OCS Study 

BOEMRE 2010-051 

 

Evaluation of NASA Aura’s Data Products 

for Use in Air Quality Studies 

over the Gulf of Mexico 

U.S. Department of the Interior

Bureau of Ocean Energy Management, 

 Regulation and Enforcement 

Gulf of Mexico OCS Region 
OCS Study 

BOEMRE 2010-051 

Evaluation of NASA Aura’s Data Products 

for Use in Air Quality Studies 

over the Gulf of Mexico 

Authors 

A. Pour Biazar 

R.T. McNider 

Mike Newchurch 

Lihua Wang 

Yun Hee Park 

With contributions from 

Maudood Khan 

The Universities Space Research Association 

Prepared under BOEMRE Cooperative Agreement 

M07AC13332/M07AC12463 

by 

University of Alabama in Huntsville 

National Space Science and Technology Center 

Earth System Science Center 

Huntsville, Alabama 25805 

Published by 

U.S. Department of the Interior 

Bureau of Ocean Energy Management, 

 Regulation and Enforcement 

Gulf of Mexico OCS Region New Orleans

December 2010 
iii

DISCLAIMER 

This report was prepared under contract between the Bureau of Ocean Energy Management, 

Regulation and Enforcement (BOEMRE) and the University of Alabama in Huntsville (UAH). 

This report has been technically reviewed by the BOEMRE, and it has been approved for 

publication. Approval does not signify that the contents necessarily reflect the views and policies 

of the BOEMRE, nor does mention of trade names or commercial products constitute 

endorsement or recommendation for use. It is, however, exempt from review and compliance 

with the BOEMRE editorial standards. 

REPORT AVAILABILITY 

This report is available only in compact disc format from the Bureau of Ocean Energy 

Management, Regulation and Enforcement, Gulf of Mexico OCS Region, at a charge of $15.00, 

by referencing OCS Study BOEMRE 2010-051. The report may be downloaded from the 

BOEMRE website through the Environmental Studies Program Information System (ESPIS). 

You will be able to obtain this report also from the National Technical Information Service in the 

near future. Here are the addresses. You may also inspect copies at selected Federal Depository 

Libraries. 

 Bureau of Ocean Energy Management, U.S. Department of Commerce 

 Regulation and Enforcement National Technical Information Service 

 Gulf of Mexico OCS Region 5285 Port Royal Road 

 Public Information Office (MS 5034) Springfield, Virginia 22161 

 1201 Elmwood Park Boulevard Phone: (703) 605-6040 

 New Orleans, Louisiana 70123-2394 Fax: (703) 605-6900 

 Telephone requests may be placed at Email: bookstore@ntis.gov

 (504) 736-2519, 1-800-200-GULF, or 

 Rush Orders: 1-800-553-6847 

 Fax: (504) 736-2620 

CITATION 

Biazar, A.P., R.T. McNider, M. Newchurch, M. Khan, Y.H. Park, and L. Wang. 2010. 

Evaluation of NASA AURA’s data products for use in air quality studies over the Gulf of 

Mexico. U.S. Dept. of the Interior, Bureau of Ocean Energy Management, Regulation 

and Enforcement, Gulf of Mexico OCS Region, New Orleans, LA. OCS Study BOEMRE 

2010-051. 82 pp. 

ACKNOWLEDGMENTS 

This work was accomplished under partial support from the Bureau of Ocean Energy 

Management, Regulation and Enforcement under Cooperative Agreement Between the 

University of Alabama in Huntsville and the Bureau of Ocean Energy Management, Regulation 

and Enforcement on Gulf of Mexico Issues, and the NASA Science Mission Directorate Applied 

Sciences Program. 
v

TABLE OF CONTENTS 

Page

LIST OF FIGURES ................................................................................................................ vii 

LIST OF TABLES................................................................................................................... xi 

1. EXECUTIVE SUMMARY ............................................................................................ 1 

1.1. PROBLEM STATEMENT.......................................................................................... 1 

1.2. OVERVIEW OF THE CURRENT PROJECT ................................................................. 2 

2. APPROACH ................................................................................................................... 5 

2.1. SATELLITE DATA.................................................................................................. 5 

2.1.1. AURA Satellite......................................................................................... 6 

2.1.1.1. Tropospheric Emission Spectrometer (TES) ............................. 7 

2.1.1.2. Ozone Monitoring Instrument (OMI)........................................ 8 

2.1.1.3. Mapping OMI and AIRS Observations Onto the CMAQ 

Model Domain......................................................................... 12 

2.1.2. Terra and Aqua Satellites (MODIS Instrument) .................................... 15 

2.1.2.1. Utilization of MODIS AOD for Initialization and 

Specification of Lateral Boundary Conditions in 

CMAQ ..................................................................................... 19 

2.2. OZONESONDE DATA........................................................................................... 22 

2.3. MODEL SIMULATIONS ........................................................................................ 24 

2.3.1. Meteorological Modeling ....................................................................... 24 

2.3.2. Emissions Processing ............................................................................. 31 

2.3.3. Photochemical Modeling........................................................................ 33 

2.3.3.1. Control Simulation (Base Case, CNTRL Simulation)............. 35 

2.3.3.2. Boundary Condition Provided by Global Model 

(RAQMS_BC Simulation)....................................................... 35 

2.3.3.3. Initial and Boundary Condition Provided from Satellite 

Observations (SAT_BC and SAT_ICBC Simulations)........... 36 

3. RESULTS AND DISCUSSION................................................................................... 45 

3.1. EVALUATION OF OMI OZONE PROFILES............................................................. 45 

3.2. MODEL PERFORMANCE IN THE FREE TROPOSPHERE........................................... 54 

3.3. MODEL PERFORMANCE IN THE BOUNDARY LAYER ............................................ 63 

3.3.1. Ozone...................................................................................................... 63 

3.3.2. PM2.5 ....................................................................................................... 68 

4. CONCLUSION AND RECOMMENDATIONS ......................................................... 77 

5. REFERENCES ............................................................................................................. 79 
vii

LIST OF FIGURES 

Page

Figure 1. A-Train constellation. ............................................................................................... 5 

Figure 2. Aura instrument measurements and their height range based on pre-launch 

design.......................................................................................................................... 6 

Figure 3. Differences between TES retrievals and ozonesonde measurements for 

different seasons in northern midlatitudes (35-60 N). . ........................................... 9 

Figure 4. Surface ozone concentrations as observed by TES for the week of August 

25-31, 2006. . ........................................................................................................... 10 

Figure 5. Example of OMI ozone profile and ozonesonde measurements interpolated 

onto the CMAQ vertical grid for Huntsville, Alabama, on August 1, 2006............. 13 

Figure 6. Aura/OMI Level 2 O3 profiles (24 layers) are mapped to CMAQ horizontal 

domain (36km x 36km) using a “drop-in-the-box” method.. ................................... 14 

Figure 7. Satellite-retrieved trace gases (OMI-NO2, OMI-HCHO, and AIRS-CO) is 

gridded to CMAQ 36km x 36km domain................................................................. 15 

Figure 8. Pixel extension algorithm (PEA) allows for increasing MODIS coverage by 

reconstructing missing values due to cloud contamination.. .................................... 18 

Figure 9. Vertical profiles of CMAQ mass concentrations scaled using the scaling 

ratio .. ..................................................................................................................... 21 

Figure 10. Ozonesonde locations for IONS-06 campaign. ...................................................... 23 

Figure 11. Locations of NOAA Ron Brown vessel during August 2006................................... 23 

Figure 12. Flow chart illustrating the model components.......................................................... 25 

Figure 13. 36-km modeling grid ................................................................................................ 25 

Figure 14. Subregions used in performance evaluation. ............................................................ 28 

Figure 15. Weather charts versus MM5 results for July 15 and 28. .......................................... 30 

Figure 16. Weather charts versus MM5 results for August 21 and 22. ..................................... 31 

Figure 17. Daily emissions totals (tons per day) for CO, NOx, SO2, NH3, Isoprene, and 

anthropogenic VOC .................................................................................................. 33 

Figure 18. Ozone (ppb) BC for south, east, north and west boundaries.. .................................. 38 

Figure 19. PAN (ppt) BC for south, east, north and west boundaries........................................ 39 

Figure 20. Formaldehyde (ppb) BC for south, east, north and west boundaries........................ 40 

Figure 21. NOx (ppt) BC for south, east, north and west boundaries. ....................................... 41 

Figure 22. Carbon monoxide (ppb) BC for south, east, north and west boundaries.................. 42 

Figure 23. Sulfate aerosol (g/m^3) BC for south, east, north and west boundaries. ............... 43 
viii

Figure 24. An example of OMI, TES, ozonesonde comparison. ............................................... 46 

Figure 25. OMI/ozonesonde comparison over United States for August 2006 during 

IONS-06 campaign. .................................................................................................. 49 

Figure 26. Scatter plot showing OMI ozone observations in the boundary layer vs. 

observations from EPA’s surface monitors for July 15 through September 7, 

2006. ......................................................................................................................... 51 

Figure 27. OMI ozone observations (left panel) versus observations by surface 

monitors (right panel) for August 3, 21, and 31, 2006. ............................................ 52 

Figure 28. Scatter plot showing ozone concentrations for Ron Brown ozonesonde 

observations at the surface versus OMI first layer observations. ............................. 54 

Figure 29. Model performance evaluated against ozonesonde measurements for August 

2006. ......................................................................................................................... 56 

Figure 30. OMI O3 profiles and model results evaluated against ozonesonde 

measurements for August 2006. ............................................................................... 57 

Figure 31. Curtain plots showing CMAQ simulated ozone concentrations from CNTRL 

simulation versus ozonesonde measurements for the month of August 2006.......... 58 

Figure 32. Curtain plots showing CMAQ simulated ozone concentrations from 

SAT_BC simulation versus ozonesonde measurements for the month of 

August 2006.............................................................................................................. 59 

Figure 33. Curtain plots showing CMAQ simulated ozone concentrations from 

SAT_ICBC simulation versus ozonesonde measurements for the month of 

August 2006.............................................................................................................. 60 

Figure 34. O3 (ppbv) spatial plots at 1900 UTC, August 21, 2006 simulated by 4 

CMAQ runs; plotted over model fields are 6 ozonesondes measurements 

launched between 1500~2300 UTC. Left panels represent CMAQ level 33 

(212 hPa). Right panels represent level 24 (501 hPa). ............................................. 62 

Figure 35. Mean observed ozone versus model predictions for CNTRL, RAQMS_BC, 

SAT_BC, and SAT_ICBC simulations for August 2006. ........................................ 66 

Figure 36. Mean normalized bias (MNB) for ozone predictions from CNTRL, 

RAQMS_BC, SAT_BC, and SAT_ICBC simulations............................................. 67 

Figure 37. Difference in ozone concentrations between SAT_BC and CNTRL 

simulations in the boundary layer for August 16, 2006............................................ 68 

Figure 38. Observed PM2.5 mass concentration (g/m3

) for different regions versus 

model predictions from CNTRL and SAT_ICBC simulations................................. 71 

Figure 39. Mean observed PM2.5 mass concentration (g/m3) for August 2006 over the 

continental U.S. versus model predictions from CNTRL, SAT_BC and 

SAT_ICBC simulations............................................................................................ 72 
ix

Figure 40. Mean normalized and fractional bias and error (MNB, MNE, MFB, MFE)

for CNTRL, SAT_BC and SAT_ICBC simulations................................................. 73 

Figure 41. Difference in surface PM2.5 mass concentration (g/m3

) between CNTRL 

and SAT_BC simulations for August 2, 2006, 18:00 GMT. .................................... 74 

Figure 42. Frontal passage of August 18-22, 2006, and its impact on PM2.5............................. 75 

Figure 43. Observed diurnal variation of PM2.5 mass concentration for few monitoring 

sites in Alabama and Mississippi versus model prediction from CNTRL 

(here marked as base) and SAT_ICBC (marked as assim) simulations. .................. 75 

Figure 44. Observed PM2.5 mass concentration for few monitoring sites in Alabama 

versus model prediction from CNTRL (here marked as base) and 

SAT_ICBC (marked as assim) simulations for the month of August 2006. ............ 76 
xi

LIST OF TABLES 

Page

Table 1. Percentage of Missing Pixels Within MM5 Area..................................................... 17 

Table 2. Speciation and Variable Name Used in the CMAQ Aerosol Module ...................... 19 

Table 3. MM5 Model Configuration. ..................................................................................... 27 

Table 4. Mathematical Formulation of Statistical Metrics Used for Evaluating 

Mesoscale Meteorological Model Performance. ...................................................... 27 

Table 5. Performance Statistics for Mesoscale Meteorological Models. ............................... 28 

Table 6. Monthly Meteorological Performance Statistics For Each of the SubRegions 

in Figure 14............................................................................................................... 29 

Table 7. Domain-wide Daily Emission Totals in Tons per Day............................................. 32 

Table 8. CMAQ Configuration Used in Simulations for This Project ................................... 34 

Table 9. Number of OMI/Sonde Pairs Used in the Evaluation of OMI Profiles for 

August 2006 for Each Model Layer. ........................................................................ 48 

Table 10. OMI / Rown Brown Vessel Surface Ozone Measurements for August 2006........... 53 

Table 11. Statistical Metrics for Air Quality Model Performance Evaluation. ........................ 63 

Table 12. Performance Statistics for CNTRL Simulation ........................................................ 65 

Table 13. Correlation Coefficient (R), Mean Normalized Bias (MNB), and Variance 

for Model PM2.5 Predictions (CNTRL and SAT_ICBC simulations) Versus 

MODIS Observations for the Frontal Event of August 18-22, 2006........................ 70 
1

1. EXECUTIVE SUMMARY 

1.1. Problem Statement 

The Bureau of Ocean Energy Management, Regulation and Enforcement (BOEMRE), a 

bureau in the U.S. Department of the Interior (DOI), is responsible for the management of 

mineral resources on the outer continental shelf (OCS), and for regulating the activities of 

offshore operators. One of BOEMRE’s regulatory obligations is the assessment of potential 

environmental impact of oil and gas operations in the Gulf of Mexico (GoM) and in particular 

the onshore air quality impact of such operations. GoM region is home to some of the most 

precious and sensitive habitats. Several coastal areas in the northeastern Gulf of Mexico have 

been designated as Wildlife Refuges and National Preserves. Some of these areas are assigned 

Class I and II status by the U.S. Environmental Protection Agency (EPA), and are subject to the 

most rigorous requirements for air quality. Due to such sensitivities, confidence in air quality 

assessments and reducing the uncertainties in the air quality studies over the GoM region is 

imperative. Furthermore, in adapting sound policies for control strategies, it is crucial to assess 

the impact of local pollution versus transboundary air pollution, and in a region such as GoM 

with scarce monitoring capability over open waters represents a challenge. 

Since air quality models are utilized for assessing the impacts of offshore operations, the 

inaccuracies in the model predictions of pollutants can significantly affect the outcome of 

impacts analysis. In assessing the potential onshore impact of ozone from OCS sources, one 

source of uncertainty in photochemical models is the specification of the background air. Errors 

in the specification of background concentrations of ozone and its precursors will propagate in 

the model and impact model predictions of ozone. Therefore, decreasing such reducible errors is 

of interest to BOEMRE. 

Regional air quality can be impacted by larger scale atmospheric motions that carry polluted 

air into a region through long range transport and cause enhancements in background levels of 

ozone and its precursors. Therefore it is essential for regional air quality modeling studies to be 

able to include transboundary air pollution. The usual practice in regional modeling is to nest the 

region within a much larger domain so that the impact of emission sources outside the region of 

study is included. Given the emission sources within the larger domain are well represented, this 

approach can reduce the propagation of the error due to ill defined boundary concentrations for 

short term forecasts. However, for regions neighboring ocean like GoM region such an approach 

may not be as effective due to the lack of information over the water. 

The GoM region is impacted by the recirculation of pollution in the southeastern United 

States. High pressure systems such as Bermuda high transport pollution from northeast over the 

waters off the east coast and recirculate it to the GoM region through southerly/southeasterly 

flow. If model simulations do not extend the domain far enough over the Atlantic Ocean to fully 

realize the impact of such recirculation, they introduce another source of uncertainty for GoM air 

quality assessments. 
2

Volatile organic compounds (VOCs) and oxides of nitrogen (NOx) are the main precursors of 

ozone in the boundary layer. They play an important role in the chemical composition of the 

troposphere. During the summer in the eastern United States there are two active natural sources 

for these compounds; i.e., hydrocarbon emissions in the boundary layer from highly vegetated 

landscape of the eastern U.S., and oxides of nitrogen from dense lightning activities in this 

region. Southeastern United States has a unique characteristic as far as air pollution is 

concerned. The region is highly vegetated and rich in biogenic hydrocarbons during the summer. 

The region is also impacted by the dominant summertime Bermuda high that creates a stagnant 

condition inducive to frequent afternoon convective activities with high frequency of lightning 

events (producing NOx). All of this is complemented by anthropogenic NOx/VOC emissions in 

the region. The abundance of natural sources of precursors together with the anthropogenic 

emissions creates a suitable environment for regional scale pollution that can impact the GoM 

region through recirculation. 

1.2. Overview of the Current Project 

Newly available satellite observations of tropospheric gases and aerosols have the potential 

to provide the necessary data to reduce the aforementioned uncertainties. In the current project 

we examined the viability of the satellite observations of ozone and aerosols for reducing the 

errors in the specification of background air and in improving the air quality model predictions. 

Such improvements will provide better estimates of ozone and aerosols for BOEMRE air quality 

assessments and the GoM region’s state agencies. 

This project originally focused on examining the ozone observations by Tropospheric 

Emission Spectrometer (TES) onboard Aura satellite. But soon after the start of the project it 

became clear that other sensors aboard Aura as well as sensors onboard other satellites like Terra 

(EOS AM) and Aqua (EOS PM) provide complimentary data that could be useful in providing a 

more comprehensive description of boundary layer and free tropospheric chemical composition. 

This project also took advantage of our active collaboration with the Smithsonian Astrophysical 

Observatory (SAO) to obtain the first available ozone profiles from Ozone Monitoring 

Instrument (OMI). A major challenge in using TES ozone measurements is the small footprint 

and poor spatial coverage. OMI on the other hand does not have those limitations but until 

recently it was only providing total column measurements of ozone. 

UAH is currently operating an ozonesonde station at the National Space Science and Technology 

Center (NSSTC). UAH was also involved in the Intercontinental Chemical Transport 

Experiment Ozonesonde Network Study 2006 (IONS-06) campaign and was one of the 

ozonesonde launch sites for evaluating ozone observations from TES. In this project, we used 

ozonesonde data from UAH and other IONS-06 stations for evaluation of TES and OMI vertical 

profiles. We also used this data for model evaluation. The Fifth-Generation NCAR/Penn State 

Mesoscale Model (MM5) (http://www.mmm.ucar.edu/mm5) and the EPA’s Community 

Multiscale Air Quality (CMAQ) (http://www.cmascenter.org) modeling system were used for 

modeling.
3

The key question we have addressed in this project is whether the satellite data can be 

utilized in air quality assessments, either for improving model performance or for evaluation. In 

doing so, we have (1) evaluated several satellite products, namely TES and OMI ozone profiles, 

and the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products, (2) devised 

techniques for the utilization of satellite data in the model, and (3) performed model simulations 

for August 2006 and evaluated the results against ozonesonde and surface observations. In the 

following each of these components will be explained in detail. 
5

2. APPROACH 

The satellite data we have considered thus far are TES and OMI ozone data and MODIS 

aerosol products. We have chosen summer of 2006, focused on the month of August, as the 

period of study. This period coincides with IONS-06 and provides a comprehensive daily 

ozonesonde observation. With respect to OMI and TES ozone, first the satellite data for the 

continental United States were extracted and evaluated against ozonesonde observations for this 

period. Then, this quality assured data were mapped onto the model domain and used in model 

simulations by modifying daily initial and boundary conditions in the simulations. In this section 

the data and the techniques for their utilization in the model are explained. Then a description of 

the model configuration for different simulations will be presented. 

2.1. Satellite Data 

Since 2002 NASA has launched a series of satellites with sun-synchronous orbits in close 

proximity to each other. They all pass the equator within few minutes of each other at around 

1:30 PM local time. Since their formation in some ways is analogous to a train that passes over 

the equator in early afternoon, the constellation of satellites is referred to as “A-Train.” 

Figure 1 illustrates the A-Train constellation with an indication of the local time of their 

overpass. Aqua, launched in 2002, is the leading satellite in this constellation making 

observations at 1:30 PM local time, and Aura, launched July 15, 2004, is the trailing satellite 

with 1:38 PM overpass. Other NASA satellites in this constellation are CloudSAT, CALIPSO 

and PARABOL (http://www.nasa.gov/missions/index.html). 

Figure 1. A-Train constellation. The times indicated are the local solar time that the satellite passes over 

equator, which is also the local time for the observation of any given point in the viewing window. 
6

In this project we have utilized data from different sensors onboard Aqua, Aura, CALIPSO, 

and a morning satellite Terra. Launched in December 18, 1999, Terra is a morning sunsynchronous satellite crossing the equator at 10:30 AM. In this project we utilized the aerosol 

products from MODIS onboard Terra and Aqua. Use of data from both morning and afternoon 

observations helped increase the spatial coverage of the data. Since the MODIS instrument 

cannot “see” through the clouds, combining the morning and the afternoon overpasses increases 

the chances of having observation for locations that were cloudy only for part of the day. 

2.1.1. AURA Satellite 

Aura (Latin for breeze) mission was launched in 2004 as part of Earth Observing System 

(EOS) program. It was launched into an ascending-node (705-km altitude) sun-synchronous 

polar orbit with a 98 ̊ inclination with an equator-crossing time of 13:45 (about 15 minutes 

behind Aqua) and 16 day repeat cycle with the design life of five years (Schoeberl et al., 2006). 

Aura carries four instruments: the High Resolution Dynamics Limb Sounder (HIRDLS), the 

Microwave Limb Sounder (MLS), the Ozone Monitoring Instrument (OMI), and the 

Tropospheric Emission Spectrometer (TES). Figure 2 shows the vertical range of Aura 

measurements for each of the instruments onboard Aura. The two sensors of interest to this 

study are TES and OMI. In the following sections these sensors and their measurements will be 

described in more detail. 

Figure 2. Aura instrument measurements and their height range based on pre-launch design. Column 

measurements are indicated in the lower part of the figure. Adapted from Schoeberl et al. (2006). 
7

2.1.1.1. Tropospheric Emission Spectrometer (TES) 

The Tropospheric Emission Spectrometer (TES) onboard the Aura satellite was designed to 

measure the global vertical distribution of tropospheric ozone as well as temperature and some 

other tropospheric species including carbon monoxide and water vapor [Beer et al., 2001; Beer, 

2006]. Initial validation of TES ozone was carried out by comparing a limited number of early 

measurements to ozonesondes, using the first version of TES nadir ozone data (V001) [Worden 

et al., 2007]. 

TES is a Fourier transform spectrometer that measures infrared emission of Earth’s 

atmosphere. Although TES can measure from both the nadir (directly below the sensor) and 

limb (side) views, nadir is presently the primary scanning geometry used. In cloud-free 

conditions, the nadir ozone profiles have approximately four degrees of freedom for signal, 

approximately two of which are in the troposphere, giving an estimated vertical resolution of 

about 6 km [Bowman et al., 2002; Worden et al., 2004; Worden et al., 2007] with a 5.3 km by 

8.3 km footprint. 

The primary measurement mode for TES is the Global Survey (GS), from which the 

instrument maps the earth in 16 orbits (~26 hours). The measurements in a global survey are 

divided into sequences which take about 82 seconds each. Prior to May 2005, each of these 

sequences consisted of 3 limb scans and 2 nadir scans. The radiances from the two nadir scans 

(which were made of the same spot) were averaged together. Nadir measurements in successive 

sequences were separated by about 544 km, while measurements from the neighboring TES orbit 

tracks were separated by 22o longitude (about 2100 km at 30 N). In the present GS 

configuration, which began on 21 May 2005, a sequence consists of three nadir scans and no 

limb scans. In the new GS mode, each nadir scan along the track is separated by about 182 km 

on the ground. The radiances are not averaged [Beer, 2006; Osterman et al., 2008] and the 

measurements provide more coverage. 

TES also has special observation modes, the most common one being the Step and Stare 

(SS), where numerous repetitive measurements are made near a given target, with scans about 6 

seconds apart and separated by 40-45 km along the ground track [Beer, 2006; Osterman et al., 

2008]. Another common special observation mode is transecting mode, in which repetitive scans 

are made where the nadir angle changes such that the footprints from subsequent scans are only 

about 12 km apart. Accumulating transect scans can thus create a nearly contiguous footprint 

spanning about 500 km. 

TES ozone is retrieved from the 9.6-μm ozone absorption band using the 995-1,070-cm-1

spectral range. The retrievals and error estimation are based on the optimal estimation approach 

[Rodgers, 2000]. TES retrievals are described in Worden et al. [2004] and Bowman et al. [2002, 

2006], with error characterization described in Kulawik et al. [2006]. Temperature, water vapor 

and ozone are simultaneously retrieved in the first step of the retrieval with other species and 

parameters retrieved in subsequent steps. A priori profile (also used as the initial guess) and 

covariance matrix are derived from ozone climatology developed using the MOZART model 

[Brasseur et al., 1998; Park et al., 2004]. To facilitate use of TES data, the averaging kernel 

matrix and prior constraint matrix are made available with the data, which may be obtained from 
8

the TES website (http://tes.jpl.nasa.gov). A detailed description of TES data is given in 

Osterman et. al. (2008) and the TES Level 2 Data User’s Guide, Version 2.0 (Osterman et al., 

2006). The user’s guide can be obtained from http://tes.jpl.nasa.gov/documents). 

Nassar et al. (2008) extensively evaluated TES observations against IONS-06 and other 

available ozonesonde data. They reported an overall positive bias of 3-10 ppb in TES ozone 

measurements. Figure 3 adapted from Nassar et al. shows the difference between TES retrievals 

and coincident ozonesonde measurements for different seasons in midlatitudes (35-60 N). As 

evident from the figure the differences both in terms of absolute difference and percentage are 

large. In particular, during the summer months which are of interest to the air quality community 

these differences are the highest in the boundary layer and in the upper troposphere. Summer of 

2006 also has the highest number of samples for comparison as the IONS-06 campaign was 

designed for the purpose of validating TES observations. The ozonesonde launch times were 

coordinated to coincide with Aura’s overpasses. It should be noted, however, that the 

ozonesonde data in these comparisons have been corrected for TES measurement sensitivity and 

vertical resolution by applying TES averaging kernels. 

The main problem in utilizing TES observations as direct input in air quality modeling 

practices is not its accuracy, but is the sparseness of observations and their relatively small 

footprint. This presents a challenge for their use in regional modeling. Figure 4 shows an 

example of TES ozone observations for the week of August 25-31, 2006, in the boundary layer. 

Ozone concentrations have been marked on the actual observations footprint on the map. As 

evident from the figure the actual footprint is so small relative to the gaps in the observations that 

it is hard to see the markings. Since the orbits are repeated every 16 days, averaging the data for 

a longer period of time will not help in filling up the gaps. This remains to be a challenge in 

absorbing/assimilating TES data in the photochemical models, since any correction made to the 

model as the result of these observations will be limited. 

2.1.1.2. Ozone Monitoring Instrument (OMI) 

One approach for better use of TES data would be the use of complementary information 

from other sensors aboard Aura. Both lower-tropospheric ozone and upper-tropospheric ozone 

signals are clearly apparent in UV backscatter instruments. The Global Ozone Monitoring 

Experiment (GOME) ozone monitoring instrument aboard the European Remote Sensing 

satellite (ERS-2) [Burrows et al., 1999; Chance et al., 1998; Hoogen et al., 1999] and Total 

Ozone Mapping Spectrometer (TOMS) [Chandra et al., 2003; Fishman and Brackett, 1997; Kim 

and Newchurch, 1998; Newchurch et al., 2001; Ziemke et al., 2000] present a rich literature of 

tropospheric ozone observations from space. The Ozone Monitoring Instrument (OMI) aboard 

Aura likewise measures tropospheric ozone. It is important to realize that one of the most 

important regions of ozone variation is in the boundary layer and that OMI should do at least as 

well as TOMS in this region and TOMS sees significant signal there [Kim et al., 2001; 

Newchurch et al., 2001]. 
9

Figure 3. Differences between TES retrievals and ozonesonde measurements for different seasons in 

northern midlatitudes (35-60 N). N indicates the number of coincidences used. Adapted 

from Nassar et al. (2008). 
10

Figure 4. Surface ozone concentrations as observed by TES for the week of August 25-31, 2006. The figure 

presents ALL the available observations (including both global survey (GS) and special observation 

(SO) modes) mapped on the actual TES observed footprint (~ 5.3x8.3 km2

). Adjacent tracks are 

separated by about 22 longitudes apart (2200km). Observations along the ground track are 

separated by about 544-km (old GS mode, prior to May 21, 2005), or 182-km (new GS mode, after 

May 21, 2005), or 40-45 km (SO mode), or 12 km in transect mode. 

OMI is a nadir-viewing near-UV/Visible charge-coupled device (CCD) spectrometer that 

was designed to measure the total columns of O3, HCHO and NO2, with a nominal ground 

footprint of 13x24 km2

 at nadir. OMI has been collecting data since August 9, 2004, and 

provides a complete global coverage in one day. OMI measurements cover a spectral region of 

264-504 nm (nanometers) with a spectral resolution between .42 and .63 nm. OMI provides 

ozone column measurements for a ground footprint of 13x24 km2

 and ozone profiles at 13x48 

km2

. OMI level 2 data can be obtained from 

http://disc.sci.gsfc.nasa.gov/data/datapool/OMI/Level2_V003. 

Through our collaborations with Dr. Xiong Liu of the Harvard-Smithsonian Center for 

Astrophysics we were able to obtain the first ozone profiles retrieved from OMI. Dr. Liu who 

had already constructed ozone profiles from GOME (Global Ozone Monitoring Experiment, a 

sensor similar to OMI) aboard a European satellite, was about to apply his technique to OMI 

when this project was started (Liu et al., 2005, 2006a, 2006b). The current project helped in 

evaluating his product as well as assessing its efficacy for air quality studies. This product would 

become part of the standard OMI data products. Dr. Liu provided O3 profiles retrieved from 

OMI measurements for the period of July-September 2006 that spans over the period of our 

study. 

OMI O3 profiles were provided on 24 fixed pressure layers in Dobson Units (DU). The 

partial column ozone O3,i (DU) can be easily converted to mean mixing ratio O3,i (ppbv) at each 

layer using the following formula (by assuming ozone is well mixed in that layer): 
11

O3,i (ppbv) = 1.251  O3,i (DU) / (Pi+1 - Pi )  1013.25  (R / (R + Zmid, i))2

Where Pi+1, Pi are the pressure in mb at two levels bounding layer i, R is the radius of earth, 

and Zmid,i is the average altitude of the layer. This formula is accurate to better than 1%. Using 

this relationship avoids the need for temperature in the conversion (Liu, personal 

communication). Similarly, the tropospheric column ozone can be converted to mean mixing 

ratio by using tropopause and surface pressure. Using mixing ratio has certain advantages over 

using tropospheric column ozone by reducing the variability simply due to the tropopause and 

surface pressure. 

Averaging kernels ( ij (  /), xxjiA ) and a priori profiles used in the retrievals are also 

reported as part of the data. Averaging kernels represent the sensitivity of retrieved partial 

column ozone (DU) at layer j to the change in ozone (DU) at layer i (from the a priori column 

ozone). They indicate retrieval sensitivity and the vertical resolution of retrievals. The 

averaging kernels are dependent on the unit used and the conversion from DU to ppbv varies 

with altitude (e.g. resulting from different T, P). Therefore, if used in evaluation this fact must 

be considered. 

Averaging kernels are generally used when the satellite retrievals are compared with 

ozonesonde or model simulations that have higher vertical resolution. They are applied to the 

higher resolution data to make them compatible with satellite data. The process involves the 

conversion of high resolution data to partial column ozone (DU) and using convolution to 

construct a modified ozone field compatible with the satellite observation. The following 

relationship is used: 

 



n

i

jaj XXAXX iaioji

1

, ,,),(

' ( )

Where n is the number of layers in the retrieval, is the a priori profile, is the 

averaging kernel, is the high resolution data (ozonesonde or model) converted to DU, and 

 is the modified O3 fields to be used in the evaluation. 

Xa,. A ji ),(

Xo,.

' X.

Since our objective is the direct insertion of satellite data in the model, we decided on having 

model vertical structure (vertical levels) as the single vertical coordinate for our exercise. 

Therefore, all other data (satellite and ozonesonde) are interpolated/integrated onto the model 

vertical layers and used for both insertion into the model and evaluation of the results. 

While for this project we have examined the efficacy of OMI O3 profiles in air quality 

studies, we are also pursuing the use of other OMI products such as formaldehyde (HCHO) and 

nitrogen dioxide (NO2) in conjunction with carbon monoxide from The Atmospheric Infrared 

Sounder (AIRS) onboard Aqua. Together these products may have the potential of providing a 

more complete representation of regional background air. 
12

erging observations into the model. Finally, OMI O3 profiles within 

the CMAQ domain are vertically interpolated onto the 39 sigma-P pressure layers of CMAQ 

mo

 low resolution OMI observations onto the high resolution model vertical layers. 

An example of vertical interpolation is demonstrated in Figure 5 for Huntsville, Alabama, on 

Aug

s from OMI. Since 

OMI measures HCHO and NO2 columns, we plan to scale them onto CMAQ vertical layers 

bas

nderestimates the boundary layer 

mixing ratio (too close to the surface). With a 45-km footprint for retrievals, AIRS mixing ratios 

will tend to be lower than in situ for all but the largest plumes. 

2.1.1.3. Mapping OMI and AIRS Observations Onto the CMAQ Model Domain 

Daily OMI O3 measurements were gridded onto CMAQ 36 km by 36 km resolution by using 

a so-called “drop-in-the-box” method. O3 retrieval for each OMI pixel is assigned to those 

CMAQ grids whose center points fall within this specific OMI pixel. For a CMAQ grid that 

receives more than one OMI O3 values, a simple averaging was applied to get the daily mean. 

The original OMI O3 data set has already filtered out some unreasonable values, and we have not 

applied any further filtering criteria yet. A “Nearest-Neighboring-Interpolation” (NNI) method 

was also used on the gridded OMI O3 data in order to fill those missing values by averaging the 

available OMI O3 values from the nearest neighboring CMAQ grids. It is assumed that if OMI 

data is missing due to some data contamination on a small scale, then NNI method will produce a 

reasonable concentration and helps in increasing the data coverage. This technique also spatially 

smoothes the concentration fields over the model domain and reduces unreasonable variations 

that may be caused from m

del. 

Our initial approach to vertical interpolation was log/linear interpolation on pressure levels. 

This approach, while reasonable for most of the troposphere, was problematic in areas where 

there is a curvature and high vertical gradient, such as boundary layer and upper troposphere. In 

our subsequent simulations we have used a robust combination of spline and linear interpolation 

to better map the

ust 1, 2006. 

We used similar approach to grid the tropospheric columns of OMI/HCHO, OMI/NO2 and 

AIRS/CO onto CMAQ horizontal domain. Fitting uncertainties for the HCHO slant columns 

typically range between 40-100%, with the lower end of this range over HCHO hot-spots. Our 

future plans are to also use formaldehyde and nitrogen dioxide observation

ed on the vertical distribution patterns from CMAQ control simulations. 

The AIRS sensor aboard Aqua spacecraft measures humidity, temperature, cloud properties, 

and the amounts of greenhouse gases. We expect to utilize AIRS CO as another complementary 

piece of information to better describe the background characteristics of boundary layer air from 

remote sensing platforms. AIRS Level 2/Level 3 Version 005 CO (without Humidity Sounder for 

Brazil, HSB) for August 2006 is available. There is 0.5 to 1.5 degrees of freedom (number of 

adjustable geophysical parameters in the retrieval; indicates the amount of information in the 

retrieval) in the AIRS CO signal that is translated into a vertically weighted total column with the 

shape of the weighting varying depending on local conditions (temperature, moisture, and CO 

amount and true profile). Nominally, AIRS CO sensitivity peaks at 500 mb, ± 200 mb. AIRS 

CO does respond to polluted boundary layers, but generally it u
13

Figure 5. Example of OMI ozone profile and ozonesonde measurements interpolated onto the CMAQ 

vertical grid for Huntsville, Alabama, on August 1, 2006. 

outmost importance as the spatial extent of highs and lows 

(ridges and troughs) can be realized. 

ncidence, but it shows the potential value of utilizing complimentary information 

together. 

Figure 6 shows OMI ozone observations at two pressure levels, one representing 485-700 

mb and the other 700-mb surface for August 21, 2006. The left panel shows OMI ozone values 

on their native tracks. The pixel sizes do not represent the real footprint of the measurements 

and have been downsized to better show the tracks. On the right panel the concentrations after 

being mapped onto the 36-kmx36-km model domain are demonstrated. As evident from the 

figure, merging all the tracks for one day provides a complete spatial coverage over the 

continental United States. This is of 

Figure 7 demonstrates OMI column nitrogen dioxide (NO2) and formaldehyde (HCHO) 

observations mapped into the 36-km model domain for the same day. One can observe the 

similarity of NO2 spatial pattern over the southeast to the mid-tropospheric ozone. This can be 

just a coi
14

Figure 6. Aura/OMI Level 2 O3 profiles (24 layers) are mapped to CMAQ horizontal domain (36km x 36km) 

using a “drop-in-the-box” method. The daily-mean profiles are then interpolated to CMAQ 39 

vertical layers (altitude-based). Left: OMI O3 plotted with fixed pixel size (not real size) at ~500 

and ~1000 mb; Right: OMI O3 mapped to CMAQ 3-D domain (36kmx36km, 39 layers); 

ozonesondes from IONS06 plotted over OMI contours. 
15

 

Figure 7. Satellite-retrieved trace gases (OMI-NO2, OMI-HCHO, and AIRS-CO) is gridded to CMAQ 

36km x 36km domain. 

2.1.2. Terra and Aqua Satellites (MODIS Instrument) 

Particulate Matter (PM) is one of the important aspects of air quality. Realistic 

representation of emission, transport, and removal of particulate matter is essential in air quality 

studies. Air quality models require datasets for initializing and specifying the lateral boundary 

conditions in the case of limited area models. Ground observations provide routine observations 

of PM but have limited spatial coverage. Column burden of atmospheric aerosols are readily 

retrieved from multi-channel satellite imagery. Satellite-retrieved aerosol optical depth (AOD) 

and mean particle size are available at spatial resolutions that are adequate for use in air quality 

models. While the total column AOD from the satellite provides the spatial distribution of 

aerosol burden, the vertical distribution of aerosols is also needed to utilize such data in air 

quality models. However, only a few satellite sensors provide vertical distribution of 

atmospheric aerosols on a limited spatial coverage (e.g., the Cloud-Aerosol Lidar and Infrared 

Pathfinder Satellite Observation, CALIPSO). In this project we examine a technique for 

utilizing Moderate Resolution Imaging Spectroradiometer (MODIS) retrieved aerosol optical 

depths in CMAQ which is a widely used air quality model. The technique uses CMAQpredicted vertical distribution of aerosols as the template for distributing satellite-derived AOD 

into atmospheric columns in CMAQ. This is based on the assumption that the spatial distribution 
16

of emission sources are reasonably defined in the model and the model performs reasonably well 

with respect to vertical mixing. Therefore, the discrepancy between the model AOD and the 

observed AOD are due to either emission source strength or the representation of aerosol 

chemistry/formation in the model. This project quantifies enhancement to air quality predictions 

derived through the use of satellite observed aerosol fields in the CMAQ model. 

MODIS is an instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites, both in 

a sun-synchronous orbit. Terra's orbit around the Earth is timed so that it passes from north to 

south across the equator in the morning (about 10:30 AM local time), while Aqua passes south to 

north over the equator in the afternoon (about 1:30 PM local time). MODIS views the entire 

Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands. This project utilizes daily 

aerosol optical thickness (AOT, hereafter used interchangeably with AOD) retrieved from 

imagery captured by MODIS sensors on both platforms. The time period considered is August 

2006. Since TERRA-MODIS acquires imagery during the morning (10:30 AM) and AQUAMODIS in the afternoon (1:30 PM), combined aerosol fields retrieved from both Terra and Aqua 

MODIS imagery can create a more complete dataset for use in CMAQ. The rationale for 

combining the data are: (1) MODIS sensor takes approximately four hours to image the entire 

region covered by the Continental United States (CONUS), and it is not possible to obtain 

simultaneous aerosol observations; (2) Aerosol retrievals are not available when cloud cover is 

present; thus, combining aerosol retrievals from the two platforms, increase the probability of 

obtaining a successful retrieval at a given location. The latter is especially true in case of partly 

cloudy scenes and/or presence of short lived clouds. 

Spatial resolution of MODIS level 2 aerosol product is 10 km by 10km at nadir, which is 

resampled to the 36km x 36km resolution equal area grid utilized by the CMAQ model. Note 

that the MODIS observed AOD, resampled to CMAQ grid, is utilized for initializing and 

providing lateral boundary conditions for CMAQ. In addition, this data is also used for 

comparing against the CMAQ-predicted aerosol fields. Since the MODIS AOD used in this 

project is a combination of observations from both Terra and Aqua platforms, it is compared 

against CMAQ simulated AOD averaged over a period (1500-2200 UTC) that is consistent with 

daytime observation window for Terra and Aqua platforms over CONUS region. 

Even after combining aerosol fields from Terra and Aqua, one of the difficulties encountered 

in creating AOD fields was missing retrievals due to presence of clouds. In order to obtain a 

smoothed AOD field, a nearest neighbor resampling approach was devised. If a missing value of 

AOD is encountered in combined Terra-Aqua product (before resampling to CMAQ grid), a 

three pixel radius neighborhood is searched for a valid AOD observation. If valid observations 

are found, then the missing value is replaced by the average of neighboring pixels. Sensitivity of 

this process to the radius of the search neighborhood was studied and a three pixel radius was 

found to be adequate. Table 1 shows the results for a case study in August 14, 2007. While 

going farther out from a missing pixel reduces the number of missing data, it also increases the 

uncertainty of the value that is assigned to the missing pixel. 

The procedure fills in the majority of the missing data pixels in the region and creates a 

smooth AOD field. In order to examine the validity of the resampling procedure, the following 

experiment was conducted. Figure 8 illustrates another example of pixel extension algorithm 
17

described above for September 7, 2006. From the original Terra-Aqua combined MODIS AOD 

image (Figure 8a), 50% of the non-zero AOD was randomly removed ( 

Figure 1b). 

Then in successive attempts the coverage was extended by increasing the radius of search 

from 1-pixel to 3-pixels. The 1-pixel extension recovered 57% of the removed data, while 2- and 

3-pixel extensions recovered 73% and 82% of the removed data respectively. The correlation 

coefficient between the recovered data and the original data remains relatively unchanged (.88) 

indicating that even going out by 3-pixels for the search does not increase the error in the 

recovered data significantly while it increases the number of recovered data considerably. 

Scatter plot between the original AOD values of the randomly removed pixels and the values 

obtained for the same locations after resampling procedure was applied to the modified field 

shows that majority of the points are clustered in along the one-to-one line. The same 

experiment was performed on the August 14, 2007, case and resulted in a correlation coefficient 

of .93 suggesting a good agreement between the original AOD field and the recovered data. 

Table 1. Percentage of Missing Pixels Within MM5 Area 

08/01 08/02 08/03 

2 pixels 31.5% 34% 34% 

3 pixels 18% 20.7% 21.8% 

4 pixels 10% 13% 14% 
18

Figure 8. Pixel extension algorithm (PEA) allows for increasing MODIS coverage by reconstructing missing values due to cl

MODIS AOD from TERRA and AQUA on Sept. 7, 2006. (b) We randomly removed 50% pixels from origin

Reconstructed image using 1, 2, and 3 pixel extension. 
19

2.1.2.1. Utilization of MODIS AOD for Initialization and Specification of Lateral 

Boundary Conditions in CMAQ 

The combined Terra-Aqua AOD data was resampled to CMAQ domain and was utilized for 

initializing and also specifying the lateral boundary conditions in CMAQ. As noted previously, 

information regarding vertical distribution of aerosols is needed for effective use of MODIS 

AOD in the model. This information is obtained from CMAQ. The dependency of the technique 

to the information from the model implies that the aerosol module used has a decisive impact on 

the aerosol speciation and distribution. In this exercise we have used the standard aerosol 

module in CMAQ as described in Binkowski and Roselle (2003) with updates described in 

Bhave et al. (2004). The aerosol distribution is modeled as a superposition of three lognormal 

modes that nominally correspond to Aitken (particles with diameter < 0.1 m), accumulation 

(particles with 0.1 < diameter < 2.5 m), and coarse (particles with diameter > 2.5 m) modes. 

In the current exercise we use two aerosol categories, fine and coarse modes. Fine mode consists 

of aerosols with aerodynamic diameter of less than 2.5 micro-meters (PM2.5), while coarse mode 

consists of aerosols with diameter greater than 2.5 micro-meters. The model results for PM2.5

concentrations are obtained by summing species concentrations over the Aitken and 

accumulation modes. CMAQ aerosol species (as represented in CMAQ aerosol module) are 

presented in Table 2. The fine mode aerosol species comprise sulfate, nitrate, ammonium, 

anthropogenic and biogenic organic carbon, elemental carbon, and other unspecified species 

originating from human activity. 

Table 2. Speciation and Variable Name Used in the CMAQ Aerosol Module 

Species Description Name

Aitken mode sulfate mass ASO4I 

Accumulation mode sulfate mass ASO4J 

Aitken mode ammonium mass ANH4I 

Accumulation mode ammonium mass ANH4J 

Aitken mode nitrate mass ANO3I 

Accumulation mode nitrate mass ANO3J 

Aitken mode anthropogenic secondary organic mass AORGAI 

Accumulation mode anthropogenic secondary organic mass AORGAJ 

Aitken mode primary organic mass AORGPAI 

Accumulation mode primary organic mass AORGPAJ 

Aitken mode secondary biogenic organic mass AORGBI 

Accumulation mode secondary biogenic organic mass AORGBJ 

Aitken mode elemental carbon mass ACEI 

Accumulation mode elemental carbon mass ACEJ 

Aitken mode unspecified anthropogenic mass A25I 

Accumulation mode unspecified anthropogenic mass A25J 

Aitken mode water mass AH2OI 

Accumulation mode water mass AH2OJ 

MODIS level 2 data provides fine mode fraction, which is the fractional contribution of fine 

mode to total AOD. Fine mode fraction data is used in this project to separate out fine and 

coarse mode AOD. However, fractional contributions by the different aerosol species to fine and 

coarse mode aerosols and knowledge of vertical distribution is still needed to utilize MODIS 
20

AOD. The following assumptions are made for closure: (1) The vertical distribution of different 

aerosol species is given by the CMAQ simulated profile for the corresponding time; (2) The 

percentage contribution of an aerosol species to the total particulate mass within a column is 

same as that obtained from CMAQ simulations for the corresponding time. Based on these 

assumptions the following ratio parameter (α) is used to scale the aerosol mass concentration in a 

CMAQ vertical column: 

CMAQ

MODIS





 

Where MODIS  and CMAQ  are MODIS and CMAQ aerosol optical depths (AOD) respectively. 

For the model CMAQ  can be calculated as: 

 

Ztop

CMAQ extdzB

0



Where is the aerosol extinction coefficient (km-1), and Z is the height in km. To arrive at 

aerosol extinction coefficient we use an empirical relationship devised from long term 

measurements at the Interagency Monitoring of Protected Visual Environments (IMPROVE) 

sites (Hand and Malm, 2005), including 

Bext

the new revisions made based on the current information 

(http://vista.cira.colostate.edu/improve/Publications/GrayLit/019_RevisedIMPROVEeq/RevisedI

MPROVEAlgorithm3.doc). The extinction coefficient is calculated as: 

   

  

  

    

6.     1 ( )(33.)

10 1 )(7.1

8.2 arg1.6

)(4.2 arg)(1.5

)(2.2 arg)(8.4

Coarse Mass Rayleigh Scattering SpecificSite 2 ppbNO

Elemental Carbon SoilFine SaltSearhf

Small Organic Mass OrganiceL Mass

Smallrhf Nitrate NitrateeLrhf

Smallrhf Sulfate SulfateeLrhf

SS

S L

ext S L

  

  

 

 



    

The algorithm uses three water growth adjustment terms. They are for use with the small size 

distribution (S) and the large size distribution (L) sulfate and nitrate compounds and for sea salt 

(fS(RH), fL(RH) and fSS(RH) respectively). Site-specific Rayleigh scattering is calculated for the 

elevation and annual average temperature of each of the IMPROVE monitoring sites. 

Hourly modeled AOD is averaged to corresponding MODIS tracking time which is from 15 

to 22 GMT to obtain the ratio α. In order to eliminate the site specific impact of Rayleigh 

scattering ( ), the adjustment is only applied if the MODIS AOD 

(

(Rayleigh _ scattering)dz

MODIS  ) is greater than Rayleigh scattering. Thus, when the impact of Rayleigh-scattering is 

greater than zero, the ratio is calculated as: 

 







  Rayleigh scattering dz

Rayleigh scattering dz

ext

MODIS

( _ )

_( )






21

 If Rayleigh-scattering impact does not exist and MODIS data are available, then the ratio is 

simply expressed as: 



 dz

ratio

ext

MODIS





For the time period considered in this study, the values of α is varied from 10-4 to 85, with 

majority of α values being less than 5. All 16 fine aerosol species in Table 2 (I and J modes, 

except water) are scaled by α. Also note that while NO2 is used in IMPROVE equation for 

calculating the aerosol extinction coefficient, it will not be scaled by α. Examples of scaled 

vertical profiles obtained using this procedure is shown in Figure 9. When the value of α is less 

than 1, it reduces the mass concentration throughout the vertical column, while values of α

greater than 1 leads to an increase throughout the column. In cases where satellite-derived AOD 

is not available the scaling procedure is not applied and model concentrations are used. 

a b

Figure 9. Vertical profiles of CMAQ mass concentrations scaled using the scaling ratio . (a) Black carbon 

and (b) anthropogenic organic mass. Blue and red lines are the original CMAQ and scaled 

concentrations, respectively. Note that  is less than 1.0 for the case shown in panel (a) and more 

than one for that shown panel (b). 
22

2.2. Ozonesonde Data 

In this project we have selected August 2006 as the study period so that we can take 

advantage of INTEX (INtercontinental chemical Transport EXperiment) Ozonesonde Network 

Study (Thompson et al., 2007a, 2007b) 2006 campaign (IONS-06). The IONS-06 campaign 

provided ozonesonde profiles (http://croc.gsfc.nasa.gov/intexb/ions06.html) for validation of 

ozone observations by the Aura satellite instruments. 

Tropospheric ozone varies significantly on time scales from months to hours. Our current 

routine ozonesonde measuring capabilities resolve tropospheric ozone variation at vertical 

resolution of 100m and temporal scales of usually 1 week, but occasionally 1 day or a minimum 

of 6 hours for short durations. Ozonesondes make in situ measurements of temperature, 

pressure, humidity and ozone from balloons launched from stations around the world. A typical 

ozonesonde measurement provides vertical profiles with a resolution of about 100 m, up to a 

maximum altitude of about 35 km (~7 mb). The accuracy of ozone measurements from sondes 

is often quoted as about ± 5% [Stratospheric Processes and their Role in Climate, International 

Ozone Commission, and Global Atmosphere Watch, 1998] and it depends on a number of 

factors. A variety of types of ozonesondes exist, but the most common type used in this work is 

the electrochemical concentration cell (ECC) sonde which relies on the oxidation reaction of 

ozone with a potassium iodide (KI) solution (Komhyr et al., 1995). ECC sondes are made by 

two different manufacturers and can operate with a range of KI solution strengths, buffer types 

and preparation procedures. Smit et al., 2007, evaluated sonde performance in a series of 

experiments using a small number of sonde measurements. Their work indicates a precision of 

better than ±(3 –5)% with an accuracy of about ±(5– 10)% up to 30-km altitude if standard 

operating procedures for ECC sondes are used. The study also suggests a median high bias of 

about 5% for ECC sondes relative to an ultraviolet (UV) photometer. 

One of the main objectives of IONS-06 campaign was to validate TES observations; 

therefore, ozonesonde launches during the summer of 2006 were scheduled in such a way to 

maximize the number of concurrent measurements with Aura’s overpasses. Figure 10 Shows 

the location of ozonesonde launch sites in North America. In this study we use the data from a 

subset of these stations (18 stations) for our evaluation work. For August 2006, about 352 

launches were made from these sites. Among them there are 24 profiles measured from NOAA 

Ron Brown ship over the Gulf of Mexico (referred as “Ron Brown” station). The locations of the 

vessel at the time of these launches are marked on the map in Figure 11. As seen in the figure, 

for most days the vessel is staying close to the coast or is sampling the Houston ship channel. 

Only on July 31, August 2 and 16 the ship is farther away from the shoreline and is sampling the 

air that is more representative of the background in GoM region. 
23

Figure 10. Ozonesonde locations for IONS-06 campaign. During IONS-06 daily ozonesondes were launched 

from these locations with an attempt for the launch time to coincide with Aura’s overpass. In this 

study we have used the measurements from a subset of 18 stations within CONUS. 

Figure 11. Locations of NOAA Ron Brown vessel during August 2006. The ship participated in IONS-06 

campaign and daily ozonesondes were launched from the locations marked on the map. 
24

2.3. Model Simulations 

The modeling paradigm for this project is presented in Figure 12. The main components in 

this modeling exercise are meteorological modeling, emissions processing, and the 

photochemical modeling. MM5 is utilized to provide the meteorological fields necessary to 

drive CMAQ. Sparse Matrix Operator Kernel Emissions (SMOKE, http://www.smokemodel.org/index.cfm) was utilized for emissions processing. Since the objective of this study is 

to quantify the efficacy of satellite observations in the air quality assessment practices, it is 

necessary to have the best representation of the physical atmosphere as well as the best 

description of the emissions into the modeling domain. Thus, in our meteorological simulations 

we have strived to use all available analyses for our modeling episode. Also, in processing the 

emissions, emissions inventory were revisited and corrected several times as problems were 

discovered. All the sensitivity simulations were performed only after a complete and satisfactory 

evaluation of meteorology and emissions. In the following each of these modeling components 

and their configurations will be reviewed. 

2.3.1. Meteorological Modeling 

Modeling of atmospheric dynamics observed during the 90 day period (July 1 to 

September 30) in 2006 was conducted using the fifth-Generation Penn State/NCAR Mesoscale 

Model (MM5) (Dudhia, 1989; Grell et al., 1994). Maintained by the National Center for 

Atmospheric Research (NCAR), MM5 is in a series of Mesoscale models first developed at Penn 

State in the early 1970's (Anthes and Warner, 1978). Since that time, it has undergone many 

changes designed to broaden its usage. These include, (1) a multiple-nest capability; (2) nonhydrostatic dynamics that allow the model to be used at a few-kilometer scale; (3) multi-tasking 

capability on shared- and distributed-memory machines; (4) four-dimensional data-assimilation 

(FDDA) capability, and (5) multiple physics options. It has been extensively used in the 

development of meteorological fields for air quality modeling application. 

Modeling simulations were conducted on a 36-km resolution grid that spans over the 

continental U.S. A Lambert Conformal map projection with origin at 40N and 97W and true 

latitudes at 33 and 45N were employed. The grid has 164 cells in the east-west and 128 cells in 

the north-south direction. The top of the modeling grid has been fixed at 50mb. It has 39 

vertical layers of varying thickness. To avoid artificial numerical mixing near the top of the 

model, vertical layers are adjusted near tropopause by increasing the number of layers in the 

upper troposphere/lower stratosphere. This would be important as the ozone vertical gradient is 

the highest at the tropopause. Figure 13 exhibits the horizontal extent of the modeling domain. 
25

Figure 12. Flow chart illustrating the model components. The main components are Meteorological model 

(MM5), emissions processor (SMOKE), and the chemical transport model (CMAQ). Satellite 

observations of ozone and AOD were assimilated through initial and boundary conditions in 

CMAQ. 

Figure 13. 36-km modeling grid. 
26

Like any other prognostic meteorological model, MM5 requires a significant amount of 

terrestrial (i.e., topography, Land Use/Land cover) and atmospheric data (e.g., gridded analyses 

fields that include at a minimum sea-level pressure, wind, temperature, relative humidity, and 

observations that contain soundings and surface reports). In this modeling project surface 

elevation, Land Use/Land Cover (LULC), soil type, and other terrestrial datasets from United 

States Geological Survey (USGS); NCEP ETA gridded analysis data at 40-km resolution 

archived at 3-hour intervals available at http://dss.ucar.edu/datasets/ds609.2; surface (land and 

ship) and upper air observational data archived at 3 and 6-hour intervals available at 

http://dss.ucar.edu/datasets/ds464.0; and hourly surface observations for over 1,000 stations in 

U.S. and Canada available at http://dss.ucar.edu/datasets/ds472.0 were utilized. 

Modeling simulations were conducted using MM5 version 3.7. Data processing was 

performed in six day segments beginning July 1 and ending October 3. The NCEP Eta-based 4-

D Data Assimilation System (EDAS) analyses were utilized for these simulations (Eta is one of 

NCEP's mesoscale numerical weather prediction models and the name "Eta" derives from the 

model's vertical coordinate known as the "eta" or "step-mountain" coordinate). The NCEP ETA 

gridded analyses data was first processed through the program PREGRID and mapped onto the 

36-km domain via the REGRIDDER. Surface, ship and upper air data are incorporated within 

the analyses fields with the help of the program LITTLE_R. Finally, INTERPF is used to 

interpolate pressure level fields generated by LITTLE_R onto MM5 sigma coordinates. 

MM5 model configuration, presented in Table 3, was determined through a brief literature 

review (Brewer et al., 2007; Kemball-Cook et al., 2005) of recent modeling projects that have 

been undertaken in support of air quality management activities. 

The model performance was evaluated using software known as METSTAT developed by 

ENVIRON corporation (http://www.camx.com/files/metstat.15feb05.tar.gz). It computes surface 

statistics for temperature, wind speed and direction, and humidity. The metrics include: Bias 

Error (B), Gross Error (E) and Root Mean Square Error (RMSE), Systematic Root Mean Square 

Error (RMSEs), Unsystematic Root Mean Square Error (RMSEu) and Index Of Agreement 

(IOA). Mathematical formulation of these variables is provided in Table 4. Monthly average 

performance statistics for August 2006 are provided in Table 5. In an effort to identify model 

biases and error over different regions of the domain, statistics have been computed for subregions shown in Figure 14 and the results presented in Table 6. 

While reviewing these statistics, one should be cautioned that summary statistics are useful in 

making only a general assessment about the adequacy of meteorological fields. For example, 

daily-mean performance statistics are likely to conceal important hour-to-hour variations. Also, 

summary statistics depend upon the number of observation-prediction pairs and generally 

improve with larger sampling sizes and longer averaging periods. This is because the probability 

of statistics being affected by extreme events (outliers) is high in smaller sample sizes. These 

and other concerns have lead USEPA to recommend that benchmarks proposed in Emery et al., 

(2001) should not be used in a “pass/fail” model, but only as a means of assessing general 

confidence in meteorological model output. 
27

Table 3. MM5 Model Configuration. 

Physics Options Option Used in the Simulation 

Nesting Type One-way 

Numerical Time Step 90 sec 

Cumulus parameterization Grell 

PBL scheme MRF 

Microphysics Reisner 1 

Radiation scheme RRTM scheme 

Land Surface scheme Noah-LSM 

Convection scheme KF2 

Observation nudging None 

3-D Grid analysis nudging Yes 

3-D Grid analysis nudging time interval 3-hour 

3-D Grid analysis nudging co-efficient GU=2.5x10-4, GV=2.5x10-4, 

GT=2.5x10-4, GQ=1.0x10-5

Surface Analysis nudging Yes 

Surface Analysis nudging time interval 3-hour 

Surface Analysis nudging co-efficient GU=2.5x10-4, GV=2.5x10-4 

Table 4. Mathematical Formulation of Statistical Metrics Used for Evaluating Mesoscale Meteorological 

Model Performance. 

Metrics Formulation 

Bias    

 

J

j

I

i

i

j

i

j OP

IJ

B

1 1

1

Gross Error  

 

J

j

I

i

i

j

i

j OP

IJ

E

1 1

1

Root Mean Square Error  

2/1

1 1

1 2





 



     

J

j

I

i

i

j

i

j OP

IJ

RMSE

Systematic Root Mean Square Error  

2/1

1 1

2 ˆ 1 



 



     

J

j

I

i

i

j

i

S j OP

IJ

RMSE

Unsystematic Root Mean Square Error  

2/1

1 1

2 ˆ 1 



 



     

J

j

I

i

i

j

i

u j PP

IJ

RMSE

Index of Agreement 





























 

J

j

I

i

o

i

jo

i

j MOMP

RMSEIJ IOA

1 1

2 . 1
28

Table 5. Performance Statistics for Mesoscale Meteorological Models. 

Statistical Measure Benchmark 

Wind Speed Bias (m/s) <0.5 

Wind Speed Total RMSE (m/s) 2.0 

Wind Speed Index of Agreement 0.6 

Wind Direction Gross Error (degree) 30.0 

Wind Direction Bias (degree) <10.0 

Temperature Bias (Kelvin) <0.5 

Temperature Gross Error (degree) 2.0 

Temperature Index of Agreement 0.8 

Humidity Bias (g/kg) <1.0 

Humidity Gross Error (g/kg) 2.0 

Humidity Index of Agreement 0.6 

Figure 14. Subregions used in performance evaluation. 
29

Table 6. Monthly Meteorological Performance Statistics 

for Each of the SubRegions in Figure 14. 

Meteorological Variable CENRAP MANEVU MWRPO VISTAS WRAP

Wind Speed Mean OBS (m/s) 2.97 2.70 2.49 2.05 3.24

Wind Speed Mean PRD (m/s) 2.47 2.35 2.41 1.94 2.43

Wind Speed Bias (m/s) -0.50 -0.35 -0.09 -0.11 -0.81

Wind Speed Gross Error (m/s) 1.26 1.48 1.17 1.21 1.64

Wind Speed RMSE (m/s) 1.65 2.03 1.50 1.54 2.11

Wind Speed Sys RMSE (m/s) 1.35 1.67 1.14 1.24 1.81

Wind Speed Unsys RMSE (m/s) 0.94 1.13 0.97 0.91 1.09

Wind Speed IOA 0.73 0.59 0.70 0.68 0.66

Wind Direction Mean OBS (deg) 149.90 189.95 167.60 160.32 255.55

Wind Direction Mean PRD (deg) 151.30 203.67 171.11 164.80 246.42

Wind Direction Bias (deg) 2.77 4.91 4.53 4.84 6.16

Wind Direction Gross Error (deg) 25.72 27.81 24.58 33.14 43.26

Temperature Mean OBS (K) 298.38 294.37 294.96 299.75 294.38

Temperature Mean PRD (K) 298.61 293.58 294.47 300.10 294.01

Temperature Bias (K) 0.23 -0.79 -0.50 0.35 -0.38

Temperature Gross Error (K) 2.08 2.09 2.08 2.11 2.99

Temperature RMSE (K) 2.81 2.70 2.68 2.85 3.87

Temperature Sys RMSE (K) 1.02 0.85 0.82 0.99 0.54

Temperature Unsys RMSE (K) 2.60 2.53 2.53 2.66 3.82

Temperature IOA 0.95 0.92 0.91 0.90 0.93

Humdity Mean OBS (g/kg) 14.38 11.54 12.71 16.37 8.13

Humdity Mean PRD (g/kg) 13.24 11.21 11.91 15.23 7.21

Humdity Bias (g/kg) -1.13 -0.33 -0.81 -1.14 -0.92

Humdity Gross Error (g/kg) 1.64 1.12 1.42 1.71 1.53

Humdity RMSE (g/kg) 2.08 1.44 1.77 2.17 2.03

Humdity Sys RMSE (g/kg) 1.23 0.69 1.10 1.28 1.32

Humdity Unsys RMSE (g/kg) 1.64 1.23 1.35 1.73 1.53

Humdity IOA 0.86 0.84 0.79 0.76 0.86

In order to examine the overall model performance with respect to realization of weather 

patterns, we also made comparisons with weather charts for the period of July 15 through 

September 6. Generally, the model performed reasonably well with respect to the predictions of 

surface temperature and winds. In the following a few examples for selected cases of interest 

will be presented. 

The general weather pattern for July 15 was similar to July 28. This was important as we 

intend to recycle CMAQ results in order to start the model simulation with a reasonable initial 

condition. To do this, we needed to run CMAQ (starting on July 15, with the standard IC) until a 

weather pattern similar to July 15 is reached. This would allow for the emissions to build-up 

over the domain of study and the atmospheric chemical composition in the model to be more 

representative of the real atmosphere. 

Figure 15 shows the weather charts for July 15 and July 28, alongside MM5 results for these 

days. As illustrated in the figure, the general wind pattern over the domain shows a reasonable 

agreement between the model and observations for these days. The overall temperature patterns 
30

are also in reasonable agreement. August 21-23 mark the stationary front experiment over 

Dallas Fort Worth, Texas. During this period a lingering stationary front extended through north 

Texas, through the southeast to the east coast. The stationary front was associated with high 

ozone events and sporadic cloudiness in the region. Figure 16 shows the weather charts for 

August 21-22 against model results for these days. Again model has been able to simulate this 

large scale event reasonably well as it captures the surface temperature gradient and low winds to 

the north and south of the front. 

The statistics together with weather chart comparisons demonstrate that the meteorological 

fields being used in emissions processing and air quality modeling are a reasonable 

representation of the physical atmosphere for the period of interest. The errors in the wind speed 

and direction, however, will be affecting our sensitivity simulations as it impacts the transport of 

observed satellite trace gas and particulate matter that we impose on the model. 

Figure 15. Weather charts versus MM5 results for July 15 and 28. 
31

Figure 16. Weather charts versus MM5 results for August 21 and 22. 

2.3.2. Emissions Processing 

Emission inventories are typically available with an annual or daily total emissions value for 

individual emissions sources or source categories. Since air quality models require emissions 

data on an hourly basis, for each model grid cell and species, emission processors are needed to 

convert the available emissions data into a form that the air quality model can ingest. The Sparse 

Matrix Operator Kernel Emissions (SMOKE) processor (Coats, 1996; Houyoux et al., 2000) is 

one such tool, and was used for creating gridded, temporalized and speciated emission files for 

this project. SMOKE is capable of generating temperature sensitive mobile source emission 

factors using EPA’s MOBILE6 emission factors model. It is also capable of generating biogenic 

emissions for this research work with the help of Biogenic Emissions Inventory System (BEIS) 

version 3.09 (Guenther et al., 2000; Pierce et al., 1998). In addition to large amounts of sourcespecific data, certain aspects of emissions processing require meteorological variables. These are 

provided by the MM5 meteorological model and include daily surface temperature for 

calculating mobile source emission factors; temperature and radiation fields for calculating 
32

biogenic emissions; and Planetary Boundary Layer (PBL) height, surface heat flux, wind speed, 

and temperature for estimating plume rise for point sources. 

In the absence of any consolidated annual emissions inventory for 2006, we are using the 

2002 annual emission inventory developed by Regional Planning Organizations (RPOs) in 

response to regulatory requirements established under the Regional Haze Rule (RHR). We are 

accounting for on-road mobile source emissions reductions that will likely result due to fleet 

turnover. We are also using the 2006 Continuous Emissions Monitoring (CEM) data for 

Electricity Generating Units (EGUs) compiled by EPA’s Clean Air Markets Division (CAMD). 

Given the overall uncertainty in emission estimates, accounting for emission reduction in two 

major source categories will provide us with a reasonable estimate of emissions for this research 

work. SMOKE derived daily average emission totals and spatial plots of NOx, VOC, CO, SO2, 

NH3, anthropogenic (AVOC) and isoprene are provided in Table 7 and Figure 17. 

Table 7. Domain-wide Daily Emission Totals in Tons per Day. 

Emission 

Source 

NOx VOC Carbon SO2 NH3 CO 

Area 3308.18 12604.77 1212.27 2550.13 6933.41 12066.45

EGU 10387.81 147.68 78.99 30651.54 46.21 2095.20

Mobile 14935.43 8801.20 214.07 329.87 0.00 99360.72

Non-EGU 12904.17 3100.11 347.01 18215.97 633.57 10320.65

Non-Road 12242.84 9819.18 919.69 1248.35 12.38 87420.60
33

Figure 17. Daily emissions totals (tons per day) for CO, NOx, SO2, NH3, Isoprene, and anthropogenic VOC. 

2.3.3. Photochemical Modeling 

Air quality modeling simulations were conducted using EPA’s Community Multiscale Air 

Quality Chemistry Transport Model (CMAQ-CTM) or Models-3 (EPA, 1999; Dennis et al., 

1996; Byun and Schere, 2006). The modeling system contains state-of-the-science 

parameterization of atmospheric processes affecting transport, transformation, and deposition of 

such pollutants as ozone, particulate matter, airborne toxics, and acidic and nutrient pollutant 

species. CMAQ-CTM incorporates output fields from the meteorological (e.g., MM5) and 
34

emissions (e.g., SMOKE) modeling systems and several other data sources through special 

processors. The meteorological data is processed using Meteorology Chemistry Interface 

Processor (MCIP), initial and boundary conditions through ICON and BCON and clear sky 

photolysis rate using JPROC (Otte et al., 2004, 2005). Initial and boundary condition processors 

allow the use of a gridded concentration field as well as the species concentration profiles that 

are available with the installation. JPROC generates the photolysis rate lookup table under clear 

sky conditions. 

MCIP version 3.3 was used to create meteorological input files for CMAQ-CTM. Most 

meteorological variables are passed through directly from the MM5 output fields. Others, such as 

dry deposition velocities, were computed by MCIP. Initial and boundary conditions for the 36-

km domain are generated from a set of predefined vertical profiles available with the CMAQ 

software. The photolysis rates processor JPROC was used to generate clear sky photolysis rates. 

The processing is similar to that of the Regional Acid Deposition Model (RADM) (Chang et al., 

1987, 1990) which uses modified extraterrestrial radiation data from the World Meteorological 

Organization (WMO) and O2 and O3 absorption cross-section data from NASA (DeMore et al., 

1994). CMAQ provides several scientific options for the most important atmospheric processes 

(e.g., gas-phase chemistry, advection). The configuration used in this project is listed in Table 8. 

This configuration is based on our past modeling practices and is similar to commonly used 

configuration in regulatory applications. 

Table 8. CMAQ Configuration Used in Simulations for This Project. 

. 

Physical Process Reference 

Horizontal and vertical advection YAMO 

Horizontal diffusion MULTISCALE 

Vertical diffusion ACM2 

Gas-phase chemistry and solver EBI_CB4 

Gas and aqueous phase 

mechanism 

CB4_AE3_AQ 

Aerosol chemistry AERO3 

Dry deposition AERO_DEPV2 

Cloud dynamics CLOUD_ACM 

The main objective of this project is to investigate the impact of initial and boundary 

conditions (IC/BC) on model predictions of ozone and to examine the usefulness of satellite 

observations in providing such critical information. Thus, a series of simulations were devised to 

achieve these objectives. Since these sensitivity simulations entirely depend on model input 

data, model configuration listed in Table 8 remains unchanged throughout these simulations. 

The results from four simulations will be discussed in this report. These are (1) control 

simulation in which CMAQ is initialized only once at the beginning of the simulation and 

continuously runs for the entire simulation period using the standard boundary condition 

(CNTRL); (2) A simulation similar to the control but a continuous BC is constructed from Realtime Air Quality Modeling System (RAQMS) global model (RAQMS_BC); (3) A simulation 

similar to control except that BC was constructed by merging satellite observations and model 
35

fields (SAT_BC); (4) A simulation similar to SAT_BC but re-initializing the model every 24 

hours by assimilating satellite observations into the model fields (SAT_ICBC). The details about 

each simulation is explained in the following. 

2.3.3.1. Control Simulation (Base Case, CNTRL Simulation) 

To establish a base case scenario as a reference point in evaluating the model performance, a 

simulation was performed using CMAQ in its standard configuration and utilizing the default 

approach for providing boundary condition (BC). The default approach constructs a vertical 

profile of key atmospheric tracers for the lateral boundaries of the study domain based on 

climatology. The default BC is particularly unrealistic in the mid/upper troposphere as it 

provides low ozone concentrations for altitudes that are exposed to stratospheric ozone incursion 

and may experience much higher ozone concentrations. 

The standard application of CMAQ, also constructs an initial condition (IC) that reflects 

clean atmosphere. While this approach works for long simulations, it is problematic for short 

term predictions. It will take the model several days to realize the impact of emissions within the 

modeling domain. The emissions will accumulate in the boundary layer while undergoing 

transport and transformation. The accumulated emissions and secondary products will be 

transported throughout the modeling domain and into the free troposphere by weather systems. 

The weather fronts that usually move from west to east over United States tend to push away the 

accumulated pollution and create a cleaner atmosphere behind the front. These processes create 

a non-homogeneous atmosphere that is very different from the homogeneous clean background 

atmosphere that the standard application of CMAQ provides as IC. 

To make the control simulation more realistic, we first run the model (with clean atmosphere 

IC) for several days until a comparable weather pattern is observed over the modeling domain 

(continental U.S. in this case). The concentration field at the end of such simulation then is used 

as the initial condition for the long simulation. This allows for the realization of the impact of 

emissions and provides a more realistic atmosphere for model initialization. It greatly impacts 

the short term model predictions. In the current exercise we allowed a 13 day spin-up time. The 

model was started on July 15, 2006, at 0 GMT and was continuously run through July 28. The 

results from July 28 were recycled as the initial condition on July 15 in all subsequent 

simulations including the control simulation. 

2.3.3.2. Boundary Condition Provided by Global Model (RAQMS_BC Simulation) 

Another approach for providing IC and BC for CMAQ simulations would be the use of a 

global model that extends far beyond the regional modeling domain and potentially can realize 

the impact of long range transport and recirculation outside the regional boundary. To establish 

a secondary reference point for evaluating the impact of direct use of satellite observations in this 

project, we constructed a continuous BC record from RAQMS global model predictions. Our 

collaborator in this project, Dr. Daewon Byun (formerly at University of Houston and currently 

with NOAA/ARL) provided the BCs for these simulations (Song et al., 2008). 
36

RAQMS data at 2 degree by 2 degree resolution were produced through the data assimilation 

of OMI ozone column with NOAA GFS Global meteorology and the satellite fire detection data 

from MODIS Rapid Response System (http://rapidfire.sci.gsfc.nasa.gov/). The details on model 

configuration and emissions data used for the RAQMS simulations are summarized in Al-Saadi 

et al. (2008). It used emissions of CO, NOx, and hydrocarbons estimated from the gridded carbon 

fuel consumption databases, satellite fire detections, and meteorology-based estimates of fire 

weather severity to estimate the amount of carbon released from active fires and ecosystemdependent emission ratios. 

Since previous spin-up simulations were used as the CMAQ IC inputs, no special IC files for 

CMAQ simulations were prepared from RAQMS outputs. To construct the BC files, 

RAQMS2CMAQ conversion code developed at the University of Houston, Institute for 

Multidimensional Air Quality Studies (IMAQS) was used. The code was initially developed to 

process RAQMS data for the 1999 simulations. The original code used for the 1999 RAQMS 

data processing required daily METCRO3D files for providing the pressure height information 

used in the vertical interpolation. But for this project we utilized a reference pressure-height 

coordinate instead. Additionally, since the recent version of MCIP did not provide all the 

necessary inputs for the conversion code, the code was modified to accommodate the limited 

information. A one-day BC file from control simulation was used to set up exactly the same 

species list in the BC file. The results of the new code were verified with those from the original 

code for one day. The interpolation with the fixed pressure levels may potentially be less precise 

than the dynamic pressure levels provided by MM5. On the other hand, interpolating with the 

fixed pressure levels may be more robust than utilizing the MM5 pressure levels, which are 

affected by the synoptic and local scale pressure perturbations. 

After the code modification was completed, a BC file was produced and verified. The results 

were compared with the University of Houston’s 23 layer MM5 simulation case. Surprisingly, 

the ozone concentrations at the top level (39th layer) for the current case was substantially lower 

than those from the UH's top level (23rd layer) ozone. This could be due to the vertical resolution 

of the current model configuration in which the top layer is too thick to represent the 

stratospheric-tropospheric exchange processes. In the current model configuration the top layer 

is very thick in order to have high vertical resolution around 10 km altitude. This has been done 

in order to minimize the artificial numerical mixing of high stratospheric ozone into the upper 

troposphere (through numerical diffusion). 

2.3.3.3. Initial and Boundary Condition Provided from Satellite Observations 

(SAT_BC and SAT_ICBC Simulations) 

The final sets of simulations are designed to investigate the efficacy of using satellite 

observations of ozone and aerosols to provide IC and BC for regional air quality simulations. It 

comprises two sets of simulations. The first sets of simulations, called SAT_BC, are similar to 

control except for the BC data they use. In SAT_BC simulations, OMI ozone profiles and 

MODIS AOD are merged with model predictions to construct daily BC files. The simulations 

are performed in 24 hour segments, starting at 0 Greenwich Mean Time (GMT), but they are 

continuous as each new segment is initialized with the output from the previous segment. In 
37

SAT_ICBC simulations, both BC and IC for each segment is provided by merging the satellite 

data with model predictions. 

In preparing BC for these simulations satellite observations for the current day are used in 

conjunction with the current day simulation. This is necessary due to the time offset between the 

observations and the model predictions. A complete spatial coverage over continental U.S. 

(CONUS) from the satellite observation is obtained by combining the data from all the tracks 

within a day which span over several hours, while representing the same local time (about 1:30 

PM) for any location. Furthermore, we are assuming that this single measurement can explain 

ozone and aerosol burden at lateral boundaries for a 24 hour simulation. Since each segment 

starts at 0:00 GMT, the current day observations would be a better representation of the air 

outside the boundary. It should be noted however that this technique is only applicable to 

retrospective modeling, such as regulatory air quality applications, where the observations for the 

simulation day are available. 

BC only impacts the interior of the domain if the flow field is transporting air from outside 

the domain into the interior. Thus, the technique used in these simulations is not adequate for the 

situations where there is an observed transient event at the boundary in conjunction with a strong 

flow field toward the domain. Since a constant BC is applied for a 24-hour segment, such an 

event can exaggerate the impact of transboundary transport. This shortcoming will be addressed 

in future refinements to our technique as we plan to introduce diurnal variation for BC by 

projecting the observations outside the boundary to arrive at the boundary conditions for each 

given time. 

In preparing IC files, satellite observations from the previous day are being utilized as they 

are closer to the model initialization time of 0:00 GMT. The techniques used for creating IC 

were described in Section 2.1. BC is simply constructed by extracting concentrations from IC 

files at model grids that reside at the boundary. 

Figures 18-23 show the BC plots for ozone (O3), peroxy acetyl nitrate (PAN), formaldehyde 

(HCHO), nitrogen oxides (NOx), carbon monoxide (CO) and sulfate aerosol for south, east, north 

and western boundaries for August 1, 2006. In each panel BC for control, RAQMS_BC, 

SAT_BC and SAT_ICBC are represented. The boundary conditions for SAT_BC and 

SAT_ICBC are identical due to the technique applied for these cases. They are presented for 

completeness and to emphasize the similarity. In Figure 18 one can observe that both 

RAQMS_BC and SAT_BC ozone concentrations are significantly different from the control. 

Also, both the satellite observation and RAQMS are generally in agreement while OMI profiles 

indicate more stratospheric O3 incursion into the mid-troposphere. Also of interest is the 

disagreement between the two in the western boundary where RAQMS indicate elevated O3

concentration in mid-troposphere that is missing in the satellite observations. The figures also 

point to another limitation for the satellite data within the daytime boundary layer (below 1-km). 

The sharp vertical gradient around 1-km is due to the fact that when OMI data is not available we 

revert back to the model value. Thus, the sharp gradient is demonstrating the contrast between 

higher OMI concentrations above 1-km and lower model values below this elevation. 
38

 

 

Figure 18. Ozone (ppb) BC for south, east, north and west boundaries. In each panel BC for control (top 

left, standard CMAQ BC), RAQMS_BC (top right, BC from RAQMS global model), SAT_BC 

(lower left, BC from satellite observations) and SAT_ICBC (lower right) simulations for August 1, 

2006, are presented. Notice that BC for SAT_BC and SAT_ICBC are identical. They are both 

included for completeness. 

Figure 19 shows similar plots for peroxyacetyl nitrate (PAN). Our technique extracts the BC 

from a modified IC file. The IC file is obtained from CMAQ output at the end of the run for the 

previous 24-hour segment. Therefore, IC and consequently BC are impacted by the emissions 

and the dynamics of the previous day. This explains the difference between BC for control and 

SAT_BC simulations. Interestingly, vertical distribution of PAN for SAT_BC shows remarkable 

agreement with RAQMS_BC. To a lesser degree, this resemblance in pattern is also manifested 

in formaldehyde distribution in Figure 20, meaning that the chemical evolution within the 

domain of study is mainly responsible for the overall characteristics of the air mass at the 

boundary as the regional model produces a similar distribution in PAN and formaldehyde as the 

global model predicts. Such general agreement for NOx is only seen on the eastern boundary as 

evident in Figure 21 while carbon monoxide does not exhibit good agreement (Figure 22). 
39

igure 19. PAN (ppt) BC for south, east, north and west 

C for sulfate aerosol (ASO4) is shown in Figure 23. This information was not extracted 

from

 

 

F boundaries. In each panel BC for control (top left, 

standard CMAQ BC), RAQMS_BC (top right, BC from RAQMS global model), SAT_BC (lower 

left, BC from model) and SAT_ICBC (lower right) simulations for August 1, 2006, are presented. 

Notice that BC for SAT_BC and SAT_ICBC are identical. They are both included for 

completeness. 

B

 RAQMS and as evident from the figure, BC for RAQMS_BC is identical to the control 

simulation. However, since SAT_BC simulation extracts this information from the interior of 

the domain, the southern and eastern boundary experience the impact of ASO4 loading. 
40

 

 

Figure 20. Formaldehyde (ppb) BC for south, east, north and west boundaries. In each panel BC for control 

(top left, standard CMAQ BC), RAQMS_BC (top right, BC from RAQMS global model), 

SAT_BC (lower left, BC from model) and SAT_ICBC (lower right) simulations for August 1, 

2006, are presented. Notice that BC for SAT_BC and SAT_ICBC are identical. They are both 

included for completeness. 
41

 

 

Figure 21. NOx (ppt) BC for south, east, north and west boundaries. In each panel BC for control (top left, 

standard CMAQ BC), RAQMS_BC (top right, BC from RAQMS global model), SAT_BC (lower 

left, BC from model) and SAT_ICBC (lower right) simulations for August 1, 2006, are presented. 

Notice that BC for SAT_BC and SAT_ICBC are identical. They are both included for 

completeness. 
42

 

 

Figure 22. Carbon monoxide (ppb) BC for south, east, north and west boundaries. In each panel BC for 

control (top left, standard CMAQ BC), RAQMS_BC (top right, BC from RAQMS global model), 

SAT_BC (lower left, BC from model) and SAT_ICBC (lower right) simulations for August 1, 

2006, are presented. Notice that BC for SAT_BC and SAT_ICBC are identical. They are both 

included for completeness. 
43

 

 

Figure 23. Sulfate aerosol (g/m^3) BC for south, east, north and west boundaries. In each panel BC for 

control (top left, standard CMAQ BC), RAQMS_BC (top right, BC from RAQMS global model), 

SAT_BC (lower left, BC from model) and SAT_ICBC (lower right) simulations for August 1, 

2006, are presented. Notice that BC for control, RAQMS_BC, and for SAT_BC and SAT_ICBC 

are identical. They are included for completeness. Aerosol species from RAQMS were not 

mapped into CMAQ and therefore the standard CMAQ concentrations are used. 
45

3. RESULTS AND DISCUSSION 

The results were evaluated in several ways in order to address some key questions in this 

project. As mentioned earlier, shortly after the start of this project we realized that TES 

observations were not adequate to provide IC/BC. On the other hand, the newly available OMI 

profiles could provide the necessary spatial coverage. But, OMI ozone profiles were untested 

and needed to be evaluated. Therefore, we were faced with the following questions: (1) what is 

the uncertainty of OMI O3 profiles over continental U.S.; (2) can OMI O3 profiles be used to 

provide IC/BC for air quality modeling practices over U.S.; (3) what is the impact of utilizing 

OMI O3 for IC/BC on CMAQ performance in the free troposphere; (4) what would be the impact 

on surface O3 predictions? In addition to addressing these questions, we also investigated the 

impact of utilizing MODIS AOD products on model aerosol predictions. 

Ozonesonde data from IONS-06 campaign were used for evaluating OMI O3 profiles and 

also to evaluate CMAQ performance in the free troposphere. EPA surface observations were 

utilized for evaluating OMI O3 observations within the boundary layer and also for evaluating 

model performance in the boundary layer. We also used MODIS observations to evaluate model 

prediction of aerosols. The results of these evaluations are discussed in detail below. 

3.1. Evaluation of OMI Ozone Profiles 

OMI ozone profiles were evaluated against IONS-06 ozonesonde measurements. We also 

evaluated the available OMI observations within the boundary layer against EPA surface 

observations. 341 ozonesonde profiles for August 2006 from 18 stations that participated in the 

IONS-06 campaign were collected. Among them there are 23 profiles measured from the 

Ronald H. Brown ship over the Gulf of Mexico (referred as “Ron Brown” station). The locations 

for the stations are illustrated in Figure 10 and the data is described in Section 2.2. 

CMAQ vertical coordinate was used as the single vertical coordinate system for evaluation. 

OMI ozone profiles were mapped onto CMAQ vertical grid structure as described in Section 

2.1.1.3. Ozonesonde data were also either interpolated (where model vertical spacing was less 

than 100 m, which is ozonesonde vertical resolution) or averaged (where model vertical spacing 

was more than 100 m). Direct comparison between TES, OMI and ozonesonde measurements at 

several sites were also performed. An example of such comparison is presented in Figure 24. 
46

 (a) (b) 

 

Figure 24. An example of OMI, TES, ozonesonde comparison. (a) Measurements for 8/21/2006, Ron Brown

ozonesonde launched at (94.73W, 29.35N), 18:50 UTC, OMI O3 measured at (94.8W, 29.18N), 

19:54 UTC, distance from sonde = 20.08 km, TES O3 measured at (97.03W, 29.15N), 19:53UTC, 

distance from sonde = 224.21 km; (b) Measurements for 8/30/2006, Ron Brown ozonesonde 

launched at (94.83W, 28.5N), 17:58 UTC, OMI O3 measured at (94.7W, 28.72N), 19:47 UTC, 

distance from sonde = 27.55 km, TES O3 measured at (95.21W, 28.06N), 19:47UTC, distance from 

sonde = 61.46 km. The red lines in the figures are ozonesonde convolved with TES averaging 

kernel, which is a reconstruction of an ozonesonde profile for TES vertical resolution. 

The figure shows ozonesonde measurements for Ron Brown vessel on August 21 and August 

30, 2006, alongside the closest OMI and TES observations. The satellite observations are about 

1-2 hours behind the ozonesonde observations. TES footprint is 224-km and 61-km away from 

the sonde locations on August 21 and August 30 respectively. The distance from OMI footprint 

to sonde location is about 20-27-km on both days. As it can be seen from the figures, both TES 

and OMI can explain the general vertical variation of ozone. Ozonesonde measurements indicate 

some fine scale vertical structure and inhomogeneity that is absent in the satellite observation. 

This is understandable as satellite observations sample a thicker layer of the atmosphere and are 

not able to explain the fine scale variations. To obtain a more comparable representation of 

ozonesonde, the sonde profile is convolved with the TES averaging kernel A (defined as the 

sensitivity of the retrieval to the true state x) and the a priori profile (xa) of the satellite 

observation, according to the equation 

xˆ

ˆ    )(   a a xxAxx [Worden et al., 2007; Rodgers and 

Connor, 2003; Rodgers, 2000], where  is the measurement error (set to zero here). If x is 
47

replaced by the sonde observation, then gives the retrieved sonde profile, which is smoothed 

in the same way as if it would have been observed by TES, and can therefore be compared to the 

actual retrieved ozone profile. Both original and convolved sondes are used in the intercomparison with the OMI profiles (Figure 24). A noticeable observation is the limitation of 

OMI in observing the boundary layer ozone. OMI observation for the lowest layer usually 

represents the average concentration for a thick layer extending from surdface to a hight of about 

800 mb (about 2-km). This means that what is considered as the OMI boundary layer 

observation for the GoM region, in actuality covers both the boundary layer and part of the lower 

free troposphere. As evident from figure 24, the lower ozone concentration (about 30 ppb) is 

confined within the marine planetary boundary layer (PBL, about 500 m) and it increases above 

the PBL to about 50 ppb at 2 km. Both OMI and TES observations are about 40 ppb which is 

close to the convolved sonde concentration.

xˆ

For a more comprehensive evaluation of OMI observations over the continental United States 

all the available ozonesonde observations were utilized. Figure 25 shows the mean bias 

( 



N

i

OMI ozonesonde

i i OO

N 1

3 3 ( ) 1 ) and mean normalized bias ( 

N 

i

ozonesonde

OMI ozonesonde

i

i i

O

OO

N 1 3

3 3 1 ( )

) for 244 pair of 

observations. The statistics below 800 mb (from surface to about 2-km, which covers model 

layers 1-17) are not reliable as the sample sizes for these layers are particularly small. Table 9

shows the number of OMI/Sonde pairs used for each vertical layer. The number of pairs 

drastically jumps from 87 in layer 15 (about 1.5 km at 840 mb) to 226 in layer 16 (about 1.8 km 

at 810 mb). As evident from Figure 25, the bias decreases as the number of pairs (sample size) 

increase from surface to about 800 mb. OMI shows a reasonable agreement with ozonesonde in 

the lower- to mid-troposphere. In the upper troposphere, while the bias increases, the normalized 

bias does not show much variation and remains below 10%. This is due to higher ozone 

concentration in the upper troposphere caused by stratospheric incursion. Therefore, a 20 ppb 

difference in ozone concentration in the upper troposphere is still relatively small. 
48

Table 9. Number of OMI/Sonde Pairs Used in the Evaluation of OMI Profiles 

for August 2006 for Each Model Layer. 

Layer Number Number of OMI/Sonde Pairs Approximate Height 

(km) 

39 243 20 

38 244 16.5 

... ... ... 

21 244 4.0 

20 243 3.5 

19 242 3 

18 243 2.5 

17 242 2.1 

16 226 1.8 

15 87 1.5 

14 47 1.2 

13 31 1.0 

12 22 0.87 

11 10 0.69 

10 8 0.51 

9 7 0.43 

8 3 0.34 

... ... ... 

6 3 0.17 

5 1 0.13 

4 0 0.084 

... ... ... 

1 0 0.008 
49

Figure 25. OMI/ozonesonde comparison over United States for August 2006 during IONS-06 campaign. 

Mean bias ( 



N

i

OMI ozonesonde

i i OO

N 1

3 3 ( ) 1

) and mean normalized bias 

( 

N 

i

ozonesonde

OMI ozonesonde

i

i i

O

OO

N 1 3

3 3 1 ( )

) from 244 pair of observations are presented. For many of the 

observations OMI did not extend below 800 mb, thus the statistics below 800 mb (about 2-km, 

model layers 1-17) is not reliable as they represent a small sample. For example the number of 

pairs for model layers 5 through 11 is 1, 3, 3, 3, 7, 8, and 10 respectively. 

OMI overpass time is about 1:30 PM local time. It is safe to assume that over land during 

August, at 1:30 PM the boundary layer is well mixed, and therefore, OMI measurements at the 
50

lowest layer is representative of the boundary layer (BL) ozone concentrations and can be 

compared to EPA’s surface observations over the continental United States. It should be 

acknowledged that OMI observations are representing a volume averaged quantity for a 

relatively large volume of air, while surface observations are point measurements. Furthermore, 

a substantial number of surface monitors are impacted by local emissions and there is a large 

spatial variability even for stations that are only separated by a few kilometers. Therefore, one 

expects to see a large scatter when surface/OMI observation pairs are compared. However, one 

also expects to see higher ozone concentrations for OMI observations when a cluster of surface 

monitors observe elevated ozone concentrations. Based on this reasoning, we constructed 

observational pairs of OMI ozone observations in the boundary layer vs. observations from 

EPA’s surface monitors for July 15 through September 7, 2006. The pairs are constructed by 

extracting daily surface observations at 14:00 local time and pairing them with OMI 

observations. 

Figure 26 shows a scatter plot illustrating these observational pairs. The figure indicates that 

neither the elevated surface concentrations nor the large variations experienced by the surface 

monitors is explained by OMI. The correlation coefficient is 0.14, indicating a relatively low 

correlation between the two observations. For OMI, the mean and standard deviation are 48.2 

and 7.9 respectively, while for surface monitors, the mean and standard deviation are 48.9 and 

16.7 respectively. The mean for OMI and surface monitors are close indicating that OMI explain 

the mean observed concentration. But the relatively small standard deviation for OMI compared 

to that of surface monitors indicate that OMI is not able to explain extreme events. 

Since the scatter plot indicated that OMI O3 measurements for the boundary layer do not 

explain the large variations seen at the surface monitors, we examined OMI spatial patterns 

against the surface observations to establish their ability in realizing high/low regions. This was 

done to see whether OMI is able to explain spatial patterns of high versus low ozone or not. 

Figure 27 shows the spatial plots for OMI and surface monitors for August 3, 21, and 31, 2006. 

These days have been chosen to represent different scenarios in the observations. On August 3 

and 21, there is a general agreement between OMI observations and that of the surface monitors. 

While OMI underestimates elevated ozone concentrations, it is able to explain the larger scale 

spatial variation seen in the surface monitors. But on August 31, there is a substantial 

disagreement between OMI and surface monitors and OMI totally misses the surface 

observations. As mentioned above, OMI’s first layer can represent boundary layer and part of 

the lower free troposphere (surface to about 800 mb). In these comparisons it is assumed that the 

first layer in OMI observations represents boundary layer. It could be that in cases such as 

August 31, OMI observation represents the higher concentration above the boundary layer. 

Based on these preliminary results, a more rigorous examination of OMI retrievals within the 

boundary layer is needed to explain the cause of such significant disagreements with the surface 

observations. Nevertheless, on days where there is a general agreement in the spatial patterns, 

there is a possibility of scalling OMI observations to be more representative of the boundary 

layer. It should be noted that this analysis pertains to the OMI measurements over land (where 

EPA surface monitors are located) and does not apply to the observations over GoM. 
51

Figure 26. Scatter plot showing OMI ozone observations in the boundary layer vs. observations from EPA’s 

surface monitors for July 15 through September 7, 2006. The pairs are constructed by extracting 

daily surface observations at 14:00 local time and pairing them with OMI observations. The 

figure indicates that OMI is not able to neither explain elevated surface concentrations nor the 

large variations experienced by the surface monitors. The correlation coefficient is 0.14, while the 

mean and standard deviation for OMI are 48.2 and 7.9 respectively, and for surface monitors are 

48.9 and 16.7 respectively. 
52

Figure 27. OMI ozone observations (left panel) versus observations by surface monitors (right panel) for 

August 3, 21, and 31, 2006. While on August 3 and 21 there is a better agreement between the 

spatial patterns, on August 31, OMI observations are not representative of the boundary layer. 

To examine the OMI observations in the marine boundary layer and over the GoM region, 

the limited number of ozonesonde observations from Ron Brown vessel were utilized. 

Unfortunately, most of the time the vessel stayed very close to the coast and on days that the ship 

is farther away (e.g., August 2 and 16) the ozonesonde data is not available. Table 10 presents 

the location and time for Ron Brown surface observations versus OMI first layer retrieval and the 
53

time of satellite overpass. These data are presented in Figure 28, which shows a scatterplot 

depicting Ron Brown surface ozone observations versus OMI first layer retrievals. While the 

sample size is small, the pattern is very similar to what is observed in Figure 26 which shows 

OMI observations versus surface stations. It seems that OMI is not able to explain the lower 

ozone concentrations within the boundary layer and tends to conform to a narrow range about the 

maean concentration. 

Table 10. OMI/Rown Brown Vessel Surface Ozone Measurements for August 2006. 

(Missing values are marked as -999) 

DAY 

August LAT LON Ron Brown time 

OMI 

time 

Ron Brown 

O3 

OMI 

O3 

2 26.76 -95.09 19 19 -999. 36.18 

3 29.63 -94.63 17 20 16.50 42.76 

4 29.68 -95.01 18 19 45.00 47.67 

6 28.00 -95.52 18 18 24.50 47.34 

7 28.11 -96.44 18 19 28.00 34.36 

9 29.87 -93.93 18 19 53.50 43.60 

11 29.75 -95.29 18 19 29.00 47.17 

12 29.21 -94.67 18 19 -999. 42.16 

13 29.68 -95.01 18 19 15.00 34.51 

15 29.68 -95.01 18 18 39.00 36.93 

16 26.63 -94.96 18 19 -999. 41.07 

17 29.64 -94.96 19 20 91.00 49.35 

18 29.31 -94.79 18 19 18.00 44.44 

21 29.35 -94.73 18 19 -999. 47.50 

22 29.00 -94.66 17 18 32.50 43.89 

23 28.55 -96.51 19 19 33.00 35.69 

24 29.32 -94.55 18 18 30.00 46.51 

26 29.68 -95.01 19 20 25.00 36.04 

27 29.68 -94.99 18 00 20.00 -999. 

28 28.95 -94.77 19 19 11.00 35.95 

29 28.50 -94.83 17 19 15.00 36.61 

30 28.50 -94.83 17 19 41.50 39.52 

31 28.50 -94.83 18 18 -999. 35.51 
54

OMI vs Ron Brown

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

0 10 20 30 40 50 60 70 80 90 10

Ron Brown BL O3 

OMI First Layer O3

0

Figure 28. Scatter plot showing ozone concentrations for Ron Brown ozonesonde observations at the surface 

versus OMI first layer observations. 

3.2. Model Performance in the Free Troposphere 

Ozonesonde measurements from IONS-06 campaign (as described in Section 2.2) are used to 

evaluate model performance in the free troposphere. For each day during August 2006, 

ozonesonde measurements within 1500~2300 UTC are compared with ozone mixing ratios at 

1900 UTC simulated by four CMAQ runs (CNTRL, RAQMS_BC, SAT_BC, and SAT_ICBC as 

described in Section 2.3). The control simulation significantly underestimates ozone 

concentrations in the upper troposphere; while ozone concentrations from all other simulations 

are closer to those measured by ozonesondes (Figure 29). The bias in terms of concentration is 

the largest in the upper troposphere. However, when the bias is presented as the percent 

difference, only the control simulation shows large deviations from Ozonesonde measurements 

from 5 to 13 km. Further improvement has been made in the interior region by applying satellite 

data as IC (SAT_ICBC) for each 24-hour segment. As evident in the figure, the best 

performance in the free troposphere is achieved by SAT_ICBC run. While SAT_ICBC 

simulation performs much better in most of the free troposphere, it overestimates ozone at upper 

layers in the model (average elevation above 12 km). This could be due to artificial diffusion at 

the model top layers. In these simulations we tried to minimize the numerical diffusion and 
55

artificial incursion of stratospheric air into the mid-troposphere by reducing the vertical grid 

spacing between 8 and 12 km. As evident from the results for RAQMS_BC, SAT_BC, and 

SAT_ICBC it seems that this approach has worked. The results from all of these simulations 

indicate that the higher ozone concentrations at the top of the model do not penetrate to lower 

layers and the mean normalized bias at 8- to 12-km elevation is relatively low. 

Figure 30 presents the same plots as Figure 29 alongside bias plots for OMI ozone profiles. 

This is to show that while for OMI the top two layers are very close to Ozonesonde 

measurements, for SAT_ICBC run, the model is underestimating the top layer and 

overestimating the layers below the top layer which is indicative of excessive mixing in the 

model top layer. In these simulations the top two layers were about 3.5 and 1.5 km thick 

compared to about 0.5-0.8 km spacing in the other layers. Since O3 vertical gradient at the top 

layer is the highest and ozone concentrations can go from less than 100 pbb to 800 ppb (Figure 

5), even a small artificial incursion from the top layer can have large impact on the lower layers. 

For our future simulations we have added two more layers at the top to keep the grid spacing to a 

maximum of 1 km. We think that this change in grid configuration will minimize the excessive 

mixing seen in the current results. 

While the overall statistics are useful in evaluating the model performance as a whole, it does 

not offer an insight into the model performance at specific regions. Examining the model results 

for each individual site offer a better understanding of the processes involved. Both 

RAQMS_BC and SAT_BC simulations show substantial improvements in the column 

distribution of ozone when compared to Ozonesonde measurements that are close to the lateral 

boundary of the domain. Over sonde stations such as Kelowna (northwest), Bratt’s Lake (north), 

Egbert and Paradox (northeast), Trinidad Heas, Table Mountain, and Holtville (west, sowthwest) 

the impact of the boundary condition in improving model performance is more noticeable 

(stations are marked on the map in Figure 10). However, over the sonde stations in the interior 

of the domain, such as Huntsville, Houston, Socorro, and Boulder, the improvements in the 

model is limited to very top layers. 
56

Figure 29. Model performance evaluated against ozonesonde measurements for August 2006. The upper 

panel shows the bias in ppb, while the lower panel expresses the bias as the percentage for four 

simulations: control, BC provided from RAQMS, BC provided from satellite observations, BC 

provided from satellite observations and ozone in IC replaced by satellite observations every 24 

hours. 
57

 (a) (b) (c) (d) 

Figure 30. OMI O3 profiles and model results evaluated against ozonesonde measurements for August 2006. 

Also plotted in this figure is the bias for OMI O3 profiles. (a) and (b) show the bias in ppb, while 

(c) and (d) express the bias as the percentage for four simulations: control, BC provided from 

RAQMS, BC provided from satellite observations, BC provided from satellite observations and 

ozone in IC replaced by satellite observations every 24 hours. 

Figures 31-33 show the curtain plots for several selected sites. Figure 31 shows the results 

from CNTRL simulation, while Figure 32 and Figure 33 show the results from SAT_BC and 

SAT_ICBC respectively. Ozonesonde measurements are mapped to the model vertical structure 

and plotted over curtain plots. Therefore, when the Ozonesonde measurements are visible on top 

of the model results, it means that there is a large disagreement between the model and the 

observations. As evident from Figure 31, the CNTRL simulation is greatly underestimating 

ozone concentration for all the stations. The underestimation is more pronounced at the stations 

to the north. This is due to the lower tropopause height for the northern latitudes and 

consequently higher ozone concentrations at lower altitudes which is absent in the CNTRL 

simulation. The results from SAT_BC show a significant improvement over CNTRL simulation. 

The improvements are more pronounced over Kelowna (northwest) and Egbert (north/northeast) 

stations and are reasonable for Holtville (west/southwest). But for the interior stations, 

Huntsville and Houston, the improvements are marginal. For these stations while some of the 

higher ozone values for the upper layer are reproduced by the model, in the mid to upper 

troposphere ozone is still underestimated. Much of this underestimation is corrected in 

SAT_ICBC simulation as evident from Figure 32. The Ozonesondes are hardly visible in this 

figure, meaning that there is a good agreement between the model and the observations. One 

also notices the higher ozone concentration for the top layer in agreement with Ozonesonde 

observations. 
58

Figure 31. Curtain plots showing CMAQ simulated ozone concentrations from CNTRL simulation versus 

ozonesonde measurements for the month of August 2006. Ozonesonde observations are resampled onto the model vertical grid structure and plotted over model predictions. Stations are 

marked on the map in Figure 10. 
59

Figure 32. Curtain plots showing CMAQ simulated ozone concentrations from SAT_BC simulation versus 

ozonesonde measurements for the month of August 2006. Ozonesonde observations are resampled onto the model vertical grid structure and plotted over model predictions. Stations are 

marked on the map in Figure 10. 
60

Figure 33. Curtain plots showing CMAQ simulated ozone concentrations from SAT_ICBC simulation versus 

ozonesonde measurements for the month of August 2006. Ozonesonde observations are resampled onto the model vertical grid structure and plotted over model predictions. Stations are 

marked on the map in Figure 10. 
61

These simulations also demonstrated that in the free troposphere, and in particular the upper 

troposphere, the transport is the dominant process in determining ozone concentration. 

Therefore, if the lateral boundary condition is correctly specified, the horizontal transport 

replenishes ozone at the top model layers within few days even when the top boundary condition 

in the model is poorly specified. This implies that the correct specification of lateral BC is able 

to explain some of the stratospheric/tropospheric exchange (STE). Tang et al. (2007) 

investigated the sensitivity of a regional chemical transport model (CTM) to the top boundary 

condition that was provided by three different global CTMs. The regional model predictions 

above 4-km were strongly impacted by the top boundary condition. But, there are several 

practical problems in specifying the top boundary from global models. First, is the mapping of 

vertical layers if the vertical coordinate systems between the global model and the regional 

model are not the same? Since the ozone vertical gradient is the largest at the top of the regional 

model, such mapping introduces large interpolation errors. Second, in most cases the dynamics 

between the two models are not similar and an STE event in the global model may not agree in 

the extent, location, and timing with the regional model. 

Figure 34 presents ozone spatial plots from four CMAQ simulations at 212 and 500 mb for 

August 21, 2006, 19:00 GMT. Plotted over CMAQ fields are Ozonesonde measurements 

launched between 15:00 and 23:00 GMT. At 212 mb, layer 33 in the 39 layer CMAQ domain, it 

can be seen that model predictions progressively are improving from CNTRL to SAT_ICBC 

simulation. What is interesting is the similarity between RAQMS_BC and SAT_BC runs. Both 

simulations indicate improvements in the northern and southern part of the domain over the 

control simulation. However, Ozonesonde measurements over Huntsville and Socorro stand out 

as they indicate higher concentrations than the model is predicting. SAT_ICBC exhibits the best 

agreement with the observation, indicating the advantage of using OMI measurements for 

initializing each modeling segment. At 500 mb, while the model predictions are somewhat 

improved, they are still under-predicting the observed values. One of the inadequacies in our 

technique in specifying the BC from OMI observations has been the use of a constant profile for 

a 24 hour simulation segment. This is evident as both SAT_BC and SAT_ICBC simulations 

miss the higher ozone concentrations to the north (at 500 mb), while RAQMS_BC is able to 

explain it. Interpolating between two daily profiles and scaling it to a diurnal pattern can serve 

as a solution and will be pursued in the future. 
62

Figure 34. O3 (ppbv) spatial plots at 1900 UTC, August 21, 2006, simulated by 4 CMAQ runs; plotted over 

model fields are 6 ozonesondes measurements launched between 1500~2300 UTC. Left panels 

represent CMAQ level 33 (212 hPa). Right panels represent level 24 (501 hPa). 
63

3.3. Model Performance in the Boundary Layer 

3.3.1. Ozone 

The performance of the model was evaluated at EPA monitoring stations. The statistical 

measures include the Mean Bias (MB), Mean Error (ME), Mean Normalized Bias (MNB), Mean 

Normalized Error (MNE), Mean Fractional Bias (MFB), and Mean Fractional Error (MFE) in 

hourly averaged concentrations predicted at these stations. Mathematical formulation of these 

metrics is provided in Table 11. Since the normalized quantities can become large when 

observations are small, a cut-off value of 60, 10, and 120 ppb for O3, SO2, and CO respectively is 

used while computing MNB and MNE statistics. Thus, whenever the observation is smaller than 

the cut-off value, the prediction-observation pair is excluded from the calculation. The hourly 

normalized bias and error metrics are presented as daily averages over all monitoring stations. 

Table 11. Statistical Metrics for Air Quality Model Performance Evaluation. 

Metrics Formulation Notes 

Mean Bias (MB)   %100 1

1

  

N

i

o

i

s

i CC

N

Computed when observation is 

greater than 0.0 

Mean Error (ME) %100 1

1

  

N

i

o

i

s

i CC

N

Same as MB 

Mean Normalized 

Bias (MNB) 

  %100 1

1

  

N

i

o

i

o

i

s

i

C

CC

N

Computed when observation is 

greater than the cut-off value 

which is set at 0.06, 0.01, and 

0.120 ppmv for O3, SO2 and 

CO. 

Mean Normalized 

Error (MNE) %100 1

1

 



N

i

o

i

o

i

s

i

C

CC

N

Same as MNB 

Mean Fraction Bias 

(MFB) 

  %100

2

1

1





 

N

i

o

i

s

i

o

i

s

i

CC

CC

N

Computed when observation is 

greater than the cut-off value 

which is set at 0.06, 0.01, and 

0.120 ppmv for O3, SO2 and 

CO. 

Mean Fraction Error 

(MFE) 

%100

2

1

1









N

i

o

i

s

i

o

i

s

i

CC

CC

N Same as MFB 

The statistical analysis was followed by visual inspection of predicted concentrations fields 

and the observations were plotted over the concentration fields. This helped in identifying 

dynamics of pollutant plumes in the region, and helped interpreting the performance issues 

related to individual monitors. For example, poor model performance at a monitoring station 
64

might be related to displacement of a plume due to error in wind direction. Finally, time series 

plots of predicted and observed hourly concentrations provided a stringent test of how well the 

model replicates the observed hourly concentration at the same time and location as the observed 

value. Problems with diurnal variation in predicted concentrations were readily apparent in the 

time series plot. Since in this study the CNTRL simulation served as the reference point to be 

compared to other simulations, it was thoroughly evaluated. 

Table 12 presents the statistics from CNTRL run for the five sub-regions shown in Figure 

14. Bias for all sub-regions is about 6 ppb with the normalized bias being -7% for the Midwest 

region. The positive bias is caused by the systematic over-prediction of ozone at night in 

CMAQ. CMAQ over-predicts ozone at night and under-predicts it during the daytime peaks. 

Since MNB does not include the lower values at night, it shows an under-prediction of 7-12% for 

different sub-regions. The under-prediction is the lowest in the Midwest region at 7%, followed 

by 9% in the Mid-Atlantic/Northeast region, 10% in the Central region, 11% in the Southeast 

region and 12% in the Western region. 

Comparing the other simulations to the control offers a mixed result. Some metrics show 

improvements, while the others indicate deterioration. However, the overall picture is consistent 

and indicates an overall increase in ozone concentration in RAQMS_BC, SAT_BC and 

SAT_ICBC simulations. That is, the nighttime over-prediction is exacerbated, but the daytime 

under-prediction is improved. Figure 35 shows the mean observed concentration for each subregion (and also the overall) versus model predictions for August 2006. RAQMS_BC overpredicts ozone in all regions while SAT_BC and SAT_ICBC exhibit a better agreement with the 

average observations for MANEVU and CENRAP regions. The average concentrations 

represent daytime, as well as nighttime observation/prediction pairs. As mentioned before, much 

of the over-prediction is due to the nighttime over-prediction. This point is better illustrated in 

Figure 36 which shows Mean Normalized Bias (MNB) for different simulations. Since data 

pairs where observed ozone is less than 60 ppb are not included in the MNB calculation, this 

metric is an indicator of model performance with respect to daytime ozone prediction (or peak 

ozone prediction). Therefore by eliminating the low nighttime values, 

Figure 36 shows that the model is under-predicting peak ozone. RAQMS_BC simulation 

which has the highest Mean Bias (MB), as illustrated in Figure 35, has the best overall 

performance during the day as evident from MNB (with the best performance in the Midwest 

region). SAT_BC and SAT_ICBC simulations overall perform better than the control simulation 

but cannot outperform RAQMS_BC. Judging from these statistics it seems that the satellite 

derived boundary condition for the western boundary is much higher than what the surface 

observations indicate. Then, the predominant west-east flow is transporting the higher ozone to 

the east and its impact diminishes as the flow reaches the eastern part of the domain. In fact, 

both SAT_BC and SAT_ICBC simulations perform poor in the west (WRAP) and outperform 

RAQMS_BC in the southeast region (VISTAS) with respect to daytime ozone predictions. This 

region is predominantly affected by the BC in the southeastern part of the domain and the 

daytime satellite profile has been able to explain the impact of transboundary flow to this region. 

Figure 37 is a snapshot of the difference in ozone concentration in the boundary layer (1 km 

altitude) between SAT_BC and the CNTRL simulations for August 16, 2006. The plot clearly 

shows the role of transport in advecting ozone from the lateral boundaries into the interior of the 
65

domain. It also shows higher ozone concentrations entering the interior of the domain from the 

northwestern boundary while the Southeast is more affected by the easterlies. This could partly 

explain the statistics discussed above. A closer look at the role of transport and the impact of the 

boundary conditions reveals that the southeast region and the GoM area is more impacted by 

these easterlies that at times carry the remnants of the northeast pollution through re-circulation. 

Table 12. Performance Statistics for CNTRL Simulation. 

 (August 1-31, 2006 at 36-km grid resolution) 

Pollutant CENRAP MANEVU MWRPO VISTAS WRAP

Ozone Mean OBS (ppb) 33 33 29 34 34 

Ozone Mean PRD (ppb) 39 39 37 39 39 

Ozone Bias (ppb) 6 6 7 6 6 

Ozone Gross Error (ppb) 12 12 13 12 12 

Ozone Normalized Bias (percent) -10 -9 -7 -11 -12 

Ozone Normalized Error (percent) 19 18 19 19 19 

Ozone Fractional Bias (percent) 24 24 27 23 23 

Ozone Fractional Error (percent) 42 44 49 43 43 

Carbon monoxide Mean OBS ppb 43 40 48 37 45 

Carbon monoxide Mean PRD (ppb) 25 24 21 27 25 

Carbon monoxide Bias (ppb) -18 -17 -28 -11 -20 

Carbon monoxide Gross Error (ppb) 24 23 35 21 27 

Carbon monoxide Normalized Bias (percent) -34 -34 -43 -24 -34 

Carbon monoxide Normalized Error (percent) 50 51 67 51 52 

Carbon monoxide Fractional Bias (percent) -44 -38 -71 -30 -45 

Carbon monoxide Fractional Error (percent) 67 69 87 62 69 

Sulfur dioxide Mean OBS (ppb) 4 4 3 3 4 

Sulfur dioxide Mean PRD (ppb) 4 5 4 4 4 

Sulfur dioxide Bias (ppb) 0 1 1 0 0 

Sulfur dioxide Gross Error (ppb) 4 4 3 3 4 

Sulfur dioxide Normalized Bias (percent) -57 -56 -61 -54 -61 

Sulfur dioxide Normalized Error (percent) 68 63 67 62 67 

Sulfur dioxide Fractional Bias (percent) 17 17 34 1 5 

Sulfur dioxide Fractional Error (percent) 86 85 80 82 80 
O3 Mean Concentration

0.0000.0050.0100.0150.0200.0250.0300.0350.0400.0450.050

VISTAS MWRPO MANEVU CENRAP WRAP 

Regions

PPM

Mean Observed (ppm) Mean Predicted (CNTRL) Mean Predicted (RAQMS) Mean Predicted (SATBC) Mean Predicted (SATICBC

66

Figure 35. Mean observed ozone versus model predictions for CNTRL, RAQMS_BC, SAT_BC, and SAT_ICBC simulation

sub-regions are marked on the map in Figure 14. 
O3 Mean Normalized Bias

-18-16-14-12-10

-8-6-4-2

0

VISTAS MWRPO MANEVU CENRAP WRAP 

Metric

Fraction (%)

Mean Norm. Bias (CNTRL) Mean Norm Bias (RAQMS) Mean Norm Bias (SATBC) Mean No

67

Figure 36. Mean normalized bias (MNB) for ozone predictions from CNTRL, RAQMS_BC, SAT_BC, and SAT_ICBC simula

where observed ozone is less than 60 ppb are not included (meaning that most of the nighttime and early morning p
68

Figure 37. Difference in ozone concentrations between SAT_BC and CNTRL simulations in the boundary 

layer for August 16, 2006. The plot clearly shows the role of transport in advecting ozone from 

the lateral boundaries 

3.3.2. PM2.5

The results fro SAT_BC and SAT_ICBC also demonstrate the impact of utilizing MODIS 

AOD products to scale model PM2.5 species. As discussed in Section 2.1.2 MODIS AOD 

products were utilized to scale model PM2.5 species to conform to satellite observations. The 

overall evaluation of the results indicates that the model performance with respect to the 

prediction of PM2.5 total mass has been greatly improved. But since the scaling is based on the 

assumption that the model partitioning and distribution of PM2.5 species is reasonable and the 

error is due to the magnitude of emissions/transformation, in cases where the partitioning was 

inaccurate, the inaccuracy was exacerbated. Our future research in this area will try to utilize the 

data from surface monitors to complement satellite observations and attempt to correct the PM2.5

partitioning as well as scaling. 

Figure 38 shows the mean observed mass concentration of PM2.5 for different regions versus 

model predictions from CNTRL and SAT_ICBC simulations. SAT_ICBC simulation shows a 

marked improvement over CNTRL in predicting PM2.5 in all regions and in particular in the 

Mid-Atlantic/Northeast region where the bias is practically negligible. The overall mean bias 
69

and error shown in Figure 39 demonstrates that while the use of satellite observations for BC 

marginally improves model performance, readjusting PM2.5 every 24 hours can significantly 

enhance model predictions of PM2.5. This is expected as correcting BC greatly impacts the 

regions closer to the lateral boundaries of the domain, but initializing the model simulation with 

a satellite derived field corrects the total mass in the entire domain. By removing the smaller 

concentrations, the improvements are even more significant as shown in Figure 40. As the 

figure shows, MFB is reduced by about 30%. 

A more detailed analysis of the results shows that episodically the satellite derived BC also 

significantly impact PM2.5 over a large region and is able to explain the impact of large scale 

transport. Figure 41 shows one such event where there is a large influx of PM2.5 from the 

southeastern boundary impacting the Gulf of Mexico, Louisiana, Texas, Oklahoma, Arkansas 

and extending into Kansas and Missouri. 

Events showing the impact of PM2.5 transport are not uncommon. But since in such events 

the transport is the dominant factor, the accuracy of model predictions of PM2.5 is dependent on 

the ability of the meteorological model in explaining the flow field. One such event is shown in 

Figure 42 where a weak cold front approaches the Southeast and gradually passes over the 

southeastern States. The figure shows MODIS daily AOD product mapped onto the CMAQ grid 

versus model AOD obtained from CMAQ aerosol predictions. Surface weather charts 

illustrating the location of the front for each day of August 18-22, 2006 are also presented in the 

figure. The correlation coefficient, Mean Normalized Bias (MNB) and Variance for model 

predictions of PM2.5 versus MODIS observations for this event is presented in Table 13. For 

August 18 and 19 the model prediction of the front is reasonable and SAT_ICBC simulation 

shows a reasonable agreement with MODIS observation. The correlation coefficients for these 

days are .8 for August 18 and .85 for August 19, an improvement over CNTRL simulation with 

correlation coefficients of .73 and .8 respectively. The spatial plots in Figure 42 also show a 

good agreement between the model predictions and observations. The weather charts indicate 

that on August 20-22, the cold front slowly moves toward the south/southeast. MODIS AOD 

plots also show a very good agreement with the weather charts as the front is marked with a high 

AOD spatial gradient. The plots indicate high AOD values to the north of the front as the 

pollution accumulates and low AOD values to the south of the front. The model also shows the 

high spatial gradient and also is in agreement with the peak AOD values and their spatial extent 

as observed by MODIS. However, the front is misplaced by the model as it shows the front 

much further to the south than the observations indicate. The front in the model is moving to the 

south/southeast much faster than the observation and therefore by missing the location of the 

front model is not able to correctly explain the location of observed PM2.5 peak. Consequently, 

the correlation coefficients for August 20-22 for SAT_ICBC simulation are not very different 

from CNTRL. MNB however goes from negative in CNTRL simulation to positive in 

SAT_ICBC, indicating that the under-prediction of PM2.5 has been corrected but the location is 

wrong. Figure 43 shows observed diurnal variation of PM2.5 mass concentration from several 

sites in Alabama and Mississippi against model predictions for the period of August 18-22, 2006. 

There is a reasonable agreement in the model predicted diurnal pattern, especially for August 18-

20, for some of the sites. SAT_ICBC simulation also shows an improvement in predicting PM2.5

concentration for August 18-20, but over-predicts PM2.5 in the latter part of this period enforcing 

the previous statements. 
70

Evaluation of model results for individual stations indicated that SAT_ICBC was able to 

better explain elevated PM2.5 episodes. 

Figure 44 shows observed PM2.5 concentrations against model predictions over several sites 

in Alabama for the month of August 2006. While the model perhaps exaggerates the extent of 

diurnal variation (for some days), it is able to realize gradual increase in PM2.5 over the State. In 

particular, both CNTRL and SAT_ICBC simulations show an increase in PM2.5 during August 

23-27, 2006, but SAT_ICBC shows a better agreement with the observations. 

Table 13. Correlation Coefficient (R), Mean Normalized Bias (MNB), and Variance for Model PM2.5

Predictions (CNTRL and SAT_ICBC simulations) Versus MODIS Observations for the 

Frontal Event of August 18-22, 2006. 

Day 18 19 20 21 22 

CNTRL SAT_ICBC CNTRL SAT_ICBC. CNTRL SAT_ICBC. CNTRL SAT_ICBC. CNTRL SAT_ICBC. 

R 0.732 0.80 0.798 0.850 0.619 0.644 0.623 0.769 0.609 0.661 

MNB -0.433 -0.064 -0.302 0.133 -0.109 0.299 -0.114 0.264 -0.168 0.140 

Variance 0.041 0.019 0.037 0.017 0.045 0.045 0.055 0.034 0.046 0.039 
PM2.5 Evaluation

-10.000

-5.000

0.0005.000

10.00015.00020.000

VISTAS: MWRPO: MANEVU: CENRAP: WRAP: Overall

Regions

Concentration (ug/m^3)

Mean Observed (ug/m^3) Mean CNTRL Mean IC/BC Mean Bias - CNTRL Mean Bias - IC/BC Mean Error - CNTRL Mean Error 

71

Figure 38. Observed PM2.5 mass concentration (g/m3) for different regions versus model predictions from CNTRL and SAT_

shown in the figure are mean bias and error in model predictions. 
PM2.5 Evaluation

-10.000

-5.000

0.0005.000

10.00015.000

Concentration (ug/m^3) Overall

Mean Observed (ug/m^3) Mean CNTRL Mean BC Mean IC/BC Mean Bias - CNTRLMean Bias - BC Mean Bias - IC/BC Mean Error - CNTRL Mean Error - BC Mean Error - IC/BC

Mean Predicted

Mean Bias

Mean Erro

72

r

Figure 39. Mean observed PM2.5 mass concentration (g/m3) for August 2006 over the continental U.S. versus model pr

SAT_BC and SAT_ICBC simulations. 
PM2.5 Evaluation

-60.000-40.000-20.000

0.000

20.00040.00060.000

1 Fraction (%)

MNB (CNTRL) MNB (SAT_BC) MNB (SAT_ICBC) MNE (CNTRL) MNE (SAT_BC) MNE (SAT_ICBC) MFB (CNTRL) MFB (SAT_BC) MFB (SAT_ICBC) MFE (CNTRL) MFE (SAT_BC) MF

Mean Normalized Bias

Mean Normalized Error

Mean Fractional Bias

Mean Fractional Erro

73

r

Figure 40. Mean normalized and fractional bias and error (MNB, MNE, MFB, MFE) for CNTRL, SAT_BC and SAT_ICBC si
74

Figure 41. Difference in surface PM2.5 mass concentration (g/m3) between CNTRL and SAT_BC 

simulations for August 2, 2006, 18:00 GMT. 
75

Figure 42. Frontal passage of August 18-22, 2006, and its impact on PM2.5. The top panel shows MODIS 

AOD, the middle panel shows model AOD based on CMAQ aerosol prediction and the bottom 

panel shows the surface charts for each day. 

Figure 43. Observed diurnal variation of PM2.5 mass concentration for few monitoring sites in Alabama and 

Mississippi versus model prediction from CNTRL (here marked as base) and SAT_ICBC 

(marked as assim) simulations. 
76

Figure 44. Observed PM2.5 mass concentration for few monitoring sites in Alabama versus model prediction 

from CNTRL (here marked as base) and SAT_ICBC (marked as assim) simulations for the 

month of August 2006. 
77

4. CONCLUSION AND RECOMMENDATIONS 

Thus far, we have demonstrated the utility of OMI O3 profiles and MODIS aerosol products 

in CMAQ. OMI O3 significantly improved model performance in the free troposphere and 

MODIS aerosol products substantially improved PM2.5 predictions. There are still issues with 

respect to the fact that neither OMI nor TES provide adequate information in the boundary layer 

with respect to O3. Our first efforts, as indicated in this report, have shown marginal 

improvements in the model performance within the boundary layer. But, with boundary layer O3

being of particular importance to the air quality community, the future work should devise 

approaches to better characterize pollution episodes. One approach could include the use of 

other AURA products such as NO2, HCHO, and CO. Since formaldehyde abundance is mainly 

limited to the boundary layer, column measurements of HCHO can make significant contribution 

to the better representation of the boundary layer chemical composition. 

With respect to the use of aerosol products, while satellite data improved model performance 

with respect to PM2.5 total mass concentration, aerosol speciation remains a challenge. The 

incorporation of satellite data relied on a key assumption that the aerosol partitioning within the 

model is reliable. Therefore, revisiting this assumption or improving the aerosol model within 

CMAQ takes higher priority. 

The current project examined the impact of the boundary conditions (BC) on the air quality 

predictions. The utilization of the satellite data for BC was helpful in the realization of 

transboundary transport of pollution and helped in better representation of the free tropospheric 

ozone. Our hypothesis that the recirculation of pollution from Northeast Corridor can play a role 

over the Gulf of Mexico (GoM) was tested and our model simulations show evidence of such 

possibility. The episodic transport of pollution by easterlies over the GoM and the southeastern 

region suggests that in particular the specification of the lateral boundaries and the background 

air in modeling practices in this region is important. 

In this project we also utilized daily information from the satellite to improve the initial 

condition in the model. This effort can lead to effectively assimilate the satellite data into the 

model fields. Since the initial conditions greatly impact the short term predictions, use of 

satellite data for IC can also potentially improve air quality forecasts. The utilization of MODIS 

AOD in specifying IC greatly improved model prediction of PM2.5 concentrations. Our overall 

assessment of the utility of satellite ozone observations for air quality studies is that the quality 

of the data for the boundary layer is not satisfactory. OMI observations are valuable in 

improving ozone fields in the free troposphere which in turn can affect ozone abundance in the 

boundary layer, but are not adequate to explain the low background ozone concentration in the 

boundary layer. Ozone in the mid- to upper-troposphere is largely dominated by transport, while 

the ozone within boundary layer is mostly affected by fast production/loss mechanisms that are 

impacted by surface emissions, chemistry and removal processes. 

The current project did not examine the role of assimilation in improving the physical 

atmosphere. Future efforts should include an improved physical atmosphere in conjunction with 

the assimilation of satellite trace gases. For example, it has been shown that the assimilation of 
78

satellite-observed clouds greatly improves model predictions of ozone within the boundary layer. 

It also eliminates one component of inconsistency between the model and the observations. One 

of the problems in satellite data assimilation is that the observed physical/chemical world is not 

always consistent with the model world. A major manifestation of this inconsistency is with 

respect to the clouds. A discrepancy between model clouds (clear sky) and the satellite clouds 

(clear sky) impacts the radiation fields, vertical transport and local circulations, the chemistry 

and microphysical properties. This means that when one component of physical or chemical 

atmosphere is perturbed (adjusted) by the assimilation of satellite data, the complete environment 

for supporting and sustaining the adjustment does not exist. Therefore, as we continue to 

introduce more chemical observations into the modeling framework, it is essential that in a 

parallel effort we make the physical environment more realistic. This means the inclusion of 

assimilation of satellite observed skin temperature, moisture, albedo, insolation, and clouds in 

conjunction with the assimilation of trace gases and aerosols. 

Finally, while satellite observation of ozone alone did not fulfill our expectations for air 

quality studies, the use of satellite observation of ozone and other trace gases in conjunction with 

surface observations needs to be investigated. For example, there are complementary satellite 

observations (as indicated before) such as nitrogen dioxide (NO2), formaldehyde (HCHO), and 

carbon monoxide (CO) that can help in specifying the background air. Also, the routine surface 

observations can be used to scale satellite observation and introduce diurnal variation to the 

measurements. Surface measurements, while detailed and continuous, are point measurements 

and lack the ability of representing a larger region. On the other hand, the information from ATrain polar orbiting satellites is an early afternoon snap shot of the atmosphere and represents an 

average quantity over a larger area. A natural extension of the efforts documented in this report 

would be to examine the feasibility of such approaches. 

The modeling simulations reported here used relatively coarse grid spacing over the 

continental United States and was able to explain the large scale impact of long range transport. 

However, a future study should examine the utilization of the techniques and the data used in this 

study for a smaller domain over the southeastern U.S. with finer grid spacing. 

Acknowledgments. This work was accomplished under partial support from Cooperative 

Agreement Between the University of Alabama in Huntsville and the Bureau of Ocean Energy 

Management, Regulation and Enforcement on Gulf of Mexico Issues, and the NASA Science 

Mission Directorate Applied Sciences Program. 
79

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