Bryan W. Shaw, Ph.D., Chairman 

Buddy Garcia, Commissioner 

Carlos Rubinstein, Commissioner 

- . : ~~·.-> .:.\ \ .: I· 

!d:~ fJLr.;·u:i: 

. !· 

Mark R. Vickery, P.G., Executive Director 10 APR -9 Pii 1:55 

TEXAS COMMISSION ON ENVIRONMENTAL QUALITY 

Protecting Texas bg Reducing and Preventing Pollution 

April6,2010 

Docket No. 2009-0802-SIP, Project No. 2009-017-SIP-NR ~ 

Docket No. 2009-0932-RUL, Rule Project No. 2009-006-101-EN · 

Docket No. 2009-0867-RUL, Rule Project No. 2009-019-101-EN 

A1 Armendariz, PhD, Regional Administrator 

U.S. Environmental Protection Agency- Region 6 

1445 Ross Avenue 

Dallas, Texas 75202-2733 . 

Dear Dr. Armendariz: 

On March 10, 2010, the Texas Commission on Environmental Quality (Commission) adopted revisions to the 

State Implementation Plan (SlP) for the control of ozone air pollution for the Houston-Galveston-Brazoria 

(HGB) area. 

The Commission adopted amendments to 30 Texas Administrative Code (TAC) Chapter 101, Subchapter A, 

General Rules, Section 101.1; and Subchapter H, Emissions Banking and Trading, Division 6, Highly Reactive 

Volatile Organic Compound Emissions Cap and Trade Program, Sections 101.390 - 101.394, 101.396, and 

101.399 - 101.401; 30 TAC Chapter 101, Subchapter H, Emissions Banking and Trading, Division 3, Mass 

Emissions C and Trade Pro Sections 101.350 101.351 and 101.353 and co · revisions to the 

SlP; and GB Attsinment Demonstration SlP Revision for the 1997 Ei t-Hour Ozone Standard. These rule 

changes and SIP revisions demonstrate that the HGB area will attain the 1997 eight-hour ozone National 

Ambient Air Quality Standard by June 15, 2019. 

Enclosed are the proposed revisions to the SlP, a public hearing certification, a complete record of the public 

hearings, and the accompanying orders.· I look forward to your expeditious approval of these SlP revisions. 

Sincerely, 

B ~,;ktw.--· B~haw,PhD. Chairman 

BWS/jmm 

Enclosures 

cc: The Honorable Rick Perry, Governor ofTexas 

Mr. Toby Baker, Office ofBudget, Planning and Policy, Office of the Governor 

Mr. Mark R Vickery, P.G., Executive Director, Texas Cor!unission on Environmental Quality 

P.O. Box 13087 Austin, Texas 78711-3087 512-239'1000 1ntemet address: www.tceq.state.tx.us 
Texas Commission on Environmental Quality 

Public Hearings 

Odober 28-29, 2009 

Houston-Galveston-Brazoria (HGB) 

Attainment Demonstration SIP Revision 

for the 1997 Eight-Hour Ozone Standard 

Projed Number 2009-017-SIP-NR 

Volume 2 of2 
REVISION TO THE STATE IMPLEMENTATION PLAN 

FOR THE CONTROL OF OZONE AIR POLLUTION 

HOUSTON-GALVESTON-BRAZORIA 1997 EIGHT-HOUR OZONE STANDARD 

NONATTAINMENT AREA 

TEXAS COMMISSION ON ENVIRONMENTAL QUALITY 

P.O. BOX 13087 

AUSTIN, TEXAS 78711-3087 

Houston-Galveston-Brazoria Attainment Demonstration 

State Implementation Plan Revision for the 1997 Eight-Hour Ozone Standard 

PROJECT NO. 2009-017-SIP-NR 

Adopted 

March 10, 2010 
xv

LIST OF APPENDICES 

Appendix Appendix Name

Appendix A Meteorological Modeling for the HGB Attainment Demonstration SIP 

Revision for the 1997 Eight-Hour Ozone Standard 

Appendix B Emissions Modeling for the HGB Attainment Demonstration SIP 

Revision for the 1997 Eight-Hour Ozone Standard 

Appendix C Photochemical Modeling for the HGB Attainment Demonstration SIP 

Revision for the 1997 Eight-Hour Ozone Standard 

Appendix D Reasonably Available Control Technology Analysis 

Appendix E Reasonably Available Control Measure Analysis 

Appendix F Evaluation of Mobile Source Control Strategies for the HoustonGalveston-Brazoria State Implementation Plan (With Detailed 

Strategies), Prepared for Houston-Galveston Area Council, Prepared by 

ENVIRON International Corporation 

Appendix G Transportation Control Measures for the HGB Attainment 

Demonstration SIP Revision for the 1997 Eight-Hour Ozone Standard 

(Excerpt from Evaluation of Mobile Source Control Strategies for the 

Houston-Galveston-Brazoria State Implementation Plan), Prepared for 

Houston-Galveston Area Council, Prepared by ENVIRON International 

Corporation 

Appendix H Voluntary Mobile Emission Reduction Program for the HGB Attainment 

Demonstration SIP Revision for the 1997 Eight-Hour Ozone Standard 

(Excerpt from Evaluation of Mobile Source Control Strategies for the 

Houston-Galveston-Brazoria State Implementation Plan), Prepared for 

Houston-Galveston Area Council, Prepared by ENVIRON International 

Corporation 

Appendix I Corroborative Analysis for the HGB Attainment Demonstration SIP 

Revision for the 1997 Eight-Hour Ozone Standard 

Appendix J Recommendations for Best Management Practices to Control and 

Reduce Inadvertent Cargo Vapor Emissions in the Tank Barge 

Community 
APPENDIX A 

Meteorological Modeling for the HGB Attainment Demonstration SIP Revision for the 1997 

Eight-Hour Ozone Standard 
A-2

APPENDIX A

Meteorological Modeling for the HGB Attainment Demonstration SIP Revision for the 1997 

Eight-Hour Ozone Standard 

INDEX 

Description Page

Section 1.1 MM5 Modeling Overview A-3

1.2 MM5 Configuration A-4

Section 2.1 MM5 Performance Evaluation Tools A-6

2.2 MM5 Modeling Performance A-12

2.3 2005ep0 Performance Evaluation A-13

2.4 2005ep1 Performance Evaluation A-17

2.5 2005ep2 Performance Evaluation A-23

2.6 2006ep0 Performance Evaluation A-32

2.7 2006ep1a Performance Evaluation A-40

2.8 2006ep1b Performance Evaluation A-50

2.9 2006ep1c Performance Evaluation A-61

2.10 2006ep1d Performance Evaluation A-68

2.11 References A-80

ATTACHMENT 1 Sea Surface Temperature (SST) Characteristics A-82

ATTACHMENT 2 Land Use and Land Cover (LULC) Characteristics A-90

ATTACHMENT 3 Data Assimilation A-99

ATTACHMENT 4 Application of Grell Cumulus Scheme A-130
A-3

METEOROLOGICAL MODELING FOR THE HGB ATTAINMENT 

DEMONSTRATION SIP 

 

1.1 MM5 Modeling Overview 

The TCEQ is using the Fifth Generation Meteorological Model (MM5, version 3.7.3) developed 

jointly by the National Center for Atmospheric Research (NCAR) and Pennsylvania State 

University (Grell et al., 1994). This model, supported by a broad user community including the 

Air Force Weather Agency, national laboratories, and academia, is being used extensively for 

regulatory air quality modeling analyses throughout the United States. MM5 modeling was 

conducted for the time periods (episodes) listed in Table 1.1-1: TexAQS II Meteorological 

Modeling Episodes. 

Table 1.1-1: Meteorological Modeling Episodes

Episode 

All Grids 

Begin Date/Time (UTC) 

Outer Grids 

End Date/Time (UTC) 

Fine Grid 

End Date/Time (UTC) 

2005ep0 May 18, 2005 06:00 June 4, 2005 06:00 June 4, 2005 06:00 

2005ep1 June 15, 2005 06:00 July 1, 2005 06:00 July 1, 2005 06:00 

2005ep2 July 25, 2005 06:00 August 9, 2005 09:00 August 9, 2005 07:00 

2006ep0 May 29, 2006 06:00 June 17, 2006 06:00 June 17, 2006 06:00 

2006ep1a August 13, 2006 06:00 August 27, 2006 09:00 August 23, 2006 07:00 

2006ep1b August 27, 2006 06:00 September 16, 2006 

09:00 

September 16, 2006 

07:00 

2006ep1c 

September 16, 2006 

06:00 October 1, 2006 09:00 October 1, 2006 07:00 

2006ep1d October 1, 2006 06:00 October 13, 2006 09:00 October 13, 2006 07:00 

A Lambert Conformal conic map projection (LCP), with geographical coordinates defined in 

Table 1.1-2: Lambert Conformal Conic Map Projection was used for the MM5 modeling. 

MM5 was configured with three two-way nested outer domains (108 km, 36 km, and 12 km 

horizontal grid resolution) to cover the United States and regional areas of interest. A one-way 4 

km fine grid domain covering the eastern half of Texas was established to focus on metropolitan 

areas with air quality degradation. Figure 1.1-1: MM5 Domains and Vertical Layers for 

Meteorological Modeling Episodes shows the MM5 nested domain configuration, which was 

established to accommodate the embedding of the CAMx nested domains with the same grid 

resolution, except for the MM5 36 km domain. That domain does not cover the far eastern part of 

the 36 km CAMx domain, so values from the MM5 108 km domain are used. Because of the 

distance from the Houston area, this replacement is expected to have a minimal effect on the 

simulation of meteorological parameters in the 4 km domain. 

Vertically, MM5 is structured with 43 layers from the surface to approximately 20 km. Twenty 

layers are within the first 3000 meters in order to resolve boundary layer phenomena and to 

provide a one-to-one mapping with the first twenty layers for CAMx. Unlike CAMx, the same 

MM5 vertical layering structure is used for all of the domains. 
Table 1.1-2: Lambert Conformal Conic Map Projection 

 First True Latitude (Alpha): 30°N 

 Second True Latitude (Beta): 60°N 

 Central Longitude (Gamma): 100°W 

 Projection Origin: (100°W, 40°N) 

 Spheroid: Perfect Sphere, Radius = 6370 km

A-4

Range Grid Points

Domain Easting (km) Northing (km) Easting Northing

108 km (-2808, 2808) (-2268, 2268) 53 43

36 km (-1296, 2160) (-1728, 972) 97 76

12 km (-648, 1080) (-1548, -360) 145 100

4 km (72, 372) (-1380, -648) 166 184

Figure 1.1-1: MM5 Domains and Vertical Layers for Meteorological Modeling Episodes 

1.2 MM5 Configuration 

The final meteorological modeling configuration for the TexAQS II episodes was the result of 

numerous sensitivity tests, model performance evaluation, model input development, previous 

modeling experience, and contracted modeling review. The pre-processing of the MM5 input 

data followed the standard program progression of TERRAIN, REGRID, and INTERPF (NCAR, 

2005b). The NESTDOWN program was used to interpolate the 12 km domain output to the 4 km 

domain input. The following bullets feature the essential parameters that were specified for the 

pre-processing programs: 

TERRAIN 

 Central Latitude and Longitude: 40.00 ° North and -100.00 ° West 

 LSMDATA=1 to create data for the land-surface model 

 Updated land use and land cover (LULC), land/water mask, vegetation fraction, and soil 

types using satellite based data 

 Further details can be found in Attachment 2: Land Use and Land Cover (LULC) 

Characteristics. 
A-5

REGRID 

 National Center for Environmental Prediction (NCEP) North American Model (NAM) 

gridded output (formerly Eta model) used for model initialization (NCEP, 2009) 

 Soil data added for NCEP - Oregon State Univ. - Air Force - Hydrological Research 

Laboratory (NOAH) land-surface model (NCAR, 2005a; Chen and Dudhia, 2001) 

INTERPF 

 Base state variables were set to Houston summer values: 1013 hPa sea-level pressure, a 

reference temperature lapse rate of 45 (K/ln p) , and a 304 °K sea-level temperature 

MM5 was configured with parameterizations and improved input data to optimize the 

performance of the wind field. Wind speed and direction are important parameters predicted by 

the meteorological model for air quality modeling purposes because the wind field determines the 

proper transport and dispersion of pollutants. To improve the representation of the wind field and 

other parameters by MM5, the land use characteristics and sea surface temperatures on all 

domains were updated with high resolution satellite measurements. In the coastal Houston area 

the temperature gradient between the land and water is a defining feature of the local 

meteorological processes (Banta et al., 2005) and these data improvements were designed to help 

MM5 resolve them. The meteorological modeling appendices for the satellite-based LULC and 

SST data provide additional details regarding their development and use in MM5. 

Based on previous modeling exercises by the TCEQ, and guidance from the EPA (EPA, 2007), 

and NCAR (Grell et. al,. 1994), the TexAQS II meteorological modeling employed data 

assimilation to improve model performance. This modeling tool allows the MM5 model to 

“relax” towards observed wind data during the model run through a process called FourDimensional Data Assimilation (FDDA), or nudging (Stauffer and Seaman, 1990; Stauffer et al., 

1991; Stauffer and Seaman, 1994). The outer domains (108 km, 36 km, and 12 km) were nudged 

towards the gridded analysis of the North American Model (NAM) run by the National Centers 

for Environmental Prediction NCEP NAM (NCEP, 2009) for winds, temperature, and water 

vapor using the MM5 default nudging parameters. The fine scale 4km domain was nudged to 

quality assured observational data obtained during the TexAQS II from upper air wind profilers. 

Details of the data and nudging methodology can be found in Attachment 3: Data Assimilation. 

Other MM5 schemes and options typically modified for air quality applications are shown in 

Table 1.2-1 below. A description of using the Grell cumulus scheme (Grell et al., 1994) on the 4 

km domain can be found in Attachment 4: Application of Grell Cumulus Scheme. The choices 

for the other parameters were based on the MM5 configurations investigated as part of the Dec. 

2004 HGB SIP revision and the Proposed HGB 2007 eight-hour ozone RFP SIP revision. 

Table 1.2-1: TexAQS II MM5 modeling scheme choices

Domain Nudging Type PBL Cumulus Radiation 

Land-Surface 

Model Microphysics 

108, 36, 12 km Analysis 3-D Eta Grell RRTM NOAH Simple Ice 

4 km Observational Eta Grell RRTM NOAH Simple Ice 

Notes: PBL = Planetary Boundary Layer 

 RRTM = Rapid Radiative Transfer Model 

 NOAH = NCEP Oregon State Air Force Hydrological Research Laboratory 

MM5 output was post-processed using the Environ program MM5CAMx to convert the 

meteorological fields to the CAMx grid and input format (Environ, 2008). The TexAQS II MM5 

output was processed with MM5CAMx options consistent with the choice of the Eta PBL scheme 

used in MM5. Since the Eta PBL scheme determines vertical mixing from a predicted turbulent 

kinetic energy (TKE), the MM5CAMx program was run with a switch to use this TKE 
information in its calculation of the vertical diffusivity coefficients required for input into CAMx. 

The minimum threshold for vertical diffusivity (KV) was set to 1.0. 

2.1 MM5 Performance Evaluation Tools 

2.1.1 Observations 

To evaluate the performance of MM5, comparisons to observed data are made. For surface data, 

the TCEQ Continuous Air Monitoring Stations (CAMS) are used for comparison. There were 

over 100 CAMS in the MM5 4 km domain and more than 40 in the Houston Region 12 area 

(Figure 2.1.1-1: All MM5 4km TCEQ CAMS (left) and Region 12 CAMS (right)) during the 2005 

and 2006 modeling periods. Because of the large number of CAMS monitors in the Houston 

region, an area wide average may smooth out smaller scale features. Zones of CAMS monitors 

were chosen to represent smaller geographic areas as defined in Figure 2.1.1-2: CAMS in Model 

Performance Zones. Evaluating model runs using the Houston Region and Zone averages instead 

of individual CAMS monitors proved to be efficient, allowing for the evaluation of more 

modeling sensitivities for the eight TexAQS II meteorological modeling episodes. 

 Figure 2.1.1-1: All MM5 4km TCEQ CAMS (left) and Region 12 CAMS (right) 

A-6
 

Downwind 

Northwest Harris Co. C26 

Conroe Relocated C78 

Northwest Core 

Westhollow C410 

Bayland Park C53 

Aldine C8 

Southeast Core 

Croquet C409 

Regional Office C81 

HRM-3 C603 

Channelview C15 

Ship Channel 

Manvel Croix Park C84 

Deer Park C35 

Wallisville Road C617 

Coastal Zone 

Clute C11 

Mustang Bayou C619 

Galveston Airport C34 

Figure 2.1.1-2: CAMS in Model Performance Zones 

The TexAQS II profiler network was used to evaluate the performance of MM5’s winds above 

the surface layer (Figure 2.1.1-3: TexAQS II profilers in 4km MM5 domain). Up to 15 profilers 

collected data during the 2005 and 2006 modeling episodes, including estimates of planetary 

boundary layer (PBL) depth. 

Figure 2.1.1-3: TexAQS II profilers in 4km MM5 domain 

A-7
2.1.2 Time Series Panel 

Time series panels comparing modeled and observed CAMS surface wind direction, wind speed, 

and temperature were created to evaluate the model’s performance over an entire episode. The 

observations are hourly averages of individual and grouped monitors (e.g., Zones, TCEQ Regionwide, or domain-wide). As an example of a time series panel, Figure 2.1.2-1: Region 12 average 

wind speed time series for 2006ep1a episode, shows the time series of wind speed averaged over 

all Region 12 monitors from the 2006ep1a episode. The X axis of the time series panels is the 

date and time in Central Standard Time (CST) of the modeling episode. The Y axis represents 

the range of values of the parameter (e.g. wind speed). The title of the panel indicates the 

geographic region, parameter (wind speed, temperature, etc), model and run name. 

The compared model hourly values are from the monitor’s corresponding model grid cell. 

MM5’s first model layer cell value (red line – MM5) and the probe height interpolated cell value 

(blue line – MM5probe) are both plotted. For wind direction, the probe height and first model 

layer values are usually the same. For wind speed the probe height is slightly slower and for 

temperature the probe height is warmer than the middle of the first model layer. The first model 

layer values are passed to the photochemical model so they are important to evaluate. Also 

shown are time series of bias and mean absolute error using the model’s first layer values. 

Figure 2.1.2-1: Region 12 average wind speed time series for 2006ep1a episode 

2.1.3 Scatter Plots 

Scatter plots of model versus observations were created to correlate how well the model 

statistically performs at monitors (groups of monitors), episode days, and model layers. As with 

the time series, the model is compared to individual, zones, region-wide, and domain-wide groups 

of monitors for every hour (per day or episode). For surface data, the observations are compared 

to the model’s first layer values. The percent of hours (all, day, or night) where the model is 

within the accuracy benchmarks (e.g. wind direction less than or equal to 30 degrees or wind 

speed less than or equal to 2 meters per second) is depicted in the upper right of the plot. Tables 

of these accuracy percentages are also presented to summarize the scatter plots. 

A-8
A linear regression line is fitted to the data and is shown in green. The correlation equation and 

coefficient of determination R2

 for the regression line is above the plot in green. For the model to 

perfectly fit the data the regression line would fall on the one-to-one line and the R2

 would be 1.0. 

The R2

 indicates how well MM5 predicts the observations, with higher values indicating better 

model performance. As the model is an imperfect representation of the real world and the 

observations have biases, errors, and limitations, a perfect fit is not expected. In fact a perfect fit 

(or very close to it) may be reason to suspect that MM5 is being nudged too hard (see Attachment 

3: Data Assimilation). For wind direction, the regression line and R2

 may not be meaningful since 

both 0° and 360° symbolize north winds. 

The plot titles are the same as the time series. For surface data scatter plots, the X axis is the 

modeled data and the Y axis is the observations. For the upper air wind data scatter plots, the X 

axis is the observations and the Y axis is the modeled data. The total number of date-time points 

(hours) and observations (hour-monitor pairs) that comprise the plot are listed next to the 

parameter name. Figure 2.1.3-1: Southwest Core Zone average wind speed scatter plot for 

2006ep1c episode shows an example or the Southeast Core Zone’s wind speed over the 2006ep1c 

episode. 

Figure 2.1.3-1: Southwest Core Zone average wind speed scatter plot for 2006ep1c episode 

2.1.4 Time-height plots 

A-9

Time-height plots represent the upper air horizontal wind conditions at profiler locations over one 

episode day. Vertical winds (rising or descending air) are not shown. The model winds (CAMx 

input) are shown as blue vectors and the observed winds (profiler) are shown as red vectors. 

Each hour of the episode day is depicted on the x-axis. Winds from just above the surface to 3 km 

are illustrated by vectors pointing in the compass direction from where the wind is blowing. A 
longer vector indicates faster wind speeds. In figure 2.1.4-1 below, at midnight CST on 

5/19/2005 a southwest wind greater than 10 m/s blows at 2.5 km above the surface at the La Porte 

profiler (red vector). The model predicts winds from the south and much lighter (blue vector). 

Observed winds were not available above 2.5 km on this day. 

Also on these plots are the model (blue) and profiler (red) estimated planetary boundary layer 

(PBL) depths shown by horizontal lines between hours. The observed data is generally only 

available during daylight hours. At 10:00 AM CST in Figure 2.1.4-1 below, the model and 

observed PBL depth agree well but differ by 500 meters at noon. 

Based on Knoderer et al. (2008), the morning rise of the PBL may be more important to ozone 

production than the peak mixing depth. The morning rise was focused on for model performance 

evaluation as well as gross differences. More information regarding the derivation of mixing 

heights from the radar wind profiler data can be found in Knoderer and MacDonald (2007). 

Figure 2.1.4-1: Time-height plot example (5/19/2005 at La Porte profiler) 

2.1.5 Surface Wind Field Plots

Surface wind field plots show the surface layer of CAMx input winds (mm5camx converted) 

compared to observations for one hour. The tail of each vector points in the compass direction 

from which the wind is blowing. Modeled winds are shown in blue; observed winds are shown in 

red. This plot type yields a visual depiction of the observed wind flow, and how well the model 

represents that flow. Galveston Bay breezes, sea breezes from the Gulf of Mexico, thunderstorm 

outflow, and other wind phenomena can be identified using these plots. Figure 2.1.5: Surface 

wind field plots for June 5, 2006 at 12:00 CST showing Galveston Bay breeze (left) and August 

28, 2006 at 16:00 CST shows an example of a Galveston Bay breeze (left) and spurious surface 

divergence patterns (right) that appear inconsistent with observations in eastern Harris County. 

A-10
Figure 2.1.5-1: Surface wind field plots for June 5, 2006 at 12:00 CST showing Galveston 

Bay breeze (left) and August 28, 2006 at 16:00 CST showing spurious surface divergence 

patterns that were not observed (right)

2.1.6 Trajectories

One of the most useful and intuitive methods for evaluating modeled wind fields is by using wind 

trajectories. A forward trajectory shows the path an air parcel from some location (say, a major 

emission source), takes as it moves with the wind. A back trajectory shows the path that an air 

parcel followed before arriving at a specific location (say, a monitor). Because the trajectories 

inherently describe source-receptor relationships, they are especially appropriate for air-quality 

applications. 

TCEQ calculated forward and back trajectories with endpoints at several locations in the HGB 

area (plus two in the Beaumont/Port Arthur area) for every hour of every episode day modeled 

using a FORTRAN program obtained from Carl Berkowitz at Pacific Northwest National 

Laboratories. This program is based on a simple kinematic model that used an inverse distancesquared weighting scheme to evaluate the u- and v-wind components of each trajectory 

(Berkowitz et al, 2005) Input for this program is five-minute average wind speed and direction 

observations from a total of 72 surface meteorological sites in and around the HGB area. This 

program was also used to calculate back-trajectories used in the PSCF-based emissions 

reconciliation work described elsewhere in this SIP revision. 

The trajectory program was used to develop sets of comparable trajectories based on 

observational data and meteorological fields as follows: First, hourly mean modeled wind 

components were extracted from the meteorological data files at the location of each of the 72 

surface meteorological sites. Five-minute observations were generated by simply replicating the 

hourly mean wind speed and direction 12 times. Observed hourly observations were similarly 

used to generate five-minute averages by replicating the hourly observations 12 times each (this 

was done to make the comparisons between the model-based and observation-based trajectories 

fair). Both the modeled and measured data were formatted for input to the FORTRAN program, 

and trajectories were generated using each set of inputs. Trajectories were generated both from 

the raw MM5 output files, and also from the CAMx input files which have been processed by the 

MM5CAMx data formatting and conversion program. Both forward and backward trajectories 

were generated for each hour of each episode day, but only forward trajectories generated from 

the CAMx inputs are used in the model performance evaluation discussed in this appendix. 

An example comparing model-based and observation trajectories is shown in Figure 2.1.6-1. In 

this plot, it is easy to see the path an air parcel takes as it leaves each starting location at 6 AM. 

The first dot along the trajectory represents the air parcel’s position after one hour, the next dot 

two hours, etc. The larger circles represent monitoring locations, colored according to the 

A-11
observed peak 8-hour ozone concentration observed at that site on that day. Both sets of 

trajectories show light wind speeds starting from the northwest, then veering clockwise through 

the day to become southeasterly by early afternoon. In this case, the modeled winds replicate the 

observations reasonably well, and at the end of the day the trajectory points from each set of 

trajectories usually agree to within 20-30 km. 

Figure 2.1.6-1: 6 AM forward wind trajectories based on modeled and observed one-hour 

average wind components for June 9, 2006

2.2 MM5 Modeling Performance 

The following section describes the performance of the final MM5 modeling configuration for 

each of the TexAQS II episodes, as listed in Table 1.1-1. Due to the large number of episode 

days, the performance evaluation will focus on the days when high ozone and notable 

meteorological phenomena occurred (Knoderer et al., 2008; Nielsen-Gammon, 2007a; NielsenGammon, 2007b; TCEQ, 2005; TCEQ, 2006). Wind field performance was deemed to be the 

most important for photochemical modeling input followed by temperature, planetary boundary 

layer height, and other important features. 

The MM5 modeling was evaluated by comparing the hourly modeled and measured wind speed, 

wind direction, and temperature for all monitors in the HGB area. Figure 2.2-1: Meteorological 

Modeling Performance Accuracy Statistics exhibits the percent of hours for which the average 

absolute difference between the modeled and measured wind speed, wind direction, and 

temperature, for all monitors in the HGB area, was within the specified accuracy benchmarks 

(Emery et. al., 2001)(e.g., wind speed less than or equal two meters per second: i.e. WSPD < 2 

m/s). All performance evaluation products are available on TCEQ’s ftp site 

(ftp://ftp.tceq.state.tx.us/pub/OEPAA/TAD/Modeling/HGB8H2/mm5). 

A-12
TexAQS II Meteorological Modeling Performance Statistics

76

82

65

76

80

67

84 83

100 100

97

100

96 96 98

78

74

80

85 83

80

69

73

98

0

10

20

30

40

50

60

70

80

90

100

2005ep0 2005ep1 2005ep2 2006ep0 2006ep1a 2006ep1b 2006ep1c 2006ep1d

Percent of Hours within Accuracy Threshold

WDIR ≤ 30 °

WSPD ≤ 2 m/s

TEMP ≤ 2 °C

Figure 2.2-1: Meteorological Modeling Performance Accuracy Statistics 

Notes: WDIR = Wind Direction 

 WSPD = Wind Speed 

 TEMP = Temperature 

Table 2-2-1: Average Houston Region 12 Percent Accuracy for all Meteorological Modeling 

Episodes

Episode 

Wind Direction (°)

Error ≤ 30 / 20 / 10 

Wind Speed (m/s)

Error ≤ 2 / 1 / 0.5 

Temperature (°C)

Error ≤ 2 / 1 / 0.5 

2005ep0 76 / 67 / 49 98 / 73 / 40 78 / 49 / 30 

2005ep1 82 / 71 / 40 100 / 93 / 60 74 / 50 / 28 

2005ep2 65 / 54 / 33 100 / 75 / 43 80 / 57 / 31 

2006ep0 76 / 63 / 45 97 / 72 / 38 85 / 57 / 33 

2006ep1a 80 / 67 / 43 100 / 83 / 43 83 / 61 / 39 

2006ep1b 67 / 54 / 33 96 / 63 / 26 80 / 52 / 25 

2006ep1c 84 / 76 / 57 96 / 57 / 29 69 / 33 / 16 

2006ep1d 83 / 66 / 33 98 / 75 / 42 73 / 51 / 32 

2.3 2005ep0 Performance Evaluation 

May-June 2005 episode (2005ep0) Summary 

High ozone days occurred on May 27 and June 2. On May 27, two monitors had 8-hour averages 

above 90 ppb, and three had values over 100 ppb, with the domain high 0f 116 ppb. June 2 had 

the episode highest values for the episode. There were three monitors reporting 8-hour ozone 

averages in the 90’s and 10 monitors over 100 ppb. Of those ten, three were over 120 ppb. The 

domain and episode peak value for 8-hour ozone was 125 ppb on June 2. 

This episode began with several days having generally tropical (e.g. southerly) flow. One these 

days, and similar days on other episodes, the background air quality is generally good. However, 

light winds still contributed to a number of exceedances. The most important meteorological 

features include a frontal passage on May 26 and the passage of a high pressure center on June 1st

. 

High ozone periods followed each of these events. 

A-13
A-14

General Model Performance 

Table 2.3-1: Average Houston Region 12 Percent Accuracy by Episode, Zone, and Peak Days

summarizes wind direction, wind speed, and temperature according to three threshold metrics. 

For the entire episode, region-wide performance is generally good, especially for wind speed. 

Previous modeling efforts used a threshold of 2 m/s to establish good performance. The present 

MM5 modeling is within that bound 98% of the time. A tighter standard can therefore be 

considered: almost three-fourths of the wind speed data is within 1 m/s of observed data. For 

wind direction accuracy, a threshold of 20 degrees is consistent with goals set in previous 

modeling. For this episode, modeled wind directions were within 20 degrees of reported data 

two-thirds of the time. 

Table 2.3-1: Average Houston Region 12 Percent Accuracy by Episode, Zone, and Peak 

Days

Number of days Geographic 

Area 

Wind direction (°) 

% Accuracy 

 < 30 / 20 / 10 

Wind Speed (m/s) 

% Accuracy 

 < 2.0 / 1.0 / 0.5 

Temperature (°C) 

% Accuracy 

 < 2.0 / 1.0 / 0.5 

Entire Episode Region 12 76 / 67 / 49 98 / 73 / 40 78 / 49 / 30 

Entire Episode Zone 1 72 / 61 / 40 94 / 68 / 39 74 / 46 / 27 

Entire Episode Zone 2 66 / 56 / 34 93 / 56 / 33 70 / 44 / 24 

Entire Episode Zone 3 69 / 56 / 34 95 / 60 / 33 75 / 46 / 26 

Entire Episode Zone 4 64 / 54 / 29 92 / 57 / 31 60 / 30 / 15 

Entire Episode Zone 5 55 / 45 / 24 90 / 53 / 29 74 / 44 / 20 

May 27 Region 12 75 / 58 / 17 100 / 83 / 63 92 / 83 / 67 

June 2 Region 12 67 / 58 / 50 100 / 96 / 58 92 / 58 / 25 

Ideally, modeled temperature would be within one degree of data. However, known temperature 

biases in MM5 often lead to using a more relaxed threshold of 2 degrees. 

One noticeable feature of this episode is that performance is best near the coast and becomes 

somewhat less as one moves towards interior zones further inland. On the other hand, it is 

encouraging to note that on May 27 and June 2, the modeled wind speed was better than the 

episode average and wind direction. 

May 27, 2005 Features 

On May 25, a frontal system approached from the northeast. From Figure 2.3-1: Region 12 

average wind direction time series for 2005ep0 episode the domain winds shift to the north and 

the front passes on the 26. On May 27, the winds veer around to the east. Thus, ozone precursors 

from two days prior – transported north by southerly winds - plus continental air from the east 

likely re-circulated into the Houston area and contributed to local ozone production. Morning 

winds were generally light and by afternoon continued to veer to the south so that the urban air 

mass crossed back over the ship channel. 
Figure 2.3-1: Region 12 average wind direction time series for 2005ep0 episode

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Figure 2.3-2: Region 12 average wind speed time series for 2005ep0 episode

June 2, 2005 Features 

Winds were generally from the northwest during the day of June 1 followed by a flow-reversal in 

the late afternoon and early evening. This rather abrupt change can be seen in wind direction 

time series. The morning of June 2 had light winds from the south generally between 1 and 2 

m/s. Thus, the peak was seen north of the ship channel later in the day, but the local emissions 

were added to those of the previous day. 

The trajectory plots in Figure 2.3-3a: 12-hour forward trajectory at 6:00 CST on June 1, 2005

and Figure 2.3-3b: 12-hour forward trajectory at 18:00 CST on June 1, 2005 show different 

performance for daytime and evening winds. As seen in the second figure, MM5 wind speeds are 

too strong as stagnation begins (although the next day statistics look good). The model will 

capture the veering to the south although is likely to shift the peak ozone a bit to the west of the 

actual location. 

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Figure 2.3-3a: 12-hour forward trajectory at 6:00 CST on June 1, 2005 

Figure 2.3-3b: 12-hour forward trajectory at 18:00 CST on June 1, 2005 

2.4 2005ep1 Performance Evaluation 

June 2005 episode (2005ep1) Summary 

During this period there were days with a typical coastal oscillation, but the generally easterly 

component to veering winds suggest that background ozone added to the local contribution. The 

21st had eleven sites in exceedance, and three monitors recording eight-hour ozone over 100 ppb. 

On June 22, 15 sites had eight-hour ozone exceedances, with an episode high ozone of 103 ppb. 

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General model performance 

The performance metrics described above for wind speed, wind direction, and temperatures look 

very good across the entire episode. Also, there does not seem to be a discernable trend of 

decreasing accuracy as one moves inland from the coast. Both the peak days of June 21 and June 

22 have very good wind speed accuracy. On the other hand, while June 22 has wind direction 

performance which matches the episode average, June 21 is somewhat less accurate. 

Table 2.4-1: Average Houston Region 12 Percent Accuracy by Episode, Zone, and Peak 

Days

Number of 

days 

Geographic 

Area 

Wind direction (°) 

% Accuracy 

 < 30 / 20 / 10 

Wind Speed (m/s) 

% Accuracy 

 < 2.0 / 1.0 / 0.5 

Temperature (°C) 

% Accuracy 

 < 2.0 / 1.0 / 0.5 

Entire Episode Region 12 82 / 71 / 40 100 / 93 / 60 74 / 50 / 28 

Entire Episode Zone 1 75 / 61 / 37 98 / 75 / 40 71 / 45 / 19 

Entire Episode Zone 2 67 / 54 / 31 98 / 81 / 43 73 / 48 / 27 

Entire Episode Zone 3 75 / 61 / 36 100 / 83 / 48 73 / 51 / 22 

Entire Episode Zone 4 66 / 52 / 30 97 / 73 / 40 61 / 33 / 16 

Entire Episode Zone 5 56 / 45 / 25 93 / 63 / 33 64 / 41 / 21 

June 21 Region 12 67 / 58 / 17 100 / 83 / 46 100 / 25 / 17 

June 22 Region 12 75 / 71 / 17 96 / 88 / 54 83 / 46 / 13 

The time series in Figure 2,4-1: Region 12 average wind direction time series for 2005ep1 

episode shows several key features of the episode. Following generally southerly flow, the 

approach of a front on the 18th caused winds to back from southwest back to the northeast. The 

high ozone day of the 21st is preceded by generally easterly flow which may contribute high 

background ozone. The days of June 21-22 showed classic flow reversal on top of regionally 

veering of winds carrying emissions around from the ship channel to the southeast and back over 

southwest Harris County. June 23 through June 28 had daily winds shifting from northeast to 

southeast leading to consistent and repeated exceedances to the west and northwest of the 

Houston downtown. 
Figure 2.4-1: Region 12 average wind direction time series for 2005ep1 episode 

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Figure 2.4-2: Region 12 average wind speed time series for 2005ep1 episode 

June 21 Features 

The MM5 wind direction and wind speed time series in Figures 2.4-1: Region 12 average wind 

direction time series for 2005ep1 episode and 2.4-2: Region 12 average wind speed time series 

for 2005ep1 episode indicate generally good performance on our two key days. Figure 2.4-3: 

Region 12 average wind direction and speed scatter plots for June 21, 2005 show performance 

for stations reporting on June 21. These plots have additional information which distinguishes 

the averages calculated for each of the day time hours from the night time hours. During two 

night time hours, MM5 slightly over-predicts wind speeds. During four night time hours, the 

average of predicted wind directions differs from the observed hourly averages by more than 

thirty degrees. These differences are likely not significant for CAMx modeling since the 

directional errors are more likely at very light wind speeds. 

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Figure 2.4-3: Region 12 average wind direction and speed scatter plots for June 21, 2005 

The good performance statistics suggest that CAMx modeling on June 21 and 22 should have 

reliable meteorological inputs. On the other hand, previous modeling experience has shown that 

when there is a strong flow reversal or a nearly complete rotation of winds through the day – 

based upon the hourly averages of HGB sites – that very localized features near Galveston Bay 

can complicate the “big picture.” One example of a subtle effect on the veering of the wind due 

to local features of Galveston Bay is shown in Figure 2.4-4: Surface wind field plots for June 21, 

2005, at 12:00 CST in which surface winds are plotted for noon on June 21st. Near Texas City, to 

the south of the ship channel, observed winds have pushed perpendicularly to the shoreline of the 

Bay. These localized winds are from the southeast while MM5 winds are still from the northeast. 

The trajectories in Figure 2.4-5: 12-hour forward trajectory at 8:00 CST on June 21, 2005,

indicate that small differences in the MM5 winds near Galveston Bay may produce a veering 

which is more gradual than actually occurred that day. 

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Figure 2.4-4: Surface wind field plots for June 21, 2005 at 12:00 CST 

Figure 2.4-5: 12-hour forward trajectory at 8:00 CST on June 21, 2006

June 22 Features 

The scatter plot in Figure 2,4-6: Region 12 average wind direction and speed scatter plots for 

June 22, 2005 indicates that on June 22 MM5 modeled wind direction was excellent throughout 

the day. As on many episode days, there is a slightly high bias in wind speeds during the night 

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time hours, as is seen in right side of the figure, but only one of those hours strays close to our 

preferred threshold. Many graphics have subtle features. The day time wind speeds in the 

scatter plot look very good, albeit with a very small negative bias. However, the consequence of 

even a tiny bias, if it is persistent can show up on a trajectory plot. In Figure 2.4-7: 12-hour 

forward trajectory at 8:00 CST on June 22, 2005 the accumulated biases across several hours 

show MM5-based trajectory patterns which are “tighter” than those based on CAMS 

observations. This is the reverse of what was seen on June 21. 

Figure 2.4-6: Region 12 average wind direction and speed scatter plots for June 22, 2005 

Figure 2.4-7: 12-hour forward trajectory at 8:00 CST on June 22, 2005 

2.5 2005ep2 Performance Evaluation 

The 2005ep2 episode features three periods of high ozone: July 28-30, August 1-3, and August 6-

7. The first period was dominated by a slow-moving and weak cold front while the two other 

periods resulted from high pressure-induced coastal oscillation. Afternoon thunderstorms 

occurred on many days during this episode, and the model only had limited success in 

reproducing these storms and the resultant wind patterns. 

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MM5 reproduced the observed wind speeds relatively well, with wind speed within two m/s 

almost 100 percent of the time for most areas (Table 2.5-1: Error percentages for 2005ep2 

episode.). Wind direction errors were higher, especially at night, when wind speeds were low 

(Figure 2.5-1). Note that observed wind direction is less accurate when wind speeds are low. 

Figures 2.5-2 and 2.5-3 exhibit the wind direction and wind speed time series of the average of all 

Houston monitors. MM5 follows the observations closely for almost all time periods. The 

overnight wind patterns appear to have the highest bias and error, due to the model’s overestimate 

of the calmer winds. Temperature performance was degraded by the presence of locally-triggered 

thunderstorms, and by the poor placement of clouds and storms associated with a slow-moving 

cold front. On most days, though, MM5 replicated the diurnal pattern of temperature well (Figure 

2.5-4: Region 12 average temperature time series for 2005ep2 episode). 

Table 2.5-1: Error percentages for 2005ep2 episode

Geographic 

Area/Zone 

Wind Direction (°)

Error ≤ 30 / 20 / 10 

Wind Speed (m/s)

Error ≤ 2 / 1 / 0.5 

Temperature (°C)

Error ≤ 2 / 1 / 0.5 

Region 12 65 / 54 / 33 100 / 75 / 43 80 / 57 / 31 

Coastal 56 / 42 / 22 99 / 76 / 42 77 / 44 / 21 

Ship Channel 54 / 40 / 24 97 / 54 / 27 80 / 53 / 31 

Southeast Core 57 / 44 / 22 98 / 66 / 36 78 / 52 / 30 

Northwest 

Core 48 / 37 / 22 94 / 60 / 29 67 / 35 / 16 

Downwind 40 / 27 / 15 84 / 42 / 23 78 / 49 / 29 

 

Figure 2.5-1: Region 12 average wind speed and direction scatter plot 

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Figure 2.5-2: Region 12 average wind direction time series for 2005ep2 episode 

Figure 2.5-3: Region 12 average wind speed time series for 2005ep2 episode 

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Figure 2.5-4: Region 12 average temperature time series for 2005ep2 episode 

July 28, 2005 

This day was the first of the 2005ep2 episode for which more than one monitor recorded an 

exceedance of the eight-hour ozone standard. Three monitors were equal or greater than 85 ppb, 

with the Seabrook Friendship Park C45 monitor observing the highest concentration of 95 ppb of 

ozone. Meteorological conditions were dominated by a stalled cold front in the Houston area, 

which didn’t push out of the area until July 30. Winds were generally from the northwest during 

the morning. A Galveston Bay breeze developed at mid-day, which was overcome by a stronger 

south-easterly sea breeze from the Gulf of Mexico. 

MM5’s wind performance on this day was quite good, especially for wind speed. Figure 2-5-5 

shows the scatter plot for wind direction and speed for all hours on 7/28/05. MM5 predicted an 

average wind speed within 2 m/s of the observations 100% of the time for the Houston area. 

Wind direction performance on July 28 was better than the episode average (Table 2.5-1: Error 

percentages for 2005ep2 episode), with outlier hours occurring mostly during the daytime wind 

shifts. Table 2.5-2: Error percentages for July 28, 2005 shows that the wind speed performance 

in all of the monitoring zones was similar, except for the Downwind zone, where MM5 overpredicted the wind speeds. This error could be due to the difficulty of replicating the slowmoving front in the northern part of the 4km domain. Temperature performance on this day was 

very good, with very high percentages of Region 12 and Zone averages within 1 degree Celsius 

of the observations. 

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Figure 2.5-5: Region 12 Average wind direction and speed scatter plots for July 28, 2005 

Table 2.5-2: Error percentages for July 28, 2005

Geographic 

Area/Zone 

Wind Direction (°)

Error ≤ 30 / 20 / 10 

Wind Speed (m/s)

Error ≤ 2 / 1 / 0.5 

Temperature (°C)

Error ≤ 2 / 1 / 0.5 

Region 12 83 / 83 / 50 100 / 67 / 25 100 / 100 / 46 

Coastal 67 / 58 / 38 100 / 75 / 46 96 / 42 / 17 

Ship Channel 67 / 42 / 13 92 / 46 / 21 100 / 92 / 63 

Southeast Core 83 / 67 / 46 100 / 83 / 33 100 / 92 / 67 

Northwest 

Core 71 / 54 / 29 100 / 67 / 29 100 / 71 / 46 

Downwind 67 / 50 / 38 67 / 08 / 04 83 / 67 / 50 

Aloft, MM5 resolved much of the flow and planetary boundary layer (PBL) structure according 

to the profiler locations. As Figure 2.5-6: Time-height plot for La Porte profiler on July 28, 2005

exhibits, winds were generally from the northwest and less than 10 m/s above the surface. MM5 

captures these winds well and simulates the growth of the PBL closely. 

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Figure 2.5-6: Time-height plot for La Porte profiler on July 28, 2005

Forward trajectories (Figure 2.5-7: 12-hour forward trajectory at 08:00 CST on July 28, 2005) 

indicated that MM5 has difficulty depicting the wind shift that occurs with the sea breeze. The 

MM5 trajectories are too long, indicating the wind speeds are slightly over–predicted, and they 

curve in the wrong direction. Because of the inaccurate transport on this day, the photochemical 

model places the high ozone plume to the south and west of the observed peak at the Seabrook 

Friendship Park C45 monitor. 

Figure 2.5-7: 12-hour forward trajectory at 08:00 CST on July 28, 2005 

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August 2, 2005

Eleven stations recorded an exceedance of the eight-hour ozone standard on this day, mostly on 

the west side of the city. The highest eight-hour ozone reading of the episode occurred at the 

West Houston monitor, with a peak of 104.6 ppb. Light winds rotated from the north in the 

morning to the south in the afternoon, following the high pressure coastal oscillation pattern. 

Scattered clouds were numerous and convective activity developed to the northeast of Houston as 

a result of the bay and sea breeze throughout the late afternoon. 

MM5 reproduced the surface wind speed and wind direction patterns acceptably on this day. The 

Houston area average time series for wind speed and direction shown above show good 

agreement between the model and the observations. Figure 2.5-8: Region 12 average wind 

direction and speed scatter plots for August 2, 2005 below displays the Houston area scatter plots 

for wind direction and speed on August 2, 2009. The scatter plot shows wind speeds fall within 

one m/s of the observations for almost all hours. However, a few nighttime wind directions do 

not show good agreement. Most of the nighttime hours experienced calm conditions, so the wind 

direction measurements may not be as accurate as the daytime measurements. The impact of 

these wind direction errors show up in Table 2.5-3: Error percentages for August 2, 2005. The 

accuracy of modeled wind directions along the coast are below the Region 12 average, and the 

downwind (to the west and northwest of downtown Houston) monitors furthest from the coast 

have very poor wind direction performance. 

Figure 2.5-8: Region 12 average wind direction and speed scatter plots for August 2, 2005 

Table 2.5-3: Error percentages for August 2, 2005

Geographic 

Area/Zone 

Wind Direction (°)

Error ≤ 30 / 20 / 10 

Wind Speed (m/s)

Error ≤ 2 / 1 / 0.5 

Temperature (°C)

Error ≤ 2 / 1 / 0.5 

Region 12 63 / 54 / 25 100 / 88 / 63 63 / 54 / 29 

Coastal 54 / 25 / 04 100 / 79 / 58 58 / 29 / 17 

Ship Channel 75 / 50 / 38 100 / 67 / 33 67 / 38 / 33 

Southeast Core 58 / 46 / 06 100 / 67 / 42 71 / 46 / 21 

Northwest 

Core 54 / 42 / 21 88 / 67 / 42 58 / 29 / 13 

Downwind 08 / 08 / 04 83 / 63 / 42 71 / 29 / 17 

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According to the upper air winds at the La Porte profiler (Figure 2.5-9: Time-height plots for the 

La Porte (left) and Jefferson County (right) profilers on August 2, 2005), MM5 replicated the 

observed winds quite well. The model had difficulty resolving the very calm winds (dots with no 

or small lines) but the flow patterns were well represented. PBL height estimates from La Porte 
were not available on this day. At the Jefferson County profiler, however the winds were well 

simulated but the PBL height was underestimated due to clouds along the coast near Beaumont in 

MM5. 

Figure 2.5-9: Time-height plots for the La Porte (left) and Jefferson County (right) profilers 

on August 2, 2005

As shown by Figure 2.5-10: 12-hour forward trajectory at 08:00 CST on August 2, 2005 the 

modeled trajectories produce similar trajectories as the observed data. However, the model 

appears to veer the winds too fast to the southeast. The higher ozone in the photochemical model 

may be pushed too far east and north of the observed peaks. 

Figure 2.5-10: 12-hour forward trajectory at 08:00 CST on August 2, 2005 

August 6, 2005

Four monitors exceeded the eight-hour ozone standard of 85 ppb on August 6, 2005, all occurring 

on the southeast side of Houston. Winds were light on this day, starting from the northwest and 

rotating to the southeast with the onset of the sea breeze just after midday. Scattered clouds were 

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observed all day with thunderstorms occurring in the afternoon to the northwest and southwest of 

Houston. 

MM5 produced a surface wind field that followed the observed patterns well on this day. The 

model appeared to be slightly behind the timing of the land/sea breeze pattern as is shown in the 

wind direction time series (Figure 2.5-2: Region 12 average wind direction time series for 

2005ep2 episode). The regional average wind speeds produced by MM5 during the morning and 

late afternoon are slightly higher than the observed speeds, which were less than 3 m/s on 

average. Also, the mid-day period is well represented during the few calm hours. The wind field 

accuracy statistics exhibit good performance by MM5 with no large deviations from the 

observations (Figure 2.5-11: Region 12 average wind direction and speed scatter plots for August 

6, 2005 and Table 2.5-4: Percent accuracy statistics for August 6, 2005). 

Figure 2.5-11: Region 12 average wind direction and speed scatter plots for August 6, 2005 

Table 2.5-4: Percent accuracy statistics for August 6, 2005

Geographic 

Area/Zone 

Wind Direction (°)

Error ≤ 30 / 20 / 10 

Wind Speed (m/s)

Error ≤ 2 / 1 / 0.5 

Temperature (°C)

Error ≤ 2 / 1 / 0.5 

Region 12 79 / 50 / 33 100 / 71 / 46 100 / 100 / 75 

Coastal 83 / 75 / 38 100 / 71 / 25 96 / 79 / 46 

Ship Channel 58 / 54 / 29 96 / 63 / 33 96 / 88 / 54 

Southeast Core 67 / 29 / 04 100 / 67 / 25 96 / 75 / 50 

Northwest 

Core 63 / 54 / 38 88 / 54 / 38 92 / 63 / 46 

Downwind 50 / 29 / 08 88 / 42 / 25 100 / 83 / 42 

At the La Porte profiler (Figure 2.5-12: Time-height plots for the La Porte (left) and Huntsville 

profilers (right) on August 6, 2005), MM5 replicated the observed wind shift from the northwest 

to the south well. The MM5 PBL estimates through 10:00 AM CST accurately followed the 

morning PBL rise but comparisons of afternoon hours were not available as the profiler estimates 

stopped at 13:00 CST. Results at the Jefferson County and Huntsville profilers also showed wellrepresented morning PBL rises. Afternoon PBL estimates were mixed, with both over- and 

under-predictions, as shown by the Huntsville profiler time-height plots. The morning PBL rise 

may be a more important metric for ozone production than peak mixing height, as reported by 

Knoderer et al. (2008). 

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Figure 2.5-12: Time-height plots for the La Porte (left) and Huntsville profilers (right) on 

August 6, 2005 

As shown by Figure 2.5-13: 12-hour forward trajectory at 08:00 CST on August 6, 2005 the modeled 

trajectories produce similar looping trajectories as the observed data. However, the model appears to 

produce winds that are too fast, especially from the east and northeast. The higher ozone in the 

photochemical model may be pushed farther west than the observed peaks. 

Figure 2.5-13: 12-hour forward trajectory at 08:00 CST on August 6, 2005 

2.6 2006ep0 Performance Evaluation 

The June 2006 episode had two days with very high 8 hour average ozone values. On June 8, the 

HGB area recorded a peak 8 hour average of 122 ppb. On June 14, there was a domain-wide 

peak of 118 ppb. Perhaps the most striking aspect of this episode, though, is that there were 

many high values across many sites and many days. 

The episode had notable tropical circulations and thunderstorms early in the modeling period. 

Throughout the majority of the period veering and backing winds were influenced by different 

frontal passages. The persistent feature leading to high ozone can be perhaps be deduced by 

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comparing Figure 2.6-1: Region 12 average wind direction time series for 2006ep0 episode, with 

Figure 2.4-1: Region 12 average wind direction time series for 2005ep1 episode. The veering 

and backing behavior during this episode are “biased” into the southwest quadrant rather than the 

southeasterly quadrant seen during the June 2005 episode. Thus, exeedances seem to have been 

more widely distributed. 

General Model Performance 

Our summary of model performance across the HGB domain and through our modeling zones is 

provided in Table 2.6-1: Average Houston Region 12 Percent Accuracy by Episode, Zone, and 

Peak Days. It is encouraging to note that domain-wide performance is above average on our two 

peak days of June 8 and June 14. It is striking that more than 75 percent of the time our wind 

direction and wind speed meet thresholds of 20 degrees and 1 m/s respectively. 

Table 2.6-1: Average Houston Region 12 Percent Accuracy by Episode, Zone, and Peak 

Days 

Number 

of days 

Geographic 

Area 

Wind direction (°) 

% Error < 30/20/10 

Wind Speed (m/s) 

% Error < 2/1/0.5 

Temperature (°C) 

% Error < 2/1/0.5 

Entire 

Episode 

Region 12 76 / 63 / 45 97 / 72 / 38 85 / 57 / 33 

June 8 Region 12 79 / 75 / 46 96 / 75 / 63 96 / 58 / 54 

June 24 Region 12 92 / 79 / 50 100 / 83 / 42 100 / 58 / 29 

Entire 

Episode 

Zone 1 69 / 57 / 35 96 / 76 / 48 81 / 55 / 29 

Entire 

Episode 

Zone 2 67 / 57 / 35 94 / 57 / 30 80 / 55 / 35 

Entire 

Episode 

Zone 3 69 / 55 / 36 93 / 56 / 25 83 / 54 / 29 

Entire 

Episode 

Zone 4 68 / 52 / 34 94 / 58 / 30 76 / 38 / 19 

Entire 

Episode 

Zone 5 52 / 39 / 21 82 / 48 / 25 85 / 53 / 31 

The time series displayed in Figures 2.6-1: Region 12 average wind direction time series for 

2006ep0 episode and 2.6-2: Region 12 average wind direction time series for 2006ep0 episode

generally support the tabulated performance above. A couple of features are worth noting. An 

approaching cold front on June 7 impacted wind direction performance on June 8. Also Tropical 

Storm Alberto disrupted predicted winds on June 12. Since a large-scale recirculation of winds 

was a key feature of June 14, the performance on the preceding couple of days may be important 

in CAMx performance 
Figure 2.6-1: Region 12 average wind direction time series for 2006ep0 episode

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Figure 2.6-2: Region 12 average wind direction time series for 2006ep0 episode 

June 8, 2006 Features 

The wind speed bias during the early morning hours seen in the regional time series presented 

earlier is clearly evident in the Figure 2.6-3: Region 12 average wind direction and speed scatter 

plots for June 8, 2006. The impact on forward trajectories started at midnight can be seen in 

Figure 2.6-4: 12-hour forward trajectory at midnight on June 8, 2006. On the other hand, 

starting the trajectories at 11 am shows wind speeds are much more consistent with observations. 

Looking next at wind direction, the regional time series for wind direction indicated a sharp 

southerly bias occurred in the early morning of June 8 . Yet, our scatter plot for wind direction 

shows apparently good over-all bias. Despite the two hours with obvious directional errors, there 

is also the difficulty discussed previously for interpreting scatter plots in which small persistent 

biases accumulate because we don’t distinguish the ordering of hour comparisons. The impact of 

this is seen in the trajectory plot in Figure 2.6-4: 12-hour forward trajectory at midnight on June 

8, 2006. For a start time of 11 CST, the good wind speed performance is reflected in trajectory 

lengths which look similar for both observational data and MM5 data. However, a subtle bias has 

appeared so that trajectories seem to be biased clockwise to the east of where they should be. 

Thus, our daily ozone peaks and requisite mixing of precursors may have been misplaced despite 

the quite good statistics and low bias. 

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Figure 2.6-3: Region 12 average wind direction and speed scatter plots for June 8, 2006 

Figure 2.6-4: 12-hour forward trajectory at midnight on June 8, 2006 

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Figure 2.6-5: 12-hour forward trajectory at 11:00 CST on June 8, 2006 

June 14, 2006 Features 

A close examination of the scatter plots for wind direction and wind speed in Figures 2.6-6: 

Region 12 average wind direction and speed scatter plots for June 14, 2006, respectively show 

some interesting features. As seen in Table 2.6-1: Average Houston Region 12 Percent Accuracy 

by Episode, Zone, and Peak Days, performance across Region 12 was very good on June 14. 

Wind direction were predicted to within 20 degrees of observations across all sites and hours 79 

percent of the time that day while wind speeds were within 1 m/s 83 percent of the time. From 

our Region 12 time series, one can see that our error is within our desired threshold of about 30 

degrees, and yet there seems to be a slight counterclockwise bias at night, and a more clockwise 

bias during the day. The small positive biases shown in our Region 12 time series for wind 

speed are well with our desired thresholds but persistent. 

The cumulative effects of a counterclockwise bias and slightly faster wind speeds impacts the 

trajectories in Figure 2.6-7: 12-hour forward trajectory at 8:00 CST on June 14, 2006. Predicted 

wind directions matched observations during most early morning hours but had a more northerly 

component near dawn. 

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Figure 2.6-6: Region 12 average wind direction and speed scatter plots for June 14, 2006 

Figure 2.6-7: 12-hour forward trajectory at 8:00 CST on June 14, 2006 

In the trajectory plot below, the clockwise bias in wind direction seen Figure 2.6-7 has become 

evident as the modeled trajectories are consistently rotated clockwise with respect to trajectories 

constructed from observational data. The similar length of the two sets of trajectories confirms 

that daytime wind speeds were very close to observed values. 
Figure 2.6-8: 12-hour forward trajectory at 14:00 CST on June 14, 2006 

One additional feature that is likely to impact air quality on June 14 can be inferred from the 

height-time cross section at the site of the La Porte profiler. Generally high background ozone 

values were observed at CAMS sites north of Houston early on this day. Inspection of Figure 

2.6-9: Time-height plots for the La Porte profiler on June 14, 2006 below, one sees that surface 

winds at La Porte were generally easterly during the early morning hours. The easterly 

components were persistent through about one kilometer above ground level (AGL). Above that 

level, the winds are generally from the northwest which is from the ship channel and portions of 

the urban core. During the middle of the day, low level winds become very light but the mixing 

height grows and any precursors that have been transported from the northwest can mix down to 

near the surface. 

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Figure 2.6-9: Time-height plots for the La Porte profiler on June 14, 2006 

2.7 2006ep1a Performance Evaluation 

The 2006ep1a episode (August 13 – 27, 2006) was the first part of the TexAQS II intensive 

monitoring effort (August 15 – October 15, 2006). As a weak cold front approached and moved 

through southeast Texas, the Houston area experienced ozone exceedances from August 16-18. 

Winds were generally light during this period, with the sea breeze dominating flow in the 

afternoons. 

MM5 reproduced the wind and temperature conditions during this episode well (Table 2.7-1: 

Percent accuracy statistics for 2006ep1a episode and Figure 2.7-1: Region 12 average wind 

direction and speed scatter plots for 2006ep1a episode 

Table 2.7-1: Percent accuracy statistics for 2006ep1a episode

 Geographic 

Area/Zone 

Wind Direction (°)

Error ≤ 30 / 20 / 10 

Wind Speed (m/s)

Error ≤ 2 / 1 / 0.5 

Temperature (°C)

Error ≤ 2 / 1 / 0.5 

Region 12 80 / 67 / 43 100 / 83 / 43 83 / 61 / 39 

Coastal 66 / 54 / 30 98 / 74 / 42 80 / 51 / 28 

Ship Channel 69 / 55 / 35 100 / 74 / 39 90 / 55 / 32 

Southeast Core 77 / 62 / 38 98 / 61 / 30 90 / 63 / 39 

Northwest 

Core 68 / 59 / 36 99 / 64 / 31 76 / 46 / 22 

Downwind 56 / 41 / 29 90 / 51 / 28 77 / 46 / 20 

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Figure 2.7-1: Region 12 average wind direction and speed scatter plots for 2006ep1a episode 

The Houston Region 12 average percent accuracy statistics are the best overall compared to other 

episodes (compare to Figure 3.3.1-2 of Chapter 3.3.1). This is further represented by the time 

series of winds and temperature where the model follows the observations closely over the entire 

episode (Figures 2.7-2: Region 12 average wind direction time series for 2006ep1a episode

through 2.7-4: Region 12 average temperature time series for 2005ep2 episode). 

 Figure 2.7-2: Region 12 average wind direction time series for 2006ep1a episode 

A-41
Figure 2.7-3: Region 12 average wind speed time series for 2006ep1a episode 

Figure 2.7-4: Region 12 average temperature time series for 2006ep1a episode 

August 16, 2006

Six monitors exceeded 85 ppb of ozone on August 16, 2006, all on the north to northeast side of 

Houston. The peak eight hour value of 95 ppb occurred at Mercer Arboretum C557. Winds were 

light from the west in the morning before stalling out around noon. A bay and sea breeze from 

A-42
the south-southeast dominated the afternoon flow. Thunderstorms developed over Louisiana in 

the late afternoon but did not impact Houston. Temperatures were in the upper 90s. 

As with most days in this episode, MM5 acceptably reproduced the wind patterns on August 16, 

2006. As Figure 2.7-5: Region 12 average wind direction and speed scatter plots for August 16, 

2006 exhibits, MM5 produced wind direction and wind speeds within the benchmarks for most 

hours. These statistics show that MM5 followed the veering winds well. For the daytime hours, 

MM5 slightly under-predicted the wind speed, and slightly over-predicted the nighttime wind 

speed. Table 2.7-2: Percent accuracy statistics for August 16, 2006 shows that same overall 

performance occurred throughout the Houston area, according to the Zone accuracy statistics. 

Figure 2.7-5: Region 12 average wind direction and speed scatter plots for August 16, 2006 

Table 2-7-2: Percent accuracy statistics for August 16, 2006

Geographic 

Area/Zone 

Wind Direction (°)

Error ≤ 30 / 20 / 10 

Wind Speed (m/s)

Error ≤ 2 / 1 / 0.5 

Temperature (°C)

Error ≤ 2 / 1 / 0.5 

Region 12 79 / 79 / 42 100 / 96 / 38 96 / 83 / 58 

Coastal 63 / 54 / 25 100 / 71 / 38 88 / 71 / 46 

Ship Channel 71 / 63 / 33 100 / 88 / 38 100 / 83 / 63 

Southeast Core 92 / 75 / 42 100 / 79 / 29 100 / 71 / 38 

Northwest 

Core 88 / 75 / 42 100 / 79 / 33 92 / 63 / 38 

Downwind 67 / 50 / 33 100 / 67 / 50 92 / 58 / 42 

Figure 2.7-6: Wind field plots for the 12:00 CST (left) and 17:00 CST (right) on August 16, 2006

shows the entire wind field of the CAMx input for August 16, 2006 at 12:00 CST; red arrows 

show observed winds, pointing in the direction that the wind blows to, and blue arrows show the 

modeled winds. For most of the observation network, winds are calm, which are indicated by 

small arrows or dots. However, along Galveston Bay, a bay breeze has developed, and the model 

winds match the observations closely. This is an important feature to capture, because the 

morning westerly winds can carry pollutants over Galveston Bay, and the bay breeze can then 

return the aged pollutants onshore, where they can encounter fresh emissions. At 17:00 CST, the 

southerly sea breeze has overtaken the bay breeze flow; the model replicates this feature properly 

as well. 

A-43
Figure 2.7-6: Wind field plots for the 12:00 CST (left) and 17:00 CST (right) on August 16, 2006 

Figure 2.7-7: Time-height plots for the La Porte (left) and Jefferson County (right) profilers 

on August 16, 2006 

At the La Porte profiler (Figure 2.7-7: Time-height plots for the La Porte (left) and Jefferson 

County (right) profilers on August 16, 2006), MM5 accurately followed the change in wind 

direction and speed throughout the lower levels of the atmosphere for the entire day. The midday hours were especially well predicted through the first kilometer, when the wind was calm. 

On other days, MM5 has difficulty accurately replicating calm conditions. The morning rise and 

afternoon decay of the PBL may be slightly under-predicted. At the Jefferson County profiler, 

MM5 successfully predicted the upper air wind flow. The calm conditions last much longer 

above the surface near Beaumont, and MM5 again simulates this well. The PBL height estimates 

were very comparable, except at 13:00 CST, when clouds may have affected this site in the 

model. 

 

As shown by Figure 2.7-8: 12-hour forward trajectory at 08:00 CST on August 16, 2006, the 

modeled and observed trajectories exhibit morning westerly winds, shifting to southeasterly in the 

afternoon. The model appears to produce winds along the ship channel with too much of an 

easterly component. The lengths of the observed and modeled trajectories are very similar, 

indicating that the wind speed was well modeled. Based on this figure, the higher ozone in the 

photochemical model may be just west of the observed peaks, due to the small wind direction 

error.

A-44
Figure 2.7-8: 12-hour forward trajectory at 08:00 CST on August 16, 2006 

August 17, 2006 

On this hot day (98 degrees Fahrenheit) winds were light from the north in the morning, calm at 

mid-day, and light east-southeast in the afternoon as the cold front slowly dissipated near 

Houston. A weak bay breeze developed along Galveston Bay at 12:00 CST and lasted through 

19:00 CST before the sea breeze from the Gulf of Mexico dominated the flow. The sea breeze 

started at 14:00 CST but had difficulty overcoming the north wind following the cold front. This 

resulted in further stagnation in the Houston area. Nielsen-Gammon (2007a) noted that the 

previous day’s ozone could have been recirculated due to the light winds of both August 16 and 

17. The partly cloudy skies, high temperatures, and light recirculating winds brought good 

conditions for ozone formation. Fourteen monitors exceeded the eight hour ozone standard, 

including the peak of 113 ppb at Tom Bass C558. Most of the exceedances occurred south of the 

Ship Channel and in western Houston. 

MM5 predicted the light wind speeds within 0.5 m/s for almost all hours of the day (Figure 2.7-9: 

Region 12 average wind direction and speed scatter plots for August 17, 2006). The wind 

direction statistics show MM5 had some difficulty with the variable winds, but this should not 

impact the photochemical modeling significantly, because the speeds were so low (Table 2.7-3: 

Percent accuracy statistics for August 17, 2006). Modeled temperatures were generally within 

one degree Fahrenheit of the observed throughout Houston. 

Table 2.7-3: Percent accuracy statistics for August 17, 2006

Geographic 

Area/Zone 

Wind Direction (°)

Error ≤ 30 / 20 / 10 

Wind Speed (m/s)

Error ≤ 2 / 1 / 0.5 

Temperature (°C)

Error ≤ 2 / 1 / 0.5 

Region 12 63 / 46 / 17 100 / 96 / 75 100 / 88 / 46 

Coastal 50 / 33 / 17 100 / 88 / 63 83 / 42 / 08 

Ship Channel 46 / 33 / 21 100 / 79 / 67 100 / 75 / 46 

Southeast Core 54 / 33 / 17 100 / 83 / 46 100 / 96 / 67 

Northwest 

Core 46 / 33 / 21 100 / 92 / 54 96 / 58 / 17 

Downwind 13 / 08 / 04 96 / 71 / 42 88 / 54 / 29 

A-45
Figure 2.7-9: Region 12 average wind direction and speed scatter plots for August 17, 2006 

The time height plots from the La Porte and Jefferson County (Figure 2-7-10: Time-height plots 

for the La Porte (left) and Jefferson County (right) profilers on August 17, 2006) show the north 

winds on August 17, 2006. Near the surface, the winds are calm, and rotate from the north to the 

southeast. MM5 follows these patterns well, except during early morning hours about one 

kilometer above the surface. The PBL estimates follow the observations very well, including the 

morning rise, though the afternoon modeled PBL at Jefferson County was too low. Modeled 

clouds moving from the east may have impacted the predicted mixing depth around Beaumont. 

Based on the observed and modeled 12 hour trajectories, MM5 simulated the wind conditions 

well along the ship channel and central Houston (Figure 2.7-11: Surface wind field plots for the 

14:00 CST (left) and 19:00 CST (right) on August 17, 2006). However, along Galveston Bay and 

the Gulf of Mexico, MM5 rotated the winds too early. This is most likely due to the modeled sea 

breeze penetrating inland too early, as shown by Figure 2.7-12: 12-hour forward trajectory at 

08:00 CST on August 17, 2006. As a result of this error, a stronger recirculation may occur in the 

photochemical model to produce more ozone than observed. 

Figure 2.7-10: Time-height plots for the La Porte (left) and Jefferson County (right) profilers on 

August 17, 2006 

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Figure 2.7-11: Surface wind field plots for the 14:00 CST (left) and 19:00 CST (right) on 

August 17, 2006 

Figure 2.7-12: 12-hour forward trajectory at 08:00 CST on August 17, 2006 

August 18, 2006 

August 18, 2006 was the last high ozone day of this episode, with only the Katy Park C559 

monitor exceeding at 89 ppb. Winds were calm in the early morning hours and then light from 

the northeast starting at 07:00 CST. By 10:00 CST the winds were steady from the east at 2 

meters/second. Wind speed peaked around 15:00 CST at 4 meters/second and slowly weakened 

as the wind turned more southeasterly. Temperatures were above 90 °F for most of the Houston 

area under scattered cumulus skies. The calm winds from the previous day and into the morning 

of August 18 may have increased the background ozone, allowing for an exceedance. 

A-47

Wind speeds were accurately represented by MM5 for this day, with almost 80% of the hours 

within one meter/second of the observations. Wind direction was well simulated except for a few 

early morning hours when MM5 predicted south-southwest winds. In reality south-southeast 

winds existed. Temperatures were under-predicted throughout the Houston area, as morning 

convection and precipitation in MM5 (not shown) may have limited afternoon temperatures and 

mixing. 
Figure 2.7-13: Region 12 average wind direction and speed scatter plots for August 18, 2006 

Table 2.7-4: Percent accuracy statistics for August 18, 2006

Geographic 

Area/Zone 

Wind Direction (°)

Error ≤ 30 / 20 / 10 

Wind Speed (m/s)

Error ≤ 2 / 1 / 0.5 

Temperature (°C)

Error ≤ 2 / 1 / 0.5 

Region 12 75 / 58 / 29 100 / 79 / 54 63 / 42 / 38 

Coastal 75 / 63 / 33 100 / 79 / 38 56 / 33 / 25 

Ship Channel 71 / 50 / 38 100 / 83 / 54 71 / 38 / 21 

Southeast Core 83 / 75 / 50 96 / 54 / 38 79 / 38 / 29 

Northwest 

Core 75 / 67 / 42 100 / 71 / 29 63 / 21 / 00 

Downwind 54 / 46 / 38 92 / 54 / 29 83 / 42 / 17 

Unlike August 16 and August 17, 2006, stagnant conditions did not occur around mid-day on 

August 18 (Figure 2.7-14: Surface wind field plots for the 12:00 CST (left) and 18:00 CST (right) 

on August 18, 2006). Instead, an easterly breeze continued until approximately 18:00 CST, as 

shown by the observations’ red arrows in the right hand of the figure. As shown by the blue 

arrows, the model tracks these conditions well. At 18:00 CST the model has too much of an 

easterly component, which may cause the modeled high ozone to be placed west and south of the 

observed peak at Katy Park. 

A-48
Figure 2.7-14: Surface wind field plots for the 12:00 CST (left) and 18:00 CST (right) on August 18, 

2006 

The upper air wind flow from the La Porte profiler (Figure 2.7-15: Time-height plots for the La 

Porte (left) and Huntsville (right) profilers on August 18, 2006) shows that the upper air winds 

had similar patterns to the surface flow. East winds exist from 07:00 – 13:00 CST before winds 

from the southeast take over for the rest of the day. MM5 represents these conditions well except 

for a few early morning hours above 1 kilometer, and near-surface winds after 14:00 CST, when 

the wind direction is slightly off. At Huntsville, northerly winds occur in the morning through the 

lowest three kilometers, before the wind speed dies at 09:00 CST. At 12:00 CST, easterly winds 

develop. MM5 generally replicates these wind conditions well. At both profilers, the peak 

mixing depth is under-predicted. The morning rise is well represented at Huntsville while PBL 

performance at La Porte is disappointing, except for a few late morning hours. 

Figure 2.7-15: Time-height plots for the La Porte (left) and Huntsville (right) profilers on August 

18, 2006

The modeled and observed trajectories on August 18, 2006 are very similar in length and 

orientation (Figure 2.7-16: 12-hour forward trajectory at 08:00 CST on August 18, 2006). The 

only difference appears to be a slight displacement to the south, due to the late turning of the 

winds to the southeast. The wind error may place the modeled ozone plumes to the south and 

west of the observed peaks. 

A-49
Figure 2.7-16: 12-hour forward trajectory at 08:00 CST on August 18, 2006 

2.8 2006ep1b Performance Evaluation 

The 2006ep1b episode has four periods of high ozone: August 31- September 1, September 3, 

September 6-7, and September 13-14, 2006 

During August 27 to September 16, 2006, there were several cold fronts that passed over 

Houston. Three of the high ozone days occurred a day after the passage of a cold front. 

The MM5 model simulated the episode well and reproduced the main meteorological features 

accurately. The error associated with the wind direction, wind speed and temperature for the 

entire episode for all sites was 80, 100, and 89 percent respectively. The error associated with the 

wind direction, wind speed and temperature for the entire episode for region 12 (HoustonGalveston) was 67, 96, and 80 percent respectively as show in Table 2.8-1: Error percentages for 

2006ep1b episode and Figure 2.8-1: Region 12 average wind speed and direction scatter plots for 

2006ep1b episode. 

A-50
Table 2.8-1: Error percentages for 2006ep1b episode

Duration Geographic 

Area/Zone 

Wind Direction (°)

Error % 30 / 20 / 10 

Wind Speed (m/s)

Error % 2 / 1 / 0.5 

Temperature (°C)

Error % 2 / 1 / 0.5 

Episode 

Average 

All Sites 80 / 65 / 39 100 / 70 / 30 89 / 57 / 27 

Episode 

Average 

Region 12 67 / 54 / 33 96 / 63 / 26 80 / 52 / 25 

Episode 

Average 

Coastal 62 / 49 / 27 90 / 56 / 32 81/ 52 / 28 

Episode 

Average 

Ship 

Channel 62 / 50 / 28 93 / 51 / 23 80 / 49 / 26 

Episode 

Average 

Southeast 

Core 59 / 48 / 26 90 / 55 / 27 81 / 51 / 29 

Episode 

Average 

Northwest 

Core 56 / 40 / 21 91 / 55/ 30 73 / 44 / 20 

Episode 

Average 

Downwind 52 / 39 / 20 82 / 37 / 20 76/ 44 / 23 

A-51

Figure 2.8-1: Region 12 average wind speed and direction scatter plots for 2006ep1b

episode 
Figure 2.8-2: Region 12 average wind direction and wind speed time series for 2006ep1b 

episode 

A-52

rne t06cp 1 b . e t c r-n s. ;. ncw u t. n e vvut c s rl u l c . g rel l 

Wind D i r deg Housto n - G al v eston -

o-r------------ --------------~~--------~== ~~~---------------- ~--~ ~~~ ~~-. 

5 

270 

2:25 

1 60 

1 .35 

90 

4 5 

'r -.~-. -r .- ,2,- .- ~~--~ ~~ -- -- -.~-r 2.7 2.9 2.9 3 0 31 01 02 03 0 4 0 5 0 6 07 0 8 0 9' , 0 , , 12 13 14- 15 1 6 

B i o s W i nd Dir de9 

27 2 a 2 9 30 3 1 01 0 2 0 3 04 O S 0 6 0 7 O B 09 10 1 1 1 2 1 .3 1 4 1 5 1 6 

M ean Abs o l u t e E ,-,-o,- W i n d O i r deg 
August 31, 2006

On that day, 14 monitors recorded an exceedance of the eight-hour ozone standard greater than 85 

ppb. The Houston Westhollow C410 monitor recorded the maximum eight-hour ozone of 127.1 

ppb at 1100 CST. Regional background levels of ozone, in the air coming into the Houston area, 

ranged from about 55 to 68 ppb as indicated by the peak eight-hour averages at Kingwood CAMS 

555, Crosby CAMS 553, and Smith Point CAMS 96. 

The sky was mostly clear during the day. Regional winds were generally light and from the 

northeast. Local winds were light northeasterly changing to light easterly in the middle of the day 

and then becoming moderate southeasterly during the late afternoon and evening hours. The high 

temperature was 94 degrees Fahrenheit (°F) at George Bush Houston Intercontinental Airport, 

92°F at Hobby Airport, and 88°F at Galveston Airport. 

The error associated with the wind direction, wind speed and temperature for the entire episode 

for region 12 (Houston-Galveston) was 71, 100, and 79 respectively as show in Table 2.8-2; 

Error percentages for August 31, 2006, and Figure 2.8-5: Region 12 Average wind direction and 

speed scatter plots for August 31, 2006. Figure 2.8-6: Time-height plot for La Porte profiler on 

August 31, 2006, shows a good agreement between the profiler winds and the MM5 output for 

most of the day and the vertical level. 

The 12-hour forward trajectories starting at 6 am on August 31, 2006 as depicted in figure 2.8-7: 

12-hour forward trajectory at 06:00 CST on August 31, 2006 show the trajectories generated 

using the MM5 data are longer and to the south of the trajectories generated using the CAMS 

data. This would results the location of the maximum ozone to be to the south of the location of 

the maximum ozone for that day. 

A-53

Figure 2.8-5: Region 12 Average wind direction and speed scatter plots for August 31, 2006 
Table 2.8-2: Error percentages for August 31, 2006 

Duration Geographic 

Area/Zone 

Wind Direction (°)

Error % 30 / 20 / 10 

Wind Speed (m/s)

Error % 2 / 1 / 0.5 

Temperature (°C)

Error % 2 / 1 / 0.5 

8/31/2006 Region 12 71 / 42 / 33 100 / 71 / 50 79 / 46 / 29 

8/31/2006 Coastal 71/50/38 88/46/33 75/42/25 

8/31/2006 Ship 

Channel 

54 / 33 / 21 96 / 63 / 58 96 / 58 / 21 

8/31/2006 Southeast 

Core 

46 / 42 / 25 100 / 67 / 50 100 / 54 / 21 

8/31/2006 Northwest 

Core 

42 / 25 / 08 92 / 63 / 42 67 / 38 / 21 

8/31/2006 Downwind 29 / 21 / 13 96 / 54 / 33 79 / 42 / 13 

Figure 2.8-6: Time-height plot for La Porte profiler on August 31, 2006

A-54
Figure 2.8-7: 12-hour forward trajectory at 06:00 CST on August 31, 2006 

September 1, 2006 

On this day, 27 monitors recorded an exceedance of the eight-hour ozone standard greater than 85 

ppb. The Houston Deer Park 2 monitor recorded the maximum eight-hour ozone of 121.8 ppb at 

1100 CST. Regional background levels of ozone, in the air coming into the Houston area, were at 

least about 63 to 66 ppb as indicated by the peak eight-hour averages at Danciger CAMS 618, 

Lake Jackson CAMS 1016, and Mustang Bayou CAMS 619. 

In the early morning, the skies were clear, becoming partly cloudy in the late morning and 

afternoon. Light west-northwesterly winds in the early morning, becoming light easterly mid-day 

with the bay breeze and then moderate southeasterly in the late afternoon and evening with the 

sea breeze. The high temperature was 96 degrees Fahrenheit (F) at George Bush Houston 

Intercontinental Airport, 92 degrees F at Hobby Airport, and 91 degrees F at Galveston Airport. 

The error associated with the wind direction, wind speed and temperature for the entire episode 

for region 12 (Houston-Galveston) was 50, 100, and 92 respectively as show in Table 2.8-3: 

Error percentages for September 1, 2006 and Figure 2.8-8: Region 12 Average wind direction 

and speed scatter plots for September 1, 2006. 

Table 2.8-3: Error percentages for September 1, 2006

Duration Geographic 

Area/Zone 

Wind Direction (°)

Error % 30 / 20 / 10 

Wind Speed (m/s)

Error % 2 / 1 / 0.5 

Temperature (°C)

Error % 2 / 1 / 0.5 

9/1/2006 Region 12 50 / 42 / 25 100 / 50 / 33 92 / 63 / 21 

9/1/2006 Coastal 29 / 13 / 04 79 / 63 / 42 96 / 79 / 42 

9/1/2006 Ship 

Channel 

67 / 63 / 21 96 / 42 / 17 75 / 42 / 38 

9/1/2006 Southeast 

Core 

54 / 38 / 29 92 / 42 / 21 79 / 63 / 25 

9/1/2006 Northwest 

Core 

42 / 25 / 13 92 / 46 / 25 83 / 29 / 04 

9/1/2006 Downwind 21 / 13 / 08 83 / 38 / 21 88 / 63 / 29 

A-55
Figure 2.8-8: Region 12 Average wind direction and speed scatter plots for September 1, 

2006 

Figure 2.8-9: Time-height plot for La Porte profiler on September 1, 2006 shows a good 

agreement between the profiler winds and the MM5 output for most of the day and the vertical 

level

\ 

Figure 2.8-9: Time-height plot for La Porte profiler on September 1, 2006 

The 12-hour forward trajectories starting at 6 am on September 1, 2006 as depicted in figure 2.8-

10: 12-hour forward trajectory at 06:00 CST on September 01, 2006 show that in the Jefferson 

County, the MM5 trajectories or similar to the observed trajectories, but slightly to the west. In 

Brazoria County, for the first four hours, the MM5 trajectories are similar to the observed 

trajectories. However, after that they are different as the MM5 trajectories travel to the west then 

north while the observed trajectories travel to the east then north. In Harris County, the MM5 and 

observed trajectories curl around and travel a very limited distance representing a stagnant 

circulation. 

A-56
Figure 2.8-10: 12-hour forward trajectory at 06:00 CST on September 01, 2006

September 7, 2006 

On September 7, 12 monitors recorded an exceedance of the eight-hour ozone standard greater 

than 85 ppb. The Houston Bayland Park C53 monitor recorded the maximum eight-hour ozone of 

110.5 ppb at 1000 CST. Regional background levels of ozone, in the air coming into the Houston 

area, were at least about 61 to 72 ppb as indicated by the peak eight-hour averages at Conroe 

CAMS 78, Kingwood CAMS 555, Crosby CAMS 553, and Galveston CAMS 34. 

As for the weather, there were occasionally some cirrus clouds with scattered cumulus clouds in 

the afternoon. Regional winds were light and from the northeast. Winds from the east-northeast 

(3-6 mph) occurred during the morning, veering to southeasterly (3-6 mph) in the late afternoon. 

The high temperature was 93 degrees F at George Bush Houston Intercontinental Airport, 

91degrees F at Hobby Airport, and 88 degrees F at Galveston Airport. 

The error associated with the wind direction, wind speed and temperature for the entire episode 

for region 12 (Houston-Galveston) was 63, 88, and 100 respectively as show in Table 2.8-4: 

Error percentages for September 7, 2006 and Figure 2.8-11: Region 12 Average wind direction 

and speed scatter plots for September 7, 2006. 

Table 2.8-4: Error percentages for September 7, 2006

Duration Geographic 

Area/Zone 

Wind Direction (°)

Error % 30 / 20 / 10 

Wind Speed (m/s)

Error % 2 / 1 / 0.5 

Temperature (°C)

Error % 2 / 1 / 0.5 

9/7/2006 Region 12 63 / 46 / 17 88 / 71 / 29 100 / 46 / 25 

9/7/2006 Coastal 42 /29 /13 83 /67 /50 100 /67/ 46 

9/7/2006 Ship 

Channel 

54 / 38 / 17 92 / 63 / 33 96 / 79 / 21 

9/7/2006 Southeast 

Core 

63 / 42 / 21 83 / 58 / 25 88 / 58 / 17 

9/7/2006 Northwest 

Core 

46 / 25 / 13 83 / 50 / 33 54 / 33 / 25 

9/7/2006 Downwind 63 / 42 / 21 83 / 38 / 21 96 / 33 / 21 

A-57
Figure 2.8-11: Region 12 Average wind direction and speed scatter plots for September 7, 

2006. 

Figure 2.8-12: Time-height plot for La Porte profiler on September 1, 2006 shows a good 

agreement between the profiler winds and the MM5 output for most of the day and the vertical 

level. 

Figure 2.8-12: Time-height plot for La Porte profiler on September 1, 2006

The 12-hour forward trajectories starting at 6 am on September 7, 2006 as depicted in figure 2.8-

13: 12-hour forward trajectory at 06:00 CST on September 7, 2006 show that most of the 

trajectories generated using the MM5 data are parallel and to the south of the trajectories 

generated using the CAMS data. 

A-58
Figure 2.8-13: 12-hour forward trajectory at 06:00 CST on September 7, 2006 

September 14, 2006 

On that day, 8 monitors recorded an exceedance of the eight-hour ozone standard greater than 85 

ppb. The West Houston C554 monitor recorded the maximum eight-hour ozone of 118.9 ppb at 

1100 CST. Regional background levels of ozone, in the air coming into the Houston area, were 

at least about 56 to 74 ppb as indicated by the peak eight-hour averages at Kingwood CAMS 555, 

Crosby CAMS 553, Smith Point C96, and Galveston CAMS 34. 

The weather was clear in the morning with scattered boundary-layer cumulus from late morning 

onward. Scattered thundershowers occurred in the afternoon south and southwest of Houston. 

East-northeasterly winds at 6-8 mph during the early morning, veering to easterly later in the 

morning and finally southeasterly at 6-10 mph during the afternoon. The high temperature was 

92 degrees Fahrenheit at George Bush Houston Intercontinental Airport, 91degrees at Hobby 

Airport, and 88 degrees at Galveston Airport. 

The error associated with the wind direction, wind speed and temperature for the entire episode 

for region 12 (Houston-Galveston) was 92, 96, and 79 respectively as show in Table 2.8-5: Error 

percentages for September 14, 2006 and Figure 2.8-14: Region 12 Average wind direction and 

speed scatter plots for September 14, 2006. 

Table 2.8-5: Error percentages for September 14, 2006 

Duration Geographic 

Area/Zone 

Wind Direction (°)

Error % 30 / 20 / 10 

Wind Speed (m/s)

Error % 2 / 1 / 0.5 

Temperature (°C)

Error % 2 / 1 / 0.5 

9/14/2006 Region 12 92 / 88 / 50 96 / 71 / 25 79 / 67 / 38 

9/14/2006 Coastal 92 / 67 / 50 96 / 58 / 33 83 / 67 / 29 

9/14/2006 Ship 

Channel 

88 / 71 / 58 96 / 71 / 33 79 / 42 / 13 

9/14/2006 Southeast 

Core 

92 / 79 / 54 96 / 58 / 25 100 / 63 / 42 

9/14/2006 Northwest 

Core 

75 / 54 / 33 100 / 63 / 38 71 / 54 / 17 

9/14/2006 Downwind 79 / 67 / 38 79 / 42 / 17 63 / 21 / 08 

A-59
A-60

Figure 2.8-14: Region 12 Average wind direction and speed scatter plots for September 14, 2006 

Figure 2.8-15: Time-height plot for La Porte profiler on September 14, 2006 shows a good 

agreement between the profiler winds and the MM5 output for most the day and the vertical 

levels, except for the lower levels from 1-6 and 10-11 CST and the upper levels at 20 CST where 

the observed winds were stronger and from the north. 

The 12-hour forward trajectories starting at 6 am on September 14, 2006 as depicted in figure 2.8-

16: 12-hour forward trajectory at 06:00 CST on September 14, 2006 show that most of the 

trajectories generated using the MM5 data are parallel and to the south of the trajectories 

generated using the CAMS data. 

Figure 2.8-15: Time-height plot for La Porte profiler on September 14, 2006
Figure 2.8-16: 12-hour forward trajectory at 06:00 CST on September 14, 2006 

2.9 2006ep1c Performance Evaluation 

The 2006ep1c episode has two periods of high ozone: September 20, 2006, and September 26-27. 

During September 16 -29, 2006, there were three cold fronts that passed over Houston on 

September 18, 24, and 28, 2006. Two of the high ozone days occurred two days after the passage 

of the first two cold fronts and the third high ozone day occurred a day after the passage of the 

third cold front. 

The MM5 model simulated the episode well and reproduced the main meteorological features 

close to the actual observations. The error associated with the wind direction, wind speed and 

temperature for the entire episode for all sites was 94, 100, and 75 respectively. The error 

associated with the wind direction, wind speed and temperature for the entire episode for region 

12 (Houston-Galveston) was 84, 96, and 69 respectively as show in Table 2.9-1: Error 

percentages for 2006ep1c episode and Figure 2.9-1: Region 12 average wind speed and direction 

scatter plots for 2006ep1c episode. 

A-61
Table 2.9-1: Error percentages for 2006ep1c episode

Duration Geographic 

Area/Zone 

Wind Direction (°)

Error % 30 / 20 / 10 

Wind Speed (m/s)

Error % 2 / 1 / 0.5 

Temperature (°C)

Error % 2 / 1 / 0.5 

Episode 

Average 

All Sites 94 / 83 / 57 100 / 69 / 37 75 / 43 / 28 

Episode 

Average 

Region 12 84 / 76 / 57 96 / 57 / 29 69 / 33 / 16 

Episode 

Average 

Coastal 85 / 74 / 39 86 / 54 /28 73 / 39 / 17 

Episode 

Average 

Ship 

Channel 81 / 69 / 47 86 / 49 / 28 62 / 28 / 12 

Episode 

Average 

Southeast 

Core 77 / 67 / 45 90 / 52 / 28 74 / 41 / 18 

Episode 

Average 

Northwest 

Core 77 / 66 / 34 85 / 45 / 23 59 / 27 / 13 

Episode 

Average 

Downwind 74 / 65 / 45 79 / 39 / 22 53 / 20 / 08 

Figure 2.9-1: Region 12 average wind direction and wind speed scatter plots for 2006ep1c 

episode 

A-62
Figure 2.9-2: Region 12 average wind direction and wind speed time series for 2006ep1c 

episode. 

The good statistical performance described in the above can be graphically tracked in the Figure 

2.9-2: Region 12 average wind direction time series for 2006ep1c episode. On one key day of 

this episode, September 20, wind speed and wind direction errors are low during most of the 

daylight hours. The other key day of September 27 has noticeable wind direction errors during 

the early to midmorning hours, and then looks good for the rest of the day. 

.

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September 20, 2006 

On that day, 1 monitor recorded an exceedance of the eight-hour ozone standard greater than 85 

ppb. The Bayland Park C53/A146 monitor recorded the maximum eight-hour ozone of 86.7 ppb 

at 1000 CST. Regional background levels of ozone, in the air coming into the Houston area, were 

at least about 62 to 65 ppb as indicated by the peak eight-hour averages at Crosby CAMS 553, 

Smith Point CAMS 96, and Galveston CAMS 34 

Skies were clear all day. Light easterly winds in the morning, becoming light to occasionally 

moderate from the east to southeast in the afternoon. The high temperature was 88 degrees 

Fahrenheit at George Bush Houston Intercontinental Airport, 86 degrees at Hobby Airport, and 

85 degrees at Galveston Airport. 

The error associated with the wind direction, wind speed and temperature for the entire episode 

for region 12 (Houston-Galveston) was 96, 100, and 71 respectively as show in Table 2.9-2: 

Error percentages for September 20, 2006 and Figure 2.9-5: Region 12 Average wind direction 

and speed scatter plots for September 20, 2006. 

Figure 2.9-6: Time-height plot for La Porte profiler on September 20, 2006 shows that the 

profiler winds at La Porte agreed well with the MM5 winds for all vertical levels for the entire 

day except for the period from 1-4 CST for all vertical levels where the profiler winds were from 

the east- southeast and the MM5 winds from the east-northeast. 

The 12-hour forward trajectories starting at 6 am on September 20, 2006 as depicted in figure 2.9-

7: 12-hour forward trajectory at 06:00 CST on September 20, 2006 show the trajectories 

generated the MM5 data are parallel to the trajectories generated using the CAMS data, but 

slightly to the south. This would push the plumes from the industrial sources to the south of the 

location of the maximum ozone for that day. 

Table 2.9-2: Error percentages for September 20, 2006

Duration Geographic 

Area/Zone 

Wind Direction (°)

Error % 30 / 20 / 10 

Wind Speed (m/s) 

Error % 2 / 1 / 0.5 

Temperature (°C) 

Error % 2 / 1 / 0.5 

9/20/2006 Region 12 96 / 83 / 42 100 / 54 / 38 71 / 21 / 8 

9/20/2006 Coastal 100 / 88 / 50 79 / 54 / 33 71 / 29 / 08 

9/20/2006 Ship 

Channel 

83 / 63 / 29 83 / 54 / 46 58 / 21 / 13 

9/20/2006 Southeast 

Core 

92 / 79 / 46 92 / 54 / 29 58 / 25 / 13 

9/20/2006 Northwest 

Core 

88 / 79 / 38 96 / 58 / 29 58 / 21 / 13 

9/20/2006 Downwind 70 / 75 / 50 83 / 50 / 29 42 / 25 / 13 
Figure 2.9-5: Region 12 Average wind direction and speed scatter plots for September 20, 2006 

Figure 2.9-6: Time-height plot for La Porte profiler on September 20, 2006

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Figure 2.9-7: 12-hour forward trajectory at 06:00 CST on September 20, 2006 

September 27, 2006 

On that day, 2 monitors recorded an exceedance of the eight-hour ozone standard greater than 85 

ppb. The Meyer Park C561/A146 monitor recorded the maximum eight-hour ozone of 90.5 ppb at 

1000 CST. Regional background levels of ozone, in the air coming into the Houston area, ranged 

from about 57 to 81 ppb as indicated by the peak eight-hour averages at Galveston CAMS 34, 

Lake Jackson CAMS 1016, Danciger CAMS 618, and Katy Park CAMS 559. 

 Skies were clear with some scattered cirrus clouds. Light west to southwest winds occurred in 

the early morning, becoming light to moderate from the southwest to south mid-day and southerly 

in the late afternoon and early evening. The high temperature was 88 degrees Fahrenheit at 

George Bush Houston Intercontinental Airport, 86 degrees at Hobby Airport, and 83 degrees at 

Galveston Airport. 

The error associated with the wind direction, wind speed and temperature for the entire episode 

for region 12 (Houston-Galveston) was 75, 100, and 67 respectively as show in Table 2.9-3: 

Error percentages for September 27, 2006 and Figure 2.9-8 Region 12 Average wind direction 

and speed scatter plots for September 27, 2006. 

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Table 2.9-3: Error percentages for September 27, 2006

Duration Geographic 

Area/Zone 

Wind Direction (°)

Error % 30 / 20 / 10 

Wind Speed (m/s) 

Error % 2 / 1 / 0.5 

Temperature (°C)

Error % 2 / 1 / 0.5 

9/27/2006 Region 12 75 / 67 / 63 100 / 54 / 17 67 / 33 / 17 

9/27/2006 Coastal 71 / 77 / 67 96 / 71 / 46 83 / 46 / 33 

9/27/2006 Ship 

Channel 

75 / 58 / 38 92 / 46 / 13 79 / 29 / 17 

9/27/2006 Southeast 

Core 

75 / 58 / 38 92 / 54 / 25 67 / 29 / 04 

9/27/2006 Northwest 

Core 

71 / 58 / 46 88 / 46 / 17 58 / 33 / 29 

9/27/2006 Downwind 79 / 75 / 42 88 / 38 / 25 63 / 33 / 17 

Figure 2.9-8: Region 12 Average wind direction and speed scatter plots for September 27, 2006 

Figure 2.9-9: Time-height plot for La Porte profiler on September 27, 2006 shows that the 

profiler winds at La Porte agreed well with the MM5 winds for all vertical levels for the entire 

day. The observed PBL, when available, agreed with the modeled PBL during most of the day. 

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Figure 2.9-9: Time-height plot for La Porte profiler on September 27, 2006

The 12-hour forward trajectories starting at 6 am on September 27, 2006 as depicted in figure 2.9-

10 show the trajectories generated the MM5 data agree with the trajectories generated using the 

CAMS data except for the first 4 hours were the former traveled to the south. 

Figure 2.9-10: 12-hour forward trajectory at 06:00 CST on September 27, 2006 

2.10 2006ep1d Performance Evaluation 

The 2006ep1d episode has two periods of high ozone: October 6-7, and October 11, 2006. 

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During October 1-11, 2006, there were two cold fronts that passed over Houston on October 6 

and 10, 2006. On October 1, 2006 the winds were 10 knots blowing from the southeast and on 

October 5, 2006 the winds were variable with a cold front still in the northern part of Texas. The 

remaining days were under the influence of a high pressure. 

The first high ozone day occurred on the same day of the passage of the first cold front, and the 

remaining two high ozone days occurred a day after the passage of the cold fronts. 

The MM5 model simulated the episode well and reproduced the main meteorological features 

close to the actual observations. The error associated with the wind direction, wind speed and 

temperature for the entire episode for all sites was 91, 100, and 74 respectively. The error 

associated with the wind direction, wind speed and temperature for the entire episode for region 

12 (Houston-Galveston) was 83, 98, and 73 respectively as show in Table 2.10-1: Error 

percentages for 2006ep1c episode and Figure 2.10-1: Region 12 average wind speed and 

direction scatter plots for 2006ep1d episode. 

Table 2.10-1: Error percentages for 2006ep1c episode 

Duration Geographic 

Area/Zone 

Wind Direction (°)

Error % 30 / 20 / 10 

Wind Speed (m/s) 

Error % 2 / 1 / 0.5 

Temperature (°C)

Error % 2 / 1 / 0.5 

Episode 

Average 

All Sites 91 / 83 / 55 100 / 74 / 42 74 / 48 / 27 

Episode 

Average 

Region 12 83 / 66/ 33 98 / 75 / 42 73 / 51 / 32 

Episode 

Average 

Coastal 75 / 59 / 35 96 / 68 / 39 76 / 42 / 19 

Episode 

Average 

Ship 

Channel 

66 / 50 / 29 94 / 62 / 32 68 / 45 / 23 

Episode 

Average 

Southeast 

Core 

72 / 60 / 36 98 / 72 / 39 83 / 52 / 27 

Episode 

Average 

Northwest 

Core 

65 / 48 / 25 95 / 73 / 41 65 / 40 / 25 

Episode 

Average 

Downwind 50 / 36 / 17 85 / 46 / 25 51 / 28 / 14 

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Figure 2.10-1: Region 12 average wind speed and direction scatter plots for 2006ep1d episode 
Figure 2.10-2: Region 12 average wind direction time series for 2006ep1d episode

October 6, 2006

Three monitors recorded an exceedance of the eight-hour ozone standard greater than 85 ppb. The 

Tom Bass C558 monitor recorded the maximum eight-hour ozone of 95.3 ppb at 1000 CST. 

Regional background levels of ozone, in the air coming into the Houston area, ranged from about 

52 to 61 ppb as indicated by the peak eight-hour averages at Conroe CAMS 78, Kingwood 

CAMS 555, and Crosby CAMS 553. 

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Mostly clear skies with a high pressure centered to the northeast. Regional winds were generally 

from the northeast. Local winds were from the east-northeast at 5-10 mph during the morning, 

becoming variable during the afternoon partly due to onset of Bay and Gulf breezes. The high 

temperature was 93 degrees Fahrenheit at George Bush Houston Intercontinental Airport, 91 

degrees at Hobby Airport, and 88 degrees at Galveston Airport. 

The MM5 model performed well for that day as can be seen in Table 2.10-2: Error percentages 

for October 6, 2006 and figure 2.10-5: Region 12 Average wind direction and speed scatter plots 

for October 6, 2006. The performance of the wind direction using the 30 degree threshold, wind 

speed using the 2 m/s threshold and temperature using the 2 degrees threshold in the HoustonGalveston region were 79, 88, and 83 respectively. 

Table 2.10-2: Error percentages for October 6, 2006 

Duration Geographic 

Area/Zone 

Wind Direction (°)

Error % 30 / 20 / 10 

Wind Speed (m/s) 

Error % 2 / 1 / 0.5 

Temperature (°C)

Error % 2 / 1 / 0.5 

10/6/2006 Region 12 79 / 75 / 50 88 / 42 / 25 83 / 50 / 21 

10/6/2006 Coastal 79 / 54 / 29 100 / 67 / 29 96 / 63 / 21 

10/6/2006 Ship 

Channel 

67 / 58 / 38 83 / 46 / 17 67 / 38 / 17 

10/6/2006 Southeast 

Core 

79 / 67 / 54 88 / 50 / 29 100 / 58 / 25 

10/6/2006 Northwest 

Core 

75 / 71 / 29 83 / 46 / 21 63 / 21 / 04 

10/6/2006 Downwind 92 / 75 / 42 50 / 21 / 0 50 / 21 / 13 

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Figure 2.10-5: Region 12 Average wind direction and speed scatter plots for October 6, 2006 

Profiler winds for La Porte profiler was missing before 6pm on that day as shown in figure 2.10-

6: Time-height plot for La Porte profiler on October 6, 2006. On the hours where there was data 

(18-23 CST), the MM5 modeled winds agreed well with the profiler data. 
Figure 2.10-6: Time-height plot for La Porte profiler on October 6, 2006 

The 12-hour forward trajectories starting at 6 am on October 11, 2006 as depicted in figure 2.10-

7: 12-hour forward trajectory at 06:00 CST on October 6, 2006 show the trajectories heading 

towards the location of maximum 8-hr ozone in southeast Houston. 

Figure 2.10-7: 12-hour forward trajectory at 06:00 CST on October 6, 2006. 

October 7, 2006 

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On that day, only one monitor recorded an exceedance of the eight-hour ozone standard greater 

than 85 ppb. The Manvel Croix Park C84 monitor recorded the maximum eight-hour ozone of 87 

ppb at 1000 CST. Regional background levels of ozone, in the air coming into the Houston area, 
ranged from about 45 to 53 ppb as indicated by the peak eight-hour averages at Conroe CAMS 

78, Kingwood CAMS 555, and Crosby CAMS 553. The high temperature was 83 degrees 

Fahrenheit at George Bush Houston Intercontinental Airport, 84 degrees at Hobby Airport, and 

81 degrees at Galveston Airport. 

Mostly clear skies in the morning with cirrus clouds in the afternoon. Winds were light to 

moderate from the northeast in the morning and early afternoon. Bay and Gulf breezes developed 

along the coast during the afternoon and with winds becoming more easterly in the late afternoon. 

The MM5 model performed very well on that day, as displayed in table 2.10-3: Error 

percentages for October 7, 2006 and figure 2.10-7: Region 12 Average wind direction and speed 

scatter plots for October 7, 2006. The performance of the wind direction using the 30 degree 

threshold, wind speed using the 2 m/s threshold and temperature using the 2 degrees threshold in 

the Houston-Galveston region were 100, 96, and 75 respectively. The daytime wind speed 

performance was better than the nighttime. The MM5 upper winds compared with the La Porte 

profiler data shown in Figure 2-10.8: Time-height plot for La Porte profiler on October 7, 2006

also compared well except for the lower winds between 13-18 and 21-22 CST, where the profiler 

winds were east-southeasterly and the MM5 winds were northwesterly. 

Table 2.10-3: Error percentages for October 7, 2006

Duration Geographic 

Area/Zone 

Wind Direction (°)

Error % 30 / 20 / 10 

Wind Speed (m/s)

Error % 2 / 1 / 0.5 

Temperature (°C)

Error % 2 / 1 / 0.5 

10/7/2006 Region 12 100 / 88 / 33 96 / 54 / 29 75 / 50 / 33 

10/7/2006 Coastal 75 / 63 / 38 96 / 50 / 25 100 / 75 / 54 

10/7/2006 Ship 

Channel 

71 / 50 / 29 83 / 42 / 25 67 / 33 / 17 

10/7/2006 Southeast 

Core 

88 / 67 / 42 96 / 50 / 33 96 / 54 / 29 

10/7/2006 Northwest 

Core 

92 / 71 / 21 83 / 71 / 38 58 / 21 / 13 

10/7/2006 Downwind 67 / 50 / 33 79 / 38 / 33 42 / 17 / 13 

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Figure 2.10-7: Region 12 Average wind direction and speed scatter plots for October 7, 2006 
Figure 2.10-8: Time-height plot for La Porte profiler on October 7, 2006

The 12-hour forward trajectories starting at 6am CST using the MM5 data (blue) compared well 

with the ones using the observed data (red), as shown in Figure 2.10-9: 12-hour forward 

trajectory at 06:00 CST on October 7, 2006. The trajectories travel to the southwest of Houston 

where the maximum 8-hour ozone values were observed. 

Figure 2.10-9: 12-hour forward trajectory at 06:00 CST on October 7, 2006 

October 11, 2006 

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On that day, 3 monitors recorded an exceedance of the eight-hour ozone standard greater than 85 

ppb. The 1850 Hawkins Camp Road C96/C638 monitor recorded the maximum eight-hour ozone 

of 90.4 ppb at 1400 CST. Regional background levels of ozone, in the air coming into the 
Houston area, ranged from about 42 to 53 ppb as indicated by the peak eight-hour averages at 

Katy Park CAMS 559, Northwest Harris County CAMS 26, and Conroe Airport CAMS 78. 

During that day, the weather was foggy with overcast low-level clouds until mid morning, then 

skies becoming mostly clear by the afternoon. Scattered medium and high cirrus clouds moved 

over the area late in the day. Light northwesterly winds in the morning, becoming light and 

variable by the early afternoon. The high temperature was 85 degrees Fahrenheit at George Bush 

Houston Intercontinental Airport, 86 degrees at Hobby Airport, and 81 degrees at Galveston 

Airport. 

The MM5 model performed well for that day as can be seen in table 2.10-4: Error percentages 

for October 11, 2006 and figure 2.10-10: Region 12 Average wind direction and speed scatter 

plots for October 11, 2006. The performance of the wind direction using the 30 degree threshold, 

wind speed using the 2 m/s threshold and temperature using the 2 degrees threshold in the 

Houston-Galveston region were 83, 100, and 79 respectively. 

Table 2.10-4: Error percentages for October 11, 2006

Duration Geographic 

Area/Zone 

Wind Direction (°)

Error % 30 / 20 / 10 

Wind Speed (m/s)

Error % 2 / 1 / 0.5 

Temperature (°C)

Error % 2 / 1 / 0.5 

10/11/2006 Region 12 83 / 58/ 17 100 / 96 / 75 79 / 63 / 25 

10/11/2006 Coastal 54 / 33 / 17 100 / 88 / 54 75 / 33 / 08 

10/11/2006 Ship 

Channel 

79 / 46 / 25 100 / 83 / 46 75 / 46 / 29 

10/11/2006 Southeast 

Core 

83 / 79 / 42 100 / 83 / 38 88 / 58 / 25 

10/11/2006 Northwest 

Core 

63 / 38 / 13 100/ 79 / 63 75 / 46 / 25 

10/11/2006 Downwind 58 / 46 / 21 100 / 83 / 50 42 / 21 / 08 

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Figure 2.10-10: Region 12 Average wind direction and speed scatter plots for October 11, 

2006 

The 12-hour forward trajectories starting at 6 am on October 11, 2006 as depicted in figure 2.10-

11: 12-hour forward trajectory at 06:00 CST on October 11, 2006, show the trajectories using the 

MM5 data traveling to near the location of maximum 8-hr ozone in the southwestern part of 

Houston close to the Bay, but somewhat to the east of the same trajectories generated using the 

CAMS observations (in red). 
Figure 2.10-11: 12-hour forward trajectory at 06:00 CST on October 11, 2006 

On the previous day (October 10, 2006), a cold front passed over Houston in the middle of the 

day bringing numerous showers and strong northwesterly winds. From Figure 2.10-12: Region 

12 average wind direction time series for October 10, 2006, and Figure 2.10-13: Region 12 

average wind speed time series for October 10, 2006, one sees a shift in wind direction from 

southwest to northwest and an increase in wind speed around 12 p.m. 

Figure 2.10-12: Region 12 average wind direction time series for October 10, 2006 

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Figure 2.10-13: Region 12 average wind speed time series for October 10, 2006 

Figure 2.10-14: Region 12 average humidity and temperature for October 10, 2006, shows a 

sharp drop in humidity and temperature around 12 CST when the cold front passed. The La Porte 

profiler shows that on that day, between 12 and 14 CST there was a shift in the wind direction 

from southwesterly to northwesterly from the surface to at least 3km above the ground, as shown 

in Figure 2.10-15: Time-height plot for La Porte profiler on October 10, 2006. Note that the 

maximum 8-hour ozone on that day was only 49 ppb.

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Figure 2.10-14: Region 12 average humidity and temperature for October 10, 2006 

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06 

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11 

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~~ --~~~~~~}g t;~-- ==).~~ ~~fJ f.7 -- ~~ l 

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o'r-- --.-~~-.-- --.---~-.--~--.---~-.--~--.---~-.---r--,---~-.--~---,JI 

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e 

6 

01 

OCT 02 OCT 03 OCT 04 

OCT OS 

OCT oe OCT 

LST 

07 OCT oa OCT 09 OCT 1 0 OCT 11 

OCT 1 2 OCT 
Figure 2.10-15: Time-height plot for La Porte profiler on October 10, 2006

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A-80

2.11 References 

Banta, R. M., C. J. Senff, J Nielsen-Gammon, L. S. Darby, T. B. Ryerson, R. J. Alvarez, S. P. 

Sandberg, E. J. Williams and M. Trainer (2005), A bad air day in Houston, Bulletin of the 

American Meteorological Society, 86(5), 657+, 10.1175/BAMS-86-5-657

Berkowitz, C.M., Spicer, C.W., Doskey, P.V., 2005. Hydrocarbon observations and ozone 

production rates in western Houston during the Texas 2000 air quality study. Atmospheric 

Environment 39 (19), 3383–3396. 

Chen, F., and J. Dudhia (2001), Coupling an advanced land-surface hydrology model with the 

Penn State - NCAR MM5 modeling system. Part I: Model implementation and sensitivity, Mon. 

Wea. Rev., 129, 569–585. 

Emery, C., E. Tai, and G. Yarwood (2001), Enhanced Meteorological Modeling and Performance 

Evaluation for Two Texas Ozone Episodes, Final Report to the Texas Natural Resource 

Conservation Commission under TNRCC Umbrella Contract No. 582-0-31984, Environ 

International Corporation, Novato, CA. 

Environ (2008), Description from MM5CAMx README file contained in 

mm5camx.21feb08.tar.gz archive, http://www.camx.com/files/mm5camx.21feb08.tar.gz, Environ 

Holdings, Inc., Last accessed May 5, 2009. 

EPA (2007), Guidance on the Use of Models and Other Analyses for Demonstrating Attainment 

of Air Quality Goals for Ozone, PM2.5, and Regional Haze, 

http://www.epa.gov/scram001/guidance/guide/final-03-pm-rh-guidance.pdf, EPA-454/B-07-002, 

162, Last accessed March 26, 2009. 

Grell, G., J. Dudhia, and D. Stauffer (1994), A description of the Fifth Generation Penn 

State/NCAR Mesoscale Model (MM5), Tech. Rep. NCAR/TN-398+STR, National Center of 

Atmospheric Research (NCAR) Tech Note. 

Janjic, Z. I. (1994), The step-mountain Eta coordinate model: Further developments of the 

convection, viscous sublayer, and turbulence closure schemes, Mon. Wea. Rev., 122, 927–945. 

Knoderer, C. A., S. A. Bratek, C. P. MacDonald (2008), Spatial and Temporal Characteristics of 

Winds and Mixing During TexAQS-II, Final Report to Texas A&M University for Texas 

Commission on Environmental Quality Proposal under Grant Activity 582-5-64593-FY08-20, 

Sonoma Technology Inc, Petaluma, CA, April 2008. 

NCAR (2005a), Appendix C: How to Use Noah Land-Surface Model Option, PSU/NCAR 

Mesoscale Modeling System Tutorial Class Notes and User’s Guide, 

http://www.mmm.ucar.edu/mm5/documents/MM5_tut_Web_notes/App-C/append-c.htm, Last 

accessed May 5, 2009. 

NCAR (2005b), MM5 Online Tutorial Home Pages, http://www.mmm.ucar.edu/mm5/On-LineTutorial/teachyourself.html, Last accessed May 5, 2009. 

NCEP, (2009), GCIP NCEP Eta model output, CISL RDA: ds609.2 Home Page, 

http://dss.ucar.edu/datasets/ds609.2/, Last accessed May 13, 2009. 

Nielsen-Gammon (2007a), Preliminary Analysis of Ozone Meteorology during TexAQS-II 

Ozone Episodes, Interim Report to the Texas Commission on Environmental Quality under 

Proposal for Grant Activity 582-5-64593-FY07-21. 
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Nielsen-Gammon (2007b), Comprehensive meteorological analysis of TexAQS II episodes, Task 

3 Deliverable for Texas Commission on Environmental Quality Proposal for Grant Activity 582-

5-64593-FY07-21. 

Stauffer, D. R., and N. L. Seaman (1990), Use of four-dimensional data assimilation in a limited 

area mesoscale model. Part I: Experiments with synoptic-scale data, Mon. Wea. Rev., 118, 1250–

1277. 

Stauffer, D. R., and N. L. Seaman (1994), Multiscale four-dimensional data assimilation, J. Appl. 

Meteor., 33, 416–434. 

Stauffer, D. R., N. L. Seaman, and F. S. Binkowski (1991), Use of four-dimensional data 

assimilation in a limited area mesoscale model. Part II: Effects of data assimilation within the 

planetary boundary layer, Mon. Wea. Rev., 119, 734–754. 

TCEQ (2005), 2005 Air Pollution Events, 

http://www.tceq.state.tx.us/compliance/monitoring/air/monops/sigevents05.html, Texas 

Commission on Environmental Quality (TCEQ), Last accessed May 21, 2009. 

TCEQ (2006), 2006 Air Pollution Events, 

http://www.tceq.state.tx.us/compliance/monitoring/air/monops/sigevents06.html, Texas 

Commission on Environmental Quality (TCEQ), Last accessed May 21, 2009. 

TCEQ (2009), Meteorological modeling performance evaluation products, Texas Commission on 

Environmental Quality (TCEQ) file transfer protocol (FTP) site, 

(ftp://ftp.tceq.state.tx.us/pub/OEPAA/TAD/Modeling/HGB8H2/mm5). 
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ATTACHMENT 1 

Sea Surface Temperature (SST) Characteristics 

Houston-Galveston-Brazoria Attainment Demonstration SIP Revision 
2.12.1 Sea Surface Temperature (SST) Characteristics 

2.12.1.1 Introduction

The temperature gradient between land and water bodies is a vital characteristic for a 

meteorological model to adequately simulate land and sea (lake) breezes. The land/sea breeze 

phenomenon has been shown to re-circulate pollutants in the Houston area, thus exacerbating its 

air quality issues (Banta et al., 2005; Darby, 2004). To improve the representation of the land/sea 

breeze in MM5, the inputs for land characteristics and sea surface temperatures (SST) were 

updated with LandSat and Geostationary Operational Environmental Satellite (GOES) based data. 

The development and use of the updated SST inputs will be discussed below while the satellitebased land use is detailed in Attachment 2: Land Use and Land Cover (LULC) Characterisrtics. 

2.12.1.2 SST Development 

SST is a constant field in MM5, meaning it is not updated or calculated by the model. It can be a 

single value per model grid square throughout the model run or it can be time-varied. Most 

analysis products used to initialize MM5, such as the National Center for Environmental 

Prediction’s (NCEP) North American Mesoscale Model (NAM) output, do not contain actual sea 

surface temperatures. In those cases the MM5 preprocessor REGRID uses skin temperature as a 

substitute. The MM5 community has determined using skin temperatures can cause unrealistic 

temperature features at coastlines, an unwanted result for the Houston-Galveston area (NCAR, 

2005). 

To overcome the coastline misrepresentations, the model can be supplied with a separate source 

of real SST data. NCEP produces a daily, high-resolution (~10 km), global SST analysis that can 

be directly input into REGRID with a modified Vtable (NCAR, 2005). The NCEP SST product 

is based on Advanced Very High Resolution Radiometer (AVHRR) satellite, ship, and buoy data 

(NCEP, 2009). The AVHRR sensor is onboard the Polar Operational Environmental Satellites 

(POES), which orbit the Earth 14.1 times per day. The satellite ground tracks do not repeat every 

day so there can be gaps in coverage. The NCEP SST data was used in the initial development of 

the 2005 and 2006 meteorological modeling and was found to yield higher resolution temperature 

gradients and improved model performance (Figure 2.12.1.2-1: Variation of NCEP SST product 

(degrees Kelvin) in MM5 along the coast of Texas and Louisiana over six hours on June 21, 

2005). 

Figure 2.12.1.2-1: Variation of NCEP SST product (degrees Kelvin) in MM5 along the coast 

of Texas and Louisiana over six hours on June 21, 2005 

The land/sea breeze coastal wind direction veers throughout the day from changing temperature 

and pressure gradients between the Gulf of Mexico and the land surface. Galveston Bay can also 

induce a bay breeze, affecting areas along its coast. Since the Bay is a shallow body of water, it 

can have stronger diurnal temperature variations than the Gulf of Mexico. A higher resolution 

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SST dataset was sought to better represent these dynamic processes where the NCEP product may 

have difficulty. 

Geostationary orbiting satellites stay fixed over the equator (i.e. synchronized with the motion of 

the Earth), thus constantly providing sensor measurements and essentially eliminating orbital data 

gaps. The GOES retrievals provide hourly 6km resolution datasets that are able to capture the 

higher resolution SST processes. The GOES data does have limitations due to missing data from 

clouds, interference, sun glint, and satellite downtime. This data was used in the final 

meteorological modeling of the 2005 and 2006 episodes. 

2.12.1.3 Satellite Data Adaptation Methodology

Because the GOES data is at a 6km resolution, the data had to be aggregated for the 12, 36, and 

108 km domains. It had to be spatially interpolated to provide data for every 4 km grid square. 

Missing data had to be filled for all domains. Two methods were used to accommodate the 

GOES SST data to the MM5 modeling domains, one internally at TCEQ and one contracted to 

the University of Houston. 

The processing of GOES data for MM5 input was first started by TCEQ’s Data Analysis Team 

(Mercado and Harper, 2007). To aggregate the satellite data to the coarse grids, TCEQ used a 

python-ArcGIS tool combination. Missing data was imputed by temporally interpolating with a 

SAS® Loess routine (Cleveland et al., 1988) as shown by the magenta dots in Figure 2.12.1.3-1: 

Example of TCEQ process for imputing missing GOES data by Loess interpolation and episodic 

medians for a single grid cell and day. If missing data still remained, possibly due to continuous 

hours of cloudiness, the grid cell was filled with the cell’s episode median value (blue stars) for 

that hour. The overall profile of the grid cell’s SST follows the black line as a combination of 

actual, interpolated, and episodic median values. 

Figure 2.12.1.3-1: Example of TCEQ process for imputing missing GOES data by Loess 

interpolation and episodic medians for a single grid cell and day

To accommodate the 6 km GOES data to the MM5 4 km domain, the same temporal interpolation 

as the coarse grids was applied but missing data still existed due to the 6 km resolution of the 

original GOES data. To overcome the spatial gaps, TCEQ applied a kriging algorithm with a 

land mask to produce more reliable estimates near coastal areas. Figure 2.12.1.3-2: Final TCEQ 

4km SST Example for August 3, 2005 at 12 UTC for the northwest Gulf of Mexico. Warmer 

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temperature are red, cooler are green, the land is brown shows an example for one hour of the 

final TCEQ SST product in the northwestern Gulf of Mexico. 
Figure 2.12.1.3-2: Final TCEQ 4km SST Example for August 3, 2005 at 12 UTC for the 

northwest Gulf of Mexico; Warmer temperature are red, cooler are green, the land is 

versity of Houston (UH) was contracted to create the tools necessary to translate the 

OES SST data to a format that could be easily input to MM5. In the process of creating those 

surface temperatures, UH fitted a sine 

rve to each grid cell’s mean monthly SST. The SST was assumed to be a function of location 

brown. 

The Uni

G

tools, UH employed a different methodology to adapt the native GOES data to the MM5 domains 

(Byun et al., 2007, Byun et al., 2008). UH used the IDL® programming language for all 

processing of the GOES data, including data aggregation. 

 

Based on the normal diurnal temperature variation of sea 

cu

(such as Galveston Bay or the middle of the Gulf of Mexico) and time (hourly and seasonal) as 

depicted in Figure 2.12.1.3-4: UH SST Function (from Byun et al., 2007). 

Figure 2.12.1.3-4: UH SST Function (from Byun et al., 2007)

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Missing hourly SST values were filled with the mean monthly SST for that hour from the sine 

curve estimate. Figure 2.12.1.3-5: Missing SST values (white cells) in the left plot are filled with 

estimates from UH’s SST function to create the right plot shows the results of applying the UH 

SST Function to estimate missing SST data. 

Figure 2.12.1.3-5: Missing SST values (white cells) in the left plot are filled with estimates 

from UH’s SST function to create the right plot 

When the TCEQ and UH processing of the GOES SST were complete, graphical comparisons 

were made to determine if gross differences appeared. The two products showed similar features, 

such as warmer coastline temperatures, and as Figure 2.12.1.3-6: TCEQ SST (left plot) compared 

to the UH SST (right plot) for August 6, 2005 at 21 UTC exhibits the cross-hatch pattern of 

replaced missing data in the UH processing shows, the UH product exhibited a cross-hatch 

pattern that was not evident in the TCEQ product. 

Figure 2.12.1.3-6: TCEQ SST (left plot) compared to the UH SST (right plot) for August 6, 

2005 at 21 UTC exhibits the cross-hatch pattern of replaced missing data in the UH 

processing

Based on the cross-hatch pattern, UH reviewed their processing and found their methodology did 

not account for the missing data created when adapting the 6 km GOES data to the 4 km MM5 

domain. Thus, the cross-hatch was a pattern of replaced missing data from the monthly mean 

estimate. To overcome this problem, UH doubled the resolution of the GOES dataset via 

horizontal interpolation for domains finer than 6 km (Byun et al., 2008) (Figure 2.12.1.3-7: The 

final UH SST product does not have the cross-hatch issues (August 6, 2005 at 21 UTC is shown)). 
Any cells missing SST data after the interpolation were filled with the monthly mean diurnal 

value. 

Figure 2.12.1.3-7: The final UH SST product does not have the cross-hatch issues (August 6, 

2005 at 21 UTC is shown)

The processing methodologies were also reviewed to determine which would be more efficient. 

To create a modeling episode’s hourly SST for each grid cell and MM5 domain, the TCEQ 

processing took approximately two working days. By contrast, the UH processing using IDL® 

created the final products within a few hours. Because the final products were similar in terms of 

SST representation, the UH methodology was chosen for its superior efficiency. 

2.12.1.4 Inputting GOES SST into MM5

 input datasets. During an MM5 run, the SST data (MM5 variable 

SEASFC) are read from the LOWBDY input files. The SST data is also included in the 

omparisons of modeled surface wind and temperature performance were made during the 

oducts. If similar model performance was observed 

To compliment the IDL® processing of the raw GOES SST data, UH developed IDL® code to 

replace SST values in MM5

T

MMINPUT input files. The UH IDL® code reads the time headers from the MM5 input files and 

replaces the TSEASFC values with the corresponding GOES SST data. All other variables are 

untouched and MM5 can be run as normal. 

2.12.1.5 MM5 Performance

C

evolution of the sea surface temperature pr

when using a higher resolution product, the higher resolution product was chosen. Table 2.12.15-

1: Table of error percentages from 4km runs for the 2005ep2 episode with different sea surface 

temperature products details the percentage of date-time points where the 4km MM5 wind and 

temperature error was less than the defined metric. On average the performance between the 

different SST types was similar, thus the UH GOES SST product was chosen for the final model 

configuration. 

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S Area/Zone Error ≤ 30 / 20 / 10 Error ≤ 2 / 1 / 0.5 Error ≤ 2 / 1 / 0.5 

Table 2.12.1.5-1 – Table of error percentages from 4km runs for the 2005ep2 episode with 

different sea surface temperature products

ST Type 

Geographic Wind Direction (°) Wind Speed (m/s) Temperature (°C)

NCEP NAM Skin 

Temp. 

Region 12 Not Available 98 / 75 / 44 83 / 55 / 28 

NCEP AVHRR SST Region 12 68 / 58 / 37 99 / 74 / 41 85 / 55 / 32 

TCEQ GOES SST Region 12 66 / 57 / 35 98 / 75 / 43 87 / 54 / 33 

UH GOES SST Region 12 66 / 53 / 35 99 / 76 / 43 87 / 55 / 28 
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Banta, R. M., C. J. S yer m .

 and M. Tr , A r

 H. K gan (200 d Mode and 

e re, F to the T ission on Environmental 

Ac 2-05-64594-FY07-03. 

yun, D. W., F. Ngan, F. Cheng, H. Kim and S. Kim (2008), Improvement of MM5 Surface 

haracteristics, Final Report to the Texas Commission on Environmental Quality for Grant 

Activity No. 582-05-64954-FY08-04. 

Cleveland, W. S., S. Devlin, E. Grosse (1988), Regression by Local Fitting, Journal of 

Econometrics, 37, 87--114. 

Darby, L.S., 2005: Cluster Analysis of Surface Winds in Houston, Texas, and the Impact of Wind 

Patterns on Ozone. J. Appl. Meteor., 44, 1788–1806. 

ITT Visual Information Systems (2009), IDL – Data Visualization Solutions web site, 

http://www.ittvis.com/ProductServices/IDL.aspx

2.12.1.6 References 

enff, T. B. R

ainer (2005)

son, J. Nielsen-Gam

bad air day in Housto

on, L. S. Darby, R

n, Bull. Am. Meteo

 J. Alvarez, S. P. 

Sandberg, ol. Soc., 86, 657–

669. 

Byun, D. W., S. Kim

ac

, F. Cheng, im and F. N 7), Improve ling Inputs: L

Use and Sea-Surf

nt 

 Temperatu inal Report exas Comm

Quality for Gra tivity No. 58

B

C

, Last accessed March 25, 2009. 

Mercado, F., C. Harper (2007), Description of GOES Data Processing Procedure, TCEQ Data 

Analysis Team Internal Draft Report. 

NCAR (2005), Chapter 5: REGRID, PSU/NCAR Mesoscale Modeling System Tutorial Class 

Notes and User’s Guide, 

http://www.mmm.ucar.edu/mm5/documents/MM5_tut_Web_notes/REGRID/regrid.htm, Last 

accessed March 11, 2009. 

NCEP, (2009), National Center for Environmental Prediction RTG_SST_HR Experimental 

Analysis web site, http://polar.ncep.noaa.gov/sst/ophi/, Last accessed March 25, 2009. 
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Land Use and Land Cover (LULC) Characteristics 

Houston-Galveston-Brazoria Attainment Demonstration SIP Revision

ATTACHMENT 2 
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2.12.2 Land Use and Land Cover (LULC) Characteristics 

2.12.2.1 Introduction

All meteorological processes (wind flow, planetary boundary layer development, temperature 

inversions, cloud for ced by the planet’s 

land-surfac vegetation 

type, soil characteristic, and topographic features all play roles in the changing weather patterns. 

For the coastal environment, the land-water interaction is a dominating feature in the local 

meteorology and must be accurately simulated by the mesoscale model. For the 2005 and 2006 

TexAQS II modeling episodes, MM5’s LULC data was updated with satellite-based data in 

attempt to improve the exchange of heat, momentum, and moisture between the surface and 

atmosphere. 

2.12.2.2 Model Representation

In MM5 most terrestrial features are represented via the TERRAIN_DOMAINx files, which are 

created by the pre-processor TERRAIN. Note that one of the key surface variables not included in 

the TERRAIN files is sea surface temperature, which is in the LOWBDY files (see Attachment 1: 

Sea Surface Temperature (SST) Characteristics). The TERRAIN program interpolates elevation, 

vegetation fraction, land use categories, soil types, and other LULC information to the domain 

resolution and sets up the land-surface model (LSM). TERRAIN also adds a land/water mask to 

determine if a grid cell is 50% or more water (e.g. lake, bay, river, ocean). 

TERRAIN offers six resolutions of United States Geological Survey (USGS) Version 2 data for 

input: 1 degree; 30, 10, 5, 2 minute; 30 second [(USGS, 2008a). For MM5 Version 3.4 and later, 

the coarse USGS data was recreated using the high-resolution global 30-second data [(Wang, 

2000). Prior version’s coarse data originated from different sources and had errors. The USGS 

Version 2 dataset is based on 1992-1993 Advanced Very High Resolution Radiometer (AVHRR) 

satellite data. 

2.12.2.3 Land Use Category Updates 

From 1992 to 2005, significant changes in the land use can be expected as urban areas expand, 

vegetation grows (dies), and land types change. For the 2005 and 2006 TexAQS II episodes, the 

TERRAIN data was updated with Landsat satellite-based data from the 2001 National Land 

Cover Database (NLCD). The 2001 NLCD is a 30-meter resolution, multi-season collaboration 

of Landsat 5 and 7 satellite data, centered on 2001, and Digital Elevation Model data [(Homer et 

al., 2007; USGS, 2008b). The University of Texas Center for Space Research (UT-CSR) 

classified the 2001 NLCD with a focus on woody vegetation for biogenic modeling purposes 

[(Wells, 2006). This analysis focused on the coastal areas of Texas, Louisiana, and Mississippi as 

shown in Figure 2.12.2.3-1: UT-CSR/NLCD map [(Byun et al., 2007) (30m resolution). Note that 

one of the key surface variables not included in the TERRAIN files is sea surface temperature, 

which is in the LOWBDY files (see Attachment 1 Sea Surface Temperature (SST) 

Characteristics). 

mation, latent and sensible heat fluxes, etc) are influen

e characteristics. The land use and land cover (LULC) attributes such as 
Figure 2.12.2.3-1 : UT-CSR/NLCD map [(Byun et al., 2007) (30m resolution) 

On contract to TCEQ Byun et al. [(2007 and 2008) evaluated the UTCSR data for use in MM5, 

referred hereafter as the UTCSR data. Based on MM5 simulations with the UTCSR data and 

their previous experience with Texas land use data, Byun et al. [(2007) found the Houston urban 

area to be underrepresented in size with the UTCSR data. To improve the urban area 

classification, a Houston urban-focused Texas Forest Service (TFS) dataset was merged with the 

UTCSR data. The TFS data is based on similar Landsat 7 data from 1999-2001 and aerial 

photography [(GEM, 2003). After the urban area correction, the UTCSR 26 category data was 

classified based on the 24 category USGS data typically used in MM5. This allowed MM5 to be 

run without code and LANDUSE.TBL modification. 

The final land use dataset for the 2005 and 2006 modeling episodes was a combination of original 

USGS version 2 data, TFS urban classifications, and UTCSR data (Figure 2.12.2.3-2: Top: USGS 

12km LULC; Bottom: UTCSR 12km LULC). In the final dataset, ‘mixed dry/irrigated cropland 

nd pasture’ replaced much of the ‘dry cropland’ in south and east Texas. Wetland areas were 

lso more numerous along the Louisiana and Texas coastlines and river bottoms. 

a

a

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Figure 2.12.2.3-2: Top: USGS 12km LULC; Bottom: UTCSR 12km LULC; note the change 

from dry cropland (off-white) to mixed dry/irrigated cropland (yellow), the increase in wetlands

ications around 

ouston from the TFS data (adapted from Byun et al., 2007) 

he 2005 and 2006 modeling episodes are from late May – early October, thus the UTCSR 

e temperature 

redictions. 

along the southeast Texas and Louisiana coasts, and the increase in urban classif

H

2.12.2.4 Vegetation Fraction Updates 

Vegetation fraction is a key terrestrial feature that MM5 uses to determine the exchange of heat 

and moisture between the surface and atmosphere, especially for evapotranspiration. The 

vegetation fraction data was reconstructed from the 30m UTCSR category data and shading 

factors (Byun et al. 2008). The standard MM5 vegetation fraction data was sourced from 5 years 

of monthly AVHRR satellite data, which provided seasonal variations. Since the UTCSR data 

was collected only in the summer periods, the improved vegetation fraction data should only be 

used during modeling of summer-time months. 

T

vegetation fraction data should be representative. Figure 2.12.2.4-1: Top: Original USGS 

Vegetation Fraction; Bottom: UTCSR Vegetation Fraction exhibits the updated UTCSR 

vegetation fraction versus the standard MM5 dataset for the southeast area of Texas and 

Louisiana. One of the biggest improvements was the removal of an artificial zone of high 

vegetation fraction east of Galveston Bay that was reflected in MM5 surfac

p

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Figure 2.12.2.4-1: Top: Original USGS Vegetation Fraction; Bottom: UTCSR Vegetation 

raction; note the removal of the artificial zones of vegetation fraction along the coast (adapted 

ody was classified as dry clay, as it does not 

ansfer heat and moisture efficiently. In that case, the river bottoms were effectively turning into 

ble (SOILPARM.TBL) was modified by replacing the lava and white sand types, 

hich are not represented in the MM5 domains. Of the soil parameters, only the ‘BB’ value of 

“Clay 1” and “Clay 2” was changed compared to the “Clay” type. The ‘BB’ parameter is part of 

the equation that associates the soil’s water content and the soil’s water potential [(Chen and 

Dudhia, 2001). Figure 2.12.2.5-1: Top: USGS Soil Types; Bottom: UTCSR Soil Types details the 

changes from adding the additional clay types.

F

from Byun et al., 2008) 

2.12.2.5 Soil Type Updates 

The gridded soil data used by the NOAH Land-Surface model is provided by NCEP’s North 

American Model (NAM, formerly Eta). The NAM resolution is approximately 32 km, too coarse 

to accurately resolve river and lake basins. Byun et al. (2008) found that around these inland 

water bodies, MM5 was simulating surface temperatures with a high bias. This problem was 

exacerbated when the soil type near the water b

tr

‘brick rivers.’ Other sources of soil data were researched but found to be similar to the NAM 

product. 

Byun et al. (2008) introduced two new clay soil types to account for the moisture content of the 

soil near water bodies. “Clay 1” represents clay with water content between one and 50%. “Clay 

2” represents clay with water content from 50 to 90%. To represent these new soil types, the soil 

parameter ta

w

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Figure 2.12.2.5-1: Top: USGS Soil Types; Bottom: UTCSR Soil Types; note the addition of 

clay1 and clay2 soil types along the rivers and coastal areas (adapted from Byun et al., 2008) 

 where UTCSR land/water mask differed from original USGS land/water mask (adapted 

2.12.2.6 Land/Water Mask Update 

A land/water mask is created by the TERRAIN preprocessor and passed to the MM5 input files. 

The land-surface model uses different surface-atmosphere exchange algorithms according to the 

land/water mask. In Houston area, many emission sources are located along the coastal interface 

and it is vital that those areas are represented as accurately as possible. When updating to the 

UTCSR data, Byun et al. (2008) discovered that some coastal grid cells were incongruent to the 

original USGS data in terms of being land or water. To overcome the land use discrepancy, the 

land/water mask was recreated using the UTCSR data (Figure 2.12.2.6-1: Black points represent 

4km cells

from Byun et al., 2008)). 

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Figure 2.12.2.6-1: Black points represent 4km cells where UTCSR land/water mask differed 

from original USGS land/water mask (adapted from Byun et al., 2008) 

2.12.2.7 Inputting UTCSR Data into MM5 TERRAIN Files

Byun et al. (2007, 2008) created Fortran programs for TCEQ to update the land use, soil, 

vegetation, and land/water mask information in MM5. The programs convert the UTCSR data to 

the MM5 domain projection and aggregate it to the domain resolution. Once the data is 

appropriately configured, a separate Fortran program replaces the USGS data in the MM5 

TERRAIN file with the corresponding UTCSR data. All other variables are untouched and MM5 

can be run as normal. 

tent 

photochemical models. While the models categorize the 

ost similar options. 

Table 2.12.2.9-1: Percent accuracy statistics for intermediate and final UTCSR LULC MM5 runs 

for all meteorological modeling episodes. compares the average performance of MM5 with the 

intermediate UTCSR LULC and with the final UTCSR LULC category updates. This version of 

the UTCSR data contains the soil type additions, land/water mask update, and TFS urban area 

correction. The table lists the percentage of hours where the model was within the benchmarks 

for wind direction, wind speed, and temperature for the all of the TexAQS II meteorological 

episodes. 

2.12.2.8 Model LULC Consistency

In previous TCEQ modeling efforts, the models used separate sources of LULC data. By 

replacing the USGS data with the UTCSR data in MM5, the underlying LULC data is consis

between the meteorology, biogenic and 

LULC data uniquely, the year and source of data are not a basis of model discrepancy. 

2.12.2.9 Model Performance

As the meteorological modeling of the TexAQS II episodes was developed, many MM5 

simulations were completed to test model configurations. Because of the timing of contract 

deliverables and TCEQ state implementation plan (SIP) timelines, model runs without the 

updated LULC, but with all other final options, were not completed. Not all episodes were able 

to be run with the same configurations over the development of the SIP modeling. However, 

comparisons of the intermediate and final LULC model runs were made for all episodes by 

choosing model runs with the m

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Table 2.12.2.9-1: Percent accuracy statistics for intermediate and final UTCSR LULC MM5 

runs for all meteorological modeling episodes; Note that UTCSR Final runs may not 

correspond to final SIP configuration. 

LULC Type Episode Zone 

Wind Dir. 

 (°) 

Error ≤ 

30 / 20 / 10 

Wind Speed 

(m/s) 

Error ≤ 

2 / 1 / 0.5 m/s 

Temp. (°C) 

Error ≤ 2 / 1 / 0.5 

UTCSR 

Intermediate 2005ep0 

Region 

12 71 / 61 / 41 96 / 70 / 35 67 / 35 / 21 

UTCSR Final 2005ep0 

Region 

12 76 / 67 / 49 98 / 73 / 40 78 / 49 / 30 

UTCSR 

Intermediate 2005ep1 

Region 

12 82 / 69 / 45 100 / 90 / 60 68 / 42 / 18 

UTCSR Final 2005ep1 

Region 

12 82 / 71 / 40 100 / 93 / 60 74 / 50 / 28 

UTCSR 

Intermediate 2005ep2 

Region 

12 65 / 52 / 32 97 / 75 / 44 73 / 44 / 24 

UTCSR Final 2005ep2 

Region 

12 67 / 54 / 33 98 / 75 / 40 78 / 57 / 32 

UTCSR 

Intermediate 2006ep0 

Region 

12 75 / 62 / 41 97 / 73 / 40 78 / 47 / 23 

UTCSR Final 2006ep0 

Region 

12 76 / 63 / 45 97 / 72 / 38 85 / 57 / 33 

UTCSR 

Intermediate 2006ep1a 

Region 

12 76 / 65 / 43 100 / 86 / 45 76 / 54 / 31 

UTCSR Final 200

Region 80 / 67 / 43 100 / 83 / 43 83 / 61 / 39 6ep1a 12 

UTCSR 

Intermediate 2006ep1b 

Region 

12 67 / 55 / 32 95 / 54 / 19 85 / 58 / 33 

UTCSR Final 2006ep1b 

Region 

12 69 / 53 / 31 96 / 56 / 22 82 / 54 / 29 

UTCSR 

Intermediate 2006ep1c 

Region 

12 87 / 76 / 53 98 / 59 / 31 61 / 32 / 15 

UTCSR Final 2006ep1c 12 

Region 84 / 76 / 57 96 / 57 / 29 69 / 33 / 16 

UTCSR 

Intermediate 2006ep1d 

Region 

12 82 / 65 / 34 98 / 74 / 37 78 / 47 / 24 

UTCSR Final 2006ep1d 

Region 

12 85 / 66 / 37 98 / 77 / 43 77 / 53 / 34 

As the tabulated statistics show, the impact of the final UTCSR LULC data was not drastic for 

winds or temperature. In many cases the final UTCSR data provided small error benefits, 

especially for temperature. For all episodes the final UTCSR data was used in the SIP modeling 

configuration. 
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nal Report to the Texas Commission on Environmental 

Quality for Grant Activity No. 582-05-64594-FY07-03. 

Byun, D. W., F. Ngan, F. Cheng, H. Kim im (2 ent of MM5 Surface 

C , Fi rt Texa n o al t 

A o. 582-05-64954-FY

Chen, F., and J. Dudhia (2001 ing an advanced land-surf drology the 

P A odeling system el on an , Mon. 

Wea. Rev., 129, 569–585. 

GEM (2003), Satellite data processing for land use and cover type classification, Final report to 

t S Pro -3-0

H ew , , N. La old, , J.N. 

V . W 00 pletion of the 2001 National Land Cover Database for the 

2.12.2.10 References 

Byun, D. W., S. Kim, F. Cheng, H. Kim and F. Ngan (2007), Improved Modeling Inputs: Land 

Use and Sea-Surface Temperature, Fi

 and S. K 008), Improvem

haracteristics nal Repo to the s Commissio n Environment Quality for Gran

ctivity N 08-04. 

), Coupl ace hy

implementati

 model with

enn State - NC R MM5 m . Part I: Mod d sensitivity

he Texas Forest ervice for ject TFS 27. 

omer, C., J. D

anDriel and J

itz, J. Fry

ickham. (2

M. Coan Hossain, C. rson, N. Her A. McKerrow

7). Com

Conterminous Uni P met ted States, hotogram ric Engineering and Remote Sensing, Vol. 73, No. 4, 

pp 3 1. 

USGS (2008), Global Land Cover Characteristics Data Base Version 2.0, 

h go bd p, L Ma . 

USGS (2008), National at (NLCD 2001), 

http://www.mrlc.gov/nlcd.php

37-34

ttp://edc2.usgs. v/glcc/glo oc2_0.ph ast accessed rch 23, 2009

 Land Cover D abase 2001

, Last accessed Mar

W 0) p on MM5 on 3, 

http://www.mmm.ucar.edu/mm op/ er.

ch 23, 2009. 

ang, W. (200 , Status Re ort Pre- and Post-processing Programs in Versi

5/worksh ws00/mesous pdf, Last accessed 09. 

Wells, G., (2006): The New Ea xas nd ificat ARC 

P n 4 8-2004- ity ustin Space 

R ustin, Texas. 

March 23, 20

stern Te

6-T2

Land Use La

T2, Univers

 Cover Class

of Texas-A

ion Project, H

roject Contact umber: H- Center for 

esearch, A
A-99

ATTACHMENT 3 

emonstration SIP Revision

Data Assimilation 

Houston-Galveston-Brazoria Attainment D
A-100

2.12.3 Data Assimilation 

2.12.3.1 Introduction

Data assim nalyses to 

produce a result closer to reality. In MM5, observed parameters are assimilated during the model 

run through a process called Four-Dimensional Data Assimilation (FDDA), or nudging, by 

adding an additional term to the equations of motion (Stauffer and Seaman, 1990; Stauffer et al., 

1991; Stauffer and Seaman, 1994). This process helps overcome errors in initialization data and 

limitations in the model’s physics that can accumulate over time. 

A MM5 model prediction can be analysis nudged to a gridded field for winds, temperature, and 

water vapor. MM5 can also be observationally nudged to high resolution meteorological data 

from sources such as surface stations, radar/sodar profilers, rawinsondes, aircraft measurements, 

etc. TCEQ has successfully used analysis and observational nudging in past SIP modeling efforts 

and others have demonstrated the benefit of nudging on model performance (ATMET, 2003; 

Byun et al., 2008; Jia and Morris, 2003; Nielsen-Gammon, 2002; Nielsen-Gammon et al., 2007; 

Otte, 2008a; Otte, 2008b). EPA recommends using FDDA to make sure the model does not 

significantly diverge from reality (EPA, 2007). Both types of data assimilation were performed 

for the meteorological modeling of the 2005 and 2006 episodes and are described herein. 

2.12.3.2 Analysis Nudging 

Analysis nudging is a process by which the model forecast is ‘relaxed’ towards a gridded field, 

which could be three-dimensional and/or surface data of winds, temperature, and water vapor. In 

MM5, analysis nudging is typically run by copying the model’s input file 

MMINPUT_DOMAINx to MMINPUT2_DOMAINx for the coarse grids only. The operational 

National Center for Environmental Prediction (NCEP) North American Mesoscale Model (NAM) 

output was used for input and analysis nudging in MM5 for the 2005 and 2006 episodes. EPA 

recommends nudging winds throughout the vertical atmosphere, temperature and moisture above 

the boundary layer only. That setup has been adopted for this modeling application. MM5 

default nudging strengths and radii of influence were used. Surface analysis nudging was not 

used. 

2.12.3.3 Observational Nudging 

Unlike analysis nudging that uses a gridded field of data, observational nudging incorporates 

individual measurements at specific locations (three-dimensional) and times into the model 

forecast. This type of nudging is usually conducted on the finest scale grid, which allows local 

measurements to influence the large-scale meteorological phenomena from the coarse domains. 

Examples of typically used sources for observational nudging include radar profilers, 

rawinsondes, surface stations, pilot balloons, satellite soundings, and aircraft. For this 

application, radar and sodar profiler wind data was chosen as the source of observational nudging. 

The 2005ep0 episode (May 18 – June 5, 2005) lacked sufficient profiler data as only data from 

Cleburne and La Porte were available. This episode was supplemented with surface observations 

from the hourly National Center for Atmospheric Research’s ds472.0 dataset (CISL, 2009). 

Observational nudging was limited to wind speed and direction on the 4km domain only. 

As part of the second Texas Air Quality Study (TexAQS II), a network of radar and sodar wind 

profilers was deployed (Figure 2.12.3.3-1: TexAQS II Profiler Locations). Sonoma Technology 

Inc. (STI) collected and quality assured the upper air wind data for use in data analysis and 

modeling projects by removing migrating birds and other artifacts. More information regarding 

STI’s quality assurance techniques can be found in their report (Knoderer and MacDonald, 2007). 

The quality assurance of the ds472.0 surface data was limited to gross error analysis, such as 

wind speed greater than 100 m/s or less than 0 m/s. 

ilation is a process by which observations are blended with modeled a
 

Figure 2.12.3.3-1: TexAQS II Profiler Locations 

The quality assured upper air wind data has a vertical resolution of 60-100 meters (m) from 40 m 

above ground level (agl) to over 10,000 m agl, depending on the type and siting of the profiler. 

MM5’s observational nudging requires the data be listed according to sigma layer at a frequency 

up to a tenth of an hour (NCAR, 2005). The TexAQS II upper air wind data was averaged within 

the MM5 sigma layers according to the vertical distance from the middle of the sigma layer (1/r 

weighting). The frequency of the profiler observations was generally hourly but some recorded 

nterval. The observations were time-shifted ahead to account 

though 

ensitivity tests were performed to test alternative configurations as detailed below. Nielsen5.deck). EPA (2007) recommends Analysis 

udging coefficients not greater than 1.0 x 10-4 for winds and 1.0 x 10-5 for moisture and 

Observational Nudging coefficients that do not cause inconsistency over the modeling domain. 

The final configuration for the 2005 and 2006 episodes used the MM5 default nudging strengths, 

data on a fifteen or thirty minute i

for the overall period of the measurement rather than the start time (Nielsen-Gammon, 2008) and 

then rounded to the tenth of the hour. The ds472.0 data was referenced to MM5 layer 43 

(surface) and rounded to the nearest tenth of the hour. 

The latitude/longitude coordinates of the profilers and ds472.0 stations were projected onto the 

Lambert Conformal MM5 4km grid, and the wind observations were rotated using a version of a 

Perl script posted to the MM5-Users listserv (Petre, 2007). TCEQ staff modified the script to 

calculate grid indices for any of the nested MM5 grids. Thus, observational nudging files were 

created for the 4 km grid and the 12 km grid for some tests. Finally, the Perl script formatted the 

Fortran binary file required for MM5 input. 

Default nudging strengths and radii of influence were used for the observational nudging, 

s

Gammon (2008b) reviewed the setup for observational nudging and found the configuration to 

perform better than alternatives. 

2.12.3.4 Configuration Summary 

FDDA in MM5 allows the user to adjust how strong the model will be nudged (relaxed) towards 

the observation or analysis field. The MM5 community has developed default nudging strength 

coefficients that appear in the mm5 run script (mm

N

A-101
A-102

with only the Analysis Nudging wind coefficient (GV=2.5 x 10-4) deviating from EPA’s 

recommended plan. The choice for the Analysis Nudging wind coefficient was based on the 

MM5 default value and previous TCEQ and contractor modeling experience. Nielsen-Gammon 

(2008b) reviewed the modeling setup for the TexAQS II episodes and determined the 

configuration to be superior to alternatives. Table 2.12.3.4-1: MM5 Nudging Parameter Values

summarizes the parameter values used.

Table 2.12.3.4-1: MM5 Nudging Parameter Values 

Nudging Coefficients (s-1) 

Radius of 

Influence 

Domain Nudging Type Winds Temp. Moisture Horiz. Vert. 

Data 

Source 

108 km Analysis 3-D 2.5E-4 2.5E-4 1.0E-5 - NCEP - NAM 

36 km Analysis 3-D 1.0E-4 1.0E-4 1.0E-5 - - NCEP 

NAM 

12 km Analysis 3-D 1.0E-4 1.0E-4 1.0E-5 - NCEP - NAM 

4 km Observational 4.0E-4 - - 240 km 0.001 

sigma 

TexAQS II 

Profiler 

2.12.3.5 Sensitivities

Alternate nudging configurations were performed to determine if other setups would provide 

results more suited to the Houston coastal area. The following sections detail those sensitivities. 

2.12.3.6 Observational Nudging on the 12km Domain 

While the 4km MM5 domain is quite extensive in size (184 by 166 4km grid cells, (see Chapter 

3: Photochemical Modeling), five TexAQS II profilers were not within its boundaries as shown in 

Figure 2.12.3.6.1: TexAQS II profilers and MM5 12km (blue) & 4km (red) domains. To 

determine if the course MM5 output could be improved in order to provide better 4km inputs, 

observational nudging was run on the 12km domain with all available quality assured TexAQS II 

profiler data. 

 

Figure 2.12.3.6-1: TexAQS II profilers and MM5 12km (blue) & 4km (red) domains 

By adding observational nudging to the 12km domain, the 12km MM5 performance was not 

significantly changed. Figure 2.12.3.6-2 – 12km Scatter plots: Texas Average Wind Speed 
Comparison shows average 12km wind speed scatter plots of all Texas ds472.0 surface 

monitoring sites versus the corresponding MM5 grid cells for every hour of the 2005ep2 episode 

(July 25 – August 9, 2005). The box in the upper right of each plot details the percentage of 

date-time points where the MM5 error was less than two, one, and 0.5 meters/second. The values 

are almost identical indicating that the 12km observational nudging did not have much impact on 

average wind speed. 

 

Figure 2.12.3.6-2: 12km Scatter plots – Texas Average Wind Speed Comparison (left plot 

e similar indicating that the observational nudging 

id not have a significant impact on the 12km domain.

Table 2.12.3.6-1: Table of error percentages from 12km runs with and without 

observational nudging for 2005ep2 episode 

Model Run Wind Dir. 

Error ≤ 30 ° 

Wind Dir. 

Error ≤ 20 ° 

Wind Spd. 

Error ≤ 2 m/s 

Wind Spd. 

Error ≤ 1 m/s 

Temp. 

Error 

≤ 2 °C 

Temp. 

Error 

≤ 1 °C 

shows standard 12km run, right plot shows 12km run w/ observational nudging) 

Table 2.12.3.6-1: Table of error percentages from 12km runs with and without observational 

nudging for 2005ep2 episode shows the error percentages for wind direction, wind speed, and 

temperature for the MM5 runs with and without 12km observational nudging when compared to 

all Texas ds472.0 sites. Again, the results ar

d

Standard 80 63 100 86 99 80 Run 

With 12km 

Nudging 80 64 100 87 98 79 

While the 12km domain did not show a significant average response, winds and temperature in 

the 4km domain exhibited more change. 4km model runs were evaluated at all TCEQ 

Continuous Air Monitoring Stations (CAMS), Houston area CAMS (TCEQ Region 12), and 

subsets of monitors called zones (Figure 2.12.3.6-3: All 4km TCEQ CAMS (left) and Region 12 

CAMS (right)). 

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A-104

 

Figure 2.12.3.6-3: All 4km TCEQ CAMS (left) and Region 12 CAMS (right) 

Figure 2.12.3.6-4: 4km wind speed scatter plots for Region 12 without (left) and with (right) 12km 

4km observational nudging for 2005ep2 episode shows the 4km performance of wind speed with 

and without observational nudging on the 12km domain for every hour of the 2005ep2 episode 

(July 25 – August 9, 2005). The two model runs perform similarly indicating that on average 

throughout the Houston area the 12km observational nudging did not have much of an impact. 

Figure 2.12.3.6-4: 4km wind speed scatter plots for Region 12 without (left) and with (right) 

12km observational nudging for 2005ep2 episode 
A-105

Because of the large number of CAMS monitors in the Houston region, an area wide average may 

smooth out smaller scale features. Zones of CAMS monitors were chosen to represent smaller 

geographic areas as shown in Figure 2.12.3.6-5: CAMS in Model Performance Zones. Evaluating 

model runs to the Houston Region and Zone averages instead of individual CAMS monitors 

proved to be efficient, allowing more sensitivities of the eight TexAQS II meteorological 

modeling episodes. 

 

Downwind 

Northwest Harris Co. C26 

Conroe Relocated C78 

Northwest Core 

Westhollow C410 

Bayland Park C53 

Aldine C8 

Southeast Core 

Croquet C409 

Regional Office C81 

HRM-3 C603 

Channelview C15 

Ship Channel 

Manvel Croix Park C84 

Deer Park C35 

Wallisville Road C617 

Coastal Zone 

Clute C11 

Mustang Bayou C619 

Galveston Airport C34 

Figure 2.12.3.6-5: CAMS in Model Performance Zones 

Compared to the Region 12 average, the wind and temperature response to 12km observational 

nudging was very similar in the Zones for the 4km domain. The change in wind speed and 

direction error was small and differed by Zone. Temperature appeared to improve slightly for all 

areas. 
A-106

Nudging? Geographic 

Area/Zone 

Wind Direction

Error ≤ 30 ° 

Wind Speed

Error ≤ 2 m/s 

Temperatur

e Error ≤ 2 

°C 

Table 2.12.3.6-2: Table of error percentages from 4km runs with and without 12km observational 

nudging for 2005ep2 episode details the response in winds and temperature on the 4km domain to 

the 12km observational nudging for the 2005ep2 episode. 

Table 2.12.3.6-2: Table of error percentages from 4km runs with and without 12km 

observational nudging for 2005ep2 episode

12km 

NO Region 12 69 97 86 

YES Region 12 68 96 88 

NO Coastal Zone 59 96 80 

YES Coastal Zone 61 96 83 

NO Ship Channel 55 94 84 

YES Ship Channel 55 94 86 

NO Southeast 

Core 59 96 80 

YES Southeast 

Core 57 94 84 

NO Northwest 

Core 47 88 65 

YES Northwest 

Core 47 91 66 

NO Downwind 39 78 80 

YES Downwind 40 80 83 

Because of the insignificant impact on the 12km and 4km do odeling with 12km 

observational nudging was not investigated further. 

2.12.3.7 Radius of Influence Tests 

The TexAQS II profilers were sited through tate of Texas and neighboring states to 

ion and for pollutant transport 

ity study where the profiler locations 

mains, MM5 m

out the s

collect upper air wind data for many areas with air quality degradat

studies. This is in contrast to the TexAQS 2000 air qual

surrounded the Houston area (Figure 2.12.3.7-1: TexAQS 2000 (blue) and TexAQS II (red) upper 

air sites). Because of the more diffuse network of upper air sites, the horizontal radius of 

influence (ROI) for observational nudging was considered to be a parameter worth investigating. 
Figure 2.12.3.7-1: TexAQS 2000 (blue) and TexAQS II (red) upper air sites

The default horizontal ROI for observational nudging is 240 km and is controlled by the 

m5.deck variable RINXY. The strength of the nudging is distanced-weighted according to a 

dii of Influence, all profilers locations are 

pacted by other profilers. Modeling with the default ROI could produce smoothed wind fields, 

m

Cressman-type function. When a 240 km ROI circle is plotted on each profiler, as in Figure 

2.12.3.7-2: TexAQS II Profilers with 240 km Ra

im

effectively losing finer details. 

Figure 2.12.3.7-2: TexAQS II Profilers with 240 km Radii of Influence 

To determine if the wind fields were being impacted, MM5 was run with ROIs of 120 and 80 km. 

Figure 2.12.3.7-3: TexAQS II Profilers with 120 (left) and 80 km (right) Radii of Influence shows 

A-107
A-108

the profilers with the smaller ROIs. With the smaller ROI, less upper air sites interact with each 

other. 

 

Figure 2.12.3.7-3: TexAQS II Profilers with 120 (left) and 80 km (right) Radii of Influence 

On an average basis, the use of a smaller ROI did not have much impact on the average error in 

the Houston area (TCEQ Region 12). Table 2.12.3.7-1: Average MM5 model performance using 

where MM5 had 

verage error less than the parameter benchmark for all Region 12 CAMS monitors and Houston 

e (km) Error ≤ 30 ° Error ≤ 2 m/s °C 

different observational nudging radii of influence displays the percent of hours 

a

zones. 

Table 2.12.3.7-1: Average MM5 model performance using different observational nudging 

radii of influence 

Geographic 

Area/Zone 

Radius of 

Influenc

Wind Direction Wind Speed Temperatur

e Error ≤ 2 

Region 12 240 (default) 65 97 73 

Region 12 120 63 99 73 

Region 12 80 60 98 74 

Coastal Zone 240 (default) 55 96 73 

Coastal Zone 120 51 97 72 

Coastal Zone 80 51 96 67 

Ship Channel 240 (default) 57 97 66 

Ship Channel 120 54 97 68 

Ship Channel 80 52 96 71 

Downwind 240 (default) 41 85 72 

Downwind 120 42 83 72 

Downwind 80 40 79 72 

Results from the ROI sensitivities were also investigated in terms of forward trajectories 

produced from surface wind sites and the corresponding surface modeled data. This metric was 

sed to determine if the wind flows were modified by the smaller ROIs. 

d) to model runs with 

 nudging ROIs of 120 (blue) and 240 km (green) for July 28, 2005 at 6 AM exhibits 

observations (red). Figure 2.12.3.7-5: Forward trajectories comparing surface observations (red) 

u

Figure 2.12.3.7-4: Forward trajectories comparing surface observations (re

observational

a 12-hour forward trajectory starting from 19 points in the Houston area at 6:00 AM CST on July 

28, 2005 and compares the model runs of 120 (blue points) and 240 km (green) with surface 
A-109

exhibits the same time period but compares the 120 km ROI run (blue) with the 80 km 

n (green). 

to model runs with observational nudging ROIs of 120 (blue) and 80 km (green) for July 28, 2005 

at 6 AM

ru

Figure orw l runs 

with ob nud 2 240 r y 28, 2005 at 6 

AM 

2.12.3.7-4: F

servational

ard trajectories c

ging ROIs of 1

omparing surfac

0 (blue) and 

e observations (

km (green) fo

red) to mode

 Jul
Figure 2.12.3.7-5: Forward trajectories comparing surface observations (red) to model runs 

with observational nudging ROIs of 120 (blue) and 80 km (green) for July 28, 2005 at 6 AM 

By evaluating the MM5 runs with different ROIs, it was determined that the default 240 km

liment those found by Nielsen-Gammon (2008b). Radius of Influence parameter tests were 

lso conducted when model sensitivities were nudged with ds472.0 data and will be described in 

the surface nudging section. 

2.12.3.8 Surface Observational Nudging 

To summarize the MM5 performance when surface nudging was used and when surface nudging 

was not used for episode 2005ep0 (May 18 - June 4, 2005), 2005ep1 (June 15 - June 30, 2005), 

and 2005ep2 (July 25 - August 9, 2005), statistics for meteorological variables were calculated 

for the Houston-Galveston area (HGB). The model performance was determined by comparing 

the CAMS hourly surface wind speed, wind direction, and temperature to the corresponding 

hourly MM5 values. The performance was evaluated by calculating the percent of compliance of 

the surface wind speed within 1m/s, the wind direction within 30 degrees, and the temperature 

within 1 degree Celsius. 

 Figure 2.12.3.8-1: Wind speed performance for episodes 2005ep0, 2005ep1, and 2005ep2 shows 

the Houston-Galveston area (HGB) daily performance of the wind speed for the three episodes 

with and without surface nudging. Wind direction and temperature are captured in Figure 

2.12.3.8-2: Wind direction performance for episodes 2005ep0, 2005ep1, and 2005ep2 and Figure 

2.12.3.8-3: Surface temperature performance for episodes 2005ep0, 2005ep1, and 2005ep2

respectively.

performed equally or better than other choices. While small differences were noted in the 

trajectories and error statistics, significant changes did not appear to occur. These conclusions 

comp

a

A-110
Region 12 Wind Speed Performance (compliance < 1 m/s)

 2005EP0 2005EP1 2005EP2

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

5/185/195/205/215/225/235/245/255/265/275/285/295/305/31

6/16/26/3

6/156/166/176/186/196/206/216/226/236/246/256/266/276/286/297/257/267/277/287/297/307/31

8/18/28/38/48/58/68/78/8

speed fddats speed upsfcfddats missing profiler

Figure 2.12.3.8-1: Wind speed performance for episodes 2005ep0, 2005ep1, and 2005ep2 

Region 12 Wind Direction Performance (compliance < 30 deg.)

 2005EP0 2005EP1 2005EP2

90%

100%

70%

80%

20%

30%

40%

50%

60%

10%

0%

5/185/195/205/215/225/235/245/255/265/275/285/295/305/31

6/16/26/3

6/156/166/176/186/196/206/216/226/236/246/256/266/276/286/297/257/267/277/287/297/307/31

8/18/28/38/48/58/68/78/8

direction fddats direction upsfcfddats missing profiler

Figure 2.12.3.8-2: Wind direction performance for episodes 2005ep0, 2005ep1, and 2005ep2 

A-111
Region 12 Temperature Performance (compliance < 1 deg.)

 2005EP0 2005EP1 2005EP2

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

5/185/195/205/215/225/235/245/255/265/275/285/295/305/31

6/16/26/3

6/156/166/176/186/196/206/216/226/236/246/256/266/276/286/297/257/267/277/287/297/307/31

8/18/28/38/48/58/68/78/8

temperature fddats temperature upsfcfddats missing profiler

 

Figure 2.12.3.8-3. Surface temperature performance for episodes 2005ep0, 2005ep1, and 

2005ep2 

Table 2.12.3.8-1: Summary of the MM5 model performance for the Houston-Galveston area 

(HGB) for wind speed, wind direction, and temperature for the high ozone days for episodes 

2005ep0, 2005ep1, and 2005ep2 shows a summary of the performance results for the three 

episodes for the Houston-Galveston (RGB) region for the days with high ozone. The wind speed 

performance results show that the MM5 runs with surface nudging performed equally or better 

than without surface nudging on 100% of the days with high ozone. As for the wind direction 

and temperature, the MM5 runs with surface nudging performed equally or better than without 

surface nudging on 52% and 52%, respectively, of the days with high ozone. 

Table 2.12.3.8-1: Summary of the MM5 model performance for the Houston-Galveston area 

(HGB) for wind speed, wind direction, and temperature for the high ozone days for 

episodes 2005ep0, 2005ep1, and 2005ep2 

A-112

 2005ep0 2005ep1 2005ep2 Total Percent 

Number of high 

ozone days 8 9 8 25 

Wind Speed nudging is equal 

or better 

8 9 8 25 100% 

Days where sfc 

Wind Direction 

Days where sfc 

nudging is equal 

or better 

5 3 5 13 52% 

Temperature 

Days where sfc 

nudging is equal 

or better 

5 3 5 13 52% 

Detailed 2005ep0 episode average model performance in percent with and without surface 

nudging for different Zones, which include: Houston-Galveston Region (R12); Zone 1 (Coastal); 

Zone 2 (Ship Channel); Zone 3 (Southeast Core); Zone 4 (Northwest Core); Zone 5 (Downwind), 

are summarized in Table 2.12.3.8-2: 2005ep0 summary of the MM5 model performance in 

percent for the Houston-Galveston area (R12) and the five Zones for compliance of wind speed 

within 1m/s, wind direction within 30 deg., and temperature within 1 deg. for the episode 

average. Of special note for this episode is that profiler data only became available on May 19 
A-113

for Cleberne (near Fort Worth) and La Porte (in HGB) and other profiler data did not become 

available until June. 

Table 2.12.3.8-2: 2005ep0 summary of the MM5 model performance in percent for the 

Houston-Galveston area (R12) and the five Zones for compliance of wind speed within 1m/s, 

wind direction within 30 deg., and temperature within 1 deg. for the episode average

2005ep0 Wind Speed Wind 

Direction Temperature 

R12 with sfc nudging 77 75 36 

upper air only (when available) 70 71 35 

Zone 1 with sfc nudging 65 72 32 

upper air only (when available) 63 62 33 

Zone 2 with sfc nudging 65 68 34 

upper air only (when available) 54 60 34 

Zone 3 with sfc nudging 63 70 32 

upper air only (when available) 55 65 33 

Zone 4 with sfc nudging 66 64 25 

upper air only (when available) 60 61 26 

Zone 5 with sfc nudging 57 58 34 

upper air only (when available) 54 56 33 

Comparisons for the 2005ep1 and 2005ep2 episodes are captured respectively in Table 2.12.3.8-

3: 2005ep1 summary of the MM5 model performance in percent for the Houston-Galveston area 

(R12) and the five Zones for compliance of wind speed within 1m/s, wind direction within 30 deg., 

and temperature within 1 deg. for the episode average and Table 2.12.3.8-4.: 2005ep2 summary 

of the MM5 model performance in percent for the Houston-Galveston area (R12) and the five 

Zones for compliance of wind speed within 1 m/s, wind direction within 30 deg., and temperature 

us a .

within 1deg. for the episode average. 

The normal expectation is that by nudging to surface data, the statistics should improve. For 

2005ep1, there is only a marginal improvement over ing upper- ir data only
A-114

area (R12) and the five Zones for compliance of wind speed within 1m/s, 

ind direction within 30 deg., and temperature within 1 deg. for the episode average

Table 2.12.3.8-3: 2005ep1 summary of the MM5 model performance in percent for the 

Houston-Galveston 

w

2005ep1 Wind Speed Wind 

Direction Temperature 

R12 with sfc nudging 94 85 38 

upper air only 90 82 42 

Zone 1 with sfc nudging 69 75 29 

upper air only 65 68 32

Zone 2 with sfc nudging 85 69 35

upper air only 80 67 37

Zone 3 with sfc nudging 86 75 34

upper air only 83 72 36

Zone 4 with sfc nudging 85 61 25

upper air only 78 64 32

Zone 5 with sfc nudging 73 55 27

upper air only 67 59 33 

. 

Table of the MM5 mod erformance percent for the 

Hous and the five Zones for compliance of wind speed within 1m/s, 

wind p ture within 1 deg. for the episode average

 2.12.3.8-4: 2005ep2 summary el p in 

ton-Galveston area (R12)

 direction within 30 deg., and tem era

2005ep2 Wind Speed Wind Direction Temperature 

R12 with sfc nudging 91 66 49 

upper air only 75 65 44 

Zone 1 with sfc nudging 78 59 38 

upper air only 74 55 37 

Zone 2 with sfc nudging 79 56 47 

upper air only 63 53 45 

Zone 3 with sfc nudging 81 55 48 

upper air only 67 57 43 

Zone 4 with sfc nudging 75 49 17 

upper air only 58 48 16 

Zone 5 with sfc nudging 55 44 48 

upper air only 43 41 43 

Sensitivity of observational nudging in MM5 to the radius of influence 

The standard radius of influence used in the observational nudging is 240 km. This number is 

representative of an appropriate range for upper air data from rawinsonde or profiler data, which 

are typically widespread (i.e., low spatial resolution). Since only one radius of influence 

parameter is permitted in MM5 when reading an observational nudging file, the value selected for 

this parameter may be more appropriate for upper air data or surface data but likely will not 

represent both sets of data well. The surface data used for the nudging is the ds472.0 surface 

observations, which have a higher spatial resolution than the profiler data, and would ideally 

require a smaller radius of influence to fully utilize all the surface data. To test the sensitivity of 

the MM5 simulations to the radius of influence, a radius of influence of 60 km was used instead 
A-115

nce for 

20 th a radius of influence of 6 fo t with a ra of 

60 high ozone day (September t t ection ’t 

ch ecreased to 60 km. Howeve he wind speed 

pe y degraded. As for the high ozone da f September, 2 he wind speed 

im e 60 k dius of influe was used. On 

Se speed and wind direction performance improved when 60km was 

us of influence. 

of 240 km. Figure 2.12.3.8-4: MM5 daily model performance for 2006ep1c with a radius of 

influence of 240 km shows the 2006ep1c episode (September 16 - 29, 2006) daily model 

performance using the standard 240 km. Figure 2.12.3.8-5: MM5 daily model performa

06ep1c wi

 km. The

0 km is the same

 20) showed tha

rmat bu

he wind dir

dius of influence

performance didn

ange when the radius of influence was d r, t

rformance slightl

proved and the w

y o

m ra

6, t

ind direction degraded when th nce 

ptember 27, both the wind

ed for the radius 

Houston (R12) Daily Max 8-Hour e 

 Speed, Dire , and Temperature ormance 

1c (Sept ,2006) Grell + Prof udging

36

71

86

27

87

97

66

18 19 20 2 22 23 24 5 26 27 29

September 2006

m mark

0

20

80

100

our M

Ozon

Wind

2006ep

ction Perf

16-29 iler N

80%

100%

42

53

44

58 60

60%

pliance w/bench

60

ax Ozone

24

20%

Per

29

0%

40%

cent co

16 17 1 2 28

40

8-H

Max Ozone Wind S /s peed <1 m Wind Dir < 30 Temperature <1

F : MM5 daily model performance for 20 1c with a radius of influence of 

2

igure 2.12.3.8-4 06ep

40 km 
Houston (R12) Daily Max 8-Hour Ozone 

Wind Speed, Direction, and Temperature Performance 

2006ep1c.d4.eta.dbemis.upsfcfddats.uhsst.utcsrlulc.grell.60km

71

86 87

97

66

80%

100%

enchmark

80

100

36

24

42

53

27 29

44

58 60

0%

20%

40%

60%

16 17 18 19 20 21 22 23 24 25 26 27

September 2006

Percent compliance w/b

0

20

40

60

8-Hour Max Ozone

28 29

Max Ozone

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Wind Speed <1 m/s Wind Dir < 30 Temperature <1

Figure 2.12.3.8-5: MM daily model performance for 2006ep1c with a radius of influence of 

60 km

Two segmented runs (partial period) of 2006ep1c were simulated to test the 60 km radius of 

influence, as well as a 30 km value. The first segment is Sep. 18-20 for the high ozone day on 

September 20. September 18 is for the MM5 spin-up and September 19 is for the CAMx spinup. The second segment is September 24-27, 2006, for the high ozone days on September 26 

and 27. September, 24 is for the MM5 spin-up and September 25 is for the CAMx spin-up. The 

full 2006ep1c episode with the 60 km radius of influence and the overlay of the two segmented 

runs with the 60 and 30 km radius of influence are displayed in Figure 2.12.3.8-6: MM5 daily 

model performance for 2006ep1c with a radius of influence of 60km, overlaid with segmented

5 

runs with 60 and 30 km radius of influence
Houston (R12) Daily Max 8-Hour Ozone 

Wind Speed, Direction, and Temperature Performance 

2006ep1c.d4.eta.dbemis.upsfcfddats.uhsst.utcsrlulc.grell.60km

0%

20%

40%

60%

80%

100%

16 17 18 19 20 21 22 23 24 25 26 27 28 29

September 2006

Percent compliance w/benchmark

Wind Speed <1 m/s Wind Dir < 30 Temperature <1 Wind Speed <1 m/s (Segment 30km) Wind Dir < 30 (Segment 30km) Temperature <1 (Segment 30km) Wind Speed <1 m/s (Segment) Wind Dir < 30 (Segment) Temperature <1 (Segment)

Figure 2.12.3.8-6: MM5 daily model performance for 2006ep1c with a radius of influence of 

60 km, overlaid with segmented runs with 60 and 30 km radius of influence. 

Table 2.12.3.8-5: Performance summary of the segmented 60 km and 30 km runs summarizes the 

performance of the segmented 60 km and 30 km runs. The upward arrows indicates improvement 

compared to the full run with 60 km, the downward arrow indicates degradation compared to the 

full run, and "same" indicates no change. 

Table 2.12.3.8-5: Performance summary of the segmented 60km and 30km runs

Sep. 19 Sep. 20 Sep. 25 Sep. 26 Sep. 27 

60km Wind 

Direction 

↓ same same ↑ ↑

 Wind Speed ↑ ↑ same same same 

 Temperature ↑ same ↓ same ↑

 

30km Wind 

Direction 

same same same same ↑

 Wind Speed ↓ same same ↓ same 

 Temperature ↓ same ↑ ↓ ↓

Sensitivity of MM5 to observation-nudging coefficient (s-1) for wind (GIV)

The observational nudging in MM5 uses a nudging coefficient parameter for the wind (GIV) set 

to 0.0004. In order to test the sensitivity of MM5 to different values for GIV, two MM5 runs 

were performed that used the profiler and the surface data (ds472) for nudging with a radius of 

influence of 60 km, instead of the standard 240km value. Figure 2.12.3.8-7.a: Performance plot 

for the wind speed using the default GIV (0.0004) and the 5 times the standard observationnudging coefficient of (5x0.0004=0.002) for the Houston (R12) shows the MM5 performance for 

Houston (R12) for episode 2006ep1c (September 16-29, 2006) with a 60 km radius of influence 

for the nudging of the wind with the standard observation-nudging coefficient of 0.0004. When 

the observation-nudging coefficient of 0.002 was used, the wind speed performance improved on 

most days including the high ozone days (September 20, 26, and 27). The entire 4 km domain is 

captured in Figure 2.12.3.8-7.b: Performance plot for the wind speed using the default GIV 

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A-118

(0.0004) and the 5 times the standard observation-nudging coefficient of (5x0.0004=0.002) for 

the entire 4 km domain. 

Sensitivity of Wind Speed Performance (1 m/s) to the Nudging Strength (GIV) for Houston (R12)

0%

20%

40%

60%

80%

100%

16 17 18 19 20 21 22 23 24 25 26 27 28 29

September 2006

Percent compliance w/benchmark

Default GIV GIV X 5

Figure 2.12.3.8-7.a: Performance plot for the wind speed using the default GIV (0.0004) and 

the 5 times the standard observation-nudging coefficient of (5x0.0004=0.002) for the 

Houston (R12) 

Sensitivity of Wind Speed Performance (1 m/s) to the Nudging Strength (GIV) for The Entire 4km Domain

100%

80%

nchmark

0%

20%

40%

16 19 20 21 23 25 27 28

ber 2006

Percent comp e

60%

liance w/b

17 18 22 24 26 29

Septem

Default GIV GIV X 5

Figure 2.12.3.8-7.b: Performance plot for the wind speed using the default GIV (0.0004) and 

the 5 times the standard observation-nudging coefficient of (5x0.0004=0.002) for the entire 4 

km domain 
A-119

observation-nudging coefficient of (5x0.0004=0.002) for the Houston 

(R12) shows that the performance of the wind direction also improved or stayed the same on all 

days except September 27, which was a high ozone day, and Figure 2.12.3.8-8.b: Performance 

plot for the wind direction using the default GIV (0.0004) and the 5 times the standard 

observation-nudging coefficient of (5x0.0004=0.002) for the entire 4 km domain shows the 

performance of the wind speed and wind direction for the entire 4 km domain. 

Figure 2.12.3.8-8.a: Performance plot for the wind direction using the default GIV (0.0004) and 

the 5 times the standard 

Sensitivity of Wind Direction Performance (30 degrees) to the Nudging Strength (GIV) for Houston (R12)

0%

20%

40%

60%

80%

100%

16 17 18 19 20 21 22 23 24 25 26 27 28 29

September 2006

Percent compliance w/benchmark

Default GIV GIV X 5

Figure 2.12.3.8-8.a: Performance plot for the wind direction using the default GIV (0.0004) 

and the 5 times the standard observation-nudging coefficient of (5x0.0004=0.002) for the 

Houston (R12) 

Sensitivity of Wind Direction Performance (30 degrees) to the Nudging Strength (GIV) for The Entire 4km 

Domain

0%

20%

40%

60%

80%

100%

Percent compliance w/benchmark

16 17 18 19 20 21 22 23 24 25 26 27 28 29

September 2006

Default GIV GIV X 5

Figure 2.12.3.8-8.b: Performance plot for the wind direction using the default GIV (0.0004) 

and the 5 times the standard observation-nudging coefficient of (5x0.0004=0.002) for the 

entire 4 km domain 

Forward Trajectories

The differences between the forward trajectories using the MM5 model runs with Grell and the 

model runs with Grell and surface nudging were evaluated. The standard trajectory plots show 

12-hour-forward trajectories plotted for 19 sites using Grell (in blue) with the corresponding 19 

trajectories from the cams surface observation in red. The corresponding 19 trajectories with 
A-120

selected using the 

LCP data file. For each of the seven days selected, the 12-hour-forward trajectory starting at 6 

am were used for both the MM5 Grell and the MM5 Grell with surface nudging. At each 5 

minute interval an error was calculated, which is the distance from that point for a given station to 

the corresponding point for the same station in the trajectory generated using the observed data. 

The error divergence was determined as the averaged error for every 5 minutes for 19 stations for 

both the Grell and the Grell with surface nudging The following figures provide a summary of the 

results for the seven days selected: 

 

For Figure 2.12.3.8-9: 12 hour forward trajectories at 19 stations starting at 6 am on May 21, 

2005. Error divergence (top) and trajectories (bottom).the error divergence with Grell and 

surface nudging is less than with Grell only. Note that both modeled trajectories divergence 

significantly after four hours. 

 

For Figure 2.12.3.8-10: 12 hour forward trajectories at 19 stations starting at 6 am on May 27, 

2005. Error divergence (top) and trajectories (bottom) the error divergence with Grell and 

surface nudging is less than with Grell only. Note that both model runs have comparable wind 

peeds (similar trajectory lengths), but there is more clockwise rotation with surface data added. 

cal. 

For Figure 2.12.3.8-12: 12 hour forward trajectories at 19 stations starting at 6 am on June 21, 

2005. Error divergence (top) and trajectories (bottom), Surface nudging does not have a 

significant improvement for either accumulated error or trajectories. 

 

For Figure 2.12.3.8-13: 12 hour forward trajectories at 19 stations starting at 6 am on June 22, 

2005. Error divergence (top) and trajectories (bottom). Surface nudging does not have a 

significant improvement for either accumulated error or trajectories. 

 

For Figure 2.12.3.8-14: 12 hour forward trajectories at 19 stations starting at 6 am on August 1, 

2005. Error divergence (top) and trajectories (bottom). the error divergence for both model runs 

is virtually identical. 

 

For Figure 2.12.3.8-15: 12 hour forward trajectories at 19 stations starting at 6 am on August 2, 

2005. Error divergence (top) and trajectories (bottom). Surface nudging does not have a 

significant improvement for either accumulated error or trajectories. 

 

Thus, for several of the high ozone days, use of surface nudging may produce trajectories which 

are “closer” to the trajectories based on observational data alone, but on several other days, there

Grell and surface nudging can be overlaid in green. The 12-hour-forward trajectories can be 

displayed starting at any hour of the day. 

 

The geographic coordinates for each trajectory at five minute intervals are available in files as 

either latitude and longitude, or as LCP (distance in km) in the north-south and east-west 

directions. For the trajectory analysis, three days with high ozone from 2005ep0, two days with 

high ozone from 2005ep1, and two days with high ozone from 2005ep2 were 

s

 

For Figure 2.12.3.8-11: 12 hour forward trajectories at 19 stations starting at 6 am on June 2, 

2005. Error divergence (top) and trajectories (bottom) the error divergence for both model runs 

is virtually identi

 

is not a significant difference is modeled trajectories. 
Forward trajectory errors average for 19 sites starting at 6am May 21, 2005

20

30

40

50

60

70

Error (Km)

0

10

0123 4 5 6 7 8 9 10 11 12

Time (hours)

error without sfc nudging error with sfc nudging

Figure 2.12.3.8-9: 12 hour forward trajectories at 19 stations starting at 6 am on May 21, 

2005. Error divergence (top) and trajectories (bottom). 

A-121
Forward trajectory errors average for 19 sites starting at 6am May 27, 2005

0

10

20

30

40

50

60

70

0 1 2 3 4 5 6 7 8 9 10 11

Time (Hr)

Error (Km)

12

error without sfc nudging error with sfc nudging

Figure 2.12.3.8-10: 12 hour forward trajectories at 19 stations starting at 6 am on May 27, 

2005. Error divergence (top) and trajectories (bottom). 

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Forward trajectory errors average for 19 sites starting at 6am June 2, 2005

0

10

20

30

40

50

60

70

0 1 2 3 4 5 6 7 8 9 10 11

Time (Hr)

Error (Km)

12

error without sfc nudging error with sfc nudging

Figure 2.12.3.8-11: 12 hour forward trajectories at 19 stations starting at 6 am on June 2, 

2005. Error divergence (top) and trajectories (bottom). 

A-123
Forward trajectory errors average for 19 sites starting at 6am June 21, 2005

0

10

20

30

40

50

60

70

0 1 2 3 4 5 6 7 8 9 10 11

Time (Hr)

Error (Km)

12

error without sfc nudging error with sfc nudging

Figure 2.12.3.8-12: 12 hour forward trajectories at 19 stations starting at 6 am on June 21, 

2005. Error divergence (top) and trajectories (bottom). 

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Forward trajectory errors average for 19 sites starting at 6am June 22, 2005

0

10

20

30

40

50

60

70

0 1 2 3 4 5 6 7 8 9 10 11

Time (Hr)

Error (Km)

12

error without sfc nudging error with sfc nudging

Figure 2.12.3.8-13: 12 hour forward trajectories at 19 stations starting at 6 am on June 22, 

2005. Error divergence (top) and trajectories (bottom). 

A-125
Forward trajectory errors average for 19 sites starting at 6am August 1, 2005

0

10

20

30

40

50

60

70

0 1 2 3 4 5 6 7 8 9 10 11

Time (Hr)

Error (Km)

12

error without sfc nudging error with sfc nudging

Figure 2.12.3.8-14: 12 hour forward trajectories at 19 stations starting at 6 am on August 1, 

2005. Error divergence (top) and trajectories (bottom). 

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Forward trajectory errors average for 19 sites starting at 6am August 2, 2005

0

10

20

30

40

50

60

70

0 1 2 3 4 5 6 7 8 9 10 11

Time (Hr)

Error (Km)

12

error without sfc nudging error with sfc nudging

Figure 2.12.3.8-15: 12 hour forward trajectories at 19 stations starting at 6 am on August 2, 

2005. Error divergence (top) and trajectories (bottom). 

A-127
2.12.3.9 References

ATMET (2003), MM5 Simulations for TexAQS 2000 Episode, Final report to the Houston 

Advanced Research Center and the Texas Commission on Environmental Quality, 12 August 

2003. 

Byun, D., F. Ngan, X. Li, D. Lee, S. Kim, H. Kim (2008), Evaluation of Retrospective MM5 and 

CMAQ Simulations of TexAQS-II Period with CAMS Measurements, 2007 TexAQS-II 

Modeling Project: Final Report to the Texas Commission on Environmental Quality for Grant 

No. 582-5-64594-FY07-02, University of Houston. 

CISL (2009), ds472.0 Home Page, Computational and Information Systems Laboratory Research 

Data Archive, http://dss.ucar.edu/datasets/ds472.0/, Last accessed March 26, 2009. 

EPA (2007), Guidance on the Use of Models and Other Analyses for Demonstrating Attainment 

of Air Quality Goals for Ozone, PM2.5, and Regional Haze, 

http://www.epa.gov/scram001/guidance/guide/final-03-pm-rh-guidance.pdf, EPA -454/B-07-002, 

162, Last accessed March 26, 2009. 

Jia, Y., R. Morris (2003), Meteorological Modeling of the August 1999 Episode to Support the 8-

Hour Ozone Early Action Compacts for Tulsa and Oklahoma City, Draft Final Report to the 

Oklahoma Department of Environmental Quality, ENVIRON International Corporation, Novato

CA. 

Knoderer, C. A., C. P. MacDonald (2007), TexAQS II radar wind profiler, radio acoustic 

sounding system, sodar and lidar data quality control and mixing height derivation, final report by 

Sonoma Technology, Inc. to Texas A&M University for Texas Commission on Environmental 

Quality Grant Activity No. 582-5-64593-FY07-20. 

NCAR (2005), Chapter 13: I/O FORMAT, PSU/NCAR Mesoscale Modeling System Tutorial 

Class Notes and User’s Guide, 

http://www.mmm.ucar.edu/mm5/documents/MM5_tut_Web_notes/IO/io.htm

, 

, Last accessed 

March 26, 2009. 

Nielsen-Gammon, J. W. (2002), Meteorological Modeling for the August 2000 HoustonGalveston Ozone Episode: PBL Characteristics, Nudging Procedure, and Performance 

Evaluation, Report to the Technical Analysis Division of the Texas Natural Resource 

Conservation Commission, Texas A&M University. 

Nielsen-Gammon, J. W., R. T. McNider, W. M. Angevine, A. B. White, and K. Knupp (2007), 

Mesoscale model performance with assimilation of wind profiler data: Sensitivity to assimilation 

parameters and network configuration, J. Geophys. Res., 112, D09119, 

doi:10.1029/2006JD007633. 

Nielsen-Gammon, J. (2008), Analysis of TexAQS II Meteorological Data, Final Report to the 

Texas Commission on Environmental Quality for Grant Activity No. 582-5-64593-FY07-20, 

Texas A&M University. 

ental Quality for Grant Activity No. 

582-5-64593-FY07-20. 

Nielsen-Gammon, J. (2008b), Model Configuration and Performance with Wind Profiler 

Nudging, A report to the Texas Commission on Environm

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Otte, T. L., 2008a: The impact of nudging in the meteorological model for retrospective air 

tte, T. L., 2008b: The impact of nudging in the meteorological model for retrospective air 

rv, http://mailman.ucar.edu/pipermail/mm5-

quality simulations. Part I: Evaluation against national observation networks. Journal of Applied 

Meteorology and Climatology, 47, 1853-1867. 

O

quality simulations. Part II: Evaluating collocated meteorological and air quality observations. 

Journal of Applied Meteorology and Climatology, 47, 1868-1887. 

Petre, J. (2006), Post to MM5-Users listse

users/2006/001040.html, Last accessed March 26, 2009. 

Junker and Y. 

in (1996), Changes to the operational ``Early'' Eta analysis/forecast system at the National 

nfiguration and recent changes. Wea. Forecasting, 10, 810-

25. 

. 

wski (1991), Use of four-dimensional data 

ssimilation in a limited area mesoscale model. Part II: Effects of data assimilation within the 

Rogers, E., T. L. Black, D. G. Deaven, G. J. DiMego, Q. Zhao, M. Baldwin, N. W. 

L

Centers for Environmental Prediction. Wea. Forecasting, 11, 391-413. 

Rogers, E., D. Deaven and G. J. DiMego (1995), The regional analysis system for the operational 

"early" Eta model: original 80-km co

8

Stauffer, D. R., and N. L. Seaman (1990), Use of four-dimensional data assimilation in a limited 

area mesoscale model. Part I: Experiments with synoptic-scale data, Mon. Wea. Rev., 118, 1250–

1277

Stauffer, D. R., and N. L. Seaman (1994), Multiscale four-dimensional data assimilation, J. Appl. 

Meteor., 33, 416–434. 

Stauffer, D. R., N. L. Seaman, and F. S. Binko

a

planetary boundary layer, Mon. Wea. Rev., 119, 734–754. 

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Application of Grell Cumulus Scheme 

ATTACHMENT 4 

Houston-Galveston-Brazoria Attainment Demonstration SIP Revision 

A-130
2.12.4 Application of Grell Cumulus Scheme 

2.12.4.1 Introduction 

Central to the development of precipitation forecasts is a correct determination of the effects of 

clouds. The first predicti ts from which forecasters 

estimated le models 

(typically representing features from 10 to 100 km), modelers have attempted to account for the 

physical effects of clouds. In a real atmosphere, convection (clouds) results from physical 

instabilities which redistributes moisture and heat in such a way as to re-stabilize the atmosphere. 

Quite a large set of meteorological literature is devoted to convective parameterizations. In this 

report, the term “parameterization” refers to a mathematical scheme or algorithm that accounts 

for a real physical process which cannot be properly resolved at a particular grid scale used for 

modeling. Since processes such as cloud formation are real, they must be accounted for or else 

the model will be inaccurate and possibly unstable. On the other hand, “resolution” is not welldefined since a given feature usually is “resolved” across several grid cells. For example, as large 

air masses move along fronts, widespread lifting can produce stratiform (wide, relatively flat) 

clouds. For a modeling domain with 20 km grid cells, this type of cloud may be resolved, but 

cumulus (puffy) clouds which can develop from strong vertical motion will not be resolved 

without a convective parameterization. However, a series of cumulus clouds might evolve a 

mesoscale structure. A frontal passage may trigger cumulus convection which leads to more 

cumulus clouds forming in the wake of the front. These so-called “mesoscale convective 

complex” (MCC) might be resolved. 

Air quality simulations of tropospheric ozone depend in large part on mostly clear episodic days 

for photochemical reactions; however, even partial cloud cover can strongly impact radiative 

processes, surface heating and PBL development. More importantly, longer episodes require a 

better characterization of clouds because an incorrect amount of precipitation can bias soil 

moisture across several days. 

Despite the recognition of cloud importance for air quality modeling, the meteorological literature 

has understandably focused on validation of convective parameterizations based upon four 

criteria related directly to precipitation events: first, does precipitation trigger at the right time? 

Secondly, does the meteorological model predict the correct peak precipitation? Thirdly, does the 

area extent of precipitation match observations? Finally, does the total precipitation agree with 

what is observed? Although clouds affect air quality in multiple and pervasive ways, validation is 

mostly qualitative in nature: do modeled clouds seem to be in approximately the same place as 

clouds observed in the visible portion of the spectrum by, say, Geostationary Operational 

Environmental Satellites (GOES). 

The next section discusses the general configuration of MM5 at the TCEQ as implemented for the 

present round of SIP modeling for the Houston-Galveston-Brazoria (HGB) nonattainment area. 

The original motivation to use a cumulus parameterization on the 4 km grid will also be discussed 

as well as the current operational rationale. A series of results will follow in section 3 with a 

discussion of those features which seem relevant for air quality modeling. Section 4 will include 

a general discussion on why the decision to try a cumulus parameterization on the 4 km MM5 

grid might seem reasonable from an air quality modeling perspective, even though TCEQ staff 

are aware that a convective scheme of any kind at a 4 km grid scale is generally not 

recommended. The final concluding section will include expected impacts on the subsequent air 

quality modeling. 

ve meteorological models tracked fron

possible precipitation. Concurrent with the development of mesosca

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2.12.4.2. MM5 Model Configuration for HGB and Motivation for the Grell Cumulus 

arameterizaton on the 4 Km Grid. 

configuration adopted for all of the TexAQS II episodes is described in 

P

The preliminary MM5 

Section 2.1 of Appendix A: Meteorological Modeling for the HGB Attainment Demonstration 

SIP Revision for the 1997 Eight-Hour Ozone Standard. In addition to noting that the Grell 

cumulus scheme was used on the three coarse grids (108 km/36 km/12 km) on all runs described 

here, it is also important to note that 2-way nesting was used on the three coarse grids. The 4 km 

MM5 grid is run as a one-way nest; i.e., the output from the 12 km grid was run through the 

NESTDOWN program to extract the initial and boundary conditions for the 4 km MM5 run. 

The motivation for applying the Grell cumulus parameterization on the MM5 4 km grid came as 

the result of a suggestion by the State Climatologist, Dr. John Nielsen-Gammon, to test whether 

the areal extent and timing of predicted cloud cover might be improved on particular episode 

days. The episode days of concern were those for which the photochemical model (CAMx) did 

not adequately replicate the observed ozone and for which the cloud coverage was poorly 

predicted. 

 

Figure 2.12.4.2-1: Lower left graphic is MM5CAMx output for cloud optical depth at 12 pm CST 

on August 2, 2005 using Grell cumulus parameterization on the 4 km domain shows an example 

of the type of graphical analysis used by TCEQ staff to examine predicted cloud coverage. The 

graphic of modeled cloud optical depth (left side) overlays a GOES image of the visible spectrum 

showing cloud fraction. The red outline in the GOES image corresponds to the MM5CAMx 

graphic. 

 

Figure 2.12.4.2-1: Lower left graphic is MM5CAMx output for cloud optical depth at 12 pm 

CST on August 2, 2005 using Grell cumulus parameterization on the 4 km domain; (Cloud 

optical depth calculated from MM5 output using Grell) 

A-132
Figure 2.12.4.2-2: Lower left graphic is MM5CAMx output for cloud optical depth at 12 pm CST 

on August 2, 2005 without using Grell cumulus parameterization applied in the 4 km domain

05 

pisode.) What is perhaps counter-intuitive is that the addition of Grell on the 4 km grid seems to 

shows the same input MM5 configuration except without Grell cumulus on the 4 km grid. 

Troubling comparisons between modeled cloud optical depth and GOES cloud fraction, as 

depicted in this figure, were the motivation, as indicated above, for investigating the addition of a 

convective parameterization on the 4 km grid. Clearly, the application of the Grell cumulus has 

an impact on the CAMx input field, and furthermore, the CAMx input field shown in Figure 

2.12.4.2-1 seems to compare more favorably with the GOES cloud fraction. (Note: August 2, 

2005 is presented as an example because it is a peak ozone day during the July-August 20

e

have reduced the role of clouds. However, the application of the Grell parameterization affects 

the simulation other meteorological variables, which can play a role in whether or not clouds are 

simulated. An additional discussion about our understanding of how the Grell parameterization 

affects other variable is presented in the next section. 

Figure 2.12.4-2: Lower left graphic is MM5CAMx output for cloud optical depth at 12 pm 

CST on August 2, 2005 without using Grell cumulus parameterization applied in the 4 km 

domain

An examination of many such graphics comparing modeled and observed cloud coverage for 

days modeled during the May-June 2005 episode, the June 2005 episode, and the reminder of the 

July-August 2005 episode led TCEQ staff to conclude that using the Grell cumulus

arameterization on the 4 km grid does not compromise the meteorological modeling 

erformance in simulating clouds. 

p

p

A-133
2.12.4.3. Operational Rationale for Using Grell Cumulus Parameterization on the 4 km 

Grid 

As indicated, the application of a cumulus parameterization impacts the simulation of other 

meteorological variables. Therefore it is not only the clouds that are affected. One noticeable 

feature of adding Grell on the 4 km grid was the smoothing of surface divergences in the wind 

field. An example of this is shown in Figure 2.12.4.3-1: Surface Wind Map for June 23, 2005, 2 

PM (CST) without Grell and Figure 2.12.4.3-2: Surface Wind Map for June 23, 2005, 2 PM (CST) 

with Grell. In the first figure the surface winds in the northern half of the domain exhibit 

divergent features, while the second figure shows these patterns to have been smoothed by the 

addition of Grell. Since these divergent features are essentially localized and temporary, 

meteorological modeling performance measures, such as statistics of accuracy in wind speed and 

direction averaged across all monitors, or graphics of the time series of modeled versus monitored 

winds over all the days of an episode, do not typically show obvious differences between MM5 

modeling with Grell and MM5 modeling without Grell on the 4 km grid, even in an area with a 

igh density of monitors, such as HGB. Therefore, although these features have been noticed by 

many MM5 modelers, generally they are not considered a source of concern. However, by 

establishing particular regions of interest, and by focusing on particular episode days, differences 

in model configuration (i.e., with and without Grell) can be discerned, and differences in 

performance measures can be distinguished, especially if surface divergent winds coincide with a 

surface monitor. 

h

Figure 2.12.4.3-1: Surface Wind Map for June 23, 2005, 2 PM (CST) without Grell 

(Observations are the red vectors)

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Figure 2.12.4.3-2: Surface Wind Map for June 23, 2005, 2 PM (CST) with Grell 

(Observations are the red vectors) 

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A-136

igure 2.12.4.3-3: Portion of HGB domain with monitors grouped by zones shows a portion of 

the HGB domain with monitors grouped by distance from the Gulf of Mexico coastline. 

Monitors within these groupings (i.e., zones) were grouped and analyzed separately. Zone 1 is 

closest to the coast and Zone 5 is furthest inland. Table 2.12.4.3-1: Monitor Site Codes used by 

the TCEQ

Figure 2.12.

F

 lists the site codes for the monitors in this figure. 

4.3-3: Portion of HGB domain with monitors grouped by zones 

A-136
Table 2.12.4.3-1: Monitor Site Codes used by the TCEQ 

Site 

Code Selected Site Name

BAYP Bayland Park, Harris Co., TX 

C35C Clinton, Harris Co., TX 

CNR2 Conroe Relocated, Montgomery Co., TX 

DRPK Deer Park, Harris Co., TX 

GALC Galveston, Galveston Co., TX 

H03H HRM-3, Haden Road, Harris Co., TX 

H08H HRM Site 8, La Porte, Harris Co., TX 

H10H HRM-10, Mt Belvieu, Chambers Co., TX 

H11H HRM-11, , Chambers Co., TX 

HALC Aldine, Houston, Harris Co., TX 

HCHV Channelview, Houston, Harris Co., TX 

HCQA Croquet, Houston, Harris Co., TX 

HNWA NW Harris, Tomball, Harris Co., TX 

HOEA Houston East, Houston, Harris Co., TX 

HROC Houston Regional Office, Harris Co., TX 

HSMA Swiss and Monroe,, Harris Co., TX 

HTCA Texas Avenue, Houston, Harris Co., TX 

HWAA North Wayside, Houston, Harris Co., TX 

LYNF Lynchburg Ferry, Harris Co., TX 

MACP Manvel Croix Park, Brazoria Co., TX 

MSTG Mustang Bayou, Brazoria Co., TX 

SBFP Seabrook Friendship Park, Harris Co., TX 

SHWH Westhollow, Houston, Harris Co., TX 

TXCT Texas City, Galveston Co., TX 

WALV Wallisville Road, Harris Co., TX 

CLTA Clute C11 

Zone 1 (Coastal) is represented by the following sites: Clute C11 (CLTA), Mustang Bayou C619 

(MSTG) and Galveston Airport C34 (GALC); 

Zone 2 (Ship Channel) is represented by the following sites: Manvel Croix Park C84 (MACP), 

Houston Deer Park C35 (DRPK) and Wallisville Road C617 (WALV); 

Zone 3 (Southeast Core) is represented by the following sites: Croquet C409 (HCQA), the 

Houston Regional Office C81 (HROC), HRM3 Haden Road (H03H) and Channelview C15 

(HCHV); 

Zone 4 (Northwest Core) is represented by the following sites: Houston Westhollow C410 

(SHWH), Houston Aldine C8 HALC), and Houston Bayland Park C53 (BAYP); and 

Zone 5 (Downwind) is represented by the following sites: Northwest Harris County C26 

(HNWA) and the relocated Conroe monitor C78 (CNR2). 

or the various episodes, performance measure differences between the model configurations 

cused on those days with high and wide-spread maximum daily eight-hour ozone 

ccuracy of the 

modeled surface winds. In particular, MM5 modeled wind speed and direction were compared to 

F

fo

concentrations. The performance measure used was a metric related to the a

A-137
surface observations from the monitors in the above zones for the selected episode days. Figure 

.12.4.3-4: Percent of averaged hourly wind speeds < 1 m/s by zone on June 2. 2005, Figure 

4.3-6 d hourly wind speeds < 1 m/s by zone on June 21, 2005, Figure 

3-8 d hourly wind speeds < 1 m/s by zone on August 2, 2005, and 

. hourly wind speeds < 1 m/s by zone on June 8, 2006

ar ction of averaged hourly modeled versus observed wind speed 

ce e typically used a wind speed metric with a 

ld l MM5 modeling was fairly good, a tighter 

d on. Figure 2.12.4.3-5: Percent of averaged hourly wind 

ns 2, 2005, Figure 2.12.4.3-7: Percent of averaged hourly 

re 1, 2005, Figure 2.12.4.3-9: Percent of averaged 

wi August 2, 2005, and Figure 2.12.4.3-11: Percent 

ag y zone on June 8, 2006, show bar charts by 

 th odeled versus observed wind directions with differences 

es are paired and color coded so that MM5 configured 

G the corresponding MM5 configured with Grell 

 m

by these metrics, of the modeled wind speed and 

d without Grell) varies by episode day and by 

o ing zones considered, the Grell configuration 

d wind direction accuracy was degraded. Of the 

 e , June 2 has poorest over all wind speed and 

August 2 has the best over all wind speed and 

ce winds are input to an air quality model, the 

e and dispersion) than the wind direction. In 

n, ge error especially at low wind speeds. This 

T Grell configuration does not compromise the 

lo

2

2.12.

2.12.4.

: Percent of average

: Percent of average

Figure 2 12.4.3-10: Percent of averaged

show b charts by zone of the fra

differen

thresho

s less than 1.0 m/s. MM5 modelers hav

 of 2.0 m/s. However, since the overal

threshol was warranted for this evaluati

directio < 30 degrees by zone on June

wind di ctions < 30 degrees by zone on June 2

hourly nd directions < 30 degrees by zone on 

of aver ed hourly wind directions < 30 degrees b

zone of e fraction of hourly averaged m

less than 30 degrees. The bars in these figur

without rell (colored blue) are set to the left and

(colored aroon) are plotted to the right. 

The differences in the accuracy, as determined 

direction between the configurations (i.e., with an

zone. F r the majority of episode days and monitor

improve wind speed accuracy although the

selected pisode days in these four sets of figures

direction

n

accuracy with the Grell configuration and 

directio accuracy. 

With the

wind sp

consideration that these modeled s

ed has more of an influence (e.g., tr

urfa

ansport

additio wind direction measurements often have lar

led the CEQ staff to conclude that using the

meteoro gical modeling performance. 

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Figure 2.12.4.3-4: Percent of averaged hourly wind speeds < 1 m/s by zone on June 2. 2005 

Figure 2.12.4.3-5: Percent of averaged hourly wind directions < 30 degrees by zone on June 

2, 2005 

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Figure 2.12.4.3-6: Percent of averaged hourly wind speeds < 1 m/s by zone on June 21, 2005

Figure 2.12.4.3-7: Percent of averaged hourly wind directions < 30 degrees by zone on June

21, 2005 

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Figure 2.12.4.3-8: Percent of averaged hourly wind speeds < 1 m/s by zone on August 2, 

2005

Figure 2.12.4.3-9: Percent of averaged hourly wind directions < 30 degrees by zone on 

August 2, 2005 

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Figure 2.12.4.3-10: Percent of averaged hourly wind speeds < 1 m/s by zone on June 8, 2006 

Figure 2.12.4.3-11: Percent of averaged hourly wind directions < 30 degrees by zone on 

June 8, 2006

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The close proximity of the Houston Ship Channel (HSC), an area with a high density of industrial 

sources, to Galveston Bay and the Gulf of Mexico with the associated land sea breeze phenomena 

creates complicated wind flow patterns of ozone precursor emissions. Therefore the TCEQ staff 

felt the wind speed and direction metrics used to evaluate the difference in the MM5 

configurations (i.e., with or without Grell on the 4 km grid) needed to be supplemented with a 

performance measure that considers the dynamic features of the winds. Therefore an additional 

performance measure was considered which compares the forward trajectory paths of modeled 

and observed air parcels advected by the winds. This performance measure is better 

characterized as a graphical evaluation rather than a metric since there are no recommended 

criteria for determining the correct path of an air parcel. This graphical evaluation was conducted 

for the same selected episode days. 

The density of trajectories needs to be high enough to represent the multitude of emission source 

locations in the HGB area that can contribute to high ozone concentrations down wind, without 

obscuring the plots. Although even with a relatively high density of trajectories, there is no 

assurance that any of the air parcels from the selected emanating locations will be advected over a 

particular monitor due to the complexity and numerous sources in the HGB area. In addition the 

density of trajectories needs to be high enough to reflect whether or not the wind pattern 

associated with the land-sea breeze phenomena has been adequately replicated. 

Although the starting point and time are arbitrary, the TCEQ staff has focused on emission source 

locations such Houston Ship Channel (HSC) and roadway intersections, as well as starting the

 although, the industrialized HSC can give rise to 

notable emissions of highly reactive pollutants at anytime. The observed forward trajectories are 

plotted based upon surface winds measured at CAMS sites from standard 10 meter towers. 

Although the data have been quality assured, CAMS winds are tabulated as hourly averages from 

shorter averages of 5 minute measurement periods. The lowest model layer simulates winds at 17 

meters, which is notably higher than the observations, so the modeled hourly averaged wind 

speeds are adjusted to reflect a 10 meter level. These caveats need to be taken into consideration 

when comparing observed and modeled forward trajectories, since they can contribute to 

accumulated differences in wind speed and direction. 

In Figure 2.12.4.3-12: 12 Hour Forward Trajectories of Air Parcels From Multiple Source 

Locations for 6 AM (CST) June 22, 2005 and Figure 2.12.4.3-13. 12 Hour Forward Trajectories 

of Air Parcels From Multiple Source Locations for 9 AM (CST) June 22, 2005, 12 hour forward 

trajectories are shown for June 22, 2005, starting at 6 AM and 9 AM (CST), respectively. These 

trajectories show the transport paths of air parcels emanating from various emission locations 

(e.g., stacks, intersection). Monitoring sites are color coded by the magnitude of the maximum 

daily eight-hour ozone concentration recorded at that site. Comparing the trajectories in these 

two plots shows how the ensemble of trajectories migrates for different starting times (i.e., 

different air parcel emanating times). 

trajectories at 6 AM and 9 AM (CST). Air parcels emanating from emission source locations

between 6 AM and 9 AM are typically those that are predominately associated with maximum

ozone concentrations occurring later in the day

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Figure 2.12.4.3-12: 12 Hour Forward Trajectories of Air Parcels From Multiple Source 

Locations for 6 AM (CST) June 22, 2005 

Figure 2.12.4.3-13: 12 Hour Forward Trajectories of Air Parcels From Multiple Source 

Locations for 9 AM (CST) June 22, 2005 

The ensemble of trajectories for the air parcels emanating at 9 AM shows that they are not carried 

as far south as the air parcels emanating at 6 AM. Therefore, between 6 and 9 AM on June 22, 

2005, the ensembles of trajectories migrated northward. 

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Also notable from both figures are the stronger winds simulated by MM5 for both the 

configuration with and without Grell, on June 22, 2005. The differences between the MM5 

simulated and CAMS observed winds, which accumulate most strongly in the first few hours, are 

especially noticeable in the Beaumont Port Arthur area and south of Houston nearer the coast. 

The accumulation of the stronger modeled winds occurring early in the trajectory carry the air 

parcels further to the southwest before the land sea breeze phenomena induced the clockwise 

rotation in wind direction. Focusing on the trajectories of the 6 AM and 9 AM air parcels 

emanating from the HSC, the trajectories derived from the MM5 simulated winds with the Grell 

configuration appear to compare slightly more favorably with the trajectories derived from the 

CAMS observed winds. 

The comparability of the trajectories derived from the winds for the two modeled configurations 

(i.e. with and without Grell) and the observed winds varied by episode day. For most of the days 

the trajectories for the two modeled configurations were somewhat similar, although they both 

generally compared less favorably with the trajectories derived from observed winds. However, 

for the episode days considered, the configuration with Grell appears to provide a slight 

improvement in the 6 AM and/or 9 AM trajectories. This led the TCEQ staff to conclude that 

using the Grell configuration does not compromise the meteorological modeling performance in 

the HGB area. 

 The “conventional wisdom”, however, is that 

onvective parameterizations are reasonable for domains with grid spacing of 10-12 km or 

that convection can be explicitly modeled at smaller scales. General modeling 

guidance support this type of model configuration (Grell, 1993, Grell, Dudhia and Stauffer, 

1994). On the other hand, grid-resolved “explicit” convection also depends upon a different set 

of parameterizations – the explicit moisture physics schemes which account for the influence on 

precipitation due to nucleation, coagulation, fall speed, supercooling of water, and various icing 

processes. 

Since TCEQ staff have chosen to investigate a non-standard configuration, this section will 

include a short series of points arguing why this is not completely unreasonable. Those points 

will recognize that 1) the traditional mix of explicit and parameterized convection is implemented 

to avoid “double counting” and thus we may expect to avoid harm by trying something new, 2) 

scale-separation between explicit and parameterized convection has been conjectured as 

necessary, but may not reflect all met regimes, 3) Grell is being used to stabilize an already 

generally fair-weather environment, and 4) the default of an explicit-only treatment is also prone 

to limitations as scale increases. 

Molinari and Dudek (1990) provide an important review of the “partitioning” between gridresolved convection and the sub-grid convective parameterization. Although these authors argue 

for a hybrid scheme in which water content from a cumulus parameterization is provided directly 

cause grid-scale condensation is computed at the end 

partitioning by the Grell cumulus 

2.12.4.4. Discussion 

The previous sections have presented an operational rationale for investigating the use of the 

Grell cumulus parameterization on a 4 km grid. 

c

greater, and 

to an explicit moisture scheme, they note, 

“Calculation techniques are such that modelers do not allow ‘double counting’; energy and 

moisture are conserved in such situations be

of the time step after all other processes have acted (e.g., Kanamitsu 1975)”. Experiments by 

TCEQ staff support other anecdotal reports that partitioning between the cumulus 

parameterization and explicit moisture physics vary across episode, and will be dependent on grid 

resolution. As pointed out below, however, the 

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parameterization might be responsible for less condensable water than other cumulus schemes 

 1990)... For grid spacing from about 3 to 20-25 km, it remains 

ncertain whether a general solution exists.” (Molinari and Dudek, pg. 339) 

 cumulus precipitation to grid-scale explicit moisture precipitation. 

995) investigate a comparison of convective parameterizations in MM5. 

that compared to other convective schemes available within the MM5 model, “... the 

rell scheme quickly switches from convective to resolved-scale dominance before the MCS 

 of explicitly modeled convective systems. Citing earlier work 

uch as Molinari and Dudek among others, these authors agreed that convective 

vection, including the upscale growth from individual 

and thus allow the explicit moisture parameterization to do most of the work. 

Molinari and Dudek cite earlier work by Arakawa and Chen (1987) who favored the idea of 

“scale separation” yet noted that nature does not create a clear example of a spectral gap between 

cloud scales and mesoscale. They note, importantly, that the lack of a spectral gap is generally 

unavoidable in the temporal development convection as well as continuous range of spatial scales 

seen in cloud systems. The discussion section of their paper makes the following assessment: 

“For models with grid spacing less than 2-3 km, the fully explicit approach is clearly superior to 

parameterized approaches, even though a 1-km grid spacing can fully simulate only the largest 

convective clouds (Lilly

u

The concern by model developers at the intermediate range of scales mentioned above is that 

parameterized convection can grow quickly to grid scale – on a large grid this could produce far 

too much precipitation. Furthermore, the likelihood of parameterized convection growing to grid 

scale increases with increased resolution. In such a situation, it would be preferable to see 

parameterized clouds become quickly secondary to grid-scale convective clouds. Although 

TCEQ staff have observed episodic variability in this regard, the Grell scheme seems most likely 

to transition from

Wang and Seaman (1

This paper investigates the various metrics described earlier in this report which are of primary 

concern for traditional precipitation forecast validation. They note the desirability of finding a 

reasonable partition between parameterized and grid-scale rainfall, in part for stability reasons. In 

particular, the note that, “This is because the sub-grid-scale convective parameterization 

represents a more rapid upward transport of heat and moisture, and hence can stabilize a column 

faster.” Although many model developers are interested in the correct evolution and intensity of 

a mesoscale convective system, we conjecture that this parameterization can stabilize a fair 

weather atmosphere. More importantly for our case study, Wang and Seaman note in their 

experiments 

G

matures...” Again, this observation is only part of a larger and traditional case study of warm and 

cold weather experiments; however, this is suggestive of the type of behavior which one would 

like to see in a cumulus parameterization if it is applied on an 4 km grid. 

The question of the applicability of applying a cumulus parameterization on a 4 km grid can be 

recast by asking whether an explicit moisture parameterization is alone sufficient to stabilize the 

atmosphere and capture appropriate precipitation. Weisman, Skamarock, and Klemp (1995) 

studied the resolution dependence

s

parameterizations were necessary for resolutions between 20 and 50 km. They summarized the 

development work of the previous decade and concluded, “nonhydrostatic cloud model 

simulations employing grid resolutions of 2 km or less have shown much success in replicating 

the observed structure and evolution of con

convective cells to full mesoscale convective systems.” Thus, at sufficient resolution, an explicit 

moisture scheme alone should be sufficient for capturing convective behavior. With sufficient 

forcing, the explicit moisture physics on a four kilometer grid may be adequate since these 

authors offer, “that 4 km resolution is sufficient to reproduce most system-scale aspects of squallline behavior over a 6-h period, with resolutions of 8 km or larger resulting in significantly slower 

system evolution and stronger system-scale circulation.” 

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2.12.4.5 Conclusion 

Air quality episodes are typically fair weather periods, and at one time clouds were ignored 

entirely by choice of short pollution events. Longer episodes may be punctuated by periods of 

precipitation, but the question of how to best characterized cloud development in a way which 

does not compromise the frequent light winds and mesoscale land-sea breeze has been an open 

question. The effects described here have been subtle, but arguably the most important objective 

of not unfairly distorting the behavior of the meteorological evolution of these episodes has been 

accomplished while improving the over-all characterization of fields that need to be input into the 

photochemical model. 

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.12.4.6 References 

a, and D. R. Stauffer, 1994: A description of the fifth generation Penn 

tate/NCAR mesoscale model (MM5), NCAR Tech. Note NCAR/TN-398-STR, 138 pp. 

Lilly, D. K., 1990: Numerical prediction of thunderstorms—has its time come? Quart. J. Roy. 

Meteor. Soc., 116, 779-798. 

Molinari, J. and M. Dudek, 1992: Parameterization of convective precipitation in mesoscale 

numerical models: A critical review. Mon. Wea. Rev., 120, 326-344. 

Wang, W., and N. L. Seaman, 1997: A comparison study of convective parameterization schemes 

in a mesoscale model, Mon. Wea. Rev., 125, 252-278. 

Weisman, M. L., W. C. Skamarock, and J. B. Klemp, 1997: The resolution dependence of 

explicitly modeled convective systems, Mon.Wea. Rev., 125, 527-548. 

 

2

Arakawa, A., and J.-M. Chen, 1987: Closure assumption in the cumulus parameterization 

problem. Short and Medium Range Numerical Weather Prediction. T. Matsuno, Ed., Meteor. 

Soc. Japan, Universal Academic Press, 107-131. 

Grell, G. A., 1993: Prognostic evaluation of assumption used by cumulus parameterizations, Mon. 

Wea. Rev., 121, 764-787. 

Grell, G. A., J. Dudhi

S