons using the 1996 

modeling year and the preliminary 2002 modeling, which also showed that there is no clear 

improvement in model performance when using finer grid resolutions. There does remain a 

possibility that the 12-km model might respond differently to emissions controls than the 36-km 

model does, even though there is no substantial difference in the model performance at the Class 

I sites. However, when we investigated this possibility in previous model emissions reduction 

sensitivity simulations using 12-km and 36-km model scenarios in the southeastern U.S., in work 

conducted for the Visibility Improvement – State and Tribal Associations of the Southeast 

(VISTAS) RPO (the WRAP equivalent for that region; see http://www.vistas-sesarm.org/), we 

found that the model grid resolution had only small effects on the model response to emissions 

reductions. A key finding of the current study is that because of the substantially higher 

resources and costs associated with performing high-resolution modeling, we do not recommend 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

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the routine application of additional 12-km modeling as part of the WRAP regional haze 

planning effort. However, this conclusion applies only to the relatively clean conditions 

experienced at Class I areas. It is likely that the finer-resolution model would provide some 

benefit for modeling higher ozone and PM2.5 concentrations relevant to health-based air quality 

standards in urban or suburban areas. 

The results presented in Section 4 for the CMAQ and CAMx model performance evaluations 

show that both models have some skill in simulating several of the PM species that contribute to 

regional haze. A key finding is that these model results are also significantly improved compared 

to results from the Section 309 modeling that was performed for calendar year 1996 (see 

http://wrapair.org/309/031215Final309TSD.pdf ). The improved model performance may be due 

to a combination of improvements in the 2002 meteorology modeling, new data sets and 

emissions models developed for the 2002 emissions inventories, and updates and improvements 

to the CMAQ and CAMx models. Both these models met many of the chosen performance goals 

for most months of the year and for most of the ambient monitoring networks for sulfate, nitrate, 

organic carbon (OC), elemental carbon (EC), and soil. Both models failed to meet performance 

goals for coarse mass (CM) and also for most PM species measured at the STN network, 

including soil, EC, OC, and nitrate. The STN monitoring sites, however, are usually located in 

areas influenced by urban emissions. Thus, poor performance at the STN sites may not be 

indicative of the model performance at Class I areas. We conclude from these results that the 

CMAQ and/or the CAMx 36-km modeling can be used, in combination with the relative 

reduction factor (RRF) approach, to evaluate the benefits of emissions reduction strategies for 

all PM species other than CM, in order to project visibility changes at Class I areas for regional 

haze planning purposes.

In the comparison of the CMAQ and the CAMx 36-km model simulations with ambient data, 

neither model exhibited superior model performance across all species and seasons. Whereas 

CMAQ exhibited better sulfate performance in the winter, CAMx summer sulfate performance 

appeared better than CMAQ’s. CMAQ nitrate performance was better in the winter, while both 

models exhibited large summer nitrate underprediction biases. CMAQ exhibited better OC 

model performance than CAMx, but uncertainties with the OMC/OC factor (see Section 4.4.3) 

and known missing processes in both the CMAQ and CAMx SOA modules (e.g., missing SOA 

from biogenic sesquiterpenes and isoprene) make these comparisons difficult to interpret. The 

models exhibited similar fairly good performance for EC, but the performance of both models 

for soil and CM was fairly poor. Combined, all of these results were also consistent with 

previous model comparisons that showed no clear improvement in model performance when 

using either the CMAQ or the CAMx model. Typically, model performance improves slightly 

for some species and degrades slightly for other species. Based on these results, a key finding is 

that both CMAQ and CAMx are acceptable for visibility modeling. The choice of model should 

be based in part on factors other than model performance, such as computer run times, disk 

storage requirements, source apportionment and/or sensitivity analysis needs, and any other 

factors that might make it more effective or convenient to use one model over the other.

Table ES-1 summarizes all of the key findings highlighted above. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

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Table ES-1. Summary of key findings from the model performance evaluation study. 

Key Findings 

Model performance does not appear to benefit significantly from using the finer-resolution grid for 

modeling the lower concentrations of PM2.5 that typically occur in the Class I areas. 

We do not recommend the routine application of additional 12-km modeling as part of the WRAP 

regional haze planning effort, due to the substantially higher resources and costs associated with 

performing high-resolution modeling. 

The 2002 model results are significantly improved compared to results from the Section 309 modeling 

that was performed for calendar year 1996. 

The CMAQ and/or the CAMx 36-km modeling can be used, in combination with the RRF approach, to 

evaluate the benefits of emissions reduction strategies for all PM species other than CM, in order to 

project visibility changes at Class I areas for regional haze planning purposes. 

Both CMAQ and CAMx are acceptable for visibility modeling. The choice of model should be based 

in part on factors other than model performance, such as computer run times, disk storage requirements, and source apportionment and/or sensitivity analysis needs. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 ix 

Contents 

Executive Summary ___________________________________________________________ ii

Tables _____________________________________________________________________xiii

Figures_____________________________________________________________________ xiv

Abbreviations ______________________________________________________________ xvii

1. Introduction ______________________________________________________________ 1

1.1 Background 1

1.1.1 Need for regional haze modeling 1

1.1.2 Role of the Western Regional Air Partnership 2

1.1.3 WRAP strategic plan 4

1.1.4 Organization of the Regional Modeling Center 4

1.2 Overview of Visibility Modeling Data Sets 5

1.3 Description of WRAP Modeling Domain 6

2. Final 2002 Emissions Inventories ____________________________________________ 10

2.1 Introduction 10

2.1.1 Emissions data overview 10

2.1.2 Emissions modeling overview 12

2.1.3 Simulation Base02a data collection and preparation 13

2.2 Stationary-Point-Source Emissions 18

2.2.1 Data sources 18

2.2.2 Emissions processing 19

2.2.3 Uncertainties and future work 19

2.3 Stationary-Area-Source Emissions 20

2.3.1 Data sources 20

2.3.2 Emissions processing 22

2.3.3 Uncertainties and future work 23

2.4 On-Road Mobile-Source Emissions 23

2.4.1 Data sources 23

2.4.2 Emissions processing 24

2.4.3 Uncertainties and future work 25

2.5 Nonroad Mobile-Source Emissions 26

2.5.1 Data sources 26

2.5.2 Emissions processing 27
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

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2.5.3 Uncertainties and future work 28

2.6 Biogenic Emissions 28

2.6.1 Data sources 28

2.6.2 Emissions processing 29

2.6.3 Uncertainties and future work 29

2.7 Fire Emissions 29

2.7.1 Data sources 29

2.7.2 Emissions processing 30

2.7.3 Uncertainties and future work 31

2.8 Dust Emissions 31

2.8.1 Data sources 31

2.8.2 Emissions processing 32

2.8.3 Uncertainties and future work 33

2.9 Ammonia Emissions 33

2.9.1 Data sources 33

2.9.2 Emissions processing 34

2.9.3 Uncertainties and future work 34

2.10 Oil and Gas Emissions 35

2.10.1 Data sources 35

2.10.2 Emissions processing 36

2.10.3 Uncertainties and future work 36

2.11 Offshore Area and Point Emissions 36

2.11.1 Data sources 36

2.11.2 Emissions processing 36

2.11.3 Uncertainties and future work 37

2.12 Commercial Marine Shipping Emissions 37

2.12.1 Data sources 37

2.12.2 Emissions processing 37

2.12.3 Uncertainties and future work 38

2.13 Summary 38

2.14 Effects of Uncertainties and Future Work 39

3. Meteorology Data ________________________________________________________ 40

3.1 Summary of Meteorology Evaluation Results 40

3.1.1 Preliminary sensitivity tests 42

3.1.2 Revised sensitivity tests 42

3.1.3 Final model configurations 44

3.1.4 Final MM5 model performance 44
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

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3.2 Uncertainties and Effects on Visibility Modeling 45

4. Air Quality and Visibility Modeling __________________________________________ 47

4.1 Description of the CMAQ and CAMx Models 47

4.1.1 Description of model versions 48

4.1.2 Selection of model science options 49

4.1.3 Description of model boundary conditions and initial conditions 50

4.2 Summary of Ambient Monitoring Data 51

4.3 Model Performance Evaluation Approach 59

4.3.1 The most useful plot types for the MPEs 59

4.3.2 Subdomains analyzed for the MPEs 67

4.3.3 Performance time periods 68

4.3.4 Description of performance metrics 68

4.4 CMAQ Model Performance Evaluation in the WRAP States for Calendar Year 

2002 70

4.4.1 Sulfate (SO4) 71

4.4.2 Nitrate (NO3) 74

4.4.3 Organic carbon (OC) 77

4.4.4 Elemental carbon (EC) 80

4.4.5 Other fine PM (soil) 83

4.4.6 Coarse mass (CM) 85

4.4.7 Ozone 87

4.5 Evaluation of CMAQ at Class I Areas for the Best-20% and Worst-20% 

Visibility Days 88

4.6 Grid Resolution Sensitivity Analysis: CMAQ 12-km Visibility Modeling for 

the WRAP Region 89

4.7 CAMx Model Performance Evaluation and Comparison to CMAQ 101

4.7.1 Sulfate (SO4) 102

4.7.2 Nitrate (NO3) 105

4.7.3 Organic carbon (OC) 108

4.7.4 Elemental carbon (EC) 111

4.7.5 Other fine PM (soil) 114

4.7.6 Coarse mass (CM) 116

4.8 Uncertainties, Errors, and Biases and Their Effects on Future Visibility 

Projection 118

4.9 Conclusions of Model Performance Evaluation 119

5. Computational Aspects of the Modeling ______________________________________ 121

5.1 Description of Hardware and Software Used 121

5.1.1 Hardware 121
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

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5.1.2 Software 123

5.2 Summary of Procedures for Modeling 124

5.2.1 The Meteorology-Chemistry Interface Processor (MCIP) 125

5.2.2 Initial condition (ICON) and boundary condition (BCON) processors 125

5.2.3 CMAQ photolysis rates processor (JPROC) 125

5.2.4 CMAQ emissions processing 125

5.2.5 CMAQ Chemical Transport Model (CCTM) 126

5.3 Computational Performance Benchmarks 126

5.4 Technology Transfer 127

6. Conclusions ____________________________________________________________ 128

References_________________________________________________________________ 130

Appendices to the Report _____________________________________________________ 134

A: Emissions Modeling and Quality Assurance 

B: Stacked-Bar Plots for CMAQ—Annual Time Series of Contributions to Light 

Extinction 

C: Stacked-Bar Plots for CMAQ—Contributions to Light Extinction for the Average 

of Best-20% Visibility Days, with Data Relaxed in Time 

D: Stacked-Bar Plots for CMAQ—Contributions to Light Extinction for the Average 

of Worst-20% Visibility Days, with Data Relaxed in Time 

E: Stacked-Bar Plots for CMAQ—Contributions to Light Extinction for the Average 

of Best-20% Visibility Days, with Data Relaxed in Time and Space 

F: Stacked-Bar Plots for CMAQ—Contributions to Light Extinction for the Average 

of Worst-20% Visibility Days, with Data Relaxed in Time and Space 

G: Stacked-Bar Plots for CAMx—Annual Time Series of Contributions to Light 

Extinction 

H: Stacked-Bar Plots for CAMx—Contributions to Light Extinction for the Average 

of Best-20% Visibility Days, with Data Relaxed in Time 

I: Stacked-Bar Plots for CAMx—Contributions to Light Extinction for the Average 

of Worst-20% Visibility Days, with Data Relaxed in Time 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

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Tables 

Table ES-1. Summary of key findings from the model performance evaluation study. ............. viii 

Table 1-1. WRAP 2003 Strategic Plan (WGA, 2003).....................................................................4 

Table 1-2. RPO Unified domain grid definition. .............................................................................6 

Table 1-3. Grid definitions for MM5 and CMAQ. ..........................................................................8 

Table 1-4. Vertical layer definition for MM5 simulations (left five columns), and approach 

for reducing CMAQ/CAMx layers by collapsing multiple MM5 layers (right five 

columns).....................................................................................................................................9 

Table 2-1. WRAP Base02a emissions categories..........................................................................12 

Table 2-2. WRAP Base02a anthropogenic emissions inventory contacts.....................................14 

Table 2-3. New Canadian spatial surrogates for Base02a. ............................................................17 

Table 2-4. Refueling SCCs removed from the non-WRAP U.S. stationary-area inventory. ........21 

Table 2-5. Base02a nonroad mobile-source inventory temporal configuration. ...........................27 

Table 2-6. Base02a fugitive and road dust SCCs. .........................................................................32 

Table 3-1. MM5 configurations from original (WRAP_0) and modified (WRAP_5) MM5 

runs...........................................................................................................................................42 

Table 3-2. MM5 Final Configuration for the WRAP 2002 36-km MM5 run after revised 

sensitivity tests were completed...............................................................................................43 

Table 3-3. MM5 Final Configuration for the WRAP 2002 36/12-km MM5 run. .........................44 

Table 4-1. Model configurations for the CMAQ and CAMx simulations.....................................49 

Table 4-2. Summary of ambient database available for the model performance evaluation.........53 

Table 4-3a. Mapping of gaseous model species to gaseous observation species. .........................56 

Table 4-3b. Mapping of particulate model species to particulate observation species..................57 

Table 4-4. Recommended statistical measures for model performance evaluation.......................69 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

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Figures 

Figure ES-1. Modeling domains used in this study. The red grid is the RPO Unified 36-km 

domain; the blue grid is the 12-km nested domain used for high-resolution modeling in 

the WRAP region..................................................................................................................... vi 

Figure 1-1. Regional Planning Organizations engaged in regional haze modeling.........................3 

Figure 1-2. Locations of the 116 Class I areas requiring regional haze plans by December 

2007 within the WRAP region...................................................................................................3 

Figure 1-3. Nesting of the 36-km CMAQ/CAMx grid within the MM5 36-km grid......................7 

Figure 1-4. Nesting of the WRAP region 12-km grid within the RPO Unified 36-km grid. ..........7 

Figure 2-1. Example Base02a NH3 emissions spatial plot, showing artificial concentration 

gradients that follow some political boundaries. .....................................................................35 

Figure 3-1. Definition of subdomains used in MM5 modeling. ....................................................41 

Figure 4-1. Locations of sites in each of the PM ambient monitoring networks...........................54 

Figure 4-2. Locations of sites in the AQS gaseous species ambient monitoring network.............54 

Figure 4-3. Example time-series plot of sulfate at the Grand Canyon IMPROVE site 

showing ambient data and two sets of model results for June 2002 (Julian dates 152 to 

180). .........................................................................................................................................60 

Figure 4-4. Example stacked-bar time-series plot for the Grand Canyon IMPROVE site 

showing ambient data (top) and model output (bottom) contributions to light extinction. .....61 

Figure 4-5. Example scatter plot comparing CMAQ v4.4 and CMAQ v4.5 model results to 

ambient data.............................................................................................................................62 

Figure 4-6. Example PAVE spatial plot with ambient data shown in symbols overlaid on 

model results shown in background for the 12-km CMAQ MPE. ..........................................63 

Figure 4-7. Example bar plot showing monthly variability in mean fractional bias (MFB) 

for two model simulations. ......................................................................................................64 

Figure 4-8. Example bugle plot showing monthly average sulfate MFB for the CMAQ v4.5 

model simulation as a function of the average of the modeled and observed sulfate 

concentration for the CASTNet, IMPROVE, and STN data. ..................................................65 

Figure 4-9. Example stacked-bar plot of contributions to light extinction for the average of 

the worst-20% visibility days at an IMPROVE site, for data relaxed in time. ........................67 

Figure 4-10. Sulfate monthly MFB for the 36-km results for CMAQ versions 4.4 and 4.5 

compared to the IMPROVE data. ............................................................................................72 

Figure 4-11. Monthly average sulfate MFB for CMAQ version 4.5 36-km results compared 

to the CASTNet, IMPROVE, and STN networks....................................................................72 

Figure 4-12. Bugle plot of monthly average sulfate MFB (top) and MFE (bottom) for 

CMAQ version 4.5 36-km results and the CASTNet, IMPROVE, and STN networks. .........73 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 xv 

Figure 4-13. Nitrate monthly MFB for the 36-km results for CMAQ for versions 4.4 and 

4.5 compared to the IMPROVE data. ......................................................................................75 

Figure 4-14. Monthly average nitrate MFB for CMAQ version 4.5 36-km results compared 

to the CASTNet, IMPROVE, and STN networks....................................................................75 

Figure 4-15. Bugle plot of monthly average nitrate MFB (top) and MFE (bottom) for 

CMAQ version 4.5 36-km results and the CASTNet, IMPROVE, and STN networks. .........76 

Figure 4-16. OC monthly MFB for the 36-km results for CMAQ versions 4.4 and 4.5 

compared to the IMPROVE data. ............................................................................................78 

Figure 4-17. Monthly average OC MFB for CMAQ version 4.5 36-km results compared to 

the IMPROVE and STN networks...........................................................................................78 

Figure 4-18. Bugle plot of monthly average OC MFB (top) and MFE (bottom) for CMAQ 

version 4.5 36-km results and the IMPROVE and STN networks. .........................................79 

Figure 4-19. EC monthly MFB for the 36-km results for CMAQ versions 4.4 and 4.5 

compared to the IMPROVE data. ............................................................................................81 

Figure 4-20. Monthly average EC MFB for CMAQ version 4.5 36-km results compared to 

the IMPROVE and STN networks...........................................................................................81 

Figure 4-21. Bugle plot of monthly average EC MFB (top) and MFE (bottom) for CMAQ 

version 4.5 36-km results and the IMPROVE and STN networks. .........................................82 

Figure 4-22. Soil monthly MFB for the 36-km results for CMAQ versions 4.4 and 4.5 

compared to the IMPROVE data. ............................................................................................83 

Figure 4-23. Bugle plot of monthly average soil MFB (top) and MFE (bottom) for CMAQ 

version 4.5 36-km results and the IMPROVE network. ..........................................................84 

Figure 4-24. CM monthly MFB for the 36-km results for CMAQ versions 4.4 and 4.5 

compared to the IMPROVE data. ............................................................................................85 

Figure 4-25. Bugle plot of monthly average CM MFB (top) and MFE (bottom) for CMAQ 

version 4.5 36-km results for the IMPROVE network. ...........................................................86 

Figure 4-26. Ozone monthly MFB for the 36-km results for CMAQ versions 4.4 and 4.5 

compared to the AQS hourly average ozone data, using a filter to exclude all hours with 

data values less than 60 ppb.....................................................................................................87 

Figure 4-27. MFB for the 12-km and 36-km CMAQ sulfate compared to each monitoring 

network. ...................................................................................................................................92 

Figure 4-28. MFB for the 12-km and 36-km CMAQ nitrate compared to each monitoring 

network. ...................................................................................................................................93 

Figure 4-29. MFB for the 12-km and 36-km CMAQ compared to IMPROVE data for OC, 

EC, soil, and CM......................................................................................................................94 

Figure 4-30. Mean fractional bias for the 12-km and 36-km CMAQ compared to AQS gasphase ozone and CO data.........................................................................................................95 

Figure 4-31. Comparison of spatial plots for the CMAQ 36-km (top) and 12-km (bottom) 

runs with IMPROVE sulfate data. ...........................................................................................96 

Figure 4-32. Comparison of spatial plots for the CMAQ 36-km (top) and 12-km (bottom) 

runs with IMPROVE nitrate data.............................................................................................97 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 xvi 

Figure 4-33. Comparison of spatial plots for the CMAQ 36-km (top) and 12-km (bottom) 

runs with IMPROVE OC data. ................................................................................................98 

Figure 4-34. Comparison of spatial plots for the CMAQ 36-km (top) and 12-km (bottom) 

runs with IMPROVE EC data..................................................................................................99 

Figure 4-35. Comparison of spatial plots for the CMAQ 36-km (top) and 12-km (bottom) 

runs with IMPROVE soil data. ..............................................................................................100 

Figure 4-36. Sulfate monthly MFB for CAMx compared to ambient data at the CASTNet, 

IMPROVE, and STN sites. ....................................................................................................103 

Figure 4-37. Comparison of CAMx and CMAQ 36-km monthly MFB for IMPROVE 

sulfate data. ............................................................................................................................103 

Figure 4-38. Bugle plot of monthly average sulfate MFB (top) and MFE (bottom) for 

CAMx 36-km results and the CASTNet, IMPROVE, and STN networks............................104 

Figure 4-39. Nitrate monthly MFB for CAMx compared to ambient data at the CASTNet, 

IMPROVE, and STN sites. ....................................................................................................106 

Figure 4-40. Comparison of CAMx and CMAQ 36-km monthly MFB for IMPROVE 

nitrate data..............................................................................................................................106 

Figure 4-41. Bugle plot of monthly average nitrate MFB (top) and MFE (bottom) for 

CAMx 36-km results versus the CASTNet, IMPROVE, and STN networks. ......................107 

Figure 4-42. OC monthly MFB for CAMx compared to ambient data at the IMPROVE and 

STN sites................................................................................................................................109 

Figure 4-43. Comparison of CAMx and CMAQ 36-km monthly MFB for IMPROVE OC 

data.........................................................................................................................................109 

Figure 4-44. Bugle plot of monthly average OC MFB (top) and MFE (bottom) for CAMx 

36-km results versus the IMPROVE and STN networks.......................................................110 

Figure 4-45. EC monthly MFB for CAMx compared to ambient data at the IMPROVE and 

STN sites................................................................................................................................112 

Figure 4-46. Comparison of CAMx and CMAQ 36-km monthly MFB for IMPROVE EC 

data.........................................................................................................................................112 

Figure 4-47. Bugle plot of monthly average EC MFB (top) and error (bottom) for CAMx 

36-km results versus the IMPROVE and STN networks.......................................................113 

Figure 4-48. Comparison of CAMx and CMAQ 36-km monthly MFB for IMPROVE soil 

data.........................................................................................................................................114 

Figure 4-49. Bugle plot of monthly average soil MFB (top) and MFE (bottom) for CAMx 

36-km results versus the IMPROVE network. ......................................................................115 

Figure 4-50. Comparison of CAMx and CMAQ 36-km monthly MFB for IMPROVE CM 

data.........................................................................................................................................116 

Figure 4-51. Bugle plot of monthly average CM MFB (top) and MFE (bottom) for CAMx 

36-km results versus the IMPROVE network. ......................................................................117 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

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Abbreviations 

ACM ....................Asymmetric Convective Model 

ADEC...................Alaska Department of Environmental Conservation 

AIM......................Aerosol Inorganic Module 

AIRMoN ..............Atmospheric Integrated Research Monitoring Network 

AIRS ....................Aerometric Information Retrieval System 

AML.....................ARC Macro Language 

AoH......................Attribution of Haze 

AQS......................Air Quality System 

ARDB ..................Acid Rain Database 

AVHRR ...............Advanced Very High Resolution Radiometer 

BART...................Best Available Retrofit Technology 

BC ........................boundary condition 

BEIS.....................Biogenic Emissions Inventory System 

BELD ...................Biogenic Emissions Landcover Database 

BM .......................Betts-Miller 

BRAVO................Big Bend Regional Aerosol and Visibility Observations Study 

CAA .....................Clean Air Act 

CAFO...................confined animal feed operations 

CALMET .............California Meteorological Model 

CALPUFF............California Puff Model 

CAMx ..................Comprehensive Air quality Model with extensions 

CAPE ...................convective available potential energy 

CARB...................California Air Resources Board 

CASTNet..............Clean Air Status and Trends Network 

CB-IV...................Carbon Bond IV (also referred to in the modeling community as CBIV, CB4) 

CCM2...................Community Climate Model 2 

CCTM ..................CMAQ Chemical Transport Model 

CEM ....................continuous emissions monitoring 

CENRAP..............Central Regional Air Planning Association 

CEP ......................Carolina Environmental Program 

CM .......................coarse mass (a term used in the context of the IMPROVE network; see also 

PMc) 

CMAQ..................Community Multiscale Air Quality 

CMAS ..................Community Modeling and Analysis System 

CMC.....................Chemical Mechanism Compiler 

CMU ....................Carnegie Mellon University 

CNG .....................compressed natural gas

CPC......................Climate Prediction Center 

CPU......................central processing unit 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 xviii 

CVS......................Concurrent Versions System

CY02....................calendar year 2002 

DDM ....................decoupled direct method 

DEAD ..................Dust Entrainment and Deposition 

dV.........................deciview 

EC ........................elemental carbon 

EDAS ...................Eta Data Assimilation System 

EEA......................European Environment Agency 

EGAS ...................Economic Growth Analysis System 

EGU .....................electric generating unit 

EPA......................U.S. Environmental Protection Agency 

ESSC....................Earth System Science Center 

FAQ......................frequently asked question 

FDDA...................four-dimensional data assimilation 

FEJF .....................Fire Emissions Joint Forum 

FIPS......................Federal Implementation Standards 

GCVTC................Grand Canyon Visibility Transport Commission 

GEOS ...................Goddard Earth Observing System 

GIS .......................geographic information system 

GUI ......................graphical user interface 

IC..........................initial condition 

IDA ......................Inventory Data Analyzer 

IDNR....................Iowa Department of Natural Resources 

IEH.......................Implicit-Explicit Hybrid 

IMPROVE............Interagency Monitoring of Protected Visual Environments 

IOA ......................index of agreement 

IPR .......................Integrated Process Rates 

IRR.......................Integrated Reaction Rates 

KF ........................Kain-Fritsch 

LAC......................light-absorbing carbon 

LADCO................Lake Michigan Air Directors Consortium 

lat-lon ...................latitude-longitude 

LPG......................liquefied petroleum gas 

LSM .....................land-surface model 

LULC ...................land use/land cover 

M4........................Mechanism 4 

MADM.................Multicomponent Aerosol Dynamics Model 

MADRID .............Model of Aerosol Dynamics, Reaction, Ionization, and Dissolution 

MANE-VU...........Mid-Atlantic/Northeast Visibility Union 

MDN ....................Mercury Deposition Network 

MFB .....................mean fractional bias 

MFE .....................mean fractional error 

MIMS...................Multimedia Integrated Modeling System 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 xix 

MM5 ....................Fifth-Generation PSU/NCAR Mesoscale Model 

M-MRF ................modified Medium-Range Forecast 

MMS ...................Minerals Management Service 

MPE .....................model performance evaluation 

MRF .....................Medium-Range Forecast 

MRLC ..................Multi-Resolution Land Characterization 

MRPO ..................Midwest Regional Planning Organization 

MWSS..................Monday-weekday-Saturday-Sunday 

NADP...................National Atmospheric Deposition Program 

NAMS..................National Air Monitoring Stations 

NASS ...................National Agricultural Statistics Service 

NCAR ..................National Center for Atmospheric Research 

NCEP ...................National Centers for Environmental Prediction 

NEI.......................National Emissions Inventory (NEI96 = 1996 version; NEI99 = 1999 version) 

NIF .......................National Emissions Inventory (NEI) Input Format 

NLCD...................National Land Cover Database 

NNRP...................NCAR/NCEP Reanalysis Project 

NORTAD.............North American Transportation Atlas Data 

NTN .....................National Trends Network 

NWS.....................National Weather Service 

OC........................organic carbon 

OMC ....................organic matter carbon 

ORD .....................Office of Research and Development 

OSAT ...................Ozone Source Apportionment Technology 

PAMS...................Photochemical Assessment Monitoring Stations 

PAVE ...................Program for the Analysis and Visualization of Environmental data 

PBL ......................planetary boundary layer 

PDM.....................Plume Dynamics Model 

PinG .....................plume-in-grid 

PM........................particulate matter 

PM10 .....................particulate matter ≤10 μm in diameter

PM2.5 ....................particulate matter ≤2.5 μm in diameter (referred to in this report as “fine 

particulate matter”)

PMc......................coarse particulate matter: PM that ranges from 2.5 to10 μm in diameter (note 

that PMc is also referred to as CM in the context of the IMPROVE network)

PPM......................Piecewise Parabolic Method 

ppmv ....................parts per million by volume

PSAT....................PM Source Apportionment Technology 

PSU ......................Pennsylvania State University

PX ........................Pleim-Xiu 

QA........................quality assurance 

QAPP ...................quality assurance project plan 

QC........................quality control 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

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QSSA ...................Quasi Steady State Approximation 

RADM..................Regional Acid Deposition Model 

RAMS ..................Regional Atmospheric Modeling System 

REMSAD.............Regional Modeling System for Aerosols and Deposition 

RHR .....................Regional Haze Rule 

RMC.....................Regional Modeling Center 

RMSE...................root mean squared error 

ROG .....................reactive organic gas 

RPO......................Regional Planning Organization 

RRF......................relative reduction factor 

RRTM ..................Rapid Radiative Transfer Model 

Rx.........................prescribed (used with “wildfires”) 

SAPRC.................Statewide Air Pollution Research Center 

SCC......................source classification code 

SEARCH..............Southeastern Aerosol Research and Characterization (monitoring network) 

SEARCH_H.........SEARCH monitoring network collecting hourly data (vs. daily [24-hour] data) 

SIP........................State Implementation Plans 

SLAMS ................State and Local Air Monitoring Stations 

SMOKE................Sparse Matrix Operator Kernel Emissions 

SMVGEAR..........Sparse-Matrix Vectorized Gear 

SOA......................secondary organic aerosols 

SST.......................sea-surface temperature 

STATSGO............State Soil Geographic 

STN......................Speciated Trends Network 

STP.......................standard temperature and pressure 

TIP........................Tribal Implementation Plan 

TM........................thematic mapper 

TOG .....................total organic gas 

TSD......................Technical support document 

TSSA....................Tagged Species Source Apportionment 

UAF......................University of Alaska Fairbanks 

UCR .....................University of California, Riverside 

UNC .....................University of North Carolina 

USDA...................U.S. Department of Agriculture 

USGS ...................U.S. Geological Survey 

VIEWS.................Visibility Information Exchange Web System 

VISTAS................Visibility Improvement – State and Tribal Associations of the Southeast 

VMT.....................vehicle miles traveled 

VOC .....................volatile organic compound(s) 

VRSM ..................Variable Size-Resolution Model 

WEQ ....................Wind Erosion Equation 

WRAP..................Western Regional Air Partnership 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 1 

1. Introduction 

The Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC) is responsible 

for performing air quality modeling simulations for the states and tribes in the WRAP region 

(which includes 14 western U.S. states, as described below) in order to provide analytical results 

used in developing implementation plans under the U.S. Environmental Protection Agency 

(EPA) Regional Haze Rule (RHR). RMC responsibilities include: 

• meteorological modeling; 

• emissions processing and modeling; 

• air quality and visibility modeling simulations; 

• analysis, display, and reporting of modeling results; and 

• storage and quality assurance of the modeling input and output files. 

The air quality and visibility modeling will be used by the WRAP states and tribes as part of 

their technical support documents (TSDs) to demonstrate reasonable progress toward attaining 

visibility goals. Key work elements of the RMC visibility modeling include the following: 

1. Evaluation of the visibility model for a historical episode—in this case, for calendar year 

2002. Output from the model simulation is compared with ambient air quality data for 

the historical episode as part of a model performance evaluation (MPE). 

2. Development of visibility planning scenarios for the regional haze baseline period of 

2000-04 and for the initial regional haze future projection period, calendar year 2018. 

3. Modeling a variety of emissions sensitivity, emissions source apportionment, and emissions control strategies to assess whether planned future regional emissions reductions 

will be sufficient to demonstrate reasonable progress toward achieving visibility goals. 

This final report discusses the activities the WRAP RMC performed in support of work element 

1. We used MPEs to evaluate the historical 2002 modeling in order to assess the suitability of 

two modeling system for subsequent planning, sensitivity, and emissions control strategy modeling. The two models are EPA’s Community Multiscale Air Quality (CMAQ) modeling system 

(Byun and Ching, 1999) and ENVIRON’s Comprehensive Air quality Model with extensions 

(CAMx) (ENVIRON, 2004). This report also discusses some model intercomparison work that 

we performed for the two modeling systems. We use all this information as the basis for 

recommending the selection of CMAQ and/or CAMx to complete work elements 2 and 3 above. 

1.1 Background 

1.1.1 Need for regional haze modeling 

The Clean Air Act (CAA) establishes special goals for visibility in many national parks, 

wilderness areas, and international parks. Through the 1977 amendments to the Clean Air Act, 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 2 

Congress set a national goal for visibility as “the prevention of any future, and the remedying of 

any existing, impairment of visibility in mandatory Class I Federal areas which impairment 

results from manmade air pollution” (40 CFR 51.300). States are required to develop State 

Implementation Plans (SIPs) to attain visibility standards; also, tribes may opt to assume responsibility for visibility programs under 40 CFR Part 49 by developing Tribal Implementation Plans 

(TIPs). The goal of the EPA RHR is to achieve natural visibility conditions at 156 Federally 

mandated Class I areas by 2064. To achieve this goal, the RHR has set up milestone years of 

2018, 2028, 2038, 2048, 2058, and 2064 to monitor progress toward natural visibility conditions. 

CAA Section 308 (§308) requires that the first visibility SIP/TIP be submitted to EPA by 2007-

2008 to demonstrate progress toward natural visibility conditions in 2018 using the 2000-2004 

five-year baseline. 

Regional haze is linked to fine particulate matter (PM2.5, which is particulate matter ≤2.5 μm in 

diameter), for which EPA has a health-based standard. PM2.5 SIPs/TIPs are to be submitted three 

years after designation of PM2.5 nonattainment areas. EPA performed the final designations of 

the PM2.5 nonattainment areas in December 2004, so the PM2.5 SIPs/TIPs are due by December 

2007. As regional haze is intricately linked to PM2.5, the PM2.5 and regional haze SIPs/TIPs are in 

the process of being aligned so that they would both be due by approximately December 2007 

(January 2008). EPA designated 8-h ozone nonattainment areas in April 2004, which makes 8-h 

ozone SIPs/TIPs also due in 2007. States and tribes may use the integrated one-atmosphere 

WRAP RMC base-year modeling as the regional component of the 8-h ozone attainment 

activities as well as for PM2.5 and regional haze. Those decisions will be based on schedule 

constraints, model performance, appropriateness of the episodes, feasibility, and need. 

1.1.2 Role of the Western Regional Air Partnership 

WRAP is one of five Regional Planning Organizations (RPOs) (Figure 1-1); the other four are the 

Central Regional Air Planning Association (CENRAP), the Midwest Regional Planning Organization (MRPO), the Visibility Improvement – State and Tribal Associations of the Southeast 

(VISTAS), and the Mid-Atlantic/Northeast Visibility Union (MANE-VU). The RPOs are responsible for coordinating development of SIPs and TIPs in selected areas of the U.S. to address the 

requirements of the RHR. The WRAP region is composed of states and tribal lands located within 

the boundaries of Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, New Mexico, 

North Dakota, Oregon, South Dakota, Utah, Washington, and Wyoming; it includes the Class I 

areas indicated in Figure 1-2. WRAP is a regional partnership of states, tribes, Federal agencies, 

stakeholders, and citizen groups that was established to initiate and coordinate activities associated with the management of regional haze and other air quality issues within the WRAP states; 

their role includes providing technical and policy tools to help states and tribes. The members of 

WRAP committees and forums (http://wrapair.org/about/orgchart.htm) consist of representatives 

from the affected stakeholder groups identified above. Committee and forum members are urged 

to communicate freely with their peers and professional associations so that they can adequately 

represent the views of that segment of the public. Each committee/forum is expected to translate 

technical materials into a form accessible to the public. More information about the WRAP 

organizational structure and activities is available at http://wrapair.org/facts/forumdescribe.html. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 3 

Figure 1-1. Regional Planning Organizations engaged in regional haze modeling. 

Figure 1-2. Locations of the 116 Class I areas requiring regional haze plans 

by December 2007 within the WRAP region. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 4 

1.1.3 WRAP strategic plan 

On September 29, 2003, WRAP released their strategic plan for the years 2003-2008 (WGA, 

2003): http://wrapair.org/WRAP/meetings/031014board/Tab_4_Strategic_Plan_Final.pdf. Prior 

to 2003, WRAP’s activities focused on developing the Section 309 (§309) SIPs/TIPs that nine 

WRAP states may elect to opt into and thereby adopt the recommendations of the Grand Canyon 

Visibility Transport Commission (GCVTC). These §309 SIPs/TIPs address visibility at the 16 

Class I areas on the Colorado Plateau (see Figure 1-2). In 2003, WRAP’s efforts turned to performing the technical analyses needed to develop the §308 SIPs/TIPs that address all 116 Class I 

areas in the WRAP region. The WRAP 2003-2008 strategic plan is broken down into two phases 

(Table 1-1). It will be reviewed and revised as needed in 2006-07. 

Table 1-1. WRAP 2003 Strategic Plan (WGA, 2003). 

Phase I 2003-2005 Phase II 2005-2007 

Purpose: Dry run for Phase II. Hedge against 

earlier due dates. 

Refine and apply Phase I approaches 

for SIP/TIP purposes. 

Scale: Regional. Regional and subregional. 

Apportionment: 1996 & 2002 source contributions. 

Areas each plan to address.

2002 source contributions. Reduction 

obligations. 

Strategies: Identify options, screen. Perform cost/benefit, select, design. 

Communications: Public education. Public acceptance. 

Major state/tribe 

submittals: 

2002 emissions inventories. Modeling run specifications. 

1.1.4 Organization of the Regional Modeling Center 

The WRAP RMC is composed of researchers from the University of California, Riverside 

(UCR), ENVIRON International Corporation, and the University of North Carolina’s Carolina 

Environmental Program (UNC-CEP). Dr. Gail Tonnesen of UCR is the Project Manager and 

Principal Investigator for the RMC. Mr. Ralph Morris and Mr. Zac Adelman lead the RMC 

efforts at ENVIRON and UNC-CEP, respectively. We are performing meteorological, emissions, 

and air quality modeling to support WRAP’s efforts to comply with the requirements of the 

RHR. UCR hosts the RMC’s computer center, which includes numerous Linux computer platforms and RAID disk storage systems. In the past, training of states, tribes, and others has been 

performed at UCR’s facilities. 

The primary modeling tools used by the RMC include the Fifth-Generation Pennsylvania State 

University/National Center for Atmospheric Research (PSU/NCAR) Mesoscale Model (MM5) 

meteorological modeling system, the Sparse Matrix Operator Kernel Emissions (SMOKE) 

emissions modeling system, and the CMAQ air quality modeling system. We have also performed some sensitivity tests using CAMx and the Regional Modeling System for Aerosols and 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 5 

Deposition (REMSAD). In addition, there are plans to apply MM5 and the California Meteorological Model/California Puff Model (CALMET/CALPUFF) modeling system to Alaska. 

During 2002 and 2003, the RMC focused on performing the technical analysis needed to develop 

the §309 SIPs/TIPs for those states and tribes that elected to pursue the §309 process. We set up 

the SMOKE emissions and CMAQ modeling system for 1996 for the western states using 

36-km-resolution MM5 data, and performed the technical analysis needed for the §309 SIPs and 

TIPs. More details can be found in the Section 309 TSD (WRAP, 2003) located at 

http://wrapair.org/309/031215Final309TSD.pdf. 

In 2003, the RMC then turned its efforts to setting up the modeling tools needed to address the 

technical requirements of the §308 SIPs/TIPs. This included setting up the MM5, SMOKE, and 

CMAQ modeling systems for calendar year 2002 (CY02) using a 36-km-resolution grid for the 

continental United States and a 12-km-resolution grid for the WRAP states. During 2004 and 

early 2005, we continued setting up the modeling infrastructure for CY02 to address the requirements of the WRAP strategic plan. 

The RMC notes and understands the importance of the guidance contained in the WRAP Tribal 

Template (http://wrapair.org/WRAP/reports/Gen-Tribe-Temp.pdf). Work products from the 

RMC are reviewed by the WRAP Modeling Forum and any interested stakeholders. 

1.2 Overview of Visibility Modeling Data Sets 

Data used in the visibility modeling discussed in this report include the following categories: 

• Emissions Data: Emissions input data were derived from many different sources, 

including data sets developed by other WRAP contractors and modeled emissions data 

from emissions models developed by EPA, WRAP, the RMC, and others. The emissions 

input data were then processed using the SMOKE system. Emissions input data and 

emissions processing are described in Section 2. 

• Meteorology Data: Meteorology data are a key input both for developing some components of the emissions inventory and for conducting air quality visibility modeling. 

Meteorology data for this study were developed by operating MM5 (Grell et al., 1994) as 

described in Section 3. 

• Visibility Model Results: Model-simulated visibility data are developed by running an air 

quality simulation model. The models used here were CMAQ and CAMx, both described 

in Section 4.1. 

• Ambient Air Quality Monitoring Data: Ambient data for gaseous and PM species are 

collected from a variety of monitoring networks (see Section 4.2). Ambient data were 

compared to the model-simulated data using procedures described in Section 4.3. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

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1.3 Description of WRAP Modeling Domain 

The 36-km coarse-grid horizontal domain used for the air quality modeling was the RPO Unified 

Continental 36-km Modeling Grid domain (abbreviated in this report as “RPO Unified domain”), 

which consists of the contiguous 48 U.S. states, contiguous lands and waters of southern Canada 

and northern Mexico, portions of the Pacific and Atlantic Oceans, most of the Gulf of Mexico, 

all of the Gulf of California, and the southern Hudson Bay region. Table 1-2 lists the parameters 

defining the RPO Unified grid. This grid resulted from discussions between the five RPOs and 

represents a compromise designed to provide an acceptable modeling domain for the Class I 

areas in the contiguous 48 U.S. states. 

Table 1-2. RPO Unified domain grid definition. 

Parameter Value 

Map projection Lambert conformal 

Alpha 33°N 

Beta 45°N 

Gamma 97°W 

x center 97°W 

y center 40°N 

This CMAQ/CAMx air quality modeling domain is nested within the MM5 meteorological 

modeling domain. The selection of the MM5 domain is described in the WRAP MM5 modeling 

protocol (ENVIRON and UCR, 2004a). Figure 1-3 shows the MM5 horizontal domain as the 

outermost (aqua) grid. Also shown in Figure 1-3 is the CMAQ 36-km grid (gray grid) nested 

within the MM5 domain. 

The MM5 36-km grid has 164 cells in the east-west dimension and 128 cells in the north-south 

dimension. The CMAQ/CAMx 36-km grid includes 148 cells in the east-west dimension, 112 

cells in the north-south dimension. Because the MM5 model grid is also nested within the Eta 

model grid, there is a possibility of modeling artifacts in the MM5 boundary cells as the Etaderived meteorological variables along the MM5 boundaries come into balance with the MM5 

algorithms. To eliminate any errors in the meteorology from these boundary effects, we chose an 

MM5 domain larger than the CMAQ/CAMx 36-km air quality domain; the MM5 domain 

provides a buffer of 8 to 9 grid cells outside each boundary of the CMAQ/CAMx domain. The 

buffer region used here exceeds the EPA suggestion of at least a 5-grid-cell buffer at each 

boundary. Figure 1-4 shows the 36-km CMAQ/CAMx continental grid and the high-resolution, 

nested 12-km grid used for high-resolution modeling in the WRAP region. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 7 

Figure 1-3. Nesting of the 36-km CMAQ/CAMx grid within the MM5 36-km grid. 

Figure 1-4. Nesting of the WRAP region 12-km grid within the RPO Unified 36-km grid. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 8 

Table 1-3 lists the numbers of rows (running east-west) and columns (running north-south) and 

the definitions of the x-y origin (i.e., the southwest corner) for the 36-km and 12-km grids for 

both MM5 and CMAQ/CAMx. For each domain, the values indicated as the x-y origin are the 

distances in meters of the domain’s southwest corner from the center of the RPO Unified grid. 

Note that the CMAQ/CAMx grid is rotated 90° relative to the MM5 grid, so rows and columns 

are reversed between the two grids. In Table 1-3, “dot” refers to the grid mesh defined at the 

vertices of the grid cells, while “cross” refers to the grid mesh defined by the grid cell centers. 

Thus, the dimension of the dot mesh is equal to the cross mesh plus one. 

Table 1-3. Grid definitions for MM5 and CMAQ. 

Southwest Corner 

Model Columns1

 

Dot (Cross)2

Rows1

Dot (Cross)2

x Origin y Origin 

MM5 36-km 129 (128) 165 (164) -2,952,000 -2,304,000 

CMAQ/CAMx 36-km 149 (148) 113 (112) -2,736,000 -2,088,000 

MM5 12-km 199 (198) 220 (219) -2,448,000 -1,008,000 

CMAQ/CAMx 12-km 208 (207) 187 (186) -2,376,000 -936,000 

1

Dot nodes are defined at grid cell vertices; cross nodes are defined at grid cell centers. 

2

Note that the CMAQ/CAMx grid is rotated 90° relative to the MM5 grid, so rows and columns are 

reversed between the two grids. 

Finally, we note that the grid definitions for the CMAQ Meteorology-Chemistry Interface 

Processor (MCIP) and CMAQ Chemical Transport Model (CCTM) are identical. The CMAQ 

vertical structure is defined primarily by the vertical grid used in the MM5 modeling. The MM5 

simulations employed a terrain-following coordinate system defined by pressure, using 34 layers 

that extend from the surface to the model top at 100 mb. Table 1-4 lists the layer definitions for 

both MM5 and CMAQ. To reduce computational cost in the CMAQ modeling, a layer-averaging 

scheme was adopted for CMAQ simulations. The effects of layer averaging were previously 

evaluated (Wang et al., 2002) and found to have a relatively minor effect on the model 

performance metrics when both the 34-layer and 19-layer CMAQ models were compared to 

ambient monitoring data. Note that CAMx was configured with exactly the same horizontal grid 

system and vertical layer structure as CMAQ.
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 9 

Table 1-4. Vertical layer definition for MM5 simulations (left five columns), and approach 

for reducing CMAQ/CAMx layers by collapsing multiple MM5 layers (right five columns). 

MM5 CMAQ 19L

Layer Sigma 

Pressure 

(mb) 

Height 

(m) 

Depth 

(m) Layer Sigma 

Pressure 

(mb) 

Height 

(m) 

Depth 

(m) 

34 0.000 100 14662 1841 19 0.000 100 14662 6536 

33 0.050 145 12822 1466 0.050 145 

32 0.100 190 11356 1228 0.100 190 

31 0.150 235 10127 1062 0.150 235 

30 0.200 280 9066 939 0.200 280 

29 0.250 325 8127 843 18 0.250 325 8127 2966 

28 0.300 370 7284 767 0.300 370 

27 0.350 415 6517 704 0.350 415 

26 0.400 460 5812 652 0.400 460 

25 0.450 505 5160 607 17 0.450 505 5160 1712 

24 0.500 550 4553 569 0.500 550 

23 0.550 595 3984 536 0.550 595 

22 0.600 640 3448 506 16 0.600 640 3448 986 

21 0.650 685 2942 480 0.650 685 

20 0.700 730 2462 367 15 0.700 730 2462 633 

19 0.740 766 2095 266 0.740 766 

18 0.770 793 1828 259 14 0.770 793 1828 428 

17 0.800 820 1569 169 0.800 820 

16 0.820 838 1400 166 13 0.820 838 1400 329 

15 0.840 856 1235 163 0.840 856 

14 0.860 874 1071 160 12 0.860 874 1071 160 

13 0.880 892 911 158 11 0.880 892 911 158 

12 0.900 910 753 78 10 0.900 910 753 155 

11 0.910 919 675 77 0.910 919 

10 0.920 928 598 77 9 0.920 928 598 153 

9 0.930 937 521 76 0.930 937 

8 0.940 946 445 76 8 0.940 946 445 76 

7 0.950 955 369 75 7 0.950 955 369 75 

6 0.960 964 294 74 6 0.960 964 294 74 

5 0.970 973 220 74 5 0.970 973 220 74 

4 0.980 982 146 37 4 0.980 982 146 37 

3 0.985 986.5 109 37 3 0.985 986.5 109 37 

2 0.990 991 73 36 2 0.990 991 73 36 

1 0.995 995.5 36 36 1 0.995 995.5 36 36 

0 1.000 1000 0 0 0 1.000 1000 0 0 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 10 

2. Final 2002 Emissions Inventories 

2.1 Introduction 

For the emissions modeling work, the WRAP RMC used improved 2002 emissions data for the 

United States, Mexico, and Canada to create a final base 2002 annual emissions database for use 

in the CMAQ and CAMx model performance evaluations. Sources for emissions inventory and 

ancillary modeling data included WRAP emissions inventory contractors, other RPOs, and EPA. 

Building from the WRAP preliminary 2002 modeling cases completed earlier (Tonnesen et al., 

2005), we integrated several updates to the inventories and ancillary data to create final 2002 

emissions input files; this simulation is referred to as 2002 base case A, or Base02a. We used 

SMOKE version 2.1 (CEP, 2004a) to prepare the inventories for input to the air quality modeling 

systems. The SMOKE simulations documented here include the generation of emissions for 

(1) annual CMAQ and CAMx simulations at a 36-km model grid resolution and (2) a short-term 

CMAQ test simulation at a 12-km grid resolution. Definitions of the WRAP modeling grids used 

for these simulations are available in Section 1.3. We performed the modeling and quality 

assurance (QA) work based on the WRAP RMC emissions QA protocol (Adelman, 2004). 

This section documents all work performed to collect, prepare, and quality-assure the emissions 

for the Base02a annual simulation. We first give a brief synopsis of the modeling concepts used 

to prepare and model the data, and then provide details on the sources of the inventories and 

ancillary emissions data, where “ancillary data” refers to all of the SMOKE input data other than 

the emissions inventories. Organized by the major emissions modeling categories (e.g., stationary point, stationary area, on-road mobile), Sections 2.2 through 2.12 describe the modeling for 

each category, problems encountered and resolutions, known uncertainties, and directions for 

future work. Section 2 concludes with a summary of the work that we performed, and a section 

discussing the effects of uncertainties and future work. Throughout this section we refer to 

Appendix A, which includes summary graphics and tables of the emissions inventory data used 

for simulation Base02a.

2.1.1 Emissions data overview 

Emissions inventories are typically divided into area, on-road mobile, point, and biogenic source 

categories. These divisions arise from differing methods for preparing the inventories, different 

characteristics and attributes of the categories, and how the emissions are processed through 

models. Generally, emissions inventories are divided into the following source categories, which 

we refer to later as “SMOKE processing categories.” 

• Stationary Area Sources: Sources that are treated as being spread over a spatial extent 

(usually a county or air district) and that are not movable (as compared to nonroad mobile 

and on-road mobile sources). Because it is not possible to collect the emissions at each 

point of emission, they are estimated over larger regions. Examples of stationary area 

sources are residential heating and architectural coatings. Numerous sources, such as dry 

cleaning facilities, may be treated either as stationary area sources or as point sources. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 11 

• Mobile Sources: Vehicular sources that travel on roadways. These sources can be computed either as being spread over a spatial extent or as being assigned to a line location 

(called a link). Data in on-road inventories can be either emissions or activity data. 

Activity data consist of vehicle miles traveled (VMT) and, optionally, vehicle speed. 

Activity data are used when SMOKE will be computing emission factors via another 

model, such as MOBILE6 (U.S. EPA, 2005a). Examples of on-road mobile sources 

include light-duty gasoline vehicles and heavy-duty diesel vehicles. 

• Point Sources: These are sources that are identified by point locations, typically because 

they are regulated and their locations are available in regulatory reports. In addition, 

elevated point sources will have their emissions allocated vertically through the model 

layers, as opposed to being emitted into only the first model layer. Point sources are often 

further subdivided into electric generating unit (EGU) sources and non-EGU sources, 

particularly in criteria inventories in which EGUs are a primary source of NOx and SO2. 

Examples of non-EGU point sources include chemical manufacturers and furniture 

refinishers. Point sources are included in both criteria and toxics inventories. 

• Biogenic Land Use Data: Biogenic land use data characterize the types of vegetation that 

exist in either county-total or grid cell values. The biogenic land use data in North 

America are available using two different sets of land use categories: the Biogenic 

Emissions Landcover Database (BELD) version 2 (BELD2), and the BELD version 3 

(BELD3) (CEP, 2004b). 

In addition to these standard SMOKE processing categories, we have added other categories, 

either to represent specific emissions processes more accurately or to integrate emissions data 

that are not compatible with SMOKE. Examples of emissions sectors that fall outside of the 

SMOKE processing categories include emissions generated from process-based models for 

representing windblown dust and agricultural ammonia (NH3) sources. An emissions category 

with data that are not compatible with SMOKE is one with gridded emissions data sets, such as 

commercial marine sources. All of the emissions categories that we used to build WRAP 

simulation Base02a are described in detail in the following sections. 

Continuing the enhancement of the emissions source categories that we initiated during the 

preliminary 2002 modeling (Tonnesen et al., 2005), we further refined the categories from the 

standard definitions listed above to include more explicit emissions sectors. The advantage of 

using more detailed definitions of the source categories is that it leads to more flexibility in 

designing control strategies, substituting new inventory or profile data into the modeling, and 

managing the input and output data from SMOKE. The major drawback to defining more emissions source categories is the increased level of complexity that results from having a larger 

number of input data sets. Another motivation behind separating the various emissions categories 

is related to the size and flexibility of the input data. Some data sets, like the CENRAP on-road 

mobile inventory, were so large that we had to process them separately from the rest of the 

sources in the on-road sector due to computational constraints. We also separated the nonroad 

mobile sector into yearly and monthly inventories to facilitate the application of uniform 

monthly temporal profiles to the monthly data. Additional details about how we prepared the 

emissions inventories and ancillary data for modeling are described in Sections 2.2 through 2.12. 

Table 2-1 summarizes the entire group of source sectors that composed simulation Base02a. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 12 

Each emissions sector listed in the table represents an explicit SMOKE simulation. As discussed 

in Section 2.1.2, after finishing all of the source-specific simulations we used SMOKE to combine all of the data into a single file for each day for input to the air quality modeling systems. 

Each subsection below on the emissions sectors describes each sector in terms of the SMOKE 

processing category, the year covered by the inventory, and the source(s) of the data. Additional 

details about the inventories are also provided, including any modifications that we made to 

prepare them for input into SMOKE. 

Table 2-1. WRAP Base02a emissions categories. 

Emissions Sector Abbreviation* 

Stationary area, including VISTAS 

county-level fires 

ar 

CENRAP county-level fires arf 

WRAP point-source fires awf 

Biogenic bg 

Ontario, Canada, point-source fires bsf 

Fugitive dust fd 

WRAP on-road mobile mb 

CENRAP on-road mobile mbvcen 

Other U.S. on-road mobile mbv 

Non-U.S. on-road mobile nusm 

CENRAP/MRPO anthropogenic NH3 nh3 

WRAP anthropogenic NH3 nh3wrap 

Seasonal/monthly nonroad mobile nrm 

Annual nonroad mobile nry 

Non-WRAP point-source fires nwf 

Offshore area ofsar 

Offshore point ofspt 

Commercial marine shipping ofsmar 

Stationary point pt 

Road dust rd 

Windblown dust wbd 

WRAP oil and gas wog 

*These abbreviations are used in the file naming of the SMOKE 

output files for each sector. 

2.1.2 Emissions modeling overview 

Emissions models such as SMOKE are computer programs that convert annual or daily estimates 

of emissions at the state or county level to hourly emissions fluxes on a uniform spatial grid that 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 13 

are formatted for input to an air quality model. For the Base02a simulation, we prepared emissions for CMAQ version 4.4 using SMOKE version 2.1 on the WRAP RMC Linux computing 

cluster (see Section 5). SMOKE integrates annual county-level emissions inventories with 

source-based temporal, spatial, and chemical allocation profiles to create hourly emissions fluxes 

on a predefined model grid. For elevated sources that require allocation of the emissions to the 

vertical model layers, SMOKE integrates meteorology data to derive dynamic vertical profiles. 

In addition to its capacity to represent the standard emissions processing categories, SMOKE is 

also instrumented with the Biogenic Emissions Inventory System, version 3 (BEIS3) model for 

estimating biogenic emissions fluxes (U.S. EPA, 2004a) and the MOBILE6 model for estimating 

on-road mobile emissions fluxes from county-level vehicle activity data (U.S. EPA, 2005a). 

To supplement the emissions estimates produced by SMOKE, we developed enhanced or 

additional emissions estimates for sources either misrepresented in the SMOKE modeling or 

completely missing from it. We used outputs from stand-alone, process-based emissions models 

to generate emissions estimates for windblown dust and agricultural NH3 sources. The WRAP 

windblown dust model generates gridded estimates of dust emissions from wind erosion for 

input to an air quality model (Mansell et al., 2004). Before developing this model, we neglected 

dust emissions estimates from wind erosion in the WRAP modeling. The WRAP NH3 model 

estimates anthropogenic NH3 emissions for input to an air quality model (Mansell, 2005). We 

used these modeled NH3 emissions to replace the estimates provided in the stationary-areasource inventory. Finally, we included an offshore commercial shipping inventory for the Pacific 

Ocean in simulation Base02a; commercial shipping is another source category that we had never 

represented in the WRAP modeling until simulation Base02a. 

SMOKE uses C-shell scripts as user interfaces to set configuration options and call executables. 

SMOKE is designed with flexible QA capabilities to generate standard and custom reports for 

checking the emissions modeling process. After modeling all of the source categories individually, including those categories generated outside of SMOKE, we used SMOKE to merge all of 

the categories together to create a single CMAQ input file per simulation day. Also, for use in 

the CAMx modeling, we converted the CMAQ-ready emissions estimates to CAMx-ready files 

using a combination of SMOKE and stand-alone Perl scripts developed by ENVIRON. We used 

SMOKE to generate CAMx inputs for all low-level emissions (including low-level point 

sources) and the CMAQ2CAMX converter to create the CAMx inputs for the elevated sources 

(including fires). Additional technical details about the version of SMOKE used for simulation 

Base02a are available from CEP (2004b). All scripts, data, and executables used to generate the 

Base02a emissions for CMAQ and CAMx are archived in a CVS repository on the WRAP RMC 

computing cluster. 

2.1.3 Simulation Base02a data collection and preparation 

This section describes the procedures that the WRAP RMC followed to collect and prepare all 

emissions data for simulation Base02a. We discuss the sources of all inventory and ancillary data 

used for this simulation. The WRAP RMC worked with emissions inventory contractors, other 

RPOs, and EPA to collect all of the data that constitute simulation Base02a. Table 2-2 lists all of 

the contacts for the various U.S. anthropogenic emission inventories we used. For the WRAP 

inventories, this table lists the contacts for the contractors who prepared the inventories; for the 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 14 

non-WRAP inventories it lists the contacts at the RPOs who provided us inventory data. We 

obtained the emissions inventories for Canada and Mexico from the U.S. EPA Emissions Factors 

and Inventory Group (EFIG) via the Clearinghouse for Inventories and Emissions Factors 

(CHIEF) website (http://www.epa.gov/ttn/chief/index.html). 

Table 2-2. WRAP Base02a anthropogenic emissions inventory contacts. 

Source Category Emissions Data Contact 

WRAP 

Stationary area (including fugitive 

dust), stationary point, WRAP oil and 

gas 

Paula Fields, ERG, Inc., 

Sacramento, CA USA 

phone: 916-361-6751; email: Paula.Fields@erg.com 

On-road and nonroad mobile, in-port, 

near-port, and commercial marine 

shipping, windblown dust, road dust, 

anthropogenic NH3, 

Alison Pollack, ENVIRON International Corporation, 

Novato, CA USA 

phone: 415-899-0710; email: apollack@environcorp.com 

All point-source fires Dave Randall, Air Sciences, Inc., 

Golden, CO USA 

phone: 303-988-2960; email: drandall@airsci.com 

CENRAP 

All Lee Warden, Oklahoma DEQ, 

Oklahoma City, OK USA 

phone: 405-702-4201; email: lee.warden@deq.state.ok.us 

VISTAS 

All Greg Stella, Alpine Geophysics, LLC, 

phone: 828-675-9045; email: gms@alpinegeophysics.com 

MANE-VU 

All Megan Schuster, MARAMA, 

Baltimore, MD USA 

phone: 410-467-0170; email: mschuster@marama.org 

MRPO 

All Marc Janssen, LADCO, 

Des Plaines, IL, USA 

phone: 847-296-2181; email:janssen@ladco.org 

As mentioned above, the refinement of these inventories involved splitting some of the inventory 

files into more specific source sectors. As the stationary-area-source emissions sector has 

traditionally been a catch-all for many types of sources, this is the inventory sector that required 

the greatest amount of preparation. Upon receiving all stationary-area-source inventories we 

extracted fugitive dust, road dust, anthropogenic NH3, and for the non-WRAP U.S. inventories, 

stage II refueling sources. We retained the dust sources as separate categories that we would 

further refine with the application of transport factors (see Section 2.8). We discarded the NH3
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 15 

and refueling sources, as these emissions are represented in the WRAP NH3 model and in the onroad mobile-source inventories, respectively. We also split the WRAP fire inventories into the 

six distinct fire categories: wildfires, agricultural fires, wildland fire use, natural prescribed, 

anthropogenic prescribed, and non-Federal rangeland fires. While we modeled these sources 

collectively for simulation Base02a, by splitting them into distinct categories we prepared them 

for fire sensitivity simulations to be completed later. 

We collected the ancillary data used for SMOKE modeling from several sources. SMOKE 

ancillary modeling data include 

• temporal and chemical allocation factors by state, county, and source classification code 

(SCC) 

• spatial surrogates and cross-reference files for allocating county-level emissions to the 

model grid 

• hourly, gridded meteorology data 

• stack defaults for elevated point sources 

• MOBILE6 configuration files 

• a Federal Implementation Standards (FIPS) codes (i.e., country/state/county codes) 

definition file 

• an SCC definition file 

• a pollutant definition file 

Except for the meteorology data and the MOBILE6 configuration files, we used default data sets 

provided by EPA as the basis for all of the ancillary data. The WRAP RMC provided the 

meteorology data for the Base02a simulations at 36-km and 12-km grid resolutions (Tonnesen et 

al., 2005). The inventory contractor who prepared the MOBILE6 inventories provided the 

MOBILE6 configuration files either directly or via an RPO representative; details about the 

sources of the MOBILE6 inputs are provided in Section 2.4. We made minor modifications to 

the chemical allocation, pollutant definition, and country/state/county codes files for new 

sources, pollutants, or counties contained in the Base02a inventories that we had not previously 

modeled. We made major modifications to the temporal and spatial allocation inputs, as 

described below. 

2.1.3.1 Temporal allocation data 

Temporally allocating annual, daily, or hourly emissions inventories in SMOKE involves 

combining a temporal cross-reference file and a temporal profiles file. 

• Temporal cross-reference files associate monthly, weekly, and diurnal temporal profile 

codes with specific inventory sources, through a combination of a FIPS code, an SCC, 

and sometimes for point sources, facility and unit identification codes. 

• Temporal profiles files contain coded monthly, weekly, and diurnal profiles in terms of a 

percentage of emissions allocated to each temporal unit (e.g., percentage of emissions per 

month, weekday, or hour). 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 16 

As a starting point for the temporal allocation data for simulation Base02a, we used the files 

from the WRAP preliminary 2002 modeling (Tonnesen et al., 2005), which originated from EPA 

(2005b). Based on guidance from the developers of some of the inventory files, we enhanced the 

temporal profiles and assignments for some source categories. 

We modified the temporal allocation data for simulation Base02a to improve the representation 

of temporal emissions patterns for certain source categories. With the delivery of the WRAP 

nonroad mobile-source inventories, we also received weekday/weekend split factors to allocate 

these data to the various days of the week (Pollack et al., 2006). Using these split factors, we 

modified the standard EPA temporal profiles for a series of nonroad SCCs specified by the 

inventory contractor. We implemented the adjusted profiles in SMOKE by modifying the 

temporal cross-reference file for the applicable FIPS and SCC combinations. Section A.1 in 

Appendix A contains additional information about how the WRAP RMC modified these profiles. 

Equations A-1 and A-2 illustrate how we applied the adjustments to standard weekly temporal 

profiles. Table A-1 lists the modified profiles by source, including the original profile number for 

reference. Table A-2 lists the weekday/weekend adjustment factors with the SCCs to which they 

were applied. Figures A-1 and A-2 illustrate the new profiles relative to the standard temporal 

profiles from which they were derived. 

Other additions to the Base02a temporal allocation data included updates that we made for some 

WRAP sources and updates made by other RPOs that are applicable to their inventories. These 

other updates to the temporal allocation files included 

• VISTAS continuous emissions monitoring (CEM)-specific profiles for EGUs in the 

VISTAS states 

• VISTAS agricultural burning profiles 

• Wildfire and prescribed fire profiles developed by VISTAS for the entire U.S. 

• MANE-VU on-road mobile profiles 

• WRAP weekly and diurnal road dust profiles 

• WRAP diurnal wildfire, agricultural fire, and prescribed fire profiles 

• WRAP on-road mobile weekly and diurnal profiles 

• CENRAP non-Federal rangeland fire profiles 

Finally, for all of the monthly and seasonal emissions inventories, we modified the temporal 

cross-reference files to apply uniform monthly profiles to the sources contained in these inventories. The monthly variability is inherent in monthly and seasonal inventories and does not need 

to be reapplied through the temporal allocation process in SMOKE. The inventories to which we 

applied uniform monthly temporal profiles included 

• WRAP, CENRAP, and MRPO nonroad mobile sources 

• WRAP on-road mobile sources 

• WRAP road dust 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 17 

2.1.3.2 Spatial allocation data 

SMOKE uses spatial surrogates and SCC cross-reference files to allocate county-level emissions 

inventories to model grid cells. Geographic information system (GIS)-calculated fractional land 

use values define the percentage of a grid cell that is covered by standard sets of land use 

categories. For example, spatial surrogates can define a grid cell as being 50% urban, 10% forest, 

and 40% agricultural. In addition to land use categories, spatial surrogates can also be defined by 

demographic or industrial units, such as population or commercial area. Similar to the temporal 

allocation data, an accompanying spatial cross-reference file associates the spatial surrogates 

(indexed with a numeric code) to SCCs. Spatial allocation with surrogates is applicable only to 

area and mobile sources. Point sources are located in the model grid cells by SMOKE based on 

the latitude-longitude coordinates of each source. Biogenic emissions are estimated based on 

1-km2

 gridded land use information that is mapped to the model grid using a processing program 

such as the Multimedia Integrated Modeling System (MIMS) Spatial Allocator (CEP, 2005). 

We used various sources of spatial surrogate information for the U.S., Canada, and Mexico 

inventories in simulation Base02a. For the U.S. and Canadian sources, we used the EPA unified 

surrogates available through the EFIG web site (EPA, 2005c). For the 36-km grid, EPA provides 

these data already formatted for SMOKE on the RPO Unified 36-km domain that we used for 

simulation Base02a. For the WRAP region’s 12-km grid, we used the EPA 12-km surrogates, 

extracting the WRAP domain in a preprocessing step before implementing these data in 

SMOKE. We modified the spatial surrogates for Canada on the RPO Unified 36-km domain by 

adding several surrogate categories that we felt were lacking in the EPA data set. Table 2-3 

provides details about the new Canadian spatial surrogates that we developed for simulation 

Base02a. For modeling Mexico, we used Shapefiles developed for the Big Bend Regional 

Aerosol and Visibility Observations Study (BRAVO) modeling to create surrogates for Mexico 

on the RPO Unified 36-km domain (U.S. EPA, 2005c). 

Table 2-3. New Canadian spatial surrogates for Base02a. 

Attribute Base02a Code Shapefile Reference 

Land area 950 can_land93_land Natural Resources Canada (1993) 

AVHRR land cover data 

Water area 951 can_land93_water Natural Resources Canada (1993) 

AVHRR land cover data 

Forest land area 952 can_land93_forest Natural Resources Canada (1993) 

AVHRR land cover data 

Agricultural land area 953 can_land93_agri Natural Resources Canada (1993) 

AVHRR land cover data 

Urban land area 954 can_land93_urban Natural Resources Canada (1993) 

AVHRR land cover data 

Rural land area 955 can_land93_rural Natural Resources Canada (1993) 

AVHRR land cover data 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 18 

Attribute Base02a Code Shapefile Reference 

Airports 956 can_airport U.S. DOT Bureau of Transportation Statistics (2005) NORTAD 

1:1,000,000 scale data 

Ports 957 can_port U.S. DOT Bureau of Transportation Statistics (2005) NORTAD 

1:1,000,000 scale data 

Roads 958 can_road1m Natural Resources Canada (2001) 

National Scale Frameworks data 

Rail 959 can_rail1m Natural Resources Canada (1999) 

National Scale Frameworks data 

For a description of the 36-km and 12-km modeling grids, refer to Section 1.3. The following 

sections provide information about the inventory data, preparation, and modeling for each of the 

major source categories that compose simulation Base02a. For a more detailed description of the 

emissions inventory data files, refer to Section A.2 in Appendix A. 

2.2 Stationary-Point-Source Emissions 

Stationary-point-source emissions data for SMOKE consist of (1) Inventory Data Analyzer 

(IDA)-formatted inventory files; (2) ancillary data for allocating the inventories in space, in time, 

and to the Carbon Bond-IV chemistry mechanism used in CMAQ and CAMx; and (3) meteorology data for calculating plume rise from the elevated point sources. This section describes 

where the RMC obtained these data, how we modeled them, and the types of QA that we performed to ensure that SMOKE processed the data as expected. 

2.2.1 Data sources 

For the stationary-point-source inventories in simulation Base02a, we used actual 2002 data 

developed by the RPOs for the U.S., version 2 of the year 2000 Canadian inventory, and the 

BRAVO 1999 Mexican inventory. The WRAP stationary-point inventory consisted of annual 

county-level and tribal data provided to us by ERG, Inc. (2006). The CENRAP (E.H. Pechan and 

CEP, 2005a) and VISTAS (Stella, 2005) stationary-point inventories consisted of an annual data 

set and monthly CEM data for selected EGUs. CENRAP and Alpine Geophysics provided these 

data directly to the RMC. We downloaded the MANE-VU and MRPO 2002 stationary-point 

inventories from the MANE-VU and the Lake Michigan Air Directors Consortium (LADCO) 

web sites, respectively. We opted to use the summer season inventory to model the entire year 

for the MANE-VU states (E.H. Pechan et al., 2005b), and from the MRPO data we used the Base 

I stationary-point inventory. The WRAP RMC entered into a nondisclosure agreement with 

Environment Canada to obtain version 2 of the 2000 Canadian point-source inventory. This 

inventory represented a major improvement over the version of the data that we had used in the 

preliminary 2002 modeling. For Mexico we used the same BRAVO 1999 inventory used in the 

preliminary 2002 modeling (Tonnesen et al., 2005). 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 19 

Simulation Base02a used updated meteorology data and improved the temporal allocation 

information relative to the preliminary 2002 modeling; the rest of the ancillary data for modeling 

stationary point sources stayed the same (Tonnesen et al., 2005). The meteorology data that we 

used to calculate plume rise for the elevated sources is version 2 of the 2002 MM5 data 

preprocessed for SMOKE and CMAQ with MCIP version 2.3 (Kemball-Cook et al., 2005). We 

made one major improvement to the temporal allocation data based on information provided by 

the VISTAS RPO: for the VISTAS sources, we added EGU-based CEM profiles developed by 

Alpine Geophysics. These additions included new monthly profiles and month-specific weekly 

and diurnal profiles. 

2.2.2 Emissions processing 

For simulation Base02a we configured SMOKE to process the annual inventories for the U.S., 

Canada, and Mexico and process hourly CEM data for the VISTAS and CENRAP states. We 

configured SMOKE to allocate these emissions up to model layer 15, which roughly corresponds 

to the maximum planetary boundary layer (PBL) heights across the entire domain throughout the 

year. As coarse particulate matter (PMc) is not an inventory pollutant but is required by the air 

quality models as an input species, we used SMOKE to calculate PMc during the processing as 

(PM10 - PM2.5). With the SMOKE option WKDAY_NORMALIZE set to “No,” we treated the 

annual inventories based on the assumption that they represent average-day data based on a 

seven-day week, rather than average weekday data. We also assumed that all of the volatile 

organic compound (VOC) emissions in the inventories are reactive organic gas (ROG), and thus 

used SMOKE to convert the VOC to total organic gas (TOG) before converting the emissions 

into CB-IV speciation for the air quality models. To capture the differences in diurnal patterns 

that are contained in the CEM temporal profiles for the VISTAS and CENRAP states, we 

configured SMOKE to generate daily temporal matrices, as opposed to using a Mondayweekday-Saturday-Sunday (MWSS) temporal allocation approach. 

To QA the stationary-point emissions, we used the WRAP emissions modeling QA protocol 

(Adelman, 2004) and a suite of graphical summaries. We used tabulated summaries of the input 

data and SMOKE script settings to document the data and configuration of SMOKE used. The 

graphical QA summaries include, for all emissions output species, daily spatial plots summed 

across all model layers, daily time-series plots, annual time-series plots, and daily vertical 

profiles. These QA graphics are available on the RMC web site at 

http://pah.cert.ucr.edu/aqm/308/QA_base02a36.plots/pt/plots/. 

2.2.3 Uncertainties and future work 

There were a group of issues with the stationary-point emissions that we left unresolved at the 

completion of simulation Base02a either because we did not feel they would have a major impact 

on the modeling results in the WRAP states or because we did not have alternative approaches 

and they represented that best available information. All of the issues were related to the sources 

outside of the WRAP region. For the non-WRAP inventories that originated as National 

Emissions Inventory (NEI) Input Format (NIF) files, incompatibilities between the NIF and IDA 

formats resulted in the omission of fields that uniquely identified the sources. As a result, 

SMOKE flagged thousands of sources outside of the WRAP states as duplicates. SMOKE treated 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 20 

these sources correctly, summing their emissions. In future modeling, all of these sources should 

be given unique identification fields so that they will be treated as separate stacks or units. 

SMOKE also flagged thousands of sources in the MRPO, MANE-VU, and CENRAP regions as 

having PM10 emissions less than their PM2.5 emissions, which is physically impossible because 

PM2.5 is a subset of PM10. This problem is indicated when the PMc values calculated by SMOKE 

are negative. SMOKE resets the value of PMc to 0 for these sources and leaves PM10 and PM2.5

unchanged. We notified the RPO representatives about this problem with their inventories before 

proceeding with these data. 

For the CEM data in the VISTAS and CENRAP regions, we encountered several inventory 

sources with entries in the hourly inventory file but no corresponding entry in the annual inventory file. SMOKE requires entries in both files, with the hourly file containing the hourly emissions values and the annual file containing the location and stack parameters. When SMOKE 

does not find an entry in the annual file for sources in the hourly file, it has no information about 

how to locate these emissions on the model grid or how to calculate the plume rise. Sources that 

have this inconsistency are ignored by SMOKE. We notified the RPO representatives about these 

problems with their inventories before proceeding with these data. 

The BRAVO 1999 Mexico inventory that we used is known to be deficient in that it contains 

only 110 sources for the entire country and does not contain any stack parameters. SMOKE 

applies default U.S. stack parameters to these sources based on the SCCs in the inventory. We 

anticipate receiving an updated Mexican inventory to use in future 2002 emissions simulations. 

Along with improved Mexican point-source emissions, we anticipate receiving improved 2002 

inventories for California, Alaska, Arizona, Montana, New Mexico, Oregon, Utah, Washington, 

Wyoming, and the WRAP tribes. We will also receive temporal profiles based on CEM data for 

WRAP, CENRAP, VISTAS, and MANE-VU EGUs. 

2.3 Stationary-Area-Source Emissions 

Stationary-area-source emissions data for SMOKE consist of IDA-formatted inventory files and 

ancillary data for allocating the inventories in space, time, and to the Carbon Bond-IV chemistry 

mechanism used in CMAQ and CAMx. This section describes where we obtained these data, 

how we modeled them, and the types of QA that we performed to ensure that SMOKE processed 

the data as expected. 

2.3.1 Data sources 

For the stationary-area-source inventories in simulation Base02a, we used actual 2002 data 

developed by the RPOs for the U.S., version 2 of the year 2000 Canadian inventory, and the 

BRAVO 1999 Mexican inventory. The WRAP stationary-area inventory consisted of annual 

county-level and tribal data provided to us by ERG, Inc. (2006). Due to the small contribution of 

the WRAP tribal inventories to the total domain emissions and the lack of readily available 

spatial allocation data for these parts of the domain, we did not use the WRAP tribal data for 

simulation Base02a. The CENRAP (E.H. Pechan and CEP, 2005a) and VISTAS Phase II (Stella, 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 21 

2005) stationary-area inventories also consisted of an annual data set and were provided directly 

to us by these RPOs. We downloaded the MANE-VU and MRPO 2002 stationary-area 

inventories from the MANE-VU and LADCO websites, respectively. We used the MRPO Base I 

stationary-area inventory for simulation Base02a. We received the Canadian 2000 inventory 

version 2 from the U.S. EPA EFIG (U.S. EPA, 2005d). For Mexico we used the same BRAVO 

1999 inventory that we used in the preliminary 2002 modeling (Tonnesen et al., 2005). 

To prepare the stationary-area inventories for modeling, we made several modifications to the 

files by removing selected sources either to model them as separate source categories or to omit 

them from simulation Base02a completely. Using guidance provided by EPA (U.S. EPA, 2004b) 

we extracted fugitive and road dust sources from all stationary-area inventories for adjustment by 

transport factors and modeling as separate source categories (see Section 2.8). We also extracted 

and discarded the stage II refueling sources (Table 2-4) from the non-WRAP U.S. inventories; 

we modeled these sources with MOBILE6 as part of the on-road mobile-source emissions. We 

left the stage II refueling emissions in the WRAP stationary-area inventory because the on-road 

mobile inventory that we received for this region did not contain these emissions. 

Table 2-4. Refueling SCCs removed from the non-WRAP U.S. stationary-area inventory. 

SCC Description 

2501060100 Storage and Transport Petroleum and Petroleum Product Storage Gasoline Service 

Stations Stage 2: Total 

2501060101 Storage and Transport Petroleum and Petroleum Product Storage Gasoline Service 

Stations Stage 2: Displacement Loss/Uncontrolled 

2501060102 Storage and Transport Petroleum and Petroleum Product Storage Gasoline Service 

Stations Stage 2: Displacement Loss/Controlled 

2501060103 Storage and Transport Petroleum and Petroleum Product Storage Gasoline Service 

Stations Stage 2: Spillage 

2501070100 Storage and Transport Petroleum and Petroleum Product Storage Diesel Service 

Stations Stage 2: Total 

2501070101 Storage and Transport Petroleum and Petroleum Product Storage Diesel Service 

Stations Stage 2: Displacement Loss/Uncontrolled 

2501070102 Storage and Transport Petroleum and Petroleum Product Storage Diesel Service 

Stations Stage 2: Displacement Loss/Controlled 

2501070103 Storage and Transport Petroleum and Petroleum Product Storage Diesel Service 

Stations Stage 2: Spillage 

Other steps that we took to prepare the stationary-area inventories included moving oil and gas 

sources from the California inventory to a separate file for explicit treatment (Section 2.10), 

confirming that there is no overlap between the anthropogenic NH3 inventory (Section 2.9) and 

stationary-area sources, moving several nonroad mobile SCCs from the Mexico inventory to the 

nonroad mobile sector, and moving area-source fires in each regional inventory to separate files. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 22 

In addition to the above inventory modifications, we made the following changes to the ancillary 

data files for simulation Base02a: We used improved temporal and spatial allocation information 

relative to the preliminary 2002 modeling; the rest of the ancillary data for modeling stationaryarea sources stayed the same as in the preliminary modeling (Tonnesen et al., 2005). We 

enhanced the spatial allocation data with additional area-based surrogates for Canada (Table 

2-3), and added surrogates for a missing county in Colorado. As part of the SMOKE modeling 

QA process we look at the assignment of emissions to spatial surrogates, focusing on the largest 

(top 10% by mass) inventory SCCs. We noticed when looking at the Canadian data for the 

preliminary 2002 modeling that forest fire emissions from the Canadian area-source inventory, 

which are relatively large sources of CO, NOx, and PM2.5, were being allocated to a surrogate for 

logging activities. We found similar discrepancies for other area and nonroad SCCs in Canada. 

To improve the representation of the Canadian emissions, we added several land-area-based 

surrogates for Canada, such as forested land area, urban land area, and rural land area, and made 

the accompanying additions to the spatial cross-reference file to associate inventory SCCs with 

these surrogates. We also added spatial surrogates for Broomfield County, CO; this county was 

included in the inventory but was not included in the base EPA surrogates. To create surrogates 

for Broomfield County, we filtered the EPA Shapefiles (U.S. EPA, 2005c) that were used to 

create surrogates for the rest of the domain with a county boundary file for Broomfield and 

surrounding counties. In adding Broomfield County to the modeling domain, we also subtracted 

parts of the surrounding four Colorado counties: Adams, Boulder, Jefferson, and Weld. 

Improvements to the temporal allocation data for simulation Base02a included the addition of 

several FIPS-specific profiles provided by VISTAS and CENRAP. These temporal profiles 

targeted mainly fire and agricultural NH3 sources in these regions, such as open burning and 

livestock operations, respectively. 

2.3.2 Emissions processing 

We configured SMOKE to process the annual stationary-area-source inventories for the U.S., 

Canada, and Mexico. As PMc is not an inventory pollutant but is required by the air quality 

models as input species, we used SMOKE to calculate PMC during the processing as (PM10 - 

PM2.5). With the SMOKE option WKDAY_NORMALIZE set to “Yes,” we treated the annual 

stationary-area inventories based on the assumption that they represent average weekday data, 

causing SMOKE to renormalize the data to a seven-day estimate before applying any temporal 

adjustments. We also assumed that all of the VOC emissions in the inventories are ROG and thus 

used SMOKE to convert the VOC to TOG before converting the emissions into CB-IV 

speciation for the air quality models. We configured SMOKE to use a MWSS temporal 

allocation approach, as opposed to a daily temporal approach. 

To QA the stationary-area emissions, we used the WRAP emissions modeling QA protocol 

(Adelman, 2004) and a suite of graphical summaries. We used tabulated summaries of the input 

data and SMOKE script settings to document the data and configuration of SMOKE used. The 

graphical QA summaries include, for all emissions output species, daily spatial plots summed 

across all model layers, daily time-series plots, and annual time-series plots. These QA graphics 

are available on the RMC web site at 

http://pah.cert.ucr.edu/aqm/308/QA_base02a36.plots/ar/plots/. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 23 

2.3.3 Uncertainties and future work 

Most of the issues that we encountered with the stationary area sources related to the removal of 

certain SCCs from the base inventories for inclusion in other source categories or complete 

omission from simulation Base02a. After removing the dust sources from the area inventories for 

modeling as separate sectors, we noted that several SCCs related to wind erosion remained in the 

Mexican inventory. As geogenic wind erosion emissions are covered by the WRAP windblown 

dust model, we removed these area-source inventory dust sources completely from simulation 

Base02a. We spent considerable effort on ensuring that we did not have overlap between the area 

inventory and the other sectors that explicitly represent sources traditionally contained in the area 

inventory, such as NH3 and dust. 

Both the Canadian and Mexican inventories presented minor problems that we resolved for 

simulation Base02a but that can be addressed more thoroughly in future simulations. The 

Canadian inventory we used contained data only at the province level—it was the equivalent of a 

statewide, rather than county-level, inventory. A higher-resolution inventory would have allowed 

us to use higher-resolution and more accurate spatial allocation data. Future modeling that uses 

Canadian data should move to the newly released municipality-level year 2000 inventories for 

Canada. 

We found a discrepancy between the state and county coding in the Mexican inventory and the 

SMOKE file that defines acceptable FIPS codes. Differences in the ordering of the Mexican state 

names between these two data sets led to some of the Mexican inventory sources being 

mislabeled in the SMOKE QA reports. We changed the state codes in the inventory and spatial 

surrogate files for two Mexican states to be consistent with the SMOKE country/state/county 

codes file. The Mexican inventory also covered only the two northern tiers of Mexican states, 

causing the southern portion of Mexico within the 36-km modeling domain to be without 

emissions. Future modeling of Mexico should use an inventory with full coverage of the country 

and a revised SMOKE country/state/county codes file that used correct spatial codes for Mexico. 

2.4 On-Road Mobile-Source Emissions 

On-road mobile-source emissions data for SMOKE consist of IDA-formatted emissions and 

vehicle activity inventory files, and ancillary data for allocating the inventories in space, in time, 

and to the Carbon Bond-IV chemistry mechanism used in CMAQ and CAMx. This section 

describes where we obtained these data, how we modeled them, and the types of QA that we 

performed to ensure that SMOKE processed the data as expected. 

2.4.1 Data sources 

For the on-road mobile-source inventories in simulation Base02a, we divided the modeling into 

three categories based on common data attributes: WRAP precomputed emissions, non-WRAP 

U.S. activities for processing with MOBILE6, and non-U.S. precomputed emissions. For the 

WRAP emissions we used actual 2002 data split into California and non-California seasonal 

inventories. In addition to the standard criteria pollutants, these files contained prespeciated 

PM2.5 emissions. For the rest of the U.S. we used annual, county-level activity and speed 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 24 

inventories with monthly, county-level MOBILE6 inputs, and hourly meteorology to estimate 

the hourly emissions with MOBILE6. For the non-U.S. inventories, we used version 2 of the 

year 2000 Canadian inventory and the BRAVO 1999 Mexican inventory. The WRAP on-road 

mobile inventory consisted of seasonal county emissions data provided to us by ENVIRON 

(Pollack et al., 2006). The CENRAP (E.H. Pechan and CEP, 2005a), VISTAS Phase II (Stella, 

2005), and MANE-VU (E.H. Pechan, 2005c) on-road mobile activity inventories consisted of 

annual county-level activities and speeds, and monthly county-level roadway data. We received 

these inventories and ancillary MOBILE6 inputs directly from the RPO inventory representatives. We used annual, county-level MRPO 2002 activity inventories that we received from 

Alpine Geophysics with the data that they provided for their VISTAS modeling (Stella, 2005). 

We received the Canadian 2000 inventory version 2 from the U.S. EPA EFIG (U.S. EPA, 

2005d). For Mexico we used the same BRAVO 1999 inventory that we used in the preliminary 

2002 modeling (Tonnesen et al., 2005). 

Simulation Base02a used improved temporal and spatial allocation information relative to the 

preliminary 2002 modeling; the rest of the ancillary data for modeling on-road mobile sources 

stayed the same as in the preliminary modeling (Tonnesen et al., 2005).We added spatial 

surrogates for Broomfield County, CO, a county that was included in the inventory but was not 

included in the base EPA surrogates. To create surrogates for Broomfield County, we filtered the 

EPA Shapefiles (U.S. EPA, 2005c) that were used to create surrogates for the rest of the domain 

with a county boundary file for Broomfield and surrounding counties. In adding Broomfield 

County to the modeling domain, we also subtracted parts of the surrounding four Colorado 

counties: Adams, Boulder, Jefferson, and Weld. 

We received temporal allocation profiles and cross-reference files directly from ENVIRON for 

use with the WRAP on-road mobile inventories (Pollack et al., 2006). We also received temporal 

profiles for the MANE-VU on-road mobile sources. 

2.4.2 Emissions processing 

We configured SMOKE to process the annual on-road mobile emissions inventory data for 

WRAP, Canada, and Mexico as precomputed area-like inventories. For the non-WRAP states, 

we used the SMOKE/MOBILE6 integration to process the annual activity inventories and 

monthly, county-based roadway information for input to CMAQ. As the WRAP inventories 

contained precomputed PM emissions, we did not have to use SMOKE to compute PM. 

ENVIRON prepared the WRAP on-road mobile inventories to represent seven-day average 

emissions (different from the area inventory, which represented average weekday emissions). 

With actual weekly average emissions, we configured SMOKE to process them by setting 

WKDAY_NORMALIZE to “No.” We also assumed that all of the VOC emissions in the 

inventories are ROG and used SMOKE to convert the VOC to TOG before converting the 

emissions into CB-IV speciation for the air quality models. We configured SMOKE to create 

daily, rather than MWSS, temporal intermediates because the WRAP on-road mobile temporal 

profiles that we received from ENVIRON contain weekly profiles that vary across the weekdays 

and do not exhibit flat weekday profiles. 

The large number of county roadway inputs for MOBILE6 we received from CENRAP forced us 

to split the non-WRAP U.S. mobile-source processing into three subsets because of memory 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 25 

limitations with SMOKE. The three subsets consisted of two sets of CENRAP simulations and a 

simulation that computed on-road mobile emissions for the MRPO, VISTAS, and MANE-VU 

states. We configured MOBILE6 to use weekly temperature averaging for computing these 

emissions within SMOKE. 

To QA the on-road mobile emissions, we used the WRAP emissions modeling QA protocol 

(Adelman, 2004) and a suite of graphical summaries. We used tabulated summaries of the input 

data and SMOKE script settings to document the data and configuration of SMOKE used. The 

graphical QA summaries include, for all emissions output species, daily spatial plots, daily timeseries plots, and annual time-series plots. These graphics are available at 

http://pah.cert.ucr.edu/aqm/308/QA_base02a36.plots/mb/plots/. 

2.4.3 Uncertainties and future work 

We approached the on-road mobile emissions preparation for simulation Base02a from three 

different directions, which were based on the forms of the input inventories and ancillary 

emissions data for different regions of the modeling domain: 

• The WRAP region used emissions estimates precomputed with MOBILE6 and processed 

like area sources with SMOKE. 

• The non-WRAP U.S. states used county-level activity data to compute emissions with 

MOBILE6. 

• The non-U.S. parts of the domain used an area-source approach for representing the onroad mobile emissions. 

Using different approaches for modeling a single emissions sector adds complexity and 

additional sources of error to the modeling because of the different assumptions that went into 

the preparation of the input data. For example, refueling emissions from the on-road mobile 

sector are represented in the WRAP area-source sector but are computed with MOBILE6 for the 

rest of the U.S. Not using MOBILE6-based emissions for the non-U.S. portion of the domain 

neglects the effects of the actual 2002 meteorology on these emissions. Applying MOBILE6 

outside of the U.S. is currently not possible because MOBILE6 is instrumented only for 

calculating U.S. emissions. The result of using MOBILE6 to calculate U.S. emissions and not 

using it to calculate the non-U.S. on-road mobile emissions estimates is that the non-U.S. 

emissions are not specific to this modeling year while the U.S. emissions are 2002-specific. 

While we used the best available information to compute the on-road mobile emissions for the 

various portions of the modeling domain, inconsistent approaches for representing these emissions may lead to unnatural emissions gradients along political boundaries. We recommend for 

future work a unified approach for at least the U.S. inventories, where either we use MOBILE6 

in SMOKE for the entire domain, or we calculate the emissions with MOBILE6 outside of 

SMOKE and then use the resulting county-based emissions inventories. 

The Mexican inventory covered only the two northern tiers of Mexican states, causing the 

southern portion of Mexico within the 36-km modeling domain to be without emissions. Future 

modeling of Mexico should use an inventory with full coverage of the country. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 26 

2.5 Nonroad Mobile-Source Emissions 

Nonroad mobile-source emissions data for SMOKE consist of annual, seasonal, and monthly 

IDA-formatted emissions inventory files and ancillary data for allocating the inventories in 

space, in time, and to the Carbon Bond-IV chemistry mechanism used in CMAQ and CAMx. 

This section describes where we obtained these data, how we modeled them, and the types of QA 

that we performed to ensure that SMOKE processed the data as expected. 

2.5.1 Data sources 

For the nonroad mobile-source inventories in simulation Base02a, we used actual 2002 data 

developed by the RPOs for the U.S., version 2 of the year 2000 Canadian inventory, and the 

BRAVO 1999 Mexican inventory. The U.S. inventories consisted of annual, seasonal, and 

monthly inventories; the non-U.S. inventories were annual data. ENVIRON provided the RMC 

with the WRAP inventories divided between California and non-California seasonal inventories, 

further subdivided into aircraft, locomotives, shipping, and all other nonroad mobile sources 

(Pollack et al., 2006). With these data ENVIRON also provided temporal adjustments to apply to 

the inventories to split them between weekday and weekend emissions (see Section A.1). We 

used these weekday/weekend splits to derive new weekly temporal profiles for these WRAP 

sources. CENRAP provided both the CENRAP and MRPO base J monthly nonroad inventories 

in SMOKE format (E.H. Pechan and CEP, 2005a). The VISTAS Phase II (Stella, 2005) and 

MANE-VU (E.H. Pechan et al., 2005c) 2002 nonroad mobile inventories consisted of annual 

county-level data. We received these inventories directly from the respective RPO inventory 

representatives. We received the Canadian 2000 inventory version 2 from the U.S. EPA EFIG 

(U.S. EPA, 2005d). For Mexico we used the same BRAVO 1999 inventory that we used in the 

preliminary 2002 modeling (Tonnesen et al., 2005). 

We had to reformat the Base02a WRAP nonroad mobile inventories before they could be input 

to SMOKE. The WRAP nonroad inventories that we received from ENVIRON contained more 

pollutants than SMOKE requires for generating CMAQ and CAMx emissions. In addition to the 

standard gas-phase and prespeciated PM2.5 pollutants, these files also contained prespeciated 

NOx, PM10, total VOC, evaporative VOC, and exhaust VOC pollutants. As evaporative and 

exhaust VOC use the same speciation profiles, we discarded these pollutants and used only the 

total VOC pollutant, which is the sum of evaporative and exhaust VOC emissions for the 

nonroad mobile sources. We also discarded the prespeciated NOx and PM10 pollutants, as these 

are not used by CMAQ or CAMx. 

Along with adding the WRAP weekday/weekend emissions splits to the temporal allocation 

files, we also created temporal input files that apply a flat, uniform monthly profile to the monthly and seasonal nonroad inventories. With the monthly and seasonal variability inherent in these 

inventories, we avoided applying redundant monthly profiles by splitting the inventories into 

seasonal/monthly and annual data. We applied the uniform monthly temporal profiles to the 

seasonal/monthly inventories and nonuniform monthly temporal profiles to the annual inventories. For the non-WRAP inventories, the weekly and diurnal temporal profiles remained 

unchanged from the preliminary 2002 modeling. Table 2-5 summarizes the temporal distribution 

of the nonroad inventories used in simulation Base02a. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 27 

Table 2-5. Base02a nonroad mobile-source inventory temporal configuration. 

Region Source Temporal Coverage 

WRAP (non-CA) Nonroad mobile Seasonal 

WRAP (CA) Nonroad mobile Seasonal 

WRAP Aircraft Seasonal 

WRAP Locomotive Annual 

WRAP In-port and near-shore shipping Annual 

CENRAP All nonroad Monthly 

VISTAS All nonroad Annual 

MRPO All nonroad Monthly 

MANE-VU All nonroad Annual 

Canada All nonroad Annual 

Mexico All nonroad Annual 

2.5.2 Emissions processing 

For simulation Base02a we configured SMOKE to process all of the nonroad mobile emissions 

inventory data as area-like inventories. As the WRAP inventories contained precomputed PM 

emissions, we did not have to use SMOKE to compute PMc. ENVIRON prepared the WRAP 

nonroad mobile inventories to represent seven-day average emissions (different from the area 

inventory, which represented weekday average emissions). With actual weekly average 

emissions, we configured SMOKE to process them by setting WKDAY_NORMALIZE to “No.” 

For the rest of the nonroad mobile inventories we processed the data as weekday average data by 

setting WKDAY_NORMALIZE to “Yes.” We also assumed that all of the VOC emissions in the 

inventories are ROG and used SMOKE to convert the VOC to TOG before converting the 

emissions into CB-IV speciation for the air quality models. We configured SMOKE to create 

MWSS temporal intermediates rather than daily temporal files because the nonroad mobile 

sources do not use weekly temporal profiles that vary across the weekdays. 

We divided the nonroad mobile emissions modeling based on whether the data were annual or 

seasonal/monthly inventories. This split facilitated the application of uniform monthly temporal 

profiles to the seasonal/monthly inventories. After processing the nonroad emissions as two 

separate categories—nonroad yearly and nonroad monthly—we combined them with the rest of 

the emissions sectors to create model-ready emissions for CMAQ and CAMx. 

The WRAP shipping inventories consisted of new data for offshore commercial marine 

emissions, new data for in-port and near-shore shipping emissions for Oregon and Washington, 

and old data for in-port and near-shore shipping emissions in California. The new gridded marine 

shipping inventory that we received for simulation Base02a (Section 2.12) covers the open ocean 

in the WRAP 36-km modeling domain all the way to the coastlines of Oregon and Washington, 

but only up to 25 miles from the coast of California. The in-port shipping inventory delivered to 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 28 

the RMC by ENVIRON is consistent with the gridded marine shipping inventories for 

Washington and Oregon in that it covers only emissions up to the Pacific coastlines for these 

states; the near-shore and offshore emissions are contained in the gridded shipping inventory. We 

did not receive new California in-port and near-shore emissions for simulation Base02a, to cover 

inland and 25 miles off the coast, and so had to use the inventory from the preliminary 2002 

modeling for these emissions. 

To QA the nonroad mobile emissions, we used the WRAP emissions modeling QA protocol 

(Adelman, 2004) and a suite of graphical summaries. We used tabulated summaries of the input 

data and SMOKE script settings to document the data and configuration of SMOKE used. The 

graphical QA summaries include, for all emissions output species, daily spatial plots, daily timeseries plots, and annual time-series plots. These QA graphics are available at 

http://pah.cert.ucr.edu/aqm/308/QA_base02a36.plots/nr/plots/. 

2.5.3 Uncertainties and future work 

We prepared nonroad mobile emissions using a combination of inventories having different 

temporal resolutions and various forms of ancillary data. These different combinations of 

information may lead to inconsistencies in how these emissions are represented across the 

modeling domain. Regarding the quality of the inventories and temporal information, there is a 

discrepancy between the WRAP states and the rest of the modeling domain; the WRAP 

inventories, spatial surrogates, and temporal profiles are more highly developed and quality 

assured than similar information for the other RPOs. In addition, the Canadian inventories 

contain only province-level information and thus have low-resolution spatial and temporal 

profiles applied to them. The Mexican nonroad emissions are deficient in the number of different 

SCCs contained in the inventory and the availability of spatial surrogates that are applicable to 

nonroad mobile sources. Improvements to the temporal profiles and spatial surrogates outside of 

the WRAP states could provide a more consistent approach to representing the nonroad 

emissions across the entire modeling domain. 

2.6 Biogenic Emissions 

Biogenic emissions data for SMOKE consist of input files to the BEIS3 model (U.S. EPA, 

2004a). BEIS3 is a system integrated into SMOKE for deriving emissions estimates of biogenic 

gas-phase pollutants from land use information, emissions factors for different plant species, and 

hourly, gridded meteorology data. The results of BEIS3 modeling are hourly, gridded emissions 

fluxes formatted for input to CMAQ or CAMx. This section describes the sources of the BEIS3 

input data that we used for simulation Base02a, how we modeled these data, and the types of QA 

that we performed to ensure that SMOKE processed the data as expected. 

2.6.1 Data sources 

For simulation Base02a we used the BELD3 land use data and biogenic emissions factors that 

we collected during the WRAP preliminary 2002 modeling (Tonnesen et al., 2005). These data 

included BELD3 1-km resolution land use estimates and version 0.98 of the BELD emissions 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 29 

factors. The Base02a biogenic emissions modeling differed from the preliminary 2002 modeling 

in the use of improved 2002 meteorology data that we developed in 2005 (Kemball-Cook et al., 

2005). 

2.6.2 Emissions processing 

We used BEIS3.12 integrated in SMOKE to prepare emissions for simulation Base02a. Most of 

the preparation for the biogenic emissions processing was completed during the preliminary 

2002 modeling. As the modeling domains did not change from the preliminary 2002 to the 

Base02a modeling, we re-used the gridded land use data and vegetation emissions factors that we 

prepared for the preliminary simulations. The major difference in the emissions processing 

between the preliminary 2002 and Base02a modeling was in the integration of improved meteorology in the Base02a simulation. 

To QA the biogenic emissions, we used the WRAP emissions modeling QA protocol (Adelman, 

2004) and a suite of graphical summaries. We used tabulated summaries of the input data and 

SMOKE script settings to document the data and configuration of SMOKE used. The graphical 

QA summaries include, for all emissions output species, daily spatial plots, daily time-series 

plots, and annual time-series plots. These QA graphics are available at 

http://pah.cert.ucr.edu/aqm/308/QA_base02a36.plots/b3/plots/. 

2.6.3 Uncertainties and future work 

We would like to explore the use of BEIS3.13 in future 2002 simulations. 

2.7 Fire Emissions 

Fire emissions data for SMOKE have traditionally been represented as county-level area-source 

inventories that were placed in only the first vertical model layer. We advanced the representation of fire emissions for air quality modeling by preparing the inventory data as point sources 

with specific latitude-longitude coordinates for each fire centroid and precomputed plume rise 

parameters that were derived from individual fire characteristics. These new inventories consist 

of annual, daily, and hourly IDA-formatted emissions inventory files and ancillary data for allocating the inventories in space, in time, and to the Carbon Bond-IV chemistry mechanism used in 

CMAQ and CAMx. This section describes where we obtained these data, how we modeled them, 

and the types of QA performed to ensure that SMOKE processed the data as expected. 

2.7.1 Data sources 

For the fire inventories in simulation Base02a, we used actual 2002 data developed by the RPOs 

for the U.S., version 2 of the year 2000 Canadian inventory, and actual 2002 data for Ontario, 

Canada. There were no fire emissions in the BRAVO 1999 Mexico inventories, so Mexican fires 

were not included in the Base02a simulation. The inventories used consisted of both area- and 

point-source data for the U.S. and Canada. Air Sciences provided us with the WRAP inventories 

divided among six different fire categories: wildfires, agricultural fires, wildland fire use, natural 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 30 

prescribed, anthropogenic prescribed, and non-Federal rangeland fires (Air Sciences, Inc., 2005). 

These inventories consisted of annual, daily, and hourly IDA-formatted files with information on 

daily emissions totals and hourly plume characteristics for each fire. We received similar pointsource fire inventories for the VISTAS states from Alpine Geophysics (Stella, 2005). In addition, 

we received county-level fire inventories represented as area sources for the VISTAS and 

CENRAP states. With these data we received monthly temporal profiles from Alpine Geophysics 

that we used to distribute these annual inventories throughout the year. The area-source 

inventories for the rest of the RPOs and Canada also contained fire emissions that were not 

distributed separately. We modeled these sources with the rest of the stationary-area-source 

sector. Finally, we received a 2002 fire inventory for Ontario, Canada from MANE-VU that is 

formatted to take advantage of the SMOKE fire plume rise algorithm (Pouliot et al., 2005). 

2.7.2 Emissions processing 

SMOKE is instrumented to distribute point-source-formatted fire inventories to the vertical 

model layers either by using a precomputed plume rise approach or by computing the plume rise 

dynamically using actual 2002 meteorology. We applied both approaches for modeling pointsource fire emissions in simulation Base02a. For the precomputed plume rise approach, SMOKE 

reads an annual inventory file with information on fire locations, a daily inventory file with daily 

emission totals for each fire, and an hourly inventory file with hourly plume bottom, plume top, 

and layer 1 fractions for each fire. SMOKE uses this information to locate the fires on the 

horizontal model grid and to distribute the plume of each fire vertically to the model layers. 

Because some of these fires have plumes that reach the model top, we set the number of 

emissions layers for processing these inventories to the full 19 layers of the meteorology. We 

applied this approach to the point-source fires for the WRAP and VISTAS regions. 

The alternative plume rise approach uses information on fuel loading and the heat flux of the 

fires to distribute the fires vertically to the model layers. The data are provided to SMOKE in the 

form of an annual inventory with information on fire locations and a daily inventory with daily 

emission totals for each fire, daily heat flux, and daily fuel loading. We applied this approach to 

the point-source fires for Ontario, Canada. 

All of the point-source fires used diurnal temporal profiles and speciation profiles for VOC and 

PM2.5 developed by Air Sciences during the preliminary 2002 modeling (Tonnesen et al., 2005). 

We modeled the area-source fires for the non-WRAP U.S. and Canada as standard stationary 

area sources. We applied monthly temporal profiles provided by VISTAS, flat weekly temporal 

profiles, and the diurnal profiles developed by Air Sciences. We used the forestland area 

surrogate to distribute these emissions from the county or province level in the inventories to the 

model grid cells. 

To QA the fire emissions, we used the WRAP emissions modeling QA protocol (Adelman, 

2004) and a suite of graphical summaries. We used tabulated summaries of the input data and 

SMOKE script settings to document the data and configuration of SMOKE used. The graphical 

QA summaries include, for all emissions output species, daily spatial plots, daily time-series 

plots, annual time-series plots, and vertical profiles. These QA graphics are available at 

http://pah.cert.ucr.edu/aqm/308/QA_base02a36.plots/allf/plots/. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 31 

2.7.3 Uncertainties and future work 

The RMC discovered several errors with the WRAP Phase II inventories. Some of these errors 

were fixed with corrections we made with guidance from Air Sciences, and others will be 

addressed by Air Sciences in Phase III of the 2002 WRAP fire inventories. The errors that we 

identified, with corrections, were as follows: 

• Missing or malformed dates in several agricultural burning events in CA. These events 

were intended to be dropped and were ultimately deleted from the inventories by the RMC. 

• Missing dates in several prescribed burning events in AZ. These records were corrected 

by Air Sciences and redistributed to the RMC. 

• Inconsistencies between the records in the hourly and annual inventory files for several 

agricultural burning events in CA. These records were corrected by Air Sciences and 

redistributed to the RMC. 

For the area-source fires outside of the WRAP region, including Canada, we applied monthly 

temporal profiles developed by VISTAS. While these profiles appear to be an improvement over 

the EPA defaults, they are specific to the VISTAS region and will misrepresent the seasonality of 

the fires in other regions of the modeling domain. In addition, we used forestland spatial surrogates to distribute these county-level (province-level for Canada) data to the model grid. Using 

spatial surrogates to locate fires is a crude approach that results in the artificial smearing of the 

emissions over too large an area. Both of these issues can be remedied by moving to a pointsource approach for representing these fires, similar to the approach used by Air Sciences for 

preparing the WRAP fire inventories. 

2.8 Dust Emissions 

Dust emissions data for SMOKE have traditionally taken the form of county-level stationaryarea-source inventories. As these emissions are correlated to meteorology, land use, and 

vegetative cover, we made several changes to how dust emissions are simulated by SMOKE to 

take these parameters into consideration. This section describes where we obtained data for 

windblown, fugitive, and road dust sources, how we modeled them, and the types of QA 

performed to ensure that SMOKE processed the data as expected. 

2.8.1 Data sources 

For the fugitive dust and road dust inventories in simulation Base02a, we used actual 2002 data 

developed by the RPOs for the U.S., version 2 of the year 2000 Canadian inventory, and the 

BRAVO 1999 Mexican inventory. We extracted the fugitive dust inventories from the 

stationary-area inventories for each of the RPOs, Mexico, and Canada. Before modeling these 

data we further divided them into construction/mining sources and agricultural sources. We 

defined the fugitive dust sources in the Base02a modeling based on guidance provided by EPA 

(2004b). ENVIRON provided WRAP (both CA and non-CA) road dust inventories (Pollack et 

al., 2006). For the rest of the RPOs and Canada, we extracted the road dust SCCs from the 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 32 

stationary-area-source inventories. The BRAVO 1999 Mexico inventory did not contain any road 

dust SCCs. Table 2-6 lists the SCCs for the various fugitive and road dust sources that we 

modeled in simulation Base02a. We applied near-source capture transport factors that are based 

on county-level vegetative cover to the fugitive and road dust inventories to prepare them for 

input to the air quality models. 

For windblown dust, we used gridded emissions prepared outside of SMOKE using a land-useand meteorology-based model developed under funding from the WRAP by ENVIRON and UCRiverside (Mansell, 2005; Mansell et al., 2005). 

Table 2-6. Base02a fugitive and road dust SCCs. 

Dust Category SCCs 

Fugitive dust (construction and mining) 2275085000, 2311010000, 2311010070, 2311020000, 

2311030000, 2325000000 

Fugitive dust (agricultural) 2801000003, 2801000005, 2801000008, 2805001000 

Road dust 2294000000, 2296000000 

2.8.2 Emissions processing 

We modeled the fugitive and road dust inventories through SMOKE using an area-source 

approach. We modeled these data on the assumption that they represented weekday, rather than 

seven-day week, emissions and thus used the SMOKE setting WKDAY_NORMALIZE to 

convert the data to a seven-day average. We configured SMOKE to compute PMc during the 

processing as (PM10 - PM2.5). We also assumed that all of the VOC emissions in the inventories 

are ROG and used SMOKE to convert the VOC to TOG before converting the emissions into 

CB-IV speciation for the air quality models. We configured SMOKE to create MWSS temporal 

intermediates rather than daily temporal files because the dust sources do not use weekly 

temporal profiles that vary across the weekdays. 

As noted above, we used SMOKE to apply near-source transport factors to the raw fugitive and 

road dust inventories to prepare them for input to the air quality models. We used U.S. transport 

factors from work done by Pace (2005) and a 2001 land use/land cover database to develop a 

SMOKE input file of county and SCC-based transport factors for the U.S., Canada, and Mexico. 

We applied these factors to create a new set of inventories adjusted for these transport factors for 

all regions except VISTAS; the VISTAS dust sources that we received already had the transport 

factors applied to them. 

We calculated the windblown dust emissions outside of SMOKE using an internally developed, 

process-based model. By “process-based” we refer to an emissions model that integrates 

information about the processes that lead to the emissions of interest, in this case windblown 

dust. The process-based windblown dust model developed by the WRAP considers wind speeds, 

precipitation history, and soil types to derive gridded dust fluxes resulting from wind 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 33 

disturbances for the modeling domain. More information on this model, its modes of operation, 

and the configuration used for simulation Base02a are available in Mansell et al. (2005). 

To QA the dust emissions, we used the WRAP emissions modeling QA protocol (Adelman, 

2004) and a suite of graphical summaries. We used tabulated summaries of the input data and 

SMOKE script settings to document the data and configuration of SMOKE used. The graphical 

QA summaries include, for all emissions output species, daily spatial plots, daily time-series 

plots, and annual time-series plots. These QA graphics are available at 

http://pah.cert.ucr.edu/aqm/308/QA_base02a36.plots/fd/plots/ for fugitive dust, 

http://pah.cert.ucr.edu/aqm/308/QA_base02a36.plots/rd/plots/ for road dust, and 

http://pah.cert.ucr.edu/aqm/308/QA_base02a36.plots/wb_dust/plots/ for windblown dust. 

2.8.3 Uncertainties and future work 

There are several improvements that we will make to the dust emissions modeling in future 

simulations. We will expand the list of fugitive dust SCCs that we extract from the stationaryarea-source inventories for application of transport factors. This expanded list is based on recent 

work by EPA (2004b). We will also explore improvements to the assumptions that we used for 

generating emissions with the WRAP windblown dust model. Areas of improvement in the 

windblown dust model include refinements to the land use data and soil characteristics, 

additional information about agricultural activities in the WRAP and CENRAP regions, detailed 

model evaluation on targeted windblown dust case studies, and the application of snow-cover 

and vegetative transport factors to these emissions (Mansell et al., 2005). 

2.9 Ammonia Emissions 

Ammonia (NH3) emissions from agricultural activities are a major source of ammonia and are 

dependent on many different environmental parameters, such as meteorology, crop and soil 

types, and land use. Traditionally these emissions have been represented in the stationary-areasource inventory as annual, county-level estimates. These estimates did not consider 

meteorology, and may have used different land use assumptions than were used in the air quality 

model simulations to which they were input. The WRAP funded development of a process-based 

agricultural NH3 emissions model to estimate NH3 emissions from several different agricultural 

sources (such as soils, livestock, and fertilizer application) that uses the same meteorology and 

land use assumptions that are used in CMAQ and CAMx. We combined the WRAP NH3 model 

emissions estimates with data provided by the other RPOs to represent agricultural NH3

emissions in simulation Base02a. 

2.9.1 Data sources 

We developed NH3 emissions for the WRAP states using the WRAP NH3 model (Mansell, 

2005), generating emissions for the following sectors: domestic sources, wild animals, fertilizers, 

soils, and livestock. CENRAP and MRPO provided monthly IDA-formatted inventories to us 

that they produced from process-based models of their own, along with temporal profiles and 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 34 

spatial cross-reference information for these sources. The rest of the U.S., Canada, and Mexico 

had agricultural NH3 emissions contained within their annual stationary-area-source inventories. 

2.9.2 Emissions processing 

We prepared the WRAP NH3 emissions outside of SMOKE using the WRAP NH3 model; details 

of this modeling are available in Mansell (2005). Due to an incorrect assumption in the soil 

emission factor used in the model, however, we had to discard the emissions from this sector. 

CENRAP and MRPO provided monthly IDA-formatted, county-level NH3 inventories that they 

developed with their own process-based models. We modeled these emissions like area sources 

with SMOKE, applying the temporal profiles and the spatial cross-referencing that we received 

from these RPOs. The agricultural NH3 emissions for the rest of the RPOs, Canada, and Mexico 

are contained within their stationary-area inventories. We applied the SMOKE default temporal 

profiles and spatial surrogates to all non-process-based NH3 emissions; details on this processing 

are provided in Section 2.3.2. 

To QA the NH3 emissions, we used the WRAP emissions modeling QA protocol (Adelman, 

2004) and a suite of graphical summaries. We used tabulated summaries of the input data and 

SMOKE script settings to document the data and configuration of SMOKE used. The graphical 

QA summaries include, for all emissions output species, daily spatial plots, daily time-series 

plots, and annual time-series plots. These QA graphics are available at 

http://pah.cert.ucr.edu/aqm/308/QA_base02a36.plots/nh3/plots/. 

2.9.3 Uncertainties and future work 

Like the other emissions categories that have traditionally been represented as stationary area 

sources, the agricultural NH3 emissions sector is affected by interregional inconsistencies in the 

way these emissions are represented. With each region of the domain representing these 

emissions differently, there are artificial concentration gradients that follow political boundaries 

in the modeling domain. Figure 2-1 illustrates these gradients in the grid cells between the 

WRAP, MRPO, and CENRAP states. Because the NH3 emissions for the MANE-VU, VISTAS, 

and non-U.S. portions of the domain are contained in the stationary-area inventories, these 

emissions do not appear in Figure 2-1. The eastern border of the Dakotas with Minnesota and the 

border of Colorado and Kansas illustrate these artificial concentration gradients. 

During the QA of the Base02a emissions, we discovered a problem with the WRAP soil NH3

estimates. The emission factor for soil NH3 that we used in developing these data produced too 

high an emission estimate from this sector. For simulation Base02a we therefore removed the 

soil NH3 sector completely from the WRAP domain. In future simulations we will include these 

emissions with a revised emission factor for NH3 emissions from soils. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 35 

Figure 2-1. Example Base02a NH3 emissions spatial plot, showing artificial 

concentration gradients that follow some political boundaries. 

2.10 Oil and Gas Emissions 

Simulation Base02a contained a handful of emissions sectors that WRAP had never modeled 

before. Emissions from oil and gas production operations have been sporadically reported by 

some states in their stationary-area-source inventories, but for the most part were missing from 

our previous modeling. In simulation Base02a, we represented oil and gas production emissions 

explicitly as both area and point sources in a handful of states across the WRAP region. 

2.10.1 Data sources 

We received oil and gas production emissions inventories for the WRAP states and for tribal 

lands in the WRAP region as stationary-area-source and stationary-point-source IDA-formatted 

inventories. ERG, Inc. provided the point-source inventories with the rest of the stationary-point 

data (ERG, Inc., 2006). ENVIRON provided the area-source oil and gas inventories for non-CA 

WRAP states and for tribal lands in the WRAP region, along with spatial surrogates for 

allocating these data to the model grid (Pollack et al., 2006). We extracted CA oil and gas 

inventories from the stationary-area-source data used in the preliminary 2002 modeling. Oil and 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 36 

gas production emissions data for outside of the WRAP region, if they exist, are contained in the 

stationary-area inventories that we received from the other RPOs. 

2.10.2 Emissions processing 

We modeled the WRAP point-source oil and gas production emissions in combination with the 

rest of the stationary-point-source emissions. The SMOKE configuration used to model these 

inventories is described in Section 2.2.2. We modeled the WRAP area-source oil and gas 

production emissions explicitly as a separate category that included WRAP and tribal inventories. These data represent weekly average emissions and did not require any renormalization 

with SMOKE. We used spatial surrogates generated by ENVIRON to allocate these annual 

county-level emissions to the model grid. For all oil and gas emissions, we applied flat temporal 

profiles to create hourly inputs to CMAQ and CAMx. 

To QA the oil and gas production emissions, we used the WRAP emissions modeling QA 

protocol (Adelman, 2004) and a suite of graphical summaries. We compared the spatial plots that 

we produced from SMOKE output to spatial plots provided by ENVIRON to ensure that we 

modeled these data correctly. We used tabulated summaries of the input data and SMOKE script 

settings to document the data and configuration of SMOKE used. The graphical QA summaries 

include, for all emissions output species, daily spatial plots, daily time-series plots, and annual 

time-series plots. These QA graphics are available at 

http://pah.cert.ucr.edu/aqm/308/QA_base02a36.plots/wog/plots/. 

2.10.3 Uncertainties and future work 

In future 2002 modeling we will replace the CA oil and gas production emissions with revised 

data provided by the California Air Resources Board (CARB). 

2.11 Offshore Area and Point Emissions 

Offshore-area-source and offshore-point-source emissions include emissions in the Gulf of 

Mexico and off the coast of California that are associated with oil and gas drilling platforms. 

2.11.1 Data sources 

We obtained year 2000 IDA-formatted point-source inventories for oil and gas platforms in the 

Gulf of Mexico from the Minerals Management Service (MMS) web site (Wilson et al., 2004). 

We combined these with point-source data for coastal California provided to us by CARB during 

the preliminary 2002 modeling. We also obtained gridded area-source emissions for platforms in 

the Gulf of Mexico from the MMS that we converted to the WRAP 36-km model grid. 

2.11.2 Emissions processing 

We modeled the offshore point and area sources as separate categories. We used SMOKE to 

locate the offshore point sources on the model grid and to vertically allocate them into 15 model 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 37 

layers. The gridded area-source inventory did not require processing with SMOKE; it required 

only a conversion from the data grid to the modeling grid using a program we developed. We 

applied flat temporal profiles to both the offshore area and point emissions, distributing them 

evenly across all hours, days, and months of the year. 

To QA the offshore platform emissions, we used the WRAP emissions modeling QA protocol 

(Adelman, 2004) and a suite of graphical summaries. We used tabulated summaries of the input 

data and SMOKE script settings to document the data and configuration of SMOKE used. The 

graphical QA summaries include, for all emissions output species, daily spatial plots, daily timeseries plots, and annual time-series plots. These QA graphics are available at 

http://pah.cert.ucr.edu/aqm/308/QA_base02a36.plots/pt/plots/ for the point sources and 

http://pah.cert.ucr.edu/aqm/308/QA_base02a36.plots/ofsar/plots/ for the area sources. 

2.11.3 Uncertainties and future work 

While the MMS data that we used were an improvement over previously modeled Gulf of 

Mexico platform inventories, the data were developed for a different modeling application that 

covered only the extreme northwestern portion of the Gulf, so they are missing large areas of the 

region of the Gulf that contain drilling platforms. The California offshore inventory represents an 

initial attempt at compiling an emission inventory for this area and contains very few sources. 

Future simulations will focus on improving these emissions by expanding the coverage of the 

offshore platform inventories for both the Gulf of Mexico and the Pacific Coast. 

2.12 Commercial Marine Shipping Emissions 

Emission inventory development for regional- and continental-scale air quality modeling has 

historically neglected offshore emissions sources beyond 25 miles offshore. Concern over the 

environmental effects of commercial shipping emissions in the Pacific on the coastal states in the 

WRAP region led to the development of a commercial marine shipping inventory for the Pacific. 

Simulation Base02a is the first WRAP modeling that uses these emissions. 

2.12.1 Data sources 

We obtained gridded annual commercial marine shipping emissions for the Pacific on the WRAP 

36-km model grid from ENVIRON for inclusion in simulation Base02a (Pollack et al., 2006). 

The commercial marine inventory contains all of the criteria pollutants contained in the nonroad 

mobile-source inventory: CO, NOx, VOC, NH3, SO2, PM10, and PM2.5. 

2.12.2 Emissions processing 

The commercial marine shipping inventory for simulation Base02a was not processed through 

SMOKE. ENVIRON delivered the data to the RMC as gridded text files on the 36-km model 

grid. We reformatted these data to the netCDF CMAQ input format with a utility we developed. 

We converted the VOC inventory pollutant to CB-IV speciation and the NOx and PM2.5

inventory pollutants to CMAQ input species with SMOKE chemical profiles for commercial 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 38 

shipping sources. We did not apply any temporal adjustments to these emissions; they use 

uniform monthly, daily, and diurnal profiles. 

To QA the commercial marine shipping emissions, we used the WRAP emissions modeling QA 

protocol (Adelman, 2004) and a suite of graphical summaries. The graphical QA summaries 

include, for all emissions output species, daily spatial plots, daily time-series plots, and annual 

time-series plots. These QA graphics are available at 

http://pah.cert.ucr.edu/aqm/308/QA_base02a36.plots/ofsar/plots/. 

2.12.3 Uncertainties and future work 

As a first attempt at representing shipping emissions in the Pacific in international waters, the 

WRAP 2002 commercial shipping inventory is a breakthrough in a historically neglected emissions category. As we evaluate the effects of these emissions on the air quality modeling, we 

anticipate that there will be refinements to the temporal profiles and to the vertical allocation of 

the emissions. Many of the stacks of large commercial ships contained in this inventory extend 

vertically above the first model layer. Future versions of this inventory should use higherresolution temporal adjustments and should allocate the emissions to the appropriate model 

layers. 

2.13 Summary 

Simulation Base02a represents a significant improvement to the preliminary 2002 emissions 

modeling previously performed by the RMC. While the preliminary 2002 modeling served its 

purpose to develop the infrastructure at the RMC for modeling large emissions data sets and 

producing annual emissions simulations, much of the input data (both as inventories and 

ancillary data) were placeholders for actual 2002 data that were being prepared through calendar 

year 2005. As these actual 2002 data sets became available to us, we integrated them into the 

SMOKE modeling and QA system that we had developed during the preliminary modeling, to 

produce a high-quality emissions data set for use in model performance evaluations of CMAQ 

and CAMx. The addition of entirely new inventory categories, like the marine shipping and oil 

and gas production sectors, added complexity to the Base02a modeling. By the end of the data 

collection phase for simulation Base02a, we had identified 22 different emissions categories to 

compose this data set (see Table 2-1). 

To facilitate the comparison between the CMAQ and CAMx simulations using the Base02a 

emissions, we used the CMAQ2CAMx utility developed by ENVIRON to convert the threedimensional emissions categories from CMAQ to CAMx format. For the low-level point and 

area sources, we used SMOKE to produce the CAMx emissions. 

Appendix A contains graphical and tabular summaries of the Base02a emissions data. Section 

A.2 contains a tabulated summary of the inventory files used in simulation Base02a. Section A.3 

contains emissions summaries for each major source category. These summaries include 

tabulated RPO and country totals for the entire modeling domain, pie charts for the entire U.S. 

and the WRAP states, and spatial plots for the annual Base02a model-ready emissions. Section 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 39 

A.4 contains tabulated and graphical emissions summaries by source category. For each major 

source category, this section provides 

• Tabulated state/province totals for the U.S., Canada, and Mexico 

• 3-D bar charts comparing the inventories between the RPOs, Canada, and Mexico 

• 2-D bar charts comparing simulation Base02a with previous simulation Pre02d 

• 2-D bar charts comparing selected inventory pollutants between the various WRAP states 

• Emissions density charts in the WRAP region for selected inventory pollutants. 

2.14 Effects of Uncertainties and Future Work 

As a continually evolving process, emissions modeling must balance access to the best available 

data sets with the ability to produce accurate results in a timely manner. New inventories are 

continually becoming available that supercede similar data. As the RMC was preparing the 

Base02a emissions for CMAQ and CAMx modeling, new data sets from the other RPOs and 

corrections or updates to the WRAP actual 2002 inventories presented themselves that we could 

not integrate into this simulation due to time constraints. We made the decision to shelve any 

new inventory information that became available after July 31, 2005, until the next round of 

2002 modeling. Known issues with the Base02a emissions include the following: 

• Missing natural emissions from sea salt, lightning, and geogenic sources 

• Errors in the CA point-, area-, and mobile-source inventories that lead to underestimations of these emissions 

• Errors in the MT on-road mobile-source inventory 

• Errors in the WA, ND, OR, and UT area-source and fugitive dust inventories 

• Missing Broomfield County, CO, data in the area-source and derivative dust inventories 

• Errors in the AZ, MT, NM, OR, UT, WA, WY, and WRAP tribal stationary-point 

inventories 

In the next round of 2002 emissions modeling to be conducted by the RMC, we anticipate 

correcting these inventory issues and any other problems that we find in simulation Base02a as 

we evaluate the air quality modeling results that use these emissions. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 40 

3. Meteorology Data 

The WRAP RMC conducted meteorological modeling of the continental Unites States using 

MM5 (Anthes and Warner, 1978; Dudhia, 1993; Grell et al., 1994). Numerous MM5 sensitivity 

tests were conducted that are documented in reports and other documents on the WRAP 

modeling web site (http://pah.cert.ucr.edu/aqm/308/mm5.shtml). Below we summarize our 

WRAP MM5 modeling, MM5 model performance, and the potential effects the MM5 modeling 

uncertainties might have on visibility modeling with CMAQ and CAMx. More details can be 

found in the WRAP MM5 modeling final report (Kemball-Cook et al., 2005; 

http://pah.cert.ucr.edu/aqm/308/reports/mm5/DrftFnl_2002MM5_FinalWRAP_Eval.pdf). 

3.1 Summary of Meteorology Evaluation Results 

We carried out MM5 simulations for the entirety of 2002 to support visibility modeling for the 

Section 308 SIPs/TIPs that are due in December 2007. During the fall of 2003, the RMC made 

an initial MM5 run for the year 2002 on the RPO Unified 36-km grid. The domain was divided 

into 11 subdomains for model evaluation, which are shown in Figure 3-1. An MM5 modeling 

protocol was prepared (ENVIRON and UCR, 2004b), which describes in detail the MM5 model 

and the setup and evaluation methods used in the 2002 modeling effort. The model performance 

evaluation for the initial 2002 MM5 run on the RPO Unified domain and results from 

preliminary MM5 sensitivity tests are discussed in Morris et al. (2004), Kemball-Cook et al. 

(2004a,b), and Emery et al. (2004). 

The model configuration for this initial run was based upon a prior 2002 MM5 application 

undertaken by the Iowa Department of Natural Resources (IDNR) (Johnson, 2003, personal 

communication), which was in turn set according to the optimal MM5 physics options that 

resulted from an in-depth sensitivity project carried out by IDNR and LADCO. While the IDNR 

simulations used MM5 version 3.5, the applications carried out for WRAP used the latest version 

of the model (v3.6.1) available at the end of 2003. Additional modifications to the physics 

configuration and application methodology were made for the WRAP simulation based on the 

latest information from EPA, IDNR, LADCO, and others. First, we opted to use the Reisner 2 

mixed-phase cloud microphysics package according to suggestions from EPA’s Office of 

Research and Development (ORD). Second, the INTERPPX option—which allows for continuous soil moisture initialization from one run segment to the next—was not used, based on poor 

performance reported by IDNR and LADCO. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 41 

Figure 3-1. Definition of subdomains used in MM5 modeling. 

The initial 2002 36-km WRAP simulation (referred to hereafter as WRAP_0) results showed that 

MM5 performs better in the central and eastern U.S. than in the West, and performs generally 

better in winter than in summer (Morris et al., 2004; Kemball-Cook et al., 2004a). In the western 

U.S., the amplitude of the diurnal temperature cycle was persistently underestimated during the 

summer, especially in the Southwest. In the desert Southwest, the humidity was greatly 

overestimated during the summer as well, and there was a pronounced cold bias. Some of these 

problems appeared to be linked to the excessive simulated precipitation generated by MM5 

during the summer, especially in the Southwest. This can have serious repercussions for CMAQ 

and CAMx modeling because too much rain can “wash out” pollutants, while the too cool, 

humid, and cloudy environment may lead to incorrect pollutant chemistry and aerosol 

thermodynamics. Temperature and humidity problems overshadowed the surface wind 

performance, which was not particularly good, but was likely affected by smaller-scale 

topographic influences that are not well represented even at the finer 12-km resolution. Wind 

performance improved quickly with height above the surface, suggesting that regional transport 

speeds/directions were reasonably represented. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 42 

3.1.1 Preliminary sensitivity tests 

Initial sensitivity tests were undertaken for a five-day period in July 2002 (July 1-5, 2002) for the 

RPO Unified 36-km domain in an attempt to find alternative MM5 configurations that would 

improve the poor summertime performance in the western U.S. Further analyses were conducted 

to compare the WRAP_0 run’s performance against that of a 36-km MM5 run made for 

VISTAS, and against operational Eta Data Assimilation System (EDAS) fields used as input to 

MM5. From these analyses, we found that using the Kain-Fritsch II scheme (as was used in the 

VISTAS 2002 MM5 modeling, compared to the Kain-Fritsch I scheme used in the initial WRAP 

modeling) improved, but did not entirely solve, the precipitation overprediction problem in the 

WRAP_0 run. We also found that removing soil moisture nudging improved temperature and 

humidity performance for the short summertime tests. Evaluation of the EDAS fields used in the 

MM5 four-dimensional data assimilation (FDDA) revealed that EDAS did not exhibit the 

summertime cold wet bias in the Southwest. This indicated that the bias was not introduced by 

the FDDA. A new model configuration (termed “WRAP_5”) was identified from these initial 

tests for new annual 2002 simulations, and the WRAP MM5 modeling protocol was updated to 

reflect this, as shown in Table 3-1. 

Table 3-1. MM5 configurations from original (WRAP_0) and modified (WRAP_5) MM5 runs.* 

Run ID LSM PBL Cumulus Microphysics 

 Analysis FDDA 

 3-D Surface 

Obs 

FDDA 

Soil Moisture 

Nudging 

WRAP_0 PX ACM KF Reisner 2 W/T/H W/T/H None Yes 

WRAP_5 PX ACM KF II Reisner 2 W/T/H None None No 

*Abbreviations: LSM = land-surface model; PBL = planetary boundary layer; FDDA = four-dimensional data 

assimilation; obs = observational; PX = Pleim-Xiu; ACM = Asymmetric Convective Model; KF = Kain-Fritsch; 

W/T/H = wind/temperature/humidity. 

Although the WRAP_5 configuration did improve humidity, temperature, and precipitation 

performance over the short July test period (especially where improvement was needed most), 

the desert Southwest continued to exhibit unsatisfactory levels of precipitation and humidity. As 

stated above, these issues will likely play crucial roles in the air quality model performance. 

3.1.2 Revised sensitivity tests 

In an attempt to further improve MM5 performance, we carried out additional 36-km MM5 test 

simulations in 2004. The latest available version of MM5 (v3.6.2) was used. Since the most 

severe problems for the initial WRAP 2002 MM5 annual simulation were the wet bias and 

precipitation overestimation in summer, we first investigated these issues for the five-day July 

test period on the 36-km grid, to maximize computational efficiency. We tested four physics 

options: (1) the cumulus parameterization; (2) the land surface models (LSMs); (3) the PBL 

models; and (4) the FDDA. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 43 

We then undertook a series of tests to determine the optimal MM5 configuration for the nested 

12-km grid of the WRAP modeling domain. In these tests, we varied only the physics options 

that we expected would ameliorate the biases in the WRAP region seen in the 36-km simulation. 

The overprediction of summer rainfall and associated surface humidity bias suggested that the 

cumulus parameterization might need to be changed. Another possible reason for the wet bias 

was the soil moisture initialization. Therefore, we focused on sensitivity tests of the soil moisture 

specification and the cumulus scheme using both the 36- and 12-km grid structures. The results 

of these sensitivity tests are discussed in Kemball-Cook et al. (2004b). The optimal model configuration for the 36-km application, referred to as the Final Configuration, is given in Table 3-2. 

Table 3-2. MM5 Final Configuration for the WRAP 2002 36-km MM5 run 

after revised sensitivity tests were completed. 

LSM PBL Cumulus Microphysics 

 Analysis FDDA 

 3-D Surface 

Obs 

FDDA 

Pleim-Xiu ACM Betts-Miller Reisner 2 W/T/H W/T/H Wind 

MM5 runs for the test period of July 1-5, 2002, on the 36-km grid made with the physics options 

indicated in Table 3-2 showed the following characteristics: 

• A dramatic reduction in the summertime cold, wet bias in the desert Southwest. 

• Surface temperature and humidity performance within benchmarks for all WRAP regions 

except the desert Southwest for temperature. 

• More accurate representation of the diurnal temperature cycle in the desert Southwest. 

• Improvements in the index of agreement for temperature, humidity, and wind speed for 

all WRAP subdomains. 

• A more realistic precipitation pattern over the western U.S. 

• Better model performance in the eastern U.S. 

• Runs made with these physics options generally represented an improvement in 

performance throughout the year, making it unnecessary to select different physics 

schemes for different seasons. 

In summary, the new configuration of MM5 represented a significant improvement in model 

performance relative to the WRAP_0 and WRAP_5 36-km MM5 runs for the five-day test 

period in July 2002. 

We then conducted an additional series of tests to determine the optimal MM5 configuration for 

the nested 12-km grid of the WRAP modeling domain. In these tests, we again focused on 

sensitivity tests of the soil moisture specification and the cumulus scheme. The main results from 

the 12-km runs over the test period of July 1-5, 2002, are as follows: 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 44 

• Use of soil moisture nudging degraded temperature and humidity model performance. 

• Initializing the soil moisture with the 36-km domain MM5 output degraded temperature 

and humidity model performance. 

• Better temperature and humidity performance was obtained by initializing the soil model 

with the EDAS analysis fields. 

3.1.3 Final model configurations 

Based upon our evaluation of surface variable and rainfall performance, we selected a 

configuration that uses the Betts-Miller cumulus scheme for the 36-km grid and no cumulus 

parameterization for the 12-km grid. This was among the best performers in terms of surface 

wind, temperature, and humidity, and performed the best for rainfall. We also chose to remove 

surface analysis nudging of temperature and humidity from both grids, based on guidance from 

reviewers that nudging to these variables can degrade performance aloft. Other MM5 model 

options remained as given in Table 3-2. Table 3-3 shows the Final Configuration for the WRAP 

36/12-km MM5 modeling that was used for the annual 2002 MM5 simulation to support 

WRAP’s regional haze modeling. The WRAP modeling protocol (ENVIRON and UCR, 2004b) 

was updated to reflect this Final Configuration. 

Table 3-3. MM5 Final Configuration for the WRAP 2002 36/12-km MM5 run. 

Grid 

Resolution LSM PBL Cumulus Microphysics 

 Analysis FDDA 

 3-D Surface 

Obs 

FDDA 

36 km Pleim-Xiu ACM Betts-Miller Reisner 2 W/T/H W Wind 

12 km Pleim-Xiu ACM None Reisner 2 W/T/H W Wind 

3.1.4 Final MM5 model performance 

At both the 36-km and 12-km resolutions, the Final Configuration for the WRAP 2002 MM5 

simulations produced results that are generally within the range of meteorological model results 

that have been used in the past for air quality applications. The 36- and 12-km MM5 runs based 

on the Final Configuration represent a significant improvement in performance over the original 

WRAP_0 run.

In the 36-km WRAP_0 simulation, surface humidity and temperature fields fell within the 

benchmarks for much of the year for most subdomains, but the model had a marked cold, wet 

bias in the western subdomains, especially during the summer. This was unfortunate because the 

worst-visibility days at western class I areas that are of high interest to WRAP generally occur 

during the summer. The surface wind field failed to meet the performance benchmark for the 

entire year in subdomains PacificNW, SW, North, and DesertSW (subdomains 1 through 4, 

respectively, in Figure 4-1). Given that the purpose of this WRAP_0 run was to provide a 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 45 

database for a 12-km nested MM5 run and air quality modeling studies centered on the western 

U.S., this was a serious problem. 

The Final Configuration 36-km and 12-km MM5 runs show improvement in the modeled 

precipitation fields, particularly in the summer in the Southwest. With a reduction in the 

overprediction of summer convective rainfall in both the 36- and 12-km runs, wet biases in the 

surface humidity and cold biases in the surface temperature are now smaller in the North and 

DesertSW subdomains in the summer months. Although the Final Configuration WRAP 36-km 

and 12-km simulations were optimized for summer performance over the WRAP region, the 

winter performance in the West did not deteriorate significantly. There was a small loss of 

accuracy, particularly in temperature and humidity, in the East, but this region is not the focus of 

WRAP. To summarize, the WRAP Final Configuration 36-km MM5 run: 

• Showed significant improvement in its surface wind performance throughout the year and 

across all subdomains, due to observational nudging of surface winds. 

• Showed significant improvement in summer rainfall and surface humidity performance in 

the WRAP region. 

• Did worse than the original WRAP run for humidity and temperature performance in the 

East. We can attribute both of these changes in performance to (1) a reduction in the areal 

coverage of rainfall and (2) an increase in convective rainfall (and therefore surface 

moisture), both of which happen in going from the Kain-Fritsch scheme to the BettsMiller scheme. 

• Showed improved temperature performance in summer in the West, and slightly worse 

performance in winter. 

• Showed a small overall degradation in performance in the East. Some of this was the 

result of eliminating the surface analysis nudging of temperature and moisture that was 

done in WRAP_0. 

Based on the results of this study, we conclude that the Final Configuration 36-km and 12-km 

WRAP MM5 runs exhibit reasonably good performance and are certainly within the bounds of 

other meteorological databases used for prior air quality modeling efforts. It is therefore 

reasonable to proceed with their use as inputs for visibility modeling. 

3.2 Uncertainties and Effects on Visibility Modeling 

Meteorology has a very large effect on the formation of fine particles, their transport to Class I 

areas, and their loss rate through deposition; meteorological conditions consequently have a very 

strong influence on visibility estimates at Class I areas. There are many scales of meteorological 

processes, from microscale eddies on the order of centimeters to meters up through synopticscale circulations on the order of hundreds of kilometers. Clearly, not all of these scales can, or 

need to be, addressed in the WRAP 36/12-km MM5 modeling. By the very nature of the MM5 

model, it can only resolve flows the size of its grid resolution (or more accurately, two times the 

grid resolution). The surface observations, on the other hand, represent point measurements that 

may or may not be representative of a volumetric average. Consequently, the evaluation of the 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 46 

MM5 model using surface point observations must be interpreted with care and acknowledgment 

of the fundamental differences between the point observations and volume-average estimates. In 

the WRAP MM5 model evaluation, we have attempted to overcome this limitation by evaluating 

the model using many observations and presenting performance metrics averaged over 

subdomains. 

In complex terrain the MM5 volume-average meteorological estimates may have more uncertainties than in flat terrain, due to terrain forcing and orographic effects. Given that many of the 

western Class I areas are located in complex terrain, these enhanced uncertainties may affect the 

WRAP visibility calculations more than in other areas of the country. Uncertainties in the 

meteorological conditions can affect the visibility estimates in many ways: 

• Meteorological uncertainties can cause incorrect transport of pollutants from major 

sources to Class I areas. 

• These uncertainties can cause incorrect dispersion of pollutants. 

• These uncertainties can cause too little or too much vertical mixing that affects transport, 

dispersion, dry deposition, and chemistry. 

• Uncertainties in temperature affect emissions (biogenic and on-road mobile sources), 

mixing, chemistry, and aerosol thermodynamics. 

• Humidity uncertainties affect chemistry, aerosol thermodynamics, and, potentially, 

visibility calculations.*

• Uncertainties in clouds affect mixing, gas-phase formation processes, and aqueous-phase 

sulfate formation. 

• Precipitation uncertainties affect the loss rate of PM and precursors due to wet deposition. 

Probably the largest model performance problem of the WRAP Final Configuration MM5 

simulation is a cold, wet bias in the desert Southwest and Pacific Northwest subdomains during 

the summer. Although the bias is not as severe as in the initial runs, it is still present. This bias 

may have the following effects: 

• The colder temperatures may inhibit the formation of secondary fine particles. 

• The wet bias may cause the presence of excessive clouds. These clouds could inhibit gasphase particle formation, but at the same time could enhance aqueous-phase sulfate 

formation. Also, the overstated precipitation from the wet bias could cause the wet 

deposition of particles and gaseous precursors to be overstated, thereby understating 

visibility impairment. 

• The cold, wet bias would also affect the thermodynamic equilibrium between particulate 

nitrate and gaseous nitric acid, resulting in an overstatement in the summer nitrate concentrations. Thus, if summer ammonium nitrate is overstated by the air quality model, it 

may be due just to meteorological model uncertainties. 

 

*

Note that EPA guidance uses monthly-average relative humidity in the visibility calculations, so uncertainties in the 

MM5 modeled humidity would not affect those calculations. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 47 

4. Air Quality and Visibility Modeling 

As discussed in Section 1, the purpose of the WRAP RMC work described in this final report 

was to evaluate air quality/visibility modeling systems for a historical episode—in this case, for 

calendar year 2002—to demonstrate the suitability of the modeling systems for subsequent 

planning, sensitivity, and emissions control strategy modeling. Model performance evaluation is 

performed by comparing output from model simulations with ambient air quality data for the 

same time period. After creating emissions and meteorology inputs for the two air quality 

models, CMAQ and CAMx (see Sections 2 and 3), the next step was to perform the visibility 

modeling and the MPEs, which are described below. This section also discusses some model 

intercomparison work that we performed for the two modeling systems. We use all this 

information as the basis for recommending the selection of CMAQ and/or CAMx to complete 

the next major work elements in RMC’s support of WRAP: 

• Development of visibility planning scenarios for the regional haze baseline period of 

2000-04 and for the initial regional haze future projection period, calendar year 2018. 

• Modeling a variety of emissions sensitivity, emissions source apportionment, and emissions control strategies to assess whether planned future regional emissions reductions 

will be sufficient to demonstrate reasonable progress toward achieving visibility goals. 

Those two work elements will be discussed in future reports. 

Section 4.1 gives summary descriptions of the CMAQ and CAMx modeling systems. Section 4.2 

discusses the ambient data monitoring networks that provided the observations we used in the 

MPEs, and Section 4.3 describes our general approach to conducting the evaluations. Sections 

4.4 and 4.5 give details on various aspects of the CMAQ MPE, while Section 4.6 describes a 

sensitivity exercise we performed to compare CMAQ 36-km and 12-km simulations. Section 4.7 

discusses the CAMx MPE, and compares CAMx results with CMAQ results. Note that although 

this report discusses MPEs for both CMAQ and CAMx, most of Section 4 focuses on CMAQ 

because most of the WRAP RMC modeling to date has been performed using the CMAQ 

system. Section 4.8 describes the how various uncertainties, errors, and biases in the modeling 

process and results may affect future visibility projections. Finally, Section 4.9 provides 

conclusions derived from the CMAQ and CAMx MPE work. 

4.1 Description of the CMAQ and CAMx Models 

EPA initially developed the CMAQ modeling system in the late 1990s. The model source code 

and supporting data can be downloaded from the Community Modeling and Analysis System 

(CMAS) Center (http://www.cmascenter.org/), which is funded by EPA to distribute and provide 

limited support for CMAQ users. CMAQ was designed as a “one atmosphere” modeling system. 

In other words, it was designed to encompass modeling of multiple pollutants and issues, 

including ozone, PM, visibility, and air toxics. This is in contrast to many earlier air quality 

models that focused on single-pollutant issues (e.g., ozone modeling by the Urban Airshed 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 48 

Model). CMAQ is an Eulerian model—that is, it is a grid-based model in which the frame of 

reference is a fixed, three-dimensional (3-D) grid with uniformly sized horizontal grid cells and 

variable vertical layer thicknesses. The number and size of grid cells and the number and 

thicknesses of layers are defined by the user, based in part on the size of the modeling domain to 

be used for each modeling project. The key science processes included in CMAQ are emissions, 

advection and dispersion, photochemical transformation, aerosol thermodynamics and phase 

transfer, aqueous chemistry, and wet and dry deposition of trace species. CMAQ offers a variety 

of choices in the numerical algorithms for treating many of these processes, and it is designed so 

that new algorithms can be included in the model. CMAQ offers a choice of three photochemical 

mechanisms for solving gas-phase chemistry: the Regional Acid Deposition Mechanism version 

2 (RADM2), a fixed-coefficient version of the SAPRC90 mechanism, and the Carbon Bond IV 

mechanism (CB-IV). 

The Comprehensive Air quality Model with extensions (CAMx) was developed by ENVIRON in 

the late 1990s as a nested-grid, gas-phase, Eulerian photochemical grid model. ENVIRON later 

revised CAMx to treat PM, visibility, and air toxics. While there are many similarities between 

the CMAQ and CAMx systems, there are also some significant differences in their treatment of 

advection, dispersion, aerosol formation, and dry and wet deposition (discussed in Section 4.1.1).

4.1.1 Description of model versions 

Both EPA and ENVIRON periodically update and revise their models as new science or other 

improvements to the models are developed. For CMAQ, EPA typically provides a new release 

about once per year. We began work on the 2002 MPE using CMAQ version 4.4, which was 

released in October 2004. In October 2005 EPA released CMAQ version 4.5, which includes the 

following updates and improvements to the modeling system: 

• A new vertical advection algorithm with improved mass conservation 

• Changes in deposition velocities for some PM species 

• A new sea-salt emissions model and inclusion of sea salt in the aerosol thermodynamics 

• An option to make vertical mixing parameters vary as a function of land use type 

We first completed the CMAQ MPE using CMAQ v.4.4. When version 4.5 was released in 

October, we redid the modeling and compared the performance of the two versions. Both sets of 

results are presented here. Note that some of the new features in CMAQ v4.5 (e.g., sea salt in the 

AE4 aerosol dynamics module, and percent urban minimum vertical diffusivity) require the 

reprocessing of the MM5 data using the new version of MCIP (MCIP v3.0). However, because 

such reprocessing could potentially jeopardize the WRAP modeling schedule, WRAP elected to 

operate CMAQ v4.5 using the MM5 data processed with a previous MCIP version, MCIP v2.3, 

and the AE3 aerosol module that does not include active sea-salt chemistry. 

ENVIRON releases updated versions of CAMx approximately every two years, or as new 

features become available. The version we used for the comparison of CMAQ and CAMx was 

CAMx v4.3beta, which has not yet been posted to the CAMx web site (www.camx.com) but is 

being used by other RPOs (e.g., MRPO, CENRAP) and will be posted in early 2006. There are 

many similarities between CMAQ and CAMx regarding the science algorithms and chemical 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 49 

mechanisms used, including the CB-IV gas-phase and RADM aqueous-phase chemistries, 

ISORROPIA aerosol thermodynamics, and Piecewise Parabolic Method (PPM) horizontal 

advection scheme. In the past, the treatment of vertical advection was a major difference between 

the two models; however, the incorporation of the new mass conservation scheme in CMAQ 

v4.5 makes its vertical advection algorithm much more similar to that of CAMx. Major 

differences between the two models that still exist are in the basic model code, in the treatment 

of horizontal diffusion SOA formation mechanisms, and in grid nesting (CAMx supports oneway and two-way nesting, whereas CMAQ supports just one-way grid nesting). Both models 

include process analysis for the gas-phase portions of the model. The publicly released version of 

CAMx supports ozone and PM source apportionment through its Ozone and PM Source 

Apportionment Technology (OSAT/PSAT) probing tools, while for CMAQ there are research 

versions of the model that include Tagged Species Source Apportionment (TSSA) for some PM 

species (e.g., sulfate and nitrate). There are also research versions of CMAQ and CAMx that 

support the Decoupled Direct Method (DDM) sensitivity tool for PM and ozone. 

The CAMx model is computationally more efficient than CMAQ. However, CAMx is currently 

supported for use on only a single central processing unit (CPU) and can perform 

multiprocessing using Open Multi-Processing (OMP) parallelization (i.e., shared memory 

multiprocessors). CMAQ parallelization, on the other hand, is implemented using Message 

Passing Interface (MPI) multiprocessing and therefore can be run using any number of CPUs. 

Depending on the number of model simulations to be performed and the manner in which they 

are set up, there can be a slight advantage either to CAMx or to CMAQ in regard to 

computational efficiency. This difference is unlikely to be important in selecting one of the 

models for the future RMC work listed at the beginning of Section 4, however. 

4.1.2 Selection of model science options 

The CMAQ and CAMx modeling systems allow a variety of choices in the science algorithms 

used for creating the numerical solution of the transport processes and chemistry. Table 4-1 

displays the science options used in the CMAQ v4.4 and v4.5 and CAMx v4.3beta model 

performance evaluations. Most science options in CMAQ remained the same, with the exception 

of the vertical advection and dry deposition. In CMAQ v4.5 we used the Yamartino advection 

scheme, which has improved mass conservation, and revised dry deposition velocities, which 

have increased rates of deposition for PM species. 

Table 4-1. Model configurations for the CMAQ and CAMx simulations. 

Model Option CMAQ version 4.4 CMAQ version 4.5 CAMx version 

4.3beta 

Model version October 2004 October 2005 2006 

Horizontal resolution 36 km 36 km and 12 km 36 km 

Number of vertical layers 19 layers 19 layers 19 layers 

Horizontal advection PPM PPM PPM 

Vertical advection PPM Yamartino Implicit 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 50 

Model Option CMAQ version 4.4 CMAQ version 4.5 CAMx version 

4.3beta 

Horizontal diffusion Spatially varying Spatially varying Spatially varying 

Vertical diffusion KV (eddy diffusion) KV (eddy diffusion) KV (eddy diffusion)

Minimum vertical diffusivity 1.0 m2/s 1.0 m2/s 1.0 m2/s 

MM5 configuration Pleim-Xiu/ACM Pleim-Xiu/ACM Pleim-Xiu/ACM 

MM5 processing MCIP2.3 pass-through MCIP2.3 pass-through MM5CAMx 

Gas-phase chemistry CB-IV CB-IV CB-IV 

Gas-phase chemistry solver MEBI/Hertel MEBI/Hertel CMC 

Secondary organic aerosol SORGAM SORGAM SOAP 

Aqueous-phase chemistry RADM RADM RADM 

Aerosol chemistry AE3/ISORROPIA AE3/ISORROPIA ISORROPIA 

Dry deposition Pleim-Xiu Revised Pleim-Xiu Wesely 

Plume-in-grid Off Off Off 

Initial concentrations CENRAP 36-km 

spin-up 

CENRAP 36-km 

spin-up 

CENRAP 36-km 

spin-up 

Boundary conditions 2002 3-hourly GEOSChem 2002 3-hourly GEOSChem 2002 3-hourly 

GEOS-Chem 

4.1.3 Description of model boundary conditions and initial conditions 

Initial conditions (ICs) are specified by the user for the first day of a model simulation. For 

continental-scale modeling using the RPO Unified 36-km domain, the ICs can affect model 

results for as many as 15 days, although the effect typically becomes very small after about 7 

days. We included a model spin-up period in each simulation to eliminate any effects from the 

ICs. For the WRAP modeling, we divided the annual simulation into four quarters, and included 

a 15-day spin-up period for the quarters beginning in April, July, and October. For the quarter 

beginning in January 2002, we ran a spin-up period covering December 16-31, 2001, using 

meteorology and emissions data that we had developed for CENRAP. The most important aspect 

of using ICs and spin-up periods is always to treat them consistently across all model simulations 

so that small differences in the ICs or spin-up periods do not affect the comparison of results 

across simulations. We considered performing new spin-up simulations for December 2001 

when we updated the 2002 base A emissions inventory; however, the effects of a new spin-up 

simulation would manifest only in the first few days of January, and this small difference would 

not justify the extra effort required. We therefore decided to use the same IC file for all model 

simulations. 

Boundary conditions (BCs) specify the concentrations of gaseous and PM species at the four 

lateral boundaries of the model domain. BCs determine the amounts of gaseous and PM species 

that are transported into the model domain when wind flow is into the domain. Boundary 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 51 

conditions affect model simulations much more than ICs do. For some areas in the WRAP region 

and for clean conditions, the BCs can be a substantial contributor to visibility impairment. 

For this study we used BC data generated in an annual simulation of the global-scale GEOS-Chem 

model that was completed by Jacob et al. (http://www-as.harvard.edu/chemistry/trop/geos/) for 

calendar year 2002. Additional data processing of the GEOS-Chem data was required before we 

could use them in CMAQ and CAMx. The data first had to be mapped to the boundaries of the 

WRAP domain, and the gaseous and PM species had to be remapped to a set of species used in the 

CMAQ and CAMx models. This work was completed by Byun and coworkers (http://wwwas.harvard.edu/chemistry/trop/geos/meetings/2005/ppt/Expanding_Model_Capabilities/GEOSCMAQ_april_4_Byun.ppt). 

4.2 Summary of Ambient Monitoring Data 

We compiled the ground-level model evaluation database for 2002 using several routine and 

research-grade ambient monitoring databases. The following specialized PM monitoring 

networks were used as sources of fine-PM data: 

• Interagency Monitoring of Protected Visual Environments (IMPROVE) network 

• Clean Air Status and Trends Network (CASTNet) 

• EPA Speciation Trends Network (STN) 

• National Atmospheric Deposition Network/National Trends Network (NADP/NTN) 

In addition, we used EPA’s Aerometric Information Retrieval System/Air Quality System 

(AIRS/AQS) database for routine gas-phase concentration measurements for O3, NO, NO2, and 

CO. Figures 4-1 and 4-2 display the locations of the monitors for the various monitoring 

networks operating during 2002. Typically, these networks provide ozone, PM, and visibility 

measurements. The types of data available from these networks are summarized in Table 4-2, 

and each of these ambient networks is briefly described below. 

IMPROVE. The Interagency Monitoring of Protected Visual Environments monitoring network 

(http://vista.cira.colostate.edu/improve) reports detailed chemical species in its measurements of 

major visibility-reducing aerosol species every three days. The PM fine mass species we used in 

the evaluation are as follows: 

• Sulfates (SO4), as sulfate ion 

• Nitrates (NO3), as nitrate ion 

• Organic carbon (OC), as organic carbon mass 

• Elemental carbon (EC), as light-absorbing carbon or carbon soot 

• Soil (SOIL), as fine soil; this is the sum of multiple inorganic elements, such as Al, Si, 

Ca, Fe, and Ti. 

These species are all measured using a 2.5-micron cut point inlet. The IMPROVE monitors also 

measure total PM10 and PM2.5 mass. These values are reported as the PM2.5 fine matter (FM) 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 52 

portion of the mass and the coarse matter (CM) portion (PM10 - PM2.5). The mapping of the 

CMAQ and CAMx species to the IMPROVE species counterparts is summarized in Tables 4-3a 

and 4-3b. Noted that for CMAQ and CAMx, water as a fine-particle species is not included 

among the mapping of IMPROVE species, because IMPROVE measures only dry particles. In 

addition, IMPROVE defines SOIL as fine soil concentration, which is the sum of the 

concentrations of several inorganic species. Although fine soil is not specifically defined in 

CMAQ, it is taken as the unspeciated portion of PM2.5 emitted species. Therefore, the CMAQ 

model species A25I and A25J are used as surrogates for IMPROVE’s fine soil concentration. 

CAMx has the capability of modeling fine and coarse crustal (FCRS and CCRS) species 

separately from the unidentified fine and coarse other PM species (FPRM and CPRM). However, 

since the CAMx other fine and coarse PM species were built from the CMAQ emissions, the 

separate modeling of the crustal species could not be performed in the WRAP modeling. 

The IMPROVE data are available both as raw data and as processed data (i.e., with concentrations recalculated based on guidance in the Regional Haze Rule). The sulfate data are available 

either as elemental sulfur or as sulfate. We use the elemental sulfur in all WRAP MPEs. The 

IMPROVE data are periodically updated by the Visibility Information Exchange Web System 

(VIEWS). For the MPEs included in this report we used the VIEWS release of the IMPROVE 

data dated May 2004. A new update of the IMPROVE data was released in September 2005; this 

version will be incorporated in our future CMAQ and CAMx MPEs. 

CASTNet. The Clean Air Status and Trends Network (CASTNet) was developed to monitor dry 

deposition. It includes measurements of ambient concentration and meteorology as well as data 

on land use, which are then used to calculate dry deposition rates. A majority of the CASTNet 

sites measure sulfate, nitrate (including both gaseous [as HNO3] and aerosol phase), and 

ammonium in seven-day filter samples. Detailed data collection procedures are described at the 

EPA CASTNet website: http://www.epa.gov/castnet/. In short, atmospheric concentration data 

are collected at each site with open-faced, three-stage filter packs. The filter pack contains a 

Teflon filter for collection of particulate species, a nylon filter for nitric acid, and a baseimpregnated cellulose (Whatman) filter for sulfur dioxide. Filter packs are exposed for one-week 

intervals, and are later extracted and analyzed for certain species. Because CASTNet reports 

gaseous species (e.g., SO2 and HNO3) in the μg/m3

 unit, while the CMAQ and CAMx models 

have gaseous species in the parts per million by volume (ppmv) unit, conversion factors 

(assumed under standard temperature and pressure [STP] conditions) of 2617.6 and 2576.7 are 

applied for the modeled SO2 and HNO3 species. It has been suggested that total nitrate (NO3 + 

HNO3), instead of individual aerosol nitrate and gaseous nitric acid, should be used for 

observation-modeled comparison, because of the possible volatilization loss from the Teflon 

filter pack used in CASTNet (Ames and Malm, 2001). Note that a ratio of 0.9841, the molecular 

weight ratio of NO3/HNO3, is applied in CASTNet’s total nitrate calculation. 

STN. EPA’s Speciation Trends Network includes about 215 monitoring stations nationwide. It 

appears to us that these 215 sites may include IMPROVE sites or sites from other networks, so 

the STN network may duplicate some data included in some of the other monitoring networks. 

However, this needs to be verified. Daily PM2.5 data are measured for 64 species in the STN 

network. We obtained some archived STN data files from the web site 

http://www.epa.gov/ttn/airs/airsaqs/detaildata/requestingaqsdata.htm. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 53 

NADP/NTN. The National Atmospheric Deposition Program/National Trends Network 

(NADP/NTN) is designed to measure wet deposition. The network is a cooperative effort 

involving state Agricultural Experiment Stations, the U.S. Geological Survey, the U.S. 

Department of Agriculture, and other governmental and private entities. It includes over 100 

sites in the continental United States, Alaska, Puerto Rico, and the Virgin Islands. The purpose 

of the network is to collect data on the chemistry of precipitation for monitoring of geographical 

and temporal long-term trends. The precipitation at each station is collected weekly and analyzed 

for hydrogen (acidity as pH), sulfate, nitrate, ammonium, chloride, and base cations (such as 

calcium, magnesium, potassium, and sodium). The NADP/NTN includes a quality assurance 

program, so we used these data without any additional QA. The NADP/NTN includes the 

Mercury Deposition Network (MDN) and the Atmospheric Integrated Research Monitoring 

Network (AIRMoN), designed to study precipitation chemistry trends with greater temporal 

resolution. Precipitation samples are collected daily from a network of nine sites and analyzed 

for the same constituents as the NADP/NTN samples. 

AQS. The Air Quality System database is EPA’s repository of “criteria air pollutants”: CO, NO2, 

SO2, O3, PM (PM10 and PM2.5), and Pb; it includes monitoring data from the 1970s forward. It 

replaced the Aeronomic Information Retrieval System (AIRS) as EPA’s main repository for 

ambient air monitoring data, including data from the State and Local Air Monitoring Stations 

(SLAMS), the National Air Monitoring Stations (NAMS), the Photochemical Assessment 

Monitoring Stations (PAMS), and other sources of data. A majority of the hourly data for ozone 

and for several gaseous components (CO, NO2, SO2, O3) can be retrieved from EPA’s web site, 

http://www.epa.gov/ttn/airs/airsaqs/detaildata/downloadaqsdata.htm, through data query 

requests. In this study, only hourly gaseous species from AQS were used for model evaluations. 

Note that, unlike CASTNet data, gaseous species in AQS are reported in the ppmv unit. 

Table 4-2. Summary of ambient database available for the model performance evaluation. 

Monitoring Network Chemical Species 

Measured 

Sampling 

Frequency; 

Duration 

Data 

Availability 

(Sites) 

IMPROVE 

(Interagency Monitoring of Protected 

Visual Environments) 

Speciated PM2.5 and PM10 1 in 3 days; 

24 hr 

~62 

CASTNet 

(Clean Air Status and Trends Network) 

Speciated PM2.5, ozone Weekly; 

week 

~72 

STN 

(Speciation Trends Network) 

Speciated PM2.5 Varies; 

varies 

~215 

NADP/NTN 

(National Acid Deposition Network/ 

National Trends Network) 

WSO4, WNO3, WNH4 Weekly; 

weekly 

~100 

AQS 

(Air Quality System) 

O3, CO, SO2, NO, NOy Hourly; 

1-hr average 

~1,536 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 54 

-2000 -1500 -1000 -500 0 500 1000 1500 2000

-1500

-1000

-500

0

500

1000

IMPROVE

CASTNet

SEARCH

STN

NADP

AQS

Figure 4-1. Locations of sites in each of the PM ambient monitoring networks. 

Figure 4-2. Locations of sites in the AQS gaseous species ambient monitoring network. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 55 

Neither the modeled PM concentrations nor the measured PM concentrations correspond directly 

with concentrations of actual PM species because of different lumping schemes used in the 

model chemistry and because of artifacts in the measurement methods. Therefore, measured 

species cannot be compared directly to modeled species, so additional calculations (“mapping 

schemes”) must be applied to in order to compare them. Tables 4-3a and 4-3b show the mapping 

schemes that we used in the CMAQ and CAMx MPEs. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

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Table 4-3a. Mapping of gaseous model species to gaseous observation species. 

Observation Species Model Species Compound IMPROVE CASTNet AQS CMAQ 

Gaseous Species 

O3 O3 O3 O3 CO CO CO CO NO NO NO NO SO2 SO2 SO2 SO2 SO2

(μg/m3) 

 TOTAL_SO2 2617.6*SO2 2617.6*SO

HNO3 HNO3 HNO3 HNO3

(μg/m3) 

 NHNO3 

(nylon filter) 

 2576.7*HNO3 2576.7*HN

NOy NO + NO2 + HONO + NO3 + 2*N2O5 + 

HNO3 + PAN + XNO2 + TPAN + HNO4 

NO + NO2 HNO3 + PA
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 57 

Table 4-3b. Mapping of particulate model species to particulate observation species. 

Observation Species Model Species 

Compound IMPROVE CASTNet STN NADP/ NTN CMAQ 

Particulate Species 

SO4 3*S TSO4 

(Teflon filter) 

SO4 wSO4 ASO4J + ASO4I PSO4 

NO3 NO3 TNO3 

(Teflon filter) 

NO3 wNO3 ANO3J + ANO3I PNO3 

Particulate NO3+SO4

 0.29*TNO3 + 

0.375*TSO4

 0.29*(ANO3I + ANO3J) + 

0.375*(ASO4I + ASO4J) 

0.29*PNO

Total NO3

(gas+particle) 

 TOTAL_NO3 ANO3I + ANO3J + 

0.9841*2576.7*HNO3 

PNO3 + 0

NH4 0.375*SO4 + 

0.29*NO3 

TNH4 (Teflon filter) 

NH4 wNH4 ANH4J + ANH4I PNH4 

OC 1.4*(OC1+ 

OC2 + OC3 + OC4 + OP) 

 OC AORGAJ + AORGAI + 

AORGPAJ + AORGPAI + AORGBJ + AORGBI 

POA + SOSOA4 

EC EC1 + EC2 + 

EC3 - OP 

 EC AECJ + AECI PEC 

TCM OC+EC SOIL 2.2*Al + 

2.49*Si + 1.63*Ca + 2.42*Fe + 1.94*Ti 

 A25I +A25J FCRS + F
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 58 

Observation Species Model Species 

Compound IMPROVE CASTNet STN NADP/ NTN CMAQ 

Particulate Species (continued) 

CM MT - FM ACORS + ASEAS + ASOIL CCRS + CPM2.5 FM PM25 ASO4J + ASO4I + ANO3J + 

ANO3I + ANH4J + ANH4I+ AORGAJ + AORGAI + AORGPAJ + AORGPAI + AORGBJ + AORGBI + AECJ + AECI + A25J + A25I 

PSO4 + PSOA1 + S+ PEC + F

RCFM 1.375*SO4 + 

1.29*NO3 + EC + OC + SOIL

 1.375*(ASO4J + ASO4I) + 

1.29*(ANO3J + ANO3I) + AORGAJ + AORGAI + AORGPAJ + AORGPAI + AORGBJ + AORGBI + AECJ + AECI + A25J + A25I 

1.375*PSPOA + SO+ SOA4 + 

PM10 MT PM25 + CM PM25 + C
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 59 

4.3 Model Performance Evaluation Approach 

The objective of an MPE is to compare model-simulated concentrations with observed data to 

determine whether the model’s performance is sufficiently accurate to justify using the model for 

simulating future conditions. There are a number of challenges in completing an annual MPE for 

regional haze. The model must be compared to ambient data from several different monitoring 

networks for both PM and gaseous species, for an annual time period, and for a large number of 

sites. The model must be evaluated for both the worst visibility conditions and for very clean 

conditions. Finally, final guidance on how to perform an MPE for fine-particulate models is not 

yet available from EPA. Therefore, we experimented with many different approaches for 

showing model performance results. The plot types that we recommend as being the most useful 

are the following: 

• Time-series plots comparing the measured and model-predicted species concentrations 

• Scatter plots showing model predictions on the y-axis and ambient data on the x-axis 

• Spatial analysis plots with ambient data overlaid on model predictions 

• Bar plots comparing the mean fractional bias (MFB) or mean fractional error (MFE) 

performance metrics (these and other performance metrics are described in Section 4.3.4) 

• “Bugle plots” showing how model performance varies as a function of the PM species 

concentration 

• Stacked-bar plots of contributions to light extinction for the average of the best-20% 

visibility days or the worst-20% visibility days at each site; the higher the light 

extinction, the lower the visibility 

These plots are all described in Section 4.3.1. UCR developed an MPE software package that we 

used to produce the full set of model evaluation metrics and plots described below. The software 

is available for download from the RMC web site and is described in a separate report, also 

available for download on the web site: 

http://pah.cert.ucr.edu/aqm/308/cmaq_faq/cmaq_ucr_mpe.shtml. 

Sections 4.3.2 and 4.3.3 describe the subdomains and performance time periods we analyzed in 

the MPEs, while the various performance metrics used are defined in Section 4.3.4. 

Each plot type or metric summarizes the MPE in a different way. It is expected that the states 

and tribes will select from among these plots those that are most useful for conveying the 

modeling results to stakeholders and for preparing the SIP/TIP technical support documents. 

4.3.1 The most useful plot types for the MPEs 

4.3.1.1 Time-series analysis at individual sites 

We created a set of time-series plots that compare the measured and model-predicted species 

concentrations. For each monitoring site, we generated either a single plot for the full year or 

separate plots by month. Plots for all sites and species are available at 

http://pah.cert.ucr.edu/aqm/308/cmaq.shtml. Figure 4-3 shows an example plot for sulfate 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 60 

ambient data and model concentrations for two different model simulations at the Grand Canyon 

IMPROVE site. 

Because of the large number of plots generated to cover all sites and all species, we also 

developed a more comprehensive comparison of model output and observations that shows the 

contribution of each PM component to haze in a single, stacked-bar time-series plot for each site. 

Each plot has two parts: the top graphic, displaying ambient data, shows the contribution of each 

PM component to daily average visibility impairment for each day that has ambient data; the 

bottom graphic, created using model output, shows the contributions to visibility impairment for 

each of the 365 modeled days. Figure 4-4 shows an example stacked-bar time-series plot for the 

Grand Canyon IMPROVE site. The two graphics presented together in each plot provide a useful 

summary of temporal variability in model performance; for example, if the model mislocates the 

high concentration by one day in time, these plots still show the close temporal association 

between the modeled and observed data.*

 The stacked-bar time-series plots use the same fixed yaxis scale for all IMPROVE sites, to facilitate the comparison of variability across sites and also 

to help establish a characteristic “fingerprint” for each IMPROVE site. These plots are very 

useful for presenting model and ambient data results to stakeholders.

Figure 4-3. Example time-series plot of sulfate at the Grand Canyon IMPROVE site 

showing ambient data and two sets of model results for June 2002 (Julian dates 152 to 180). 

 

*

Note that this type of information is lost in the animated spatial plots discussed in Section 4.3.1.3. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 61 

Figure 4-4. Example stacked-bar time-series plot for the Grand Canyon IMPROVE site 

showing ambient data (top) and model output (bottom) contributions to light extinction.
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 62 

4.3.1.2 Scatter plots for each site 

We created scatter plots showing model predictions on the y-axis and ambient data on the x-axis 

for all data sites, either individually for each month or for the entire year. This provides a quick 

summary of the model performance, although it does not include any information about temporal 

or spatial variability. Figure 4-5 shows an example of a scatter plot for modeled sulfate and 

IMPROVE data sulfate. The plot-generation software we use also calculates all of the various 

performance metrics (see Section 4.3.4) while producing the scatter plot, and automatically 

includes several metrics in the text label on the plot. Typically, we prepare a set of these plots for 

each month of 2002 to show seasonal variability in the model performance, and we usually 

compare two different model simulations—for example, Figure 4-5 shows model-predicted 

sulfate for CMAQ v4.4 and CMAQ v4.5 compared to the IMPROVE data. The full set of scatter 

plots is available at http://pah.cert.ucr.edu/aqm/308/cmaq.shtml. 

Figure 4-5. Example scatter plot comparing CMAQ v4.4 and CMAQ v4.5 

model results to ambient data. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 63 

4.3.1.3 Spatial analysis plots 

We developed spatial plots that use the Program for the Analysis and Visualization of 

Environmental data (PAVE) visualization software to overlay ambient data on top of the model 

predictions. With one plot for each day during 2002, there is a set of 365 plots for each species 

measured at the IMPROVE sites. These are available on the RMC web site 

(http://pah.cert.ucr.edu/aqm/308/cmaq.shtml), where we use a graphics tool to conveniently 

animate the plots. Figure 4-6 shows an example plot. These plots show the IMPROVE data 

superimposed in colored symbols over the model-simulated data. The more often the background 

color (model output) matches the color of the diamond symbol (ambient data), the better the 

model performance; a glaring mismatch in color indicates poor model performance at that site. 

These plots are useful for showing spatial distributions of the data as well as instances where 

poor model performance has occurred because the model has mislocated the high concentrations 

by just a few grid cells from the observed high concentrations. These plots are also useful for 

presenting the model and ambient data to stakeholders. 

Figure 4-6. Example PAVE spatial plot with ambient data shown in symbols 

overlaid on model results shown in background for the 12-km CMAQ MPE. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 64 

4.3.1.4 Bar plots of averaged model performance metrics: Fractional bias and 

error 

We used the MPE software to calculate the average of the model performance metrics for each 

month (see Section 4.3.4), and summarized these results using bar plots to compare the monthly 

average mean fractional bias (MFB) or mean fractional error (MFE) for each species. This 

provides a quick summary of the variability in seasonal model performance. These plots are also 

useful for quickly comparing model performance results for different models and for model 

sensitivity experiments. Figure 4-7 is an example bar plot showing monthly variability in MFB. 

Additional plots are available at http://pah.cert.ucr.edu/aqm/308/cmaq.shtml. 

Figure 4-7. Example bar plot showing monthly variability in 

mean fractional bias (MFB) for two model simulations. 

4.3.1.5 Bugle plots showing model performance goals and criteria 

We produced “bugle plots” to show how model performance varies as a function of the PM 

species concentration. Monthly average error and bias terms are plotted in a bugle plot as a 

function of the species concentration on the x-axis (see Figure 4-8 for an example). These plots 

help characterize differences in model performance for clean versus polluted conditions. They 

also include curves showing model performance goals and model performance criteria, 

explained below. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 65 

Figure 4-8. Example bugle plot showing monthly average sulfate MFB for the 

CMAQ v4.5 model simulation as a function of the average of the modeled and observed 

sulfate concentration for the CASTNet, IMPROVE, and STN data. 

Bugle plots were first suggested by James Boylan of the Georgia Department of Natural 

Resources. As defined by Boylan and Russell (2005), the performance goals are MFB ≤ ± 30% 

and MFE ≤ +50%, and the performance criteria are MFB ≤ ± 60% and MFE ≤ +75%. The 

performance goals are the more stringent of the two sets of metrics; we believe a goodperforming model would achieve these. The performance criteria are less strict. If the criteria are 

equaled or exceeded, it suggests potential problems with the model simulation. The goals and 

criteria increase at lower concentrations according to the following equations: 

Performance Goal:

Performance Criteria:

where Co is the observation concentration and Cm is the model-predicted concentration. 

Note that the performance goal curves in our bugle plots are arbitrarily defined based on ozone 

performance goals and may represent an unreasonable goal for PM performance. The model 

125 75 3 0.75 /

0.5( )

≤ +

− +

g m

Co Cm

FE e μ

140 60 3 0.5 /

0.5( )

≤ ± +

− +

g m

Co Cm

FB e μ

150 50 3 0.75 /

0.5( )

≤ +

− +

g m

Co Cm

FE e μ

170 30 3 0.5 /

0.5( )

≤ ± +

− +

g m

Co Cm

FB e μ
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 66 

criteria curves indicate the skill of a wide array of previous PM modeling studies. We are not 

suggesting that these performance goals/criteria be generally adopted or that they are the most 

appropriate to use. Rather, we are using them just to frame and put the PM model performance 

into context and to facilitate model performance intercomparison across episodes, species, 

models, and sensitivity tests. Additional plots are available on the RMC web site at 

http://pah.cert.ucr.edu/aqm/308/cmaq.shtml. 

4.3.1.6 Analysis of best-20% and worst-20% visibility days 

We prepared stacked-bar plots of contributions to light extinction for the average of the best-20% 

visibility days or the worst-20% visibility days at each site. Each plot shows one of three types of 

information: 

1. Observed and modeled data matched (paired) in space and time. 

2. Observed and modeled data relaxed (also referred to as “unpaired”) in time; see Section 

4.3.3 for a definition of “relaxed in time.” 

3. Observed and modeled data relaxed (unpaired) in both space and time; see Sections 4.3.2 

and 4.3.3 for definitions of “relaxed in space” and “relaxed in time,” respectively. 

Figure 4-9 shows an example relaxed-in-time plot. In this plot, the first stacked bar is for the 

ambient data, and the second stacked bar is for the model prediction for the same grid cell and 

day of the ambient measurement. For the best-20% average plots, the next three stacked bars are 

for the best visibility day selected from expanding time windows of 3 days (i.e., including one 

day before and after the measurement), 7 days, and 15 days. For the worst-20% average plots, 

the worst visibility day is selected from the expanding time windows. 

The purpose of this comparison is to determine whether the model can simulate the full range of 

clean to polluted conditions that we observe in the ambient data at the individual sites. It 

increases our confidence in the model as a “reasonable version of reality” if it does well at 

simulating this range. However, the comparison should not be used as a primary model 

performance evaluation test because it is likely to obscure errors in the model. In particular, we 

do not want to select the best-fit model results from the relaxed in time and space analysis 

because this would be an arbitrary use of the model results to demonstrate a match to the data 

without any evidence that the match was achieved by correctly simulating the air quality. Such a 

use would also increase the potential for compensating errors in the model and give us an 

exaggerated overconfidence in the model performance. Instead, these plots should be used as a 

corroborative analysis method designed to gauge more generally our confidence in the reliability 

of the model for simulating future conditions. Additional plots are available on the RMC web 

site at http://pah.cert.ucr.edu/aqm/308/cmaq.shtml. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 67 

Figure 4-9. Example stacked-bar plot of contributions to light extinction for the average of 

the worst-20% visibility days at an IMPROVE site, for data relaxed in time. 

4.3.2 Subdomains analyzed for the MPEs 

All MPE results in this report include only those monitoring sites that are located within the 

WRAP region—that is, within the 12-km grid map for the western states shown in blue in Figure 

1-2. Monitoring sites in other RPO regions are not included because our analysis is designed to 

evaluate model performance only for the WRAP region. We included the same set of monitoring 

sites for both the WRAP 36-km and 12-km domains so the model results for the two grids of 

different resolutions are directly comparable. For most of the model evaluation plots described in 

Section 4.3.1, we used the model concentration from the grid cell in which the monitor site is 

located. Normally, we do not consider the exact location of the site within the grid cell, and we 

do not attempt to interpolate from surrounding model grid cells. However, in calculating the 

performance metrics for the average of the best-20% and worst-20% days (see Section 4.3.1.6), 

we performed a “relaxed-in-space” comparison in which we compared the model prediction to 

the highest concentration in the surrounding nine grid cells for the 20% worst days, and to the 

lowest concentration in the surrounding nine grid cells for the 20% best days. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 68 

4.3.3 Performance time periods 

The CMAQ and CAMx model simulations were performed for calendar year 2002, and the 

models output the hourly average concentrations for all gaseous and PM species needed for 

comparing the models to the ambient monitoring data. Because the data collection methods and 

averaging periods differ across the various ambient monitoring networks, for comparing modeled 

and observed data we always calculate an average of the modeling data that is consistent with the 

monitoring period used at each of the monitoring networks. This can range from a 1-h average 

for gaseous species at the AQS sites, to a 24-h average at the IMPROVE sites, to a 1-wk average 

at the CASTNet sites. We present the MPE results in a variety of ways, ranging from very 

detailed analyses at each monitoring site to more general summaries of the model results for 

monthly and annual average bias and error over all sites in a given monitoring network. As 

discussed in Section 4.3.1.6, the model performance plots for the average of the best-20% and 

worst-20% days include a “relaxed-in-time” comparison of the modeled and observed data in 

which the highest model concentration is selected within a narrowly defined time window to 

compare with the worst-20% visibility days, and the lowest model concentration is selected 

within a narrowly defined time window to compare with the best-20% visibility days. 

4.3.4 Description of performance metrics 

Model performance metrics are statistical measures used to quantify the model’s performance 

relative to the observed data in terms of bias and error. These statistics can be computed in a 

variety of different ways, as shown in Table 4-4. Traditionally, ozone model performance 

evaluations were performed using the mean normalized error (MNE) and mean normalized bias 

(MNB) performance metrics. However, this approach overemphasized model overpredictions 

because the minimum and maximum bounds on the mean normalized bias ranged from 0 to 

infinity. For ozone modeling, an observed concentrations cutoff is used (typically 60 ppb) that 

keeps the MNB and MNE performance metrics from “blowing up.” For the WRAP model 

performance evaluation this was especially problematic because we must evaluate model 

performance on the cleanest days when ambient data concentrations are low, leading to very 

large computed values of MNB and MNE. 

An alternate approach is to use mean fractional bias (MFB) and mean fractional error (MFE). For 

the MFB metric, the minimum and maximum range in bias is symmetric about zero and can 

extend to +200%; thus, it does not overemphasize the importance of model overpredictions. 

Similarly, the MFE is bounded from 0 to +200% and cannot “blow up” to inordinately high 

numbers like the MNB and MNE can. In our model performance evaluation, we computed and 

saved all of the bias and error metrics shown in Table 4-4. However, we usually present only the 

mean fractional bias and gross error metrics, as they have been found to provide the most useful 

descriptions of model performance, especially when combined with the graphical displays 

discussed in Section 4.3.1. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 69 

Table 4-4. Recommended statistical measures for model performance evaluation. 

Measure Mathematical Expression Notation 

Accuracy of unpaired 

peak (Au) 

peak

peak

u

peak

O

P − O Opeak = peak observation; 

u Ppeak = unpaired peak prediction within 

certain surrounding grid cells of peak 

observation 

Accuracy of paired peak 

(Ap) 

peak

peak

O

P − O Ppeak = paired (in both time and space) 

peak prediction 

Coefficient of 

determination (r

2

) 

∑ ∑

∑

= =

=

− −

⎥

⎦

⎤ ⎢

⎣

⎡ − −

N

i

N

i

i i

N

i

i i

P P O O

P P O O

1 1

2 2

2

1

( ) ( )

( )( )

Pi = prediction at time and location i; 

Oi = observation at time and location i; 

P = arithmetic average of Pi, i=1,2,..., N; 

O = arithmetic average of Oi, i=1,2,...,N 

Normalized Mean Error 

(NME) 

∑

∑

=

=

−

N

i

i

N

i

i i

O

P O

1

1

Reported as % 

Root Mean Squared 

Error (RMSE) ( )

2

1

1

1 2

⎥

⎦

⎤ ⎢

⎣

⎡ ∑ − =

N

i

Pi Oi N

Mean Fractional Error 

(MFE) ∑= +

− N

i i i

i i

P O

P O

N 1

2 Reported as % 

Mean Absolute Gross 

Error (MAGE) ∑=

−

N

i

Pi Oi N 1

1

Mean Normalized Gross 

Error (MNGE) ∑=

− N

i i

i i

O

P O

N 1

1 Reported as % 

Mean Bias (MB) 

∑( ) =

−

N

i

Pi Oi N 1

1

Mean Normalized Bias 

(MNB) ( ) ∑=

− N

i i

i i

O

P O

N 1

1 Reported as % 

Mean Fractional Bias 

(MFB) ∑= ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

+

− N

i i i

i i

P O

P O

N 1

2 Reported as % 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 70 

Measure Mathematical Expression Notation 

Normalized Mean Bias 

(NMB) 

∑

∑

=

=

−

N

i

i

N

i

i i

O

P O

1

1

( ) Reported as % 

Bias Factor (BF) ∑=

N

i i

i

O

P

N 1

1 Bias factor = 1 + MNB; reported as ratio 

notation (prediction:observation) 

4.4 CMAQ Model Performance Evaluation in the WRAP States for 

Calendar Year 2002 

In the CMAQ MPE discussions below, for each species we present three types of plots that 

summarize the results as monthly averages for all sites in each monitoring network. These plots 

include: 

• A bar plot showing the CMAQ version 4.4 and CMAQ version 4.5 MFBs compared to 

the IMPROVE data 

• A bar plot of MFB for those species with data from multiple monitoring networks 

• Bugle plots of MFB and MFE for each species 

This is the minimum set of plots needed to evaluate model performance. Additional plots 

reflecting model performance at the individual IMPROVE monitoring sites are included in the 

appendices to this report and on the RMC web site. We focus here on MPE results for the 

IMPROVE sites because IMPROVE data are measured in Class I areas, making these data the 

most pertinent to the Regional Haze Rule and the ultimate purpose of the model performance 

evaluation; however, results for the other monitoring networks are also included on the RMC 

web site.
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 71 

4.4.1 Sulfate (SO4) 

Figure 4-10 shows the CMAQ v4.4 and v4.5 modeled sulfate compared to the IMPROVE sulfate 

data. CMAQ v4.4 overpredicted sulfate in the winter and underpredicted it in the summer. 

CMAQ v4.5 predicted consistently lower sulfate than did CMAQ v4.4, which meant that CMAQ 

v4.5 had improved performance (i.e., lower positive bias) in the winter months and poorer 

performance (i.e., increased negative bias) in the summer months. The reduced sulfate in CMAQ 

v4.5 may be caused in part by increased deposition rates for particles in CMAQ v4.5. 

Figure 4-11 compares the CMAQ v4.5 sulfate to the IMPROVE, CASTNet, and STN networks. 

The model performance is similar for all three networks, although negative bias is slightly larger 

for the STN sites, which are more influenced by urban conditions than either CASTNet or 

IMPROVE sites. 

Figure 4-12 shows the bugle plots of MFB and MFE for sulfate for the same three monitoring 

networks. There is a slight tendency for CMAQ v4.5 to overpredict the lowest sulfate 

concentrations (i.e., the winter sulfate at the IMPROVE sites), and to underpredict the higher 

concentrations. The model’s sulfate performance is quite good, with all of the IMPROVE and 

CASTNet monthly average MFBs and MFEs falling within the model performance goals, and 

the STN results close to the model performance goals. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 72 

Figure 4-10. Sulfate monthly MFB for the 36-km results for CMAQ 

versions 4.4 and 4.5 compared to the IMPROVE data. 

Figure 4-11. Monthly average sulfate MFB for CMAQ version 4.5 

36-km results compared to the CASTNet, IMPROVE, and STN networks. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 73 

Figure 4-12. Bugle plot of monthly average sulfate MFB (top) and MFE (bottom) for 

CMAQ version 4.5 36-km results and the CASTNet, IMPROVE, and STN networks. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 74 

4.4.2 Nitrate (NO3) 

Figure 4-13 shows the CMAQ v4.4 and v4.5 modeled nitrate compared to the IMPROVE nitrate 

data. As for sulfate, CMAQ v4.4 overpredicted nitrate in the winter and underpredicted it in the 

summer. Again as for sulfate, CMAQ v4.5 predicted consistently lower nitrate than did CMAQ 

v4.4, which meant that CMAQ v4.5 had improved performance (i.e., lower positive bias) in the 

winter months and poorer performance (i.e., increased negative bias) in the summer months. The 

reduced nitrate in CMAQ v4.5 may also be caused by increased deposition rates for particles in 

CMAQ v4.5. 

Figure 4-14 compares the CMAQ v4.5 nitrate to the IMPROVE, CASTNet, and STN networks. 

The model performance is generally similar for all three networks, with the exception of larger 

negative bias for the STN sites in the winter months. 

Figure 4-15 shows the nitrate bugle plots for the three monitoring networks. For the IMPROVE 

and CASTNet data, CMAQ v4.5 has the largest underpredictions for nitrate in the summer 

months, when the nitrate concentrations are lowest, and there is a small tendency to overpredict 

nitrate in the winter months at the CASTNet and IMPROVE sites, when the nitrate concentrations are greatest. The model meets the performance goals for both the CASTNet and IMPROVE 

data, but it fails to meet both the performance goals and the less stringent performance criteria 

for most months for the STN data. A possible explanation is that the STN sites are located near 

urban areas with high NOx emissions and high nitrate concentrations, and that the coarse grid 

resolution of the 36-km model causes artificial dilution and underpredictions for these sites. 

However, results from the 12-km CMAQ simulations (discussed in Section 4.6) do not support 

this hypothesis; the 12-km CMAQ model has very similar performance to the 36-km model, with 

large underpredictions of nitrate for the STN sites. 

If we focus primarily on the results at the IMPROVE and CASTNet sites, the model performance 

for nitrate is reasonably good. Although there are large underpredictions for the summer months, 

the summer also has high temperatures and low nitrate concentrations, so even large negative 

bias errors in nitrate in the summer months will have a relatively small effect on the error in total 

light extinction and visibility. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 75 

Figure 4-13. Nitrate monthly MFB for the 36-km results for CMAQ 

for versions 4.4 and 4.5 compared to the IMPROVE data. 

Figure 4-14. Monthly average nitrate MFB for CMAQ version 4.5 

36-km results compared to the CASTNet, IMPROVE, and STN networks. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 76 

Figure 4-15. Bugle plot of monthly average nitrate MFB (top) and MFE (bottom) for 

CMAQ version 4.5 36-km results and the CASTNet, IMPROVE, and STN networks. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

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4.4.3 Organic carbon (OC) 

Figure 4-16 shows the CMAQ v4.4 and v4.5 modeled OC compared to the IMPROVE OC data. 

CMAQ v4.4 overpredicted OC during all 12 months, with the positive MFB varying from 15% 

to 40%. CMAQ v4.5 had significantly improved performance, with lower OC predictions and 

MFB close to zero for several months and a maximum MFB of about 20%. 

Figure 4-17 compares the CMAQ v4.5 OC to the IMPROVE and STN networks. The CMAQ 

results had uniformly large negative biases for the STN sites. 

Figure 4-18 shows the OC bugle plots for the two monitoring networks. For IMPROVE, CMAQ 

v4.5 meets the performance goals for MFB for all months, and it meets the performance goals for 

MFE for all but two months. In contrast, the model fails to meet the performance goals for OC at 

the STN sites for all 12 months, and it fails to meet the less stringent performance criteria for 

most months. A couple comments on the OC model performance should be made: 

• The STN OC measurements are believed to be less reliable than the IMPROVE data, 

since the former are not blank corrected. 

• The OC measurements were adjusted to organic matter carbon (OMC) using a 1.4 factor. 

This value is likely too low; OMC/OC ratios between 1.6 and 2.2 have been suggested, 

with an average value of 1.8. Using a 1.8 OMC/OC ratio factor would reduce the OC 

overprediction bias. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 78 

Figure 4-16. OC monthly MFB for the 36-km results for CMAQ 

versions 4.4 and 4.5 compared to the IMPROVE data. 

Figure 4-17. Monthly average OC MFB for CMAQ version 4.5 

36-km results compared to the IMPROVE and STN networks. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

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Figure 4-18. Bugle plot of monthly average OC MFB (top) and MFE (bottom) for 

CMAQ version 4.5 36-km results and the IMPROVE and STN networks. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

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4.4.4 Elemental carbon (EC) 

Figure 4-19 shows the CMAQ v4.4 and v4.5 modeled EC compared to the IMPROVE EC data. 

CMAQ v4.4 underpredicted EC during the winter and overpredicted it during the summer. 

CMAQ v4.5 predicted lower EC concentrations than did CMAQ v4.4; this resulted in larger 

negative bias during the winter months, but improved performance during the summer months, 

with MFB close to zero in July and August. 

Figure 4-20 compares the CMAQ v4.5 EC to the IMPROVE and STN networks. CMAQ had 

better performance for EC at the STN sites, with smaller negative bias in most months and small 

overpredictions of EC in summer months. 

Figure 4-21 shows the EC bugle plots for both monitoring networks. CMAQ v4.5 met the 

performance goals for EC for both the IMPROVE and STN data. Interestingly, the model had 

opposite trends in performance for the two networks. For IMPROVE the modeled negative bias 

increased for the months with the lowest measured EC, while for STN the model tended toward 

negative bias for the those months with the highest EC concentrations. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

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Figure 4-19. EC monthly MFB for the 36-km results for CMAQ 

versions 4.4 and 4.5 compared to the IMPROVE data. 

Figure 4-20. Monthly average EC MFB for CMAQ version 4.5 

36-km results compared to the IMPROVE and STN networks. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

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Figure 4-21. Bugle plot of monthly average EC MFB (top) and MFE (bottom) 

for CMAQ version 4.5 36-km results and the IMPROVE and STN networks. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

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4.4.5 Other fine PM (soil) 

Figure 4-22 shows the CMAQ v4.4 and v4.5 modeled soil compared to the IMPROVE soil data. 

CMAQ v4.4 overpredicted soil during the winter months and underpredicted it during the 

summer months. CMAQ v4.5 predicted lower soil concentrations than did CMAQ v4.4; this 

resulted in larger negative bias during the winter months, but improved performance during the 

summer months. (Note that a plot comparing CMAQ performance across multiple ambient 

monitoring networks is not included for soil because these data are measured only at the 

IMPROVE network.) 

Figure 4-23 shows the soil bugle plots for the IMPROVE network. CMAQ v4.5 met the 

performance goals for soil for most months, and when it failed to meet the performance goals it 

did meet the performance criteria. 

Figure 4-22. Soil monthly MFB for the 36-km results for CMAQ 

versions 4.4 and 4.5 compared to the IMPROVE data. 
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Figure 4-23. Bugle plot of monthly average soil MFB (top) and MFE (bottom) 

for CMAQ version 4.5 36-km results and the IMPROVE network. 
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4.4.6 Coarse mass (CM) 

Figure 4-24 shows the CMAQ v4.4 and v4.5 modeled CM compared to the IMPROVE CM data. 

CMAQ v4.4 underpredicted CM during most months, and the underpredictions became worse 

with CMAQ v4.5. (Note that a plot comparing CMAQ performance across multiple ambient 

monitoring networks is not included for CM because these data are measured only at the 

IMPROVE network.) 

Figure 4-25 shows the CM bugle plots for the IMPROVE network. CMAQ v4.5 failed to meet 

both the performance goals and the performance criteria for CM for most months. 

Figure 4-24. CM monthly MFB for the 36-km results for CMAQ 

versions 4.4 and 4.5 compared to the IMPROVE data. 
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Figure 4-25. Bugle plot of monthly average CM MFB (top) and MFE (bottom) 

for CMAQ version 4.5 36-km results for the IMPROVE network. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

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4.4.7 Ozone 

Although ozone does not directly affect visibility, it is an important oxidant that affects the 

production of nitrate, sulfate, and OC from secondary organic aerosols. Thus, the model must 

accurately simulate ozone to correctly represent the formation of several SOA species. Figure 

4-26 compares the ozone performance for CMAQ v4.4 and v4.5 for the AQS data. Following 

traditional ozone model performance guidance, we excluded all ambient data for which the 

measured concentration was less than 60 ppb. As a result, the comparison was weighted toward 

those days with high ambient ozone, and this tended to cause the model to underpredict the data, 

especially for the winter months during which there were few days with measured ozone greater 

than 60 ppb. We also performed this analysis without the filter on the ambient data so that all 

ozone data were included in the comparison. In that comparison (not shown here) the model 

tended to overpredict ozone, with larger overpredictions occurring during the winter months. 

Overall, we expect CMAQ to overpredict total oxidant and to slightly overpredict formation of 

SOA for low ambient ozone conditions, and to slightly underpredict formation of SOA for high 

ambient ozone conditions. 

Figure 4-26. Ozone monthly MFB for the 36-km results for CMAQ versions 4.4 and 4.5 

compared to the AQS hourly average ozone data, using a filter 

to exclude all hours with data values less than 60 ppb. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

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4.5 Evaluation of CMAQ at Class I Areas for the Best-20% and 

Worst-20% Visibility Days 

Plots of the model performance averaged for the best-20% and worst-20% visibility days at each 

IMPROVE site in the Class I areas of the WRAP region are included in the following appendices: 

Appendix B: Stacked-Bar Plots for CMAQ—Annual Time Series of Contributions to Light 

Extinction 

Appendix C: Stacked-Bar Plots for CMAQ—Contributions to Light Extinction for the 

Average of Best-20% Visibility Days, with Data Relaxed in Time 

Appendix D: Stacked-Bar Plots for CMAQ—Contributions to Light Extinction for the 

Average of Worst-20% Visibility Days, with Data Relaxed in Time 

Appendix E: Stacked-Bar Plots for CMAQ—Contributions to Light Extinction for the 

Average of Best-20% Visibility Days, with Data Relaxed in Time and Space 

Appendix F: Stacked-Bar Plots for CMAQ—Contributions to Light Extinction for the 

Average of Worst-20% Visibility Days, with Data Relaxed in Time and Space 

These plots were prepared for the CMAQ v4.4 simulation using the 2004 release of the VIEWS 

IMPROVE data. For all plots we used the default, monthly average relative humidity at each site 

to calculate the light extinction for each PM species. Each appendix contains a total of 88 plots, 

one plot for each of the WRAP IMPROVE sites. As discussed in Section 4.3.1, the annual 

stacked-bar time-series plots are useful for comparing the model results for model-simulated 

total light extinction to the observed light extinction calculated for each IMPROVE site. An 

example of these plots was shown in Figure 4-4. Comparing the plots across the 88 sites, it is 

apparent that many of the sites have a unique pattern or “fingerprint” of seasonal variation in the 

contributions of different PM species to total light extinction. In general the model provides a 

reasonably good approximation of the fingerprint of the ambient data at many of the IMPROVE 

sites. An advantage of these plots is that they indicate good performance in a qualitative sense 

when the model predicts peak PM concentrations within a day or two of the ambient data peaks. 

Because the data are sparse relative to the model results (i.e., sampling occurs only every third 

day), a strictly quantitative evaluation using error and bias metrics would indicate very poor 

model performance on some days, when for those same days the stacked-bar time-series plot 

indicates relatively good performance. These plots are useful for indicating the ability of the 

model to simulate observed conditions reasonably well, and they provide a sense of increased 

confidence in the model’s skill compared to simply relying on the tabulated error and bias 

metrics. 

Appendices C and D present stacked-bar plots of contributions to light extinction for the average 

of the best-20% and worst-20% visibility days, respectively, with the modeled and observed data 

relaxed in time. The motivation for these plots, as discussed in Section 4.3.1.6, is to determine 

whether the model can simulate the extremes of clean and polluted conditions at the IMPROVE 

sites. An example plot was shown in Figure 4-9. 

Paging through the PDF file of these plots shows that at most sites the model overpredicts the 

observed data on the cleanest days and underpredicts the observed data on the most polluted days 

when matched precisely in space and time. However, comparing the bar plot for the 3-day time 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 89 

window to the observed data, it is also apparent that the model can match the extremes of clean 

and polluted conditions at many sites simply by expanding the comparison by one day before 

and after the measured data. For the 7-day and 15-day time windows, there is some additional 

decrease in modeled extinction for the cleanest days (and some additional increase in modeled 

extinction for the most polluted days), but most of the change occurs within the 3-day time 

window. These results indicate that the model can simulate the extremes of clean and polluted 

conditions, or that the model is able to simulate the dynamic range of visibility conditions 

observed at the IMPROVE sites. As noted earlier, however, this should not be construed to 

represent a validation of the model, because the “improvements” in model performance may be 

an artifact of compensating errors in the model input data. Instead, these plots are useful as 

supporting evidence that the model can predict the extremes of the clean and polluted condition, 

and they show that the model is not unresponsive to combinations of conditions of input data that 

could predict clean or polluted extremes. 

Appendices E and F are analogous to Appendices C and D, except that the comparisons of model 

results and observed data are relaxed in both time and space. Given the known errors in wind 

direction and wind speed, it is plausible that, at least in some cases, the model may mislocate the 

extreme clean and polluted events by one day, or may spatially offset the extremes by one or 

more grid cells.

4.6 Grid Resolution Sensitivity Analysis: CMAQ 12-km Visibility 

Modeling for the WRAP Region 

Using CMAQ, we performed a model sensitivity simulation using a 12-km horizontal resolution 

grid for the WRAP nested subdomain shown in blue in Figure 1-4. First, a 12-km simulation of 

the MM5 model and emissions processing at the 12-km resolution were completed in order to 

develop the high-resolution input data required for this sensitivity simulation. Compared with a 

36-km grid, a 12-km grid requires a factor of 9 increase in the number of grid cells needed to 

represent the same geographic area. The area in each 12-km grid cell is only 144 km2 compared 

to 1,296 km2 in each 36-km grid cell. Possible benefits of using a finer-resolution 12-km grid 

instead of a 36-km grid include the following: 

• Better resolved and possibly more accurate meteorology and emissions input data. 

• Less numerical dispersion, resulting from reduced artificial dilution compared to point 

sources being averaged over the coarse 36-km grid. 

• Less numerical dispersion in the advection algorithms. 

• Improved accuracy resulting from higher topographical resolution. 

• More accurate treatment of nonlinear photochemical reactions. 

• More precise location of the ambient monitoring sites relative to topographical features 

and emissions sources. 

The primary disadvantage of running a 12-km model is the increased time and compute resource 

costs: (1) the additional cost of modeling the much larger number of grid cells required to 

represent the nested subregion, and (2) the additional cost of having to perform a coarse-grid 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 90 

model simulation as well as the 12-km simulation, because the 36-km simulation is needed for 

developing boundary conditions for the 12-km grid. 

Figures 4-27 and 4-28 show the CMAQ 12-km and 36-km model MFB compared to the 

IMPROVE, CASTNet, NADP/NTN, and STN data for sulfate and nitrate, respectively. Figure 

4-29 displays the CMAQ 12-km and 36-km model MFB compared to the IMPROVE OC, EC, 

SOIL, and CM. Finally, Figure 4-30 shows the CMAQ 12-km and 36-km model MFB compared 

to the AQS gas-phase ambient data for ozone and CO, where the ozone MFB is calculated using 

the ambient data filter to exclude measured ozone values less than 60 ppb, as described earlier. 

Each of these plots shows the same general result: although there are small differences between 

the MFBs calculated for the 12-km and the 36-km models, their performance is quite similar. It 

is not apparent that one version of the model is in any way superior to the other. These results 

indicate that there would be no advantage in terms of our MPE to running the more resourceintensive 12-km model instead of the 36-km model. 

We also examined the spatial resolution differences between the CMAQ 12-km and 36-km 

results by preparing PAVE plots that have ambient IMPROVE data overlaid on the model 

results, using the same spatial domains for both versions of the model—in other words, we 

extracted data from the 36-km domain results to make the plots directly comparable to the 12-km 

model domain. The daily spatial plots can be viewed on the web site using the animation tool at 

this link: 

http://pah.cert.ucr.edu/aqm/308/cmaq.shtml#base02aV4512kvs36k. 

To compare the 12-km and 36-km plots via the web site, it is necessary to open two browsers so 

that both a 12-km and a 36-km plot can be viewed at the same time. Note that the IMPROVE 

data are measured only every third day, so only one-third of these plots will show both model 

and ambient data. 

To illustrate this comparison here, we selected a pair of plots for a single day for the IMPROVE 

sulfate, nitrate, OC, EC, and soil; these results are shown, respectively, in Figures 4-31 through 

Figure 4-35. Each pair of plots shows the same general result. The spatial features or plumes in 

the concentrations are more detailed in the 12-km plot than in the 36-km plot, and the peak 

model value in the domain is usually considerably greater in the 12-km model than in the 36-km 

model. This is a result of the reduced numerical dispersion in the 12-km model, as discussed 

above. (Note that these differences in the domain peak concentration can be read only in the text 

at the bottom of each plot, where the grid cells with the domain-wide minimum and maximum 

are identified in each plot. The differences in peak values between the 12-km and 36-km results 

are not apparent in the plots themselves because the color scale was chosen at the low range of 

the modeled concentration, in order to distinguish concentration gradients in the areas of the 

domain that have generally clean conditions with low PM concentrations. Choosing a greater 

value in the color range would primarily identify urban areas and would not distinguish PM 

concentration gradients in or near Class I areas.) 

Next, we note that in each pair of plots, the increased spatial detail and increased peak 

concentration do not significantly change the general appearance of the plots nor do they change 

the regions that experience high or low PM concentrations. Thus, the spatial plots are consistent 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 91 

with the results of the model performance metric comparison for the 12-km and 36-km model 

results discussed above: there appears to be no significant benefit in using the finer-resolution 

grid for modeling the lower concentrations of PM that typically occur at Class I areas. 

There remains a possibility that the 12-km model might have a different response to emissions 

controls than the 36-km model does, even though there is no substantial difference in the model 

performance at the Class I sites. However, in a separate study we investigated this possibility in 

previous model emissions reduction sensitivity simulations using the VISTAS 12-km and 36-km 

model scenarios, and we found that the model grid resolution had only small effects on the 

model response to emissions reductions. 

Bearing out the results of the current sensitivity study described in this subsection, past model 

sensitivity experiments performed using high-resolution grids have shown little or no 

improvement in MPEs for finer-resolution grids. These studies include 12-km and 4-km model 

simulations performed in parts of the WRAP domain, and sensitivity simulations using a 12-km 

nested subregion in the VISTAS 36-km coarse grid. Similar results have also been observed by 

National Park Service and EPA staff when modeling PM for the southwestern U.S. and Big Bend 

regions. Thus, the results described here are generally consistent with our expectations based on 

previous modeling studies. A possible explanation for the lack of notable improvement in the 

12-km model simulation is that the effects on model performance resulting from uncertainty and 

errors in model inputs and in model formulation (see Section 4.8) have a significantly greater 

effect on model performance than does any performance benefit resulting from the better spatial 

resolution. In fact, the use of a higher resolution may actually degrade model performance if 

there are slight biases in the model inputs. For example, a slight error in the wind direction might 

cause a plume to miss a monitor when a high-resolution grid is used, whereas a coarse-resolution 

grid allows the plume to impact the monitor due to enhanced numerical dispersion. If it is indeed 

the case that the effects of these uncertainties and errors far outweigh any performance benefit of 

using a higher resolution, we will need to achieve substantial improvements in the ability to 

model meteorology, emissions, and chemistry before we can expect to realize benefits from 

using a finer spatial resolution. However, it is important to note that this general finding is 

offered only within the context of regional haze modeling in the western U.S., where the receptor 

areas of interest (Class I areas) are primarily in remote areas away from most major source 

regions. For modeling elevated ozone and PM2.5 concentrations that primarily occur in or near 

major urban areas or near high-density source regions, the model performance benefits of higher 

resolution grids may be more apparent. 

In summary, because of the substantial increase in the time and compute resource costs of 

performing high-resolution modeling, we do not recommend the routine application of additional 

12-km modeling as part of the WRAP planning effort. However, this conclusion applies only to 

the clean conditions experienced at Class I areas. It is likely that the finer-resolution model 

would provide some benefit for modeling higher ozone and PM2.5 concentrations relevant to 

health-based air quality standards in urban or suburban areas. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 92 

IMPROVE 

sulfate

-50.0-40.0-30.0-20.0-10.0

0.0

10.020.0

Fractional Gross Error 12km 36km

12km -0.4 -5.4 -40.8 -33.3 3.236km 1.0 -0.8 -38.1 -23.1 10.8

January February June July November

CASTNET sulfate

-70-60-50-40-30-20-10

0

10

Fraction Gross Error 

12km -20.5 -21.7 -56.8 -35.2 36km -24.6 -19.4 -61.1 -35.1 

January February June July 

NADP sulfate

-40-30-20-10

0

10203040

Fraction Gross Error 12km 36km

12km 27.8 4.6 -4.1 -14.5 17.736km -8 -14.9 -15.3 -34 6.9

January February June July November

STN sulfate

-45-40-35-30-25-20-15-10

-5

05

Fraction Gross Error 

12km 0 1.9 -25.2 -37.8 36km -32.7 -29.3 -42.7 -39.5 

January February June July 

Figure 4-27. MFB for the 12-km and 36-km CMAQ sulfate compared to each monitoring ne
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 93 

IMPROVE 

nitrate

-200.0-150.0-100.0

-50.0

0.0

50.0

Fractional Gross Error 12km 36km

12km -24.0 -12.1 -152.0 -168.0 -44.336km -4.5 10.7 -137.0 -156.0 -15.1

January February June July November

CASTNET

nitrate

-30-20-10

0

102030

Fractional Gross Error 

12km 6.4 14.9 -22.9 -25.2 36km 10.6 23.2 -25.2 -23.9 

January February June July 

NADP

nitrate

-70-60-50-40-30-20-10

0

10

Fractional Gross Error 12km 36km

12km 0.6 -8.9 -17.9 -48.2 -8.336km -30.7 -27.5 -30.5 -62.3 -15.4

January February June July November

STN

nitrate

-160-140-120-100

-80-60-40-20

0

Fractional Gross Error 

12km -56 -56.9 -124 -145 36km -60 -64.4 -125 -137 

January February June July 

Figure 4-28. MFB for the 12-km and 36-km CMAQ nitrate compared to each monitoring ne
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 94 

IMPROVE

OC

-50.0-40.0-30.0-20.0-10.0

0.0

10.020.030.0

Fractional Gross Error 12km 36km

12km -0.4 -6.3 -29.1 -38.5 -26.836km 9.7 20.5 -0.2 -2.4 -3.0

January February June July November

IMPROVE

EC

-60.0-50.0-40.0-30.0-20.0-10.0

0.0

Fractional Gross Error 

12km -48.4 -43.4 -18.4 -13.8 36km -47.5 -41.4 -7.9 -0.3 

January February June July 

IMPROVE

SOIL

-80.0-60.0-40.0-20.0

0.0

20.040.060.0

Fractional Gross Error 12km 36km

12km 45.9 10.6 -55.9 -50.6 -12.736km 43.5 11.9 -39.8 -21.7 4.8

January February June July November

IMPROVE

CM

-160.0-140.0-120.0-100.0

-80.0-60.0-40.0-20.0

0.0

Fractional Gross Error 

12km -52.8 -88.8 -138.0 -151.0 36km -49.9 -84.5 -138.4 -148.0 

January February June July 

Figure 4-29. MFB for the 12-km and 36-km CMAQ compared to IMPROVE data for OC, EC, so
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 95 

AQS

O3

-45

-40

-35

-30

-25

-20

-15

-10

-5

0

Fractional Gross Error

12km

36km

12km -37.7 -19.5 -12.1 -23.2 -36.5

36km -38.1 -16.4 -7.5 -17.5 -30.9

January February June July November

AQS

CO

-100

-80

-60

-40

-20

0

Fractional Gross Error

12km

36km

12km -76.8 -78.1 -58.5 -55.3 -72.7

36km -90.4 -90 -62.3 -58 -83.3

January February June July November

Figure 4-30. Mean fractional bias for the 12-km and 36-km CMAQ 

compared to AQS gas-phase ozone and CO data. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 96 

Figure 4-31. Comparison of spatial plots for the CMAQ 36-km (top) 

and 12-km (bottom) runs with IMPROVE sulfate data. 
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 97 

Figure 4-32. Comparison of spatial plots for the CMAQ 36-km (top) 

and 12-km (bottom) runs with IMPROVE nitrate data. 
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 98 

Figure 4-33. Comparison of spatial plots for the CMAQ 36-km (top) 

and 12-km (bottom) runs with IMPROVE OC data. 
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Figure 4-34. Comparison of spatial plots for the CMAQ 36-km (top) 

and 12-km (bottom) runs with IMPROVE EC data. 
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Figure 4-35. Comparison of spatial plots for the CMAQ 36-km (top) 

and 12-km (bottom) runs with IMPROVE soil data. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 101 

4.7 CAMx Model Performance Evaluation and Comparison to CMAQ 

As noted in Section 4.1, CAMx is a three-dimensional photochemical grid model that supports 

aerosols, mercury, air toxics and other reactive and nonreactive compounds. The model currently 

contains two main aerosol treatments: (1) the CMU multisectional approach, where aerosols can 

grow and shrink among, say, 10 size sections; and (2) a two-section coarse/fine approach that 

assumes all secondary PM species are in the fine mode. CAMx contains several of the same 

modules as CMAQ (e.g., CB-IV gas-phase chemistry, RADM aqueous-phase chemistry, 

ISORROPIA aerosol thermodynamics, and PPM advection solver), but there are significant 

differences in the core model formulation and some of the major processes. 

For our MPE study, we performed an annual CAMx simulation on the same 36-km grid that was 

used for the CMAQ modeling. Emissions data were consistent with the CMAQ data and were 

either processed using SMOKE with the same emissions input data or converted from CMAQ 

binary files to CAMx binary files using custom software. The same horizontal (36 km) and 

vertical (19 layers) grid structure was used in the CAMx and CMAQ simulations. 

In this section we present summary results for the CAMx monthly averaged MFBs compared to 

the ambient monitoring data and to the CMAQ model results. More detailed results are included 

on the project web site: http://pah.cert.ucr.edu/aqm/308/cmaq.shtml#CAMxvsCMAQV4536k

and in the following appendices to this report:

Appendix G: Stacked-Bar Plots for CAMx—Annual Time Series of Contributions to Light 

Extinction 

Appendix H: Stacked-Bar Plots for CAMx—Contributions to Light Extinction for the 

Average of Best-20% Visibility Days, with Data Relaxed in Time 

Appendix I: Stacked-Bar Plots for CAMx—Contributions to Light Extinction for the 

Average of Worst-20% Visibility Days, with Data Relaxed in Time 

These are analogous to Appendices B, C, and D for the CMAQ results. A useful approach for 

comparing the CMAQ and CAMx model results at individual IMPROVE sites is to open the 

corresponding CMAQ and CAMx appendices and compare the results side by side for each site. 

In the discussion below, we focus on the monthly average bias for each model in our 

comparisons of results. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

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4.7.1 Sulfate (SO4) 

When the model predictions were averaged over all IMPROVE sites, CAMx predicted greater 

sulfate concentrations than did CMAQ. Figure 4-36 shows the MFB for CAMx compared to 

each of the monitoring networks. CAMx overpredicted sulfate at the IMPROVE and CASTNet 

sites during the winter months, but during the summer months had good agreement with the 

observed sulfate, with small bias. CAMx had small negative (underprediction) bias throughout 

the year at the more urban STN sites. 

Figure 4-37 shows the MFB for both CAMx and CMAQ sulfate predictions using IMPROVE 

data at sites in the WRAP states. For the winter months when CMAQ had small positive bias, 

CAMx had larger positive bias. The winter performance was poorest for both models in 

December, for which CMAQ and CAMx had 38% and 80% positive biases, respectively. The 

sign of the model bias was reversed for the summer months, when CMAQ had negative bias 

of -40% while CAMx had close to zero bias. 

Figure 4-38 shows the bugle plots for the CAMx monthly averaged bias and error for all three 

ambient data networks in the WRAP states. Comparing Figure 4-38 to the CMAQ bugle plots in 

Figure 4-12 shows very similar performance for the two models, with larger sulfate overpredictions and larger error at the lower sulfate levels during winter months, and improved CAMx 

performance at the STN sites throughout the year. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

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CAMx SO4

-80

-60

-40

-20

0

20

40

60

80

100

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Fractional Bias(%)

CASTNET IMPROVE STN

Figure 4-36. Sulfate monthly MFB for CAMx compared to ambient data at 

the CASTNet, IMPROVE, and STN sites. 

IMPROVE SO4

-80

-60

-40

-20

0

20

40

60

80

100

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Fractional Bias(%)

CAMx

CMAQ V45

Figure 4-37. Comparison of CAMx and CMAQ 36-km monthly MFB 

for IMPROVE sulfate data. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

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Figure 4-38. Bugle plot of monthly average sulfate MFB (top) and MFE (bottom) for 

CAMx 36-km results and the CASTNet, IMPROVE, and STN networks. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 105 

4.7.2 Nitrate (NO3) 

Figure 4-39 shows the nitrate MFB for CAMx compared to each of the monitoring networks in 

the WRAP States. CAMx had a large positive bias for nitrate at the IMPROVE and CASTNet 

sites during the winter months and a large negative bias during the summer months. The model 

had a small negative bias during the winter at the STN sites. 

Figure 4-40 shows the nitrate MFB for both CAMx and CMAQ compared to the IMPROVE 

data. For the winter months, CMAQ had good model performance with small bias, while CAMx 

had large positive bias. The performance was similar for both models during the summer months, 

both having large negative bias. However, underprediction of nitrate during the summer is not of 

concern at most IMPROVE sites because nitrate typically has very low observed concentrations 

and only small contributions to visibility impairment during summer months. 

Figure 4-41 shows the bugle plots for the CAMx monthly averaged bias and error for all three 

ambient data networks. Comparing Figure 4-41 to the CMAQ nitrate bugle plots in Figure 4-15 

shows that CMAQ generally met the performance goals at the IMPROVE and CASTNet sites 

while CAMx failed to meet the performance goals and criteria during the months with higher 

nitrate concentration (i.e., the winter months). Both models failed to meet performance criteria 

for some months at the STN sites, although CAMx had better performance than CMAQ for that 

network. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

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CAMx NO3

-200

-150

-100

-50

0

50

100

150

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Fractional Bias(%)

CASTNET IMPROVE STN

Figure 4-39. Nitrate monthly MFB for CAMx compared to ambient data at 

the CASTNet, IMPROVE, and STN sites. 

IMPROVE NO3

-200

-150

-100

-50

0

50

100

150

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Fractional Bias(%)

CAMx

CMAQ v45

Figure 4-40. Comparison of CAMx and CMAQ 36-km monthly MFB 

for IMPROVE nitrate data. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 107 

Figure 4-41. Bugle plot of monthly average nitrate MFB (top) and MFE (bottom) for 

CAMx 36-km results versus the CASTNet, IMPROVE, and STN networks. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

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4.7.3 Organic carbon (OC) 

Figure 4-42 shows the OC MFB for CAMx compared to each of the monitoring networks with 

OC data. CAMx had negative bias of about -20% to -40% for OC throughout the year at the 

IMPROVE sites, and larger negative bias at the STN sites. 

Figure 4-43 shows the OC MFB for both CAMx and CMAQ compared to the IMPROVE data. 

CMAQ had generally better performance than CAMx for OC when considering the monthly 

average over all IMPROVE sites. CMAQ had a small positive bias of about 0% to -20% with no 

seasonal pattern, while CAMx had a negative bias throughout the year. These results use an 

OMC/OC ratio of 1.4. If we were to use a ratio of 1.8, as discussed in Section 4.4.3, the CMAQ 

positive bias would be reduced and the CAMx negative bias would be increased. 

Figure 4-44 shows the OC bugle plots for the CAMx monthly averaged bias and error. 

Comparing Figure 4-44 to the CMAQ OC bugle plots in Figure 4-18 shows that CMAQ met the 

performance goals and criteria for IMPROVE while CAMx failed to meet the performance goals 

and marginally met the criteria. Both models failed to meet performance goals at the STN sites, 

although CMAQ met the performance criteria for some months for that network. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

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CAMx OC

-120

-100

-80

-60

-40

-20

0

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Fractional Bias(%)

IMPROVE STN

Figure 4-42. OC monthly MFB for CAMx compared to ambient data at 

the IMPROVE and STN sites. 

IMPROVE OC

-60

-50

-40

-30

-20

-10

0

10

20

30

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Fractional Bias(%)

CAMx

CMAQ V45

Figure 4-43. Comparison of CAMx and CMAQ 36-km monthly MFB 

for IMPROVE OC data. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 110 

Figure 4-44. Bugle plot of monthly average OC MFB (top) and MFE (bottom) for 

CAMx 36-km results versus the IMPROVE and STN networks. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 111 

4.7.4 Elemental carbon (EC) 

Figure 4-45 shows the EC MFB for CAMx compared to each of the monitoring networks. CAMx 

had nearly zero bias in winter, and relatively small positive bias in spring and summer at the 

IMPROVE sites and larger negative bias at the STN sites. 

Figure 4-46 shows the EC MFB for both CAMx and CMAQ compared to the IMPROVE data. 

CMAQ predicted lower EC concentrations than did CAMx, and CMAQ had a relatively large 

negative bias of about -15% to -30% in the winter months and a small negative bias in the 

summer months, while CAMx had good performance in the winter and a relatively large positive 

bias during the spring and summer. 

Figure 4-47 shows the EC bugle plots for the CAMx monthly averaged bias and error. 

Comparing Figure 4-47 to the CMAQ EC bugle plots in Figure 4-21 shows that both models met 

the performance goals. CAMx might have slightly better overall performance in terms of bias for 

EC because its summer overpredictions are slightly smaller than the CMAQ winter 

underpredictions. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

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CAMx EC

-60

-50

-40

-30

-20

-10

0

10

20

30

40

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Fractional Bias(%)

IMPROVE STN

Figure 4-45. EC monthly MFB for CAMx compared to ambient data at 

the IMPROVE and STN sites. 

IMPROVE EC

-60

-50

-40

-30

-20

-10

0

10

20

30

40

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Fractional Bias(%)

CAMx

CMAQ V45

Figure 4-46. Comparison of CAMx and CMAQ 36-km monthly MFB for 

IMPROVE EC data. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 113 

Figure 4-47. Bugle plot of monthly average EC MFB (top) and error (bottom) for CAMx 

36-km results versus the IMPROVE and STN networks. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 114 

4.7.5 Other fine PM (soil) 

Figure 4-48 shows the soil MFB for CAMx and CMAQ compared to IMPROVE data. CAMx 

overpredicted soil throughout the year, with a large positive bias in winter months and a smaller 

positive bias in summer months, while CMAQ had a relatively small positive bias in winter 

months with a larger negative bias in summer months. (Note that a plot showing the soil MFB 

for CAMx compared across multiple monitoring networks is not included because soil data are 

measured only at the IMPROVE network.) 

Figure 4-49 shows the soil bugle plots for the CAMx monthly averaged bias and error. Comparing Figure 4-49 to the CMAQ soil bugle plots in Figure 4-23 shows that both models met the 

performance criteria but each model failed to meet the performance goals for a few months, with 

CAMx erring on the side of overprediction and CMAQ erring on the side of underprediction. 

Note that CAMx has two separate species for “other fine PM,” one for crustal and one for 

“other.” When developing emission inventories for CAMx, all fine PM from dust sources (e.g., 

road dust, agricultural dust, construction dust, and windblown dust) are assigned to the fine 

crustal species that is typically compared to the IMPROVE soil measurement. However, because 

in our MPE study the CAMx emissions were obtained from the CMAQ emissions that do not 

make this distinction, the fine crustal species could not be separately modeled and compared 

with the IMPROVE soil measurements. This likely explains the difference in the CAMx soil 

performance in this study compared to other studies (e.g., for MRPO and CENRAP) where fine 

crustal was modeled as a separate species in CAMx. 

IMPROVE SOIL

-80

-60

-40

-20

0

20

40

60

80

100

120

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Fractional Bias(%)

CAMx

CMAQ V45

Figure 4-48. Comparison of CAMx and CMAQ 36-km monthly MFB for 

IMPROVE soil data. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 115 

Figure 4-49. Bugle plot of monthly average soil MFB (top) and MFE (bottom) for CAMx 

36-km results versus the IMPROVE network. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 116 

4.7.6 Coarse mass (CM) 

Figure 4-50 shows the CM MFB for CAMx and CMAQ compared to the IMPROVE data. The 

models have very similar performance for CM, showing a large negative bias throughout the 

year, with the highest underprediction bias occurring during the summer months. (Note that a 

plot showing the CM MFB for CAMx compared across multiple monitoring networks is not 

included because CM data are measured only at the IMPROVE network.) 

Figure 4-51 shows the CM bugle plots for the CAMx monthly averaged bias and error. 

Comparing Figure 4-51 to the CMAQ CM bugle plots in Figure 4-25 shows that both models fail 

to meet both the performance goals and the performance criteria. 

The poor model performance for coarse mass is consistent with our results in previous modeling 

studies. It may result in part from the difficulty of accurately modeling sources and transport of 

windblown dust or missing sources of CM emissions in the emissions inventory data. The 

measured CM concentrations may also be due to local sources that cannot be accounted for in the 

coarse (36-km) grid resolution used in the CMAQ and CAMx modeling. 

IMPROVE CM

-160

-140

-120

-100

-80

-60

-40

-20

0

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Fractional Bias(%)

CAMx

CMAQ V45

Figure 4-50. Comparison of CAMx and CMAQ 36-km monthly MFB 

for IMPROVE CM data. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 117 

Figure 4-51. Bugle plot of monthly average CM MFB (top) and MFE (bottom) for CAMx 

36-km results versus the IMPROVE network. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 118 

4.8 Uncertainties, Errors, and Biases and Their Effects on Future 

Visibility Projection 

The 2002 base case version A model results discussed in this report have large uncertainties and 

known errors and bias in the input data. The levels of uncertainty in the emissions inventories 

depend on the source category and range from about ±30% for sources with the least uncertainty 

to more than a factor of 3 uncertainty for those sources with the greatest uncertainty, such as 

NH3, biogenic emissions, and fires. It is also known that the gas-phase photochemical mechanism is incomplete and uncertain, and that there are error and bias in the meteorology modeling. 

Moreover, there remains uncertainty in the treatment of aqueous chemistry, aerosol thermodynamics, and rates of removal of PM and gaseous species by wet and dry deposition. The 

magnitude and effects of errors in input data are likely to vary depending on the location in the 

modeling domain. It is possible that the effects of errors in emissions and chemistry on modeled 

visibility at Class I areas may be mitigated somewhat because Class I areas tend to be distant 

from the largest emissions source areas. This means that the effects of errors may be reduced by 

near-field removal of pollutants, and that errors may be cancelled out by compensating errors in 

input data. The effects of errors in meteorology, however, may increase with greater transport 

distances because of biases in wind speed and direction. There are also errors and biases in the 

monitoring data that were used in the MPE, which in some cases may be as large as the errors in 

some of the input data. Because of the nonlinearity of the chemical reactions and the complex 

interactions of errors in the input data, it is not possible to quantitatively estimate the aggregate 

uncertainty in the model predictions. However, we can conclude that we should expect the model 

to exhibit large errors and bias when compared to the ambient data. It is likely that major 

advances would be required in our ability to model the emissions and meteorology data before 

we can hope to substantially improve model performance for long-term visibility modeling 

beyond the levels of error and bias described in the results above. 

It is also likely that the effects of errors in the base case model performance will be mitigated 

somewhat because the model predictions are being used in a relative sense to project future-year 

fine-particle and visibility levels. That is, relative reduction factors (RRFs) are developed as the 

ratio of future-year to current-year modeling results and are then applied to the current-year 

visibility observations to project future-year visibility, so some uncertainties in the modeling 

results may cancel each other. When used in a relative sense, it is most important that the model 

correctly predict the direction of the change in visibility in response to changes in emissions. In 

previous model sensitivity simulations that we have performed for VISTAS, we found that the 

model response to changes in emissions was not very sensitive to changes in the base case 

modeling, including changes in grid resolution, changes in photochemical mechanism used, and 

changes in the base case NH3 emissions inventory. We expect that the model responsiveness for 

the WRAP scenarios used here will also be not very sensitive to similar changes in model inputs 

and choice of science options. 

The key issue with respect to model uncertainty is whether the model correctly simulates the 

direction of change in visibility in response to emissions changes. It is likely that for sulfate, 

nitrate, and ammonium species, this will depend on acid-to-base ratios. There are only very 

limited NH3 and ammonium ambient monitoring data available, and it is not possible to evaluate 

the model’s ability to distinguish accurately acidic versus basic conditions. Collection of 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 119 

additional NH3 and ammonium data should be a high priority for future monitoring efforts so 

that this can be better evaluated in future modeling studies. 

In spite of these uncertainties, the model performance is quite good for sulfate, and the formation 

of sulfate and responsiveness of sulfate to SOx controls is relatively insensitive to errors in 

modeled ozone and NH3 concentrations. This is true because there are multiple oxidation 

pathways to convert SOx to sulfuric acid, and because sulfuric acid can form sulfate even at low 

NH3 levels. Therefore, we expect the model to be most reliable for predicting the effects of 

changes in SOx on changes in visibility. The same reasoning holds true for several other PM 

compounds, including OC, EC, soil, and CM. The species that have the largest uncertainty are 

nitrate and ammonium, but these species tend to represent only a small fraction of total PM on 

many of the worst visibility days, particularly in summer months. 

4.9 Conclusions of Model Performance Evaluation 

The results presented above for the CMAQ and CAMx 36-km base case version A model performance evaluations for calendar year 2002 show that the models have some skill in simulating 

several of the PM species that contribute to regional haze. Also, these model results are significantly improved compared to the Section 309 modeling that was performed using calendar year 

1996 (see http://wrapair.org/309/031215Final309TSD.pdf). The improved model performance 

may result from a combination of improvements in the 2002 meteorology modeling, new data 

sets and emissions models developed for the 2002 emissions inventories, and updates and 

improvements to the CMAQ and CAMx models. 

The CAMx model performance evaluation, and the 2002 base case version A CMAQ 12-km 

simulation, did not show clear-cut improvement in model performance over the CMAQ 36-km 

simulation. Neither CMAQ nor CAMx exhibited improved model performance across all species 

and seasons. Whereas CMAQ exhibited better sulfate performance in the winter, the CAMx 

summer sulfate performance appeared better than CMAQ’s. CMAQ nitrate performance was 

better in the winter, while both models exhibited large summer nitrate underprediction biases. 

CMAQ exhibited better OC model performance than CAMx, but uncertainties with the 

OMC/OC factor and known missing processes in both the CMAQ and CAMx SOA modules 

(e.g., missing SOA from biogenic sesquiterpenes and isoprene) make these comparisons difficult 

to interpret. The models exhibited similar fairly good performance for EC, while both models 

showed fairly poor performance for soil and CM. Despite the significant increase in computer 

resources needed for the 12-km simulation, CMAQ model performance for the 36-km and 12-km 

grids were nearly identical, with neither model configuration being superior to the other when 

examining across all species and seasons. These results are consistent with previous WRAP 

model comparisons using the 1996 modeling year and the preliminary 2002 modeling, which 

showed that there is no clear improvement in model performance when using alternate models or 

a higher grid resolution. Typically, model performance improves slightly for some species and 

degrades slightly for other species. 

In summary, the CMAQ and CAMx models both met many of the performance goals for most 

months of the year and for most of the ambient monitoring networks for sulfate, nitrate, OC, EC, 

and soil. Both models failed to meet performance goals for coarse mass and also for most PM 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 120 

species measured at the STN network, including soil, EC, OC, and nitrate. The STN data, 

however, are located in areas influenced by urban emissions. Thus, poor performance at the STN 

sites may not be indicative of the models’ performance at Class I areas. 

We conclude from our results that the CMAQ and/or CAMx 36-km modeling can be used in 

combination with the RRF approach to evaluate the benefits of emissions reduction strategies for 

all PM species other than CM to project visibility changes at Class I areas. We also conclude that 

there is little benefit in using the 12-km grid resolution and many detriments, due to the 

increased computer run times and disk storage, and consequently increased likelihood for the 

introduction of errors in the analysis. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

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5. Computational Aspects of the Modeling 

5.1 Description of Hardware and Software Used 

5.1.1 Hardware 

At a minimum, a basic computer hardware system for visibility modeling should include the 

components described in this section. Several different options described here range from building your own computer systems (least expensive, but most effort) to the purchase of turnkey 

systems from computer vendors (most expensive, but least effort). 

5.1.1.1 Compute nodes 

We recommend PC/Linux computer systems based on x86 and x86-64 architecture, i.e., Intel or 

AMD CPUs. Given their relatively low cost and high performance, these PC/Linux systems offer 

the best performance-to-cost ratio of any computing platforms. The one disadvantage of 

PC/Linux systems is that they tend to be less reliable, having more down time than the more 

expensive compute hardware alternatives. More expensive and more reliable systems include 

workstations from IBM, SUN, and SGI. PC/Linux systems can be purchased from a variety of 

leading vendors. A more economical option for states and tribes with some hardware expertise is 

to purchase components and build your own compute nodes. At UCR we purchase parts and 

build our own nodes. This option is about one-half to one-third the cost of purchasing prebuilt 

compute nodes from vendors. An advantage of this approach is that when these compute systems 

fail, you will have the experience and knowledge needed to rapidly repair them. With the cost 

savings in building our own PCs at UCR, we can maintain a large inventory of spare parts so that 

dead computers can be brought back online the same day. 

There is a significant cost benefit in the purchase of the dual-CPU servers. We have also found 

the best price-to-performance ratio using the AMD Athlon systems. The AMD Opteron systems 

may have a slight advantage when performing simulations using parallel code with 8 or more 

CPUs. We have found the Intel Xeon CPUs to be more tolerant of high temperatures than are the 

AMD CPUs, so the Intel CPUs might be a better choice for environments that do not have excellent temperature control. AMD now offers (as of July 2005) a new dual-core Opteron CPU. Intel 

now offers a 64-bit, 2-GB L2 Cache 90nm dual-core CPU. AMD also offers energy-efficient 

versions of the Opteron CPUs that may function better in rooms with limited air conditioning 

control. 

For large-scale modeling, we recommend that several dual-CPU systems be installed, along with 

a high-speed gigabit network switch and a separate file server with a RAID5 disk storage system. 

5.1.1.2 Options for RAM memory 

At a minimum, all systems should be configured with at least 1 GB of RAM. System cost increases significantly with 2 GB or more of RAM. We have found that 1 GB of RAM is sufficient 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 122 

for modeling on the RPO Unified 36-km domain. There may be some benefit in installing 2 GB 

of RAM on compute nodes used for 12-km modeling domains. All file servers should include 

2 GB of RAM. 

5.1.1.3 Data storage systems 

For large-scale modeling, we typically require the ability to store data sets as large as several 

terabytes (TB). Large disk storage systems are built using redundant arrays of independent disks 

(RAID) (also referred to as redundant arrays of inexpensive disks). RAID systems should be 

configured as RAID5 or RAID6, which reserve part of the disk space for recovering data when 

one of the disk drives fails. Disks in the RAID systems should be “hot-swappable,” that is, it 

should be possible to replace a failed drive without shutting down the RAID system. Ideally, the 

RAID system should be configured with a standby spare drive so that the RAID can rebuild the 

data system immediately when a disk fails. At UCR we have experienced four catastrophic 

failures of RAID5 systems during the past five years, in which we lost all data on the RAID even 

though we used RAID5 with standby spares. Two of these failures were due to unreliable IBM 

Deskstar hard drives, and the others were due to failures of the RAID5 hardware itself. Thus, 

even a RAID5 or RAID6 system should not be considered 100% reliable, and all critical data 

should be backed up to a second RAID5 system and also archived to tape or backup disk drives. 

For all large primary data storage systems, we recommend that the disk systems be hard mounted 

(soft mounting will result in corrupted writes of output data). 

5.1.1.4 Network equipment 

Gigabit Ethernet switches and adapters are relatively inexpensive, so we strongly recommend the 

use of Gigabit network equipment. The older 10/100 network switches have significant performance problems. There are other, higher-speed options available—for example, Myrinet—but 

these would probably be cost prohibitive. We have obtained excellent performance on our 

Gigabit Ethernet, and we do not recommend the more expensive Myrinet. 

5.1.1.5 Backup, archiving, and data transfer 

Because of the large numbers and sizes of files involved in our modeling projects, we use 

(1) Firewire/USB disk storage for archiving and backup, and (2) redundant RAID5 systems for 

critical data backup. 

Transfer of files over the Internet is not feasible for large files due to very slow transfer times. 

However, transfer by ftp can be used for small files (typically less than 1 GB). 

5.1.1.6 Server room 

A key aspect of computer hardware systems is an adequate room to house the computers. When 

more than two or three compute nodes and servers are used together, they have high noise and 

heat loads. A server room should have the following attributes: (1) secure, locked room with 

limited access; (2) good air conditioning and humidity control; (3) automated monitoring and 

alarm systems in case climate control fails; (4) a clean, dust-free space; and (5) high-speed 

Internet access for electronic transfer of data. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

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5.1.2 Software 

Software required for visibility modeling includes the components described below. 

5.1.2.1 The operating system 

Red Hat Enterprise Linux is currently our operating system (OS) of choice. A disadvantage is 

that it must be purchased from Red Hat, but the purchase includes support from Red Hat. A less 

expensive option is CentOS, which is a free repackaging of Red Hat's Enterprise server without 

Red Hat support. Until recently we used and recommended free Linux distributions such as 

Fedora and CentOS. However, the lack of support can result in considerable staff time spent in 

installing and debugging hardware installed on these systems; also, we do not recommend use of 

Fedora because it is no longer being updated. If you need OS support or have complex hardware 

configurations, then the use of Red Hat Enterprise Linux or other commercial Linux distributions 

is strongly recommended. SuSE is another very well supported commercial Linux distribution. 

The basic installation of a Linux operating system should be rather straightforward and sufficient 

for air quality modeling. For a relatively simple hardware configuration and staff with experience 

in maintaining the OS, the free distribution of CentOS could be a good choice. 

5.1.2.2 The Fortran compiler 

A Fortran compiler is an integral part of the software. Both Portland Group Fortran Compiler and 

Intel Fortran Compiler can be used to compile CMAQ, CAMx, and all relevant software 

programs. At UCR we use the Portland Group Fortran compiler. 

5.1.2.3 NetCDF 

The netCDF libraries were developed to support large, 3-D data sets using a binary, machineindependent, self-describing file structure. The netCDF libraries are required to compile the 

CMAQ model. The netCDF package can be downloaded from the following web site: 

http://www.unidata.ucar.edu/software/netcdf. 

5.1.2.4 Input/Output Applications Programming Interface (I/O API) 

The Input/Output Applications Programming Interface (I/O API) libraries are built on the 

netCDF libraries and were developed specifically for use in SMOKE and CMAQ. You can 

download the I/O API library from http://www.baronams.com/products/ioapi. To build the I/O 

API library, first edit the configuration file for the appropriate architecture and compiler, then 

build the library by typing “make”. 

In the past we have had problems when the I/O API libraries and the CMAQ model were 

compiled with different compiler versions. We therefore recommend that the same compiler be 

used to compile all of CMAQ and all of its supporting libraries. 

5.1.2.5 MPICH 

MPICH is a freely available, portable implementation of MPI, which is the standard for messagepassing libraries. MPICH can be downloaded from http://www-unix.mcs.anl.gov/mpi/mpich. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

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Either MPICH1 or MPICH2 will work with the CMAQ code. For CAMx users, the current 

CAMx release uses only OMP and not MPICH, so there is no need to compile the MPICH 

library. 

5.1.2.6 Parallel applications 

To run CMAQ on more than one machine via MPICH, three things need to be configured. 

First, the users’ home directories have to be available on each machine. Typically this would be 

done via NFS (network file system). Here at UCR, in addition to the home directories, the 

software that does not come standard with the operating system is placed on an NFS server, 

along with all of our data. Our various NFS shares are mounted on all of our compute nodes, 

making all of our data and applications available everywhere they are needed. To help reduce 

network overhead, we use the Linux automounter daemon so that NFS shares are mounted only 

when they are accessed, and then automatically unmounted again after being inactive for a set 

period of time. We also discovered that the “hard” mount option is necessary to eliminate data 

corruption problems. 

The second thing to set up is “authentication.” Authentication means a mechanism that allows 

the users to log in with the same user name and password on any of the machines on your 

network. Two common ways to achieve authentication are with NIS or OpenLDAP. 

The third thing is passwordless logins for the MPICH jobs. This can be achieved either via an rsh 

server or an ssh server. With rsh, you simply create a .rhosts file in your home directory with the 

names or IPs of the machines you want to be able to log in to without a password. Using ssh is 

somewhat more complicated. The RMC staff can provide a UNIX script that can be run inside 

the .ssh directory in your home directory if the simpler rsh option is not available on your 

system. 

5.2 Summary of Procedures for Modeling 

The CMAQ modeling system includes interface processors to incorporate the outputs of the 

meteorology and emissions processors and to prepare the requisite input information for initial 

and boundary conditions and photolysis rates to the CMAQ Chemistry Transport Model 

(CCTM). 

The most current CMAQ model and data are available from the CMAS download center. The 

download center includes the CMAS Model Clearinghouse and Data Clearinghouse. Additional 

information is available from the Community Modeling and Analysis System (CMAS) help 

desk. Although one could run CMAQ on a Windows platform, we recommend running it on 

UNIX/Linux platforms. 

This section briefly summarizes the procedures for air quality modeling with CMAQ. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

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5.2.1 The Meteorology-Chemistry Interface Processor (MCIP) 

The CMAQ model requires inputs of three-dimensional gridded wind, temperature, humidity, 

cloud/precipitation, and boundary layer parameters. The current version of CMAQ can utilize 

output fields from the PSU/NCAR MM5 meteorological model and the Weather Research and 

Forecast (WRF) Modeling System. 

MM5 is a state-of-the science atmosphere model that has proven useful for air quality applications and has been used extensively in past local, state, regional, and national modeling efforts. 

The MM5 modeling system software is freely provided and supported by the Mesoscale Prediction Group in the Mesoscale and Microscale Meteorology Division of NCAR. 

The first step is to run MM5 to generate the meteorological fields for CMAQ. Detailed information on how we set up and run MM5 is at WRAP MM5 Webpage. 

After running MM5, the next step is to run the Meteorology-Chemistry Interface Processor 

(MCIP) to provide the complete set of meteorological data in netCDF binary format required by 

the CCTM. 

5.2.2 Initial condition (ICON) and boundary condition (BCON) processors 

Initial conditions (ICONs) and boundary conditions (BCONs) are necessary to solve the partial 

and ordinary differential equations in air quality models such as CMAQ. The CMAQ ICON and 

BCON processors typically generate IC and BC files from two sources. The first is a timeinvariant set of vertical concentration profiles. The second source is from existing CCTM 3-D 

concentration files; this source is usually used in one-way nested simulations. 

5.2.3 CMAQ photolysis rates processor (JPROC) 

The CMAQ photolysis rates processor (JPROC) estimates a table of clear-sky photolysis rates 

for a specific date. Many chemical reactions in the atmosphere are initiated by the photodissociation of numerous trace gases (e.g., ozone, HCHO, carbonyls, H2O2, HONO). These 

photodissociative reactions are responsible for most of the smog buildup. Accurate photodissociation reaction rate (or photolysis rate) estimates must be made to accurately model and 

predict the effects of air pollution. 

The Total Ozone Mapping Spectrometer (TOMS) total ozone column data could also be used in 

the JPROC processor to better estimate the photolysis rates for the specific date. 

5.2.4 CMAQ emissions processing 

CMAQ requires 3-D gridded emissions input files. The current emissions files are generated by 

the Sparse Matrix Operator Kernel Emissions (SMOKE) system. The SMOKE emissions 

processor can be downloaded from the CMAS home page. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

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Reports and documents on emissions processing with SMOKE for the WRAP CMAQ 

simulations are available on the following web site: http://pah.cert.ucr.edu/aqm/308/docs.shtml. 

5.2.5 CMAQ Chemical Transport Model (CCTM) 

The CCTM simulates the relevant and major atmospheric chemistry, transport, and deposition 

processes involved throughout the modeling domains. 

Key science processes in CCTM include (1) advection and diffusion; (2) gas-phase chemistry 

(CB-IV and SAPRC); (3) aerosols; (4) cloud dynamics and chemistry; (5) photolysis rates; and 

(6) plume-in-grid. The two steps to set up and run CCTM involve (1) using the “bldit” script to 

create the CCTM executable; and (2) using the run scripts to execute the CCTM processor 

Reports and documents on the WRAP CMAQ 36-km and 12-km simulations are available on the 

following web site: http://pah.cert.ucr.edu/aqm/308/docs.shtml. 

5.3 Computational Performance Benchmarks 

CMAQ Suite: The most current CMAQ model (v4.5) is used for the benchmark. 

Fortran/C Compiler: Portland Group Fortran 90 compiler (pgf90 v6.0-5) and GNU C compiler 

(gcc version 3.4.4 20050721 [Red Hat 3.4.4-2]) are used for the benchmark. 

Computing Platform: Dual-CPU AMD Opteron 246 systems, each running Red Hat Enterprise 

Linux . Each system has 2 GB of RAM, an 80-GB system disk, and gigabit Ethernet. The 64-bit 

Opteron CPUs are backwardly compatible with the x86 instruction set, although a 64-bit 

operating system is required to benefit from the 64-bit extensions. Communication for these 

machines is provided by a high-speed gigabit Ethernet switch. 

CMAQ Run Setup: The CMAQ annual runs are divided into four seasons with 15 days of spinup period back-dating from the first day of the season. The last hour from the previous day is the 

initial condition for the subsequent day. Parallel processing (MPI; see above) is used in these 

simulations. 

Run time: The run time (wall-clock time) ranges from 2.25 hours with two CPUs to 1-1.5 hours 

with four CPUs for the WRAP 36-km simulations. Of the wall-clock time, approximately 65% of 

the time is used in computation and 35% by the system to handle I/O. The run time for a 24-hour 

WRAP 12-km simulation is approximately 3.5 hours with six CPUs. 

Run-time Memory Requirement: For each CMAQ job, the total run-time memory requirement 

(dependent on the WRAP domain and model physics) is approximately 422 Mb for the 36-km 

domain and 970 Mb for the 12-km domain. 

Disk Storage: CMAQ I/O files are stored on IDE-SCSI RAID5 array units that use inexpensive 

IDE disks but interface with our file servers via an Ultra 160 SCSI port. Total disk storage 

requirements for a WRAP 36-km simulation, for each day, are 1.4 GB for input files and 2.6 GB 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 127 

for output files. For the 12-km domain, the disk requirements for input and output files are 

3.3 GB and 5.9 GB, respectively. 

5.4 Technology Transfer 

Technology transfer involves transferring the models, model evaluation tools, and data sets to the 

Nation Park Services, states, and tribes so that they can perform additional studies of emissions 

reduction strategies. It also includes support in the setup and operation of the models. A relatively small budget is included in the current WRAP RMC work plan for technology transfer 

activities. 

Because of the large numbers and sizes of files, the two main options we commonly use include: 

• Transfer by ftp: Transfer of files over the Internet is not feasible for large files due to 

very slow transfer times. However, ftp and e-mail and can be used for small files 

(typically less than 1 GB). 

• Firewire/USB drives: For large numbers and sizes of CMAQ files, Firewire/USB drives 

with capacities of 250 GB or higher are used. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

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6. Conclusions 

The primary objectives of this report were 

• to document the model performance evaluations using the CMAQ and CAMx models for 

the WRAP calendar year 2002 visibility modeling, 

• to make recommendations on the configuration of the modeling systems, and 

• to determine whether the modeling systems are sufficiently reliable to be used as one of 

several tools in the process of developing visibility SIPs and TIPs. 

Concerning model performance for visibility-related species, both the CMAQ and CAMx models 

met many of the performance goals for most months of the year and for most of the ambient 

monitoring networks for sulfate, nitrate, OC, EC, and soil. A key component of the modeling 

effort was the development of performance metrics and approaches for presenting complex 

modeling results in a way that effectively communicates the model performance. Among the new 

approaches that we developed in this effort, the annual stacked-bar time-series plots and the 

stacked-bar plots for the average of the best-20% and worst-20% visibility days at each individual IMPROVE site are especially useful for summarizing the model performance. These plots 

are included in Appendices B through I. 

Both models failed to meet performance goals when compared against data from the Speciation 

Trends Network (STN) for coarse mass and also for most PM species measured, including soil, 

EC, OC, and nitrate. The STN data, however, are located in areas influenced by urban emissions. 

Thus, poor performance at the STN sites may not be indicative of the quality of model performance at Class I areas. 

As discussed in this report, there remain substantial uncertainties in the data used in the modeling 

systems as well as in the systems themselves. These include 

• Uncertainties in the model formulation, including the gas-phase photochemical reactions 

and aerosol formation 

• Known error, bias, and uncertainty in some of the meteorology input data 

• Large uncertainties in some components of the emissions inventory data 

• Errors and uncertainty in the observational data used in the MPE 

Even with these uncertainties, the model visibility predictions for calendar year 2002 are 

generally consistent with the ambient data at most sites within the WRAP region. The models are 

reasonably successful in reproducing both the seasonal trends and the relative contributions of 

most PM2.5 components to visibility impairment at the individual IMPROVE sites, as illustrated 

in the stacked-bar time-series plots. The models are able to simulate most PM2.5 components 

reasonably well because the fundamental processes that contribute to emissions of primary PM, 

formation of secondary PM, and transport and removal of PM2.5 are generally well understood. 

For example, the oxidation of SOx, NOx, and VOC to form sulfate, nitrate, and SOA are well 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 129 

understood in general terms. It is considerably more difficult, however, to simulate these 

processes precisely, and the additional error and uncertainty in the emissions and meteorology 

data make it very difficult for the models to accurately simulate the concentrations of each 

component of PM2.5 at a particular location on any given day. Therefore, the model results 

should be thought of as providing verisimilitude of ambient visibility conditions in general, 

rather than an accurate prediction of visibility on any particular day. 

The key issue in considering the usefulness of the CMAQ and CAMx models is whether they can 

be used to accurately predict the changes in future visibility at Class I areas as a result of reductions in emissions of PM2.5 and its precursors. Given the uncertainties discussed above, it is 

difficult to judge the accuracy of model predictions in absolute terms for predicting future 

visibility changes. However, it is likely that the models will accurately predict the direction of 

change in visibility impacts for all components of PM2.5. Moreover, when used in combination 

with the relative reduction factor approach, the models may also predict the magnitude of change 

in visibility impacts for most components of PM2.5. This conclusion is based on the relative 

insensitivity of the models’ response to changes in their input data. We have considerably less 

confidence in the reliability of the models for predicting changes in visibility impacts for coarse 

mass because of large uncertainties in our ability to model the emissions, dispersion, transport, 

and deposition of coarse mass. 

Finally, we note that the model results for calendar year 2002 are significantly improved 

compared to the Section 309 modeling that was performed using calendar year 1996 (see 

http://pah.cert.ucr.edu/rmc/index.shtml and http://wrapair.org/309/031215Final309TSD.pdf). 

The improved model performance may result from a combination of improvements in the 2002 

meteorology modeling, new data sets and emissions models developed for the 2002 emissions 

inventories, and updates and improvements to the CMAQ and CAMx models. 

Based on our comparisons of the 36-km and 12-km modeling results presented here for CMAQ, 

and also based on the results of previous modeling studies, we conclude that there is no 

significant benefit in using the finer-resolution grid for modeling the concentrations of PM2.5 that 

typically occur at the Class I areas. It is possible that fine-resolution modeling would provide 

more benefit for ozone and PM2.5 modeling closer to urban areas or in regions with higher PM2.5

concentrations. However, for the relatively clean PM2.5 conditions at the WRAP Class I 

IMPROVE sites and the larger source-receptor relationships in the West, the effects of grid 

resolution are generally small. Therefore, we do not recommend the routine application of 

additional 12-km modeling as part of the WRAP regional haze planning effort, especially 

because of the significantly increased cost that would be involved in doing so. 

Based on all of our results, we conclude that either the CMAQ or the CAMx 36-km modeling 

can be used in combination with the RRF approach to evaluate the benefits of emissions 

reduction strategies for all PM species other than coarse mass to project visibility changes at 

Class I areas for regional haze planning purposes. The choice of one model or the other should 

be based on factors such as the amount of effort, resources, and time required to complete the 

model simulations. To the extent that resources are available, it may be useful to perform some 

simulations with each model because obtaining similar results from two different models would 

add to the weight of evidence supporting the visibility modeling results. 
Final Report for the WRAP 2002 Visibility Model Performance Evaluation 

 130 

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Appendices to the Report 

This report references nine appendices. Their combined size makes them too large to include 

within this document file. They can be downloaded from the RMC web site. Their titles and 

locations are as follows: 

A: Emissions Modeling and Quality Assurance (referenced in Section 2) 

http://pah.cert.ucr.edu/aqm/308/reports/final/2002_MPE_report_Appendix_A.pdf 

B: Stacked-Bar Plots for CMAQ—Annual Time Series of Contributions to Light 

Extinction (referenced in Section 4.5) 

2002_MPE_report_Appendices_B-I.zip 

C: Stacked-Bar Plots for CMAQ—Contributions to Light Extinction for the Average 

of Best-20% Visibility Days, with Data Relaxed in Time (referenced in Section 4.5) 

2002_MPE_report_Appendices_B-I.zip 

D: Stacked-Bar Plots for CMAQ—Contributions to Light Extinction for the Average 

of Worst-20% Visibility Days, with Data Relaxed in Time (referenced in Section 4.5) 

2002_MPE_report_Appendices_B-I.zip 

E: Stacked-Bar Plots for CMAQ—Contributions to Light Extinction for the Average 

of Best-20% Visibility Days, with Data Relaxed in Time and Space (referenced in 

Section 4.5) 

2002_MPE_report_Appendices_B-I.zip 

F: Stacked-Bar Plots for CMAQ—Contributions to Light Extinction for the Average 

of Worst-20% Visibility Days, with Data Relaxed in Time and Space (referenced in 

Section 4.5) 

2002_MPE_report_Appendices_B-I.zip 

G: Stacked-Bar Plots for CAMx—Annual Time Series of Contributions to Light 

Extinction (referenced in Section 4.7) 

2002_MPE_report_Appendices_B-I.zip

H: Stacked-Bar Plots for CAMx—Contributions to Light Extinction for the Average 

of Best-20% Visibility Days, with Data Relaxed in Time (referenced in Section 4.7) 

2002_MPE_report_Appendices_B-I.zip

I: Stacked-Bar Plots for CAMx—Contributions to Light Extinction for the Average 

of Worst-20% Visibility Days, with Data Relaxed in Time (referenced in Section 4.7) 

2002_MPE_report_Appendices_B-I.zip