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.usTexas 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 of2REVISION TO THE STATE IMPLEMENTATION PLAN
FOR THE CONTROL OF OZONE AIR POLLUTION

HOUSTON-GALVESTON-BRAZORIA 1997 EI GHT-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, 2010xv 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 th e 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 Sour ce 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 th e HGB Attainment Demonstration SIP Revision for the 1997 Eight-Hour Ozone Standard

Appendix J Recommendations for Best Ma nagement Practices to Control and Reduce Inadvertent Cargo Vapor Emissions in the Tank Barge CommunityAPPENDIX A

Meteorological Modeling for the HGB Attainment Demonstration SIP Revision for the 1997 Eight-Hour Ozone StandardA-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

A-4 1.2 MM5 Configuration

Section 2.1 MM5 Performance Evaluation Tools A-6

A-12 2.2 MM5 Modeling Performance

A-13 2.3 2005ep0 Performance Evaluation

A-17 2.4 2005ep1 Performance Evaluation

A-23 2.5 2005ep2 Performance Evaluation

A-32 2.6 2006ep0 Performance Evaluation

A-40 2.7 2006ep1a Performance Evaluation

A-50 2.8 2006ep1b Performance Evaluation

A-61 2.9 2006ep1c Performance Evaluation

A-68 2.10 2006ep1d Performance Evaluation

A-80 2.11 References

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

ATTACHMENT 2 Land Use and La nd Cover (LULC) Characteri s tics A-90

ATTACHMENT 3 Data Assimilation A-99

ATTACHMENT 4 Application of Grell Cumulus Scheme A-130A-3 METEOROLOGICAL MODELING FOR THE HGB ATTAINMENT
DEMONSTRATION SIP

1.1 MM5 Modeling Overview The TCEQ is using the Fifth Generation Meteor ological Model (MM5, version 3.7.3) developed jointly by the National Center for Atmosphe ric 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 (epi sodes) 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 or der to resolve boundary layer phenomena and to provide a one-to-one mapping with the first tw enty 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 Ran g e Grid Point s Do m a i n Easting ( k m ) Northing ( k m ) E a s t i n g N o r t h i n g 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 eval uation, 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 inte rpolate the 12 km domain output to the 4 km domain input. The following bullets feature the esse ntial 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 Attachme nt 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) us ed for model initialization (NCEP, 2009)

 Soil data added for NCEP - Oregon State Un iv. - Air Force - Hydrological Research Laboratory (NOAH) land-surface model (N CAR, 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 di rection 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 charac teristics 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 mode ling appendices for the satellite-based LULC and SST data provide additional details regard ing 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. Th is 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 dom ains (108 km, 36 km, and 12 km) were nudged towards the gridded analysis of the North Amer ican 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: App lication of Grell Cumulus Scheme. The choices for the other parameters were based on the MM5 co nfigurations 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 determ ines vertical mixing from a predicted turbulent kinetic energy (TKE), the MM5CAMx program was run with a switch to use this TKEinformation in its calculation of the vertical diffusivity coefficients required for input into CAMx. The minimum threshold for vertical diffusivity (K V ) 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 mo re 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 la rge number of CAMS monitors in the Houston region, an area wide average may smooth out smal ler scale features. Zones of CAMS monitors were chosen to represent smaller geogra phic areas as defined in Figure 2.1.1-2: CAMS in Model Performance Zones . Evaluating model runs using the Hous ton Region and Zone averages instead of individual CAMS monitors proved to be e fficient, 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-6Downwind

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 ev aluate 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-72.1.2 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. Th e 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 th e 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 mode l 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 we re 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 indi vidual, 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 direc tion 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-8A linear regression line is fitted to the data and is shown in green. The correlation equation and
coefficient of determination R 2 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 R 2 would be 1.0.
The R 2 indicates how well MM5 predicts the obser vations, with higher va lues 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 th at MM5 is being nudged to o hard (see Attachment 3: Data Assimilation). For wind direction, the regression line and R 2 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 m odeled 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 wi nd speed scatter plot for 2006ep1c episode

2.1.4 Time-height plots

A-9 Time-height plots represent the up per air horizontal wind conditions at profiler locations over one episode day. Vertical winds (rising or descendi ng air) are not shown. The model winds (CAMx input) are shown as blue vectors and the observe d 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 compa ss direction from where the wind is blowing. Alonger 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 prof iler (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 in formation 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; obser ved winds are shown in red. This plot type yields a visual depicti on 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 id entified using these plots. Figure 2.1.5: Surface wind field plots for June 5, 2006 at 12:00 CS T 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 inconsiste nt with observations in eastern Harris County.

A-10Figure 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 w ith 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 NationalLaboratories. 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 meteorologi cal sites in and around the HGB area. This program was also used to calculate back-tra jectories 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 repli cating 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 we re 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 co nversion 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 perform ance 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-11observed peak 8-hour ozone concentration observe d 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 th e performance of the final MM5 modeling configuration for each of the TexAQS II episodes, as listed in Tabl e 1.1-1. Due to the large number of episode days, the performance evaluation will focus on the days when high ozone and notablemeteorological phenomena occurred (Knoderer et al., 2008; Nielsen-Gammon, 2007a; NielsenGammon, 2007b; TCEQ, 2005; TCEQ, 2006). Wind fi eld 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 a nd 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-12TexAQS 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 2006ep1dPercent 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, w ith the domain high 0f 116 ppb. June 2 had the episode highest values for the episode. Th ere 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 bac kground 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 1 st

. High ozone periods followed each of these events.

A-13A-14 General Model Performance
Table 2.3-1: Average Houston Region 12 Percent Accu racy 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 dir ections 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 zon es 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 sout herly winds - plus continental air from the east likely re-circulated into the Houston area and c ontributed 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

A-15Figure 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 rath er 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.

A-16Figure 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
21 st had eleven sites in exceedance, and three m onitors recording eight-hour ozone over 100 ppb. On June 22, 15 sites had eight-hour ozone exceed ances, with an episode high ozone of 103 ppb.

A-17A-18 General model performance The performance metrics described above for wind speed, wind direction, and temperatures look very good across the entire episode. Also, ther e 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 18 th caused winds to back from southw est back to the northeast. The high ozone day of the 21 st 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 th e 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

A-19Figure 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 informa tion 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.

A-20Figure 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 ha nd, previous modeling experience has shown that when there is a strong flow reversal or a near ly 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 21 st . 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.

A-21Figure 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

A-22time 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 s ubtle features. The day time wind speeds in the scatter plot look very good, albeit with a very sm all negative bias. However, the consequence of even a tiny bias, if it is persistent can s how 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-m oving and weak cold front while the two other periods resulted from high pressure-induced coast al oscillation. Afte rnoon thunderstorms occurred on many days during this episode , and the model only had limited success in reproducing these storms and the resultant wind patterns.

A-23MM5 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, especi ally at night, when wind speeds were low (Figure 2.5-1). Note that observed wind directi on is less accurate when wind speeds are low. Figures 2.5-2 and 2.5-3 exhibit the wind direction an d 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

A-24Figure 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

A-25Figure 2.5-4: Region 12 average temperature time series for 2005ep2 episode

July 28, 2005 This day was the first of the 2005ep2 episode fo r which more than one monitor recorded an exceedance of the eight-hour ozone standard. Thr ee monitors were equal or greater than 85 ppb, with the Seabrook Friendship Park C45 monitor obs erving 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 sp eed for all hours on 7/28/05. MM5 predicted an average wind speed within 2 m/s of the observa tions 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 do main. Temperature performance on this day was very good, with very high percentages of Regi on 12 and Zone averages within 1 degree Celsius
of the observations.

A-26Figure 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 pl anetary 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.

A-27Figure 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 in accurate transport on this day, the photochemical model places the high ozone plume to the south a nd 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

A-28August 2, 2005
Eleven stations recorded an exceedance of the ei ght-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, followi ng the high pressure coastal oscillation pattern.
Scattered clouds were numerous and convective ac tivity 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 dir ection patterns acceptably on this day. The Houston area average time series for wind sp eed 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 pl ots 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 nigh ttime 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

A-29 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 re presented. PBL height estimates from La Portewere not available on this day. At the Jeffers on 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 standa rd 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

A-30observed 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 timi ng 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 ob served speeds, which were less than 3 m/s on average. Also, the mid-day period is well repr esented 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 hour s were not available as the profiler estimates stopped at 13:00 CST. Results at the Jefferson County and Huntsv ille profilers also showed wellrepresented morning PBL rises. Afternoon PBL estimates were mixed, with both over- and under-predictions, as shown by the Huntsville profile r time-height plots. The morning PBL rise may be a more important metric for ozone pr oduction than peak mixing height, as reported by Knoderer et al. (2008).

A-31Figure 2.5-12: Time-height plots for the La Po rte (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 fart her 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 hi gh 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 a nd 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 leadi ng to high ozone can be perhaps be deduced by

A-32A-33 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 “biase d” into the southwest quadrant rather than the southeasterly quadrant seen during the June 2005 ep isode. 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-wi de performance is above average on our two peak days of June 8 and June 14. It is striki ng 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 performanceFigure 2.6-1 : Region 12 average wind direction time series for 2006ep0 episode

A-34Figure 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 in terpreting 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 sp eed performance is reflected in trajectory lengths which look similar for both observational da ta 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.

A-35Figure 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

A-36Figure 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 wi nd 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 degr ees 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 bi as 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.

A-37A-38 Figure 2.6-6: Region 12 average wind direction and speed scatt er 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 bi as 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 Hous ton 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.

A-39Figure 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 th e 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 expe rienced 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

A-40Figure 2.7-1: Region 12 average wind direction and speed scatter plots for 2006ep1a episode

The Houston Region 12 average percent accuracy st atistics 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-41Figure 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 o ccurred at Mercer Arboret um C557. Winds were light from the west in the morning before st alling out around noon. A bay and sea breeze from

A-42the south-southeast dominated the afternoon flow . Thunderstorms developed over Louisiana in the late afternoon but did not impact Houst on. Temperatures were in the upper 90s.

As with most days in this episode, MM5 accep tably 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 th e veering winds well. For the daytime hours, MM5 slightly under-predicted the wind speed, a nd 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-43Figure 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 ofthe atmosphere for the entire day. The midday hours were especially well pr edicted through the first kilometer, when the wind was calm. On other days, MM5 has difficulty accurately rep licating calm conditions. The morning rise and afternoon decay of the PBL may be slightly unde r-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 si mulates 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 w esterly 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 observe d 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 obs erved peaks, due to the small wind direction

error.

A-44Figure 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 Ga lveston 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 m onitors 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-45Figure 2.7-9: Region 12 average wind direction and speed scatter plots for August 17, 2006

The time height plots from the La Po rte 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 wi nds 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 fo llow 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 traj ectories, 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 Port e (left) and Jefferson County (right) profilers on August 17, 2006

A-46Figure 2.7-11: Surface wind field plots for the 14:00 CST (left) and 19:00 CST (right) on August 17, 2006

Figure 2.7-1 2 : 12-ho ur 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. Temperatur es 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 backgr ound ozone, allowing for an exceedance.

A-47 Wind speeds were accurate ly represented by MM5 for this day, with almost 80% of the hours
within one meter/second of the observations. Wi nd direction was well simulated except for a few early morning hours when MM5 predicted south-s outhwest winds. In reality south-southeast winds existed. Temperatures were under-predict ed throughout the Houston area, as morning convection and precipitation in MM5 (not show n) 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 hi gh ozone to be placed west and south of the observed peak at Katy Park.

A-48Figure 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 di es at 09:00 CST. At 12:00 CST, easterly winds develop. MM5 generally replicates these wind c onditions well. At both profilers, the peak mixing depth is under-predicted. The morning ri se 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 pl ace the modeled ozone plumes to the south and west of the observed peaks.

A-49Figure 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- Septem ber 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 per cent respectively. The e rror associated with thewind direction, wind speed and temperature for the entire episode for region 12 (HoustonGalveston) was 67, 96, and 80 percent r espectively 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-50Table 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 a nd direction scatter plots for 2006ep1b episodeFigure 2.8-2: Region 12 average wind direction and wind speed time series for 2006ep1b episode

A-52 rn e t0 6c p 1 b

. e t c**r-n s.** **;.** nc w u**t.** n e vvu t c s rl u l c

. g re l l

Win d D i r deg Ho ust o n

- G al v esto n

-**o-r ------------** **--------------~~--------~ ==** **~~~ ----------------** **~--~ ~~~** **~~ -.** **5** 270
2:2 5

1 60
1 .3 5

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 ea n Ab s o l u t e E ,- ,-o ,- W i n d O i r de gAugust 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 Ge orge Bush Houston Intercontinental Airport, 92°F at Hobby Airport, and 88°F at Galveston Airport.

The error associated with the wind direction, wi nd 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 maxi mum ozone to be to the south of the location of the maximum ozone for that day.

A-53 Figure 2.8-5: Region 12 A v erage wind direction and speed scatter plots for August 31, 2006Table 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-54Figure 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, a nd Mustang Bayou CAMS 619.

In the early morning, the skies were clear, b ecoming 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 southeaster ly 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 Ai rport, and 91 degrees F at Galveston Airport.

The error associated with the wind direction, wi nd 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-55Figure 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 obser ved 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 th e east then north. In Harris County, the MM5 and observed trajectories curl around and travel a very limited distance representing a stagnant
circulation.

A-56Figure 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 exceed ance of the eight-hour ozone standard greater than 85 ppb. The Houston Bayland Park C53 mon itor recorded the maximum eight-hour ozone of 110.5 ppb at 1000 CST. Regional bac kground levels of ozone, in the air coming into the Houston area, were at least about 61 to 72 ppb as indicat ed 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 so me 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 Geor ge Bush Houston Intercontinental Airport, 91degrees F at Hobby Airport, and 88 degrees F at Galveston Airport.

The error associated with the wind direction, wi nd 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-57Figure 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 pa rallel and to the south of the trajectories generated using the CAMS data.

A-58Figure 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 th e air coming into the Houston area, were at least about 56 to 74 ppb as indicated by th e 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 scat tered boundary-layer cumulus from late morning
onward. Scattered thundershowers occurred in th e 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, wi nd 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-59A-60 Figure 2.8-14: Region 12 Average wind direction and speed scatter plots for September 14, 2006

Figure 2.8-1 5 : Time-height plot for La Porte profiler on September 14, 2006 shows a good agreement between the profiler winds and the MM 5 output for most the day and the vertical levels, except for the lower leve ls from 1-6 and 10-11 CST and th e 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 pa rallel 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, 2006Figure 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 oz one: September 20, 2006, and September 26-27.

During September 16 -29, 2006, there were th ree 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 associ ated 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 a nd temperature for the entire episode for region 12 (Houston-Galveston) was 84, 96, and 69 re spectively 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-61Table 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-62Figure 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 Septembe r 27 has noticeable wind direction errors during the early to midmorning hours, and then looks good for the rest of the day.

.

A-63A-64 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 le vels of ozone, in the air coming into the Houston area, were at least about 62 to 65 ppb as indicated by th e 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, wi nd 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 ver tical 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 tr ajectories generated using the CAMS data, but slightly to the south. This would push the plum es 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 / 13Figure 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

A-65Figure 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 cl ouds. Light west to southwest winds occurred in the early morning, becoming light to moderate fro m 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, wi nd 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

.

A-66Table 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.

A-67Figure 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.

A-68During 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 associ ated 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 a nd 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

A-69 Figure 2.10-1: Region 12 average wind speed and direction sca tter plots for 2006ep1d episodeFigure 2.10-2: Region 12 average wind di rection 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 oz one, 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.

A-70Mostly 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 temperatur e 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

A-71 Figure 2.10-5: Region 12 Average wind directio n 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 ag reed 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

A-72 On that day, only one monitor recorded an ex ceedance 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 th e peak eight-hour averages at Conroe CAMS 78, Kingwood CAMS 555, and Crosby CAMS 55 3. 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 morn ing 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 b ecoming 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. Th e 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 be tween 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

A-73 Figure 2.10-7: Region 12 Average wind directio n and speed scatter plots for October 7, 2006Figure 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

A-74 On that day, 3 m onitors 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 le vels of ozone, in the air coming into theHouston 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 ove rcast 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 temper ature 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

A-75 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 ma ximum 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 di rection time series for October 10, 2006

A-76Figure 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 surf ace 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.

A-77Figure 2.10-14: Region 12 a verage humidity and temperature for October 10, 2006

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A-79A-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+, 10.1 175/BAMS-86-5-657

Berkowitz, C.M., Spicer, C.W., Doskey, P.V ., 2005. Hydrocarbon observations and ozone production rates in western Houston during th e 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, Fi nal 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 coor dinate model: Further developments of the convection, viscous sublayer, and turbulence cl osure 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 Re port to Texas A&M University for Texas Commission on Environmental Quality Proposal under Grant Activit y 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/M M5_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 Texa s Commission on Environmental Quality under Proposal for Grant Activity 582-5-64593-FY07-21.A-81 Nielsen-Gammon (2007b), Comprehensive meteorological analysis of TexAQS II episodes, Task
3 Deliverable for Texas Commission on Environmen tal 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), Multi scale 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/ m onitoring/air/ m onops/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/ m onitoring/air/ m onops/sigevents06.html , Texas Commission on Environmental Quality (TCEQ), Last accessed May 21, 2009.

TCEQ (2009), Meteorological modeling perform ance 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 ).A-82 ATTACHMENT 1

Sea Surface Temperature (SST) Characteristics
Houston-Galveston-Brazoria Attainme nt Demonstration SIP Revision2.12.1 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 polluta nts 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 characteris tics and sea surface temperatures (SST) were updated with LandSat and Geostati onary Operational Environmental Satellite (GOES) based data. The development and use of the updated SST inpu ts will be discussed below while the satellitebased land use is detailed in Attachment 2: La nd Use and Land Cover (LULC) Characterisrtics.

2.12.1.2 SST Development SST is a constant field in MM5, meaning it is not up dated or calculated by the model. It can be a single value per model grid square throughout th e model run or it can be time-varied. Most analysis products used to initialize MM5, su ch 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-resolu tion (~10 km), global SST analysis that can be directly input into REGRID with a modi fied Vtable (NCAR, 2005). The NCEP SST product is based on Advanced Very High Resolution Ra diometer (AVHRR) satellite, ship, and buoy data (NCEP, 2009). The AVHRR sensor is onboard th e 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 NC EP 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 T exas 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

A-83SST dataset was sought to better represent these dynamic processes where the NCEP product may have difficulty.

Geostationary orbiting satellites stay fixed over th e equator (i.e. synchronized with the motion of the Earth), thus constantly providing sensor measu rements and essentially eliminating orbital data gaps. The GOES retrievals provide hourly 6km re solution 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 dow ntime. 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 in ternally 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 sat ellite 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 kr iging 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

A-84 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. Th e 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 variati on 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)

A-85A-86 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 app eared. The two products showed similar features, such as warmer coastline temperat ures, 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 doubl ed 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 do es 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. B ecause 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 ra w 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 highe r resolution product was chosen. Table 2.12.15-
1: Table of error percentages from 4km runs fo r 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 me tric. On average the performance between the different SST types was similar, thus the UH GOES SST product was chosen for the final model configuration.

A-87A-88 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 / 28A-89 Banta, R. M., C. J. S y er 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 a nd 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 Hous ton, Texas, and the Im pa ct of Wind
Patterns on Ozone. J. Appl. Meteor.

, 44 , 1788–1806.

ITT Visual Information System s (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. Ni elsen-Ga m bad air day in Housto on, L. S. Darby, R n, Bull. Am. Meteo J. Alvarez, S. P. ol. Soc., 86, 657– Sandberg,
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/M M5_tut_Web_notes/REG R ID/r egrid.htm , Last accessed March 11, 2009.

NCEP, (2009), National Center for Environm ental Prediction RTG_SST_HR Experimental Analysis web site, http://polar.ncep.noaa.gov/sst/ophi/ , Last accessed March 25, 2009.A-90 Land Use and Land Cover (LULC) Characteristics
Houston-Galveston-Brazoria Attain ment Demonstration SIP Revision ATTACHMENT 2A-91 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 in teraction 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 typ es, 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 Stat es Geological Survey (USGS) Version 2 data for input: 1 degree; 30, 10, 5, 2 minute; 30 second [(US GS, 2008a). For MM5 Version 3.4 and later, the coarse USGS data was recreated using th e 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 Advan ced 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 sate llite-based data from the 2001 National Land Cover Database (NLCD). The 2001 NLCD is a 30-meter resolution, mu lti-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 wood y vegetation for biogenic modeling purposes [(Wells, 2006). This analysis fo cused on the coastal areas of Texa s, 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 asFigure 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 For est Service (TFS) dataset was merged with the
UTCSR data. The TFS data is based on si milar 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 classificati ons, and UTCSR data (Figure 2.12.2.3-2: Top: USGS

2km LULC; Bo 1 ttom: UTCSR 12km LULC ). In the final dataset, ‘mixed dry/irrigated cropland

nd pasture’ replaced much of the ‘dry croplan d’ in south and east Texas. Wetland areas were lso more numerous along the Louisiana a nd Texas coastlines and river bottoms. a

a

A-92Figure 2.12.2.3-2: Top: USGS 12km LULC; Bottom: UTCSR 12km LULC ; note the change from dry cropland (off-white) to mixed dry/irrigate d 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, a nd the increase in urban classif
H

2.12.2.4 Vegetation Fraction Updates Vegetation fraction is a key terrestrial feature th at MM5 uses to determine the exchange of heat and moisture between the surface and atmosphe re, especially for evapotranspiration. The vegetation fraction data was r econstructed from the 30m UTCS R 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 im proved vegetation fracti on 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 wa s the removal of an artificial zone of high vegetation fraction east of Galveston Bay that was reflected in MM5 surfac

p

A-93Figure 2.12.2.4-1: Top: Original USGS Vegetation Fract ion; 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 cas e, 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 cont ent 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 wate r 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 ty pes to account for the moisture content of the soil near water bodies. “Clay 1” represents clay with water content betw een 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

A-94Figure 2.12.2.5-1: Top: USGS Soil Types; Bottom: UTCSR Soil Types ; note the addition of lay1 and clay2 soil types along the rive c rs and coastal areas (adapted from Byun et al., 2008)

where UTCSR land/water mask differed fr om original USGS land/water mask (adapted 2.12.2.6 Land/Water Mask Update A land/water mask is created by the TERRAIN pr eprocessor and passed to the MM5 input files.
The land-surface model uses different surface-atmo sphere exchange algorithms according to the land/water mask. In Houston area, many emissi on 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

km cells 4 from Byun et al., 2008) ).

A-95Figure 2.12.2.6-1: Black points represent 4k m 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 progr ams 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. Wh ile 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 UT CSR LULC category updates. This version of the UTCSR data contains the soil type additi ons, 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
b etween the meteorology, biogeni c and LULC data uniquely, the year and sou rce of data are not a basis of model discrepancy.

2.12.2.9 Model Performance As the meteorological modeling of the Tex AQS II episodes was developed, many MM5 simulations were completed to test model confi gurations. 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 th e 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

A-96A-97 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

s the tabulated statistics show, the impact of the fi A nal 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.A-98 nal Report to the Texas Comm ission 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 a nd 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

a nd 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 d sensitivity enn State - NC R MM5 m . Part I: Mod

he Texas Forest ervice for ject TFS 27.

omer, C., J. D
anDriel and J itz, J. Fry ickham. (2 M. Coan Ho ssain, C. rson, N. Her A. McKerrow 7). Com
C ni P met ng Sens

onterminous U ted States, hotogram r ic Engineeri and Remote ing , Vol. 73, No. 4, pp 3 1.

USGS (2008), Global Land Cover Charact eristics 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 acce ssed rch 23, 2009

Land Cove r 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-proc essing Program s in Versi
5/worksh ws00/mesous pdf , Last acc essed 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 La nd Use La T2, Univers Cover Class
of Texas-A ion Project, H
Center for roject Contact umber: H- esearch, AA-99 ATTACHMENT 3

emonstration SIP Revision Data Assimilation Houston-Galveston-Brazoria Attain ment DA-100 2.12.3 Data Assimilation

2.12.3.1 Introduction
Data assim nalyses to produce a result closer to reality. In MM5, ob served parameters are assimilated during the model run through a process called Four-Dimensi onal Data Assimilation (FDDA), or nudging, by adding an additional term to the equations of mo tion (Stauffer and Seaman, 1990; Stauffer et al., 1991; Stauffer and Seaman, 1994). This process he lps 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 usin g FDDA to make sure the model does not significantly diverge from reality (EPA, 2007). Bo th types of data assimilation were performed for the meteorological modeling of the 2005 an d 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 da ta of winds, temperature, and water vapor. In MM5, analysis nudging is typically ru n by copying the model’s input file MMINPUT_DOMAINx to MMINPUT2_DOMAINx fo r the coarse grids only. The operational National Center for Environmental Prediction (N CEP) 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 influen ce 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 (t hree-dimensional) and times into the model forecast. This type of nudging is usually cond ucted on the finest scale grid, which allows local measurements to influence the large-scale me teorological 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 prof iler 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 Atmospheri c 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 w as 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 observa tions are blended with modeled aFigure 2.12.3.3-1: TexAQS II Profiler Locations

The quality assured upper air wind data has a ver tical resolution of 60-100 meters (m) from 40 m above ground level (agl) to over 10,000 m agl, de pending 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

eigh w ting). The frequency of the profiler observa tions was generally hourly but some recorded
nterval. The observations were time-shifted ahead to account

thoughensitivity 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 ep isodes used the MM5 default nudging strengths, data on a fifteen or thirt y 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 ob servations were rotated using a version of a Perl script posted to the MM5-Users listserv (P etre, 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.

efault nudging strengths and radii of influence were used for the observational nudging, D

s Gammon (2008b) reviewed the setup for observa tional 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 scrip t (mm
N

A-101A-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 th e 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

lternate nudging configurations were performed to determine if other setups would provide A

re sults 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 profile rs 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 im proved in order to provide better 4km inputs, observational nudging was run on the 12km domain w ith 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 SpeedComparison shows average 12km wind speed scatte r plots of all Texas ds472.0 surface monitoring sites versus the corresponding MM5 gr id cells for every hour of the 2005ep2 episode (July 25 – August 9, 2005). The box in the uppe r 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 Averag e 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

ll Texas ds472.0 sites. Again, the results ar a d

Standard 80 99 80 63 100 86 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) ).

A-103A-104 Figure 2.12.3.6-3: All 4km TCEQ CAMS (left) and Region 12 CAMS (right)

F igure 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 do main 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 nudgi ng did not have much of an impact.

F igure 2.12.3.6-4: 4km wind speed scatter plots for Region 12 without (left) and with (right) 12km observational nudging for 2005ep2 episodeA-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 av erages instead of individual CAMS monitors proved to be efficient, allowing more sens itivities 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. Te mperature 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
ai r quali collect upper air wind data for many areas with ty degradat studies. This is in contrast to the TexAQS 2000 air qual

s urrounded 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 consid ered 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 th e 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 circ le 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 imp acted, 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-107A-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 di d 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 perfor mance 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 inv estigated in terms of forward trajectories produced from surface wind sites and the corres ponding 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

igure 2.12.3.7-4: Forward trajectories comparing surface ob servations (re F 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 surfaceA-109 exhibits the same time peri od 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 omparin g surfac 0 (blue) and e observations ( km (green) fo red) to mode
JulFigure 2.12.3.7-5: Forward trajectories comparin g surface observations (red) to model runs with observational nudging ROIs of 120 (blue) an d 80 km (green) for July 28, 2005 at 6 AM

By evaluating the MM5 runs with different RO Is, 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 su rface 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), statis tics 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 evaluate d 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 performan ce 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 chang es did not appear to occur. These conclusions

comp

a

A-110Region 12 Wind Speed Performance (compliance < 1 m/s)

2005EP0 2005EP1 2005EP2

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%5/185/195/205/215/225/235/245/255/265/275/285/295/305/316/16/26/36/156/166/176/186/196/206/216/226/236/246/256/266/276/286/297/257/267/277/287/297/307/318/18/28/38/48/58/68/78/8

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%5/185/195/205/215/225/235/245/255/265/275/285/295/305/316/16/26/36/156/166/176/186/196/206/216/226/236/246/256/266/276/286/297/257/267/277/287/297/307/318/18/28/38/48/58/68/78/8

direction fddats direction upsfcfddats missing profiler Figure 2.12.3.8-2: Wind direction performance for episodes 2005ep0, 2005ep1, and 2005ep2

A-111Region 12 Temperature Performance (compliance < 1 deg.)

2005EP0 2005EP1 2005EP2

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%5/185/195/205/215/225/235/245/255/265/275/285/295/305/316/16/26/36/156/166/176/186/196/206/216/226/236/246/256/266/276/286/297/257/267/277/287/297/307/318/18/28/38/48/58/68/78/8

temperature fddats temperature upsfcfddats missing profiler

F igure 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 performan ce for the Houston-Galveston area (HGB) for wind speed, wind direction, and temp erature for the high ozone days for episodes 2005ep0, 2005ep1, and 2005ep2 shows a summary of the perfo rmance 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 nudgi ng performed equally or better than without surface nudging on 52% and 52%, respectivel y, 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: Hous ton-Galveston Region (R12); Zone 1 (Coastal); Zone 2 (Ship Channel); Zone 3 (S outheast 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 19A-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 Zo nes 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

uppe r 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

ones for compliance of wind speed within 1 m/s, wind direction within 30 deg Z ., and temperature

us a . within 1deg. for the episode average

.

The normal expectation is that by nudging to su rface data, the statistics should improve. For

005ep1, there is only a marginal improve 2 ment over ing upper- ir data onlyA-114 area (R12) and the five Zo nes 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

up per air only 65 68 32

Zo ne 2 with sfc nudging 85 69 35

up per air only 80 67 37

Zo ne 3 with sfc nudging 86 75 34

up per air only 83 72 36

Zo ne 4 with sfc nudging 85 61 25

up per air only 78 64 32

Zo ne 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 Zo nes 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 ai r data from rawinsonde or profiler data, which are typically widespread (i.e., low spatial reso lution). 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 uppe r 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 resolu tion than the profiler data, and would ideally require a smaller radius of influence to fully utili ze 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 insteadA-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 hi gh ozone da f September, 2 he wind speed
im e 60 k dius of influe was used. On
Se speed and wind di rection 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

nce ind direction degraded when th ptember 27, both the wind
ed for the radius

Houston (R 12) D aily 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 2006mmark

0 20 80 100our M Ozon
Wind 2006ep ct ion Perf
16-29 iler N

80% 1 00%

42 53

44 58 60 60%pliance w/bench

60ax Ozone

24

20%Per 29

0% 40%cent co

16 17 1 2 28 408-H

Max Oz one Wind S /s peed <1 m Wind Di r < 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 kmHouston (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 2006Percent compliance w/b

0 20 40 608-Hour Max Ozone

28 29

Max Ozone

A-116 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 we re 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 onSeptember 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 infl uence 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 influenceHouston (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 2006Percent 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 r uns. The upward arrows indicates improvement compared to the full run with 60 km, the downw ard 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 co efficient 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 surf ace 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 plotfor 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 w as 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

A-117A-118 (0.0004) and the 5 times the standard observation-n udging 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 2006Percent 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 2006Percent comp e 60%liance w/b

17 18 22 2 4 26 29
Septem

Default GIV GIV X 5 lt GIV (0.0004) and Figure 2.12.3.8-7.b: Performance plot for the wind speed using the defau the 5 times the standard observation-nudging coe fficient of (5x0.0004=0.002) for the entire 4
km domainA-119 observation-nudging coeffic ient 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 hi gh 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 (5x0.0 004=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 2006Percent compliance w/benchmark

Default GIV GIV X 5

F igure 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-nudgin g 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-nudgin g 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 surf ace observation in red. The corresponding 19 trajectories withA-120 selected using the
LCP data file. For each of the seven days select ed, 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 poin t for a given station to the corresponding point for the same station in th e trajectory generated using the observed data. The error divergence was determined as the averag ed 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 st ations 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 st ations 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 sta tions 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 sta tions 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 observati onal data alone, but on several other days, there Grell and surface nudging can be overlaid in gr een. 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 tw o days with high ozone from 2005ep2 were

s

For Figure 2.12.3.8-11 : 12 hour forward trajectories at 19 st ations starting at 6 am on June 2, 2005. Error divergence (top) and trajectories (bottom ) the error divergence for both model runs s virtually identi i

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 70Error (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 st arting at 6 am on May 21, 05. Error divergence (top) and trajectories (bottom). 20

A-121Forward 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).

A-122Forward 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 forwar d trajectories at 19 stations st arting at 6 am on June 2, 2005. Error divergence (top) and trajectories (bottom).

A-123Forward 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).

A-124Forward 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-125Forward 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 sta tions starting at 6 am on August 1, 2005. Error divergence (top) and trajectories (bottom).

A-126Forward 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-1272 .12.3.9 References

ATMET (2003), MM5 Simulations for TexAQS 2 000 Episode, Final report to the Houston Advanced Research Center and the Texas Commission on Environmental Quality, 12 August
2003.

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A-129Application of Grell Cumulus Scheme ATTACHMENT 4

Houston-Galveston-Brazoria Attainme nt Demonstration SIP Revision

A-1302.12.4 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 atmo sphere, 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 de voted 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 formati on 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. Howeve r, a series of cumulus clouds might evolve a mesoscale structure. A frontal passage may tri gger 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 troposphe ric ozone depend in large part on mostly clear episodic days for photochemical reactions; however, even par tial cloud cover can strongly impact radiative processes, surface heating and PBL development. More importantly, longer episodes require a
better characterization of clouds because an in correct amount of precipitation can bias soil moisture across several days.

Despite the recognition of cloud importance for ai r quality modeling, the meteorological literature has understandably focused on validation of co nvective parameterizations based upon four criteria related directly to precipitation events: fi rst, 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 qua lity in multiple and pervasive ways, validation is mostly qualitative in nature: do modeled clouds see m 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-Ga lveston-Brazoria (HGB) nonattainment area. The original motivation to use a cumulus paramete rization 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 m odeling 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 in clude expected impacts on the subsequent air quality modeling. ve meteorological models tracked fron possible precipitation. Concurrent with the development of mesosca

A-1312.12.4.2. MM5 Model Configuration for HG B and Motivation for the Grell Cumulus arameterizaton on the 4 Km Grid

. configuration adopted for a ll 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 nes ting 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 pa rameterization 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 tho se 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 TC EQ 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 calc ulated from MM5 output using Grell)

A-132Figure 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 Gre ll cumulu s parameterization applied in the 4 km domain

05 pisode.) What is perhaps counte r-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. Clea rly, 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 durin g the July-August 20

e have reduced the role of clouds. However, the application of the Grell parameterization affects the simulation other meteorological variables, whic h 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 cumu lus parameterization applied in the 4 km
domain

An examination of many such graphics compar ing 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-1332.12.4.3. Operational Rationale for Using G rell Cumulus Parameterization on the 4 km
Grid As indicated, the application of a cumulus pa rameterization impacts the simulation of other meteorological variables. Therefore it is not onl y 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 m odeled 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 Gre ll on the 4 km grid, even in an area with a

igh density of monitors, such as HGB. Therefor e, although these features have been noticed by many MM5 modelers, generally they are not c onsidered 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 withou t 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)

A-134Figure 2.12.4.3-2: Surface Wind Map for June 23, 2005, 2 PM (CST) with Grell (Observations are the red vectors)

A-135A-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 dist ance fro m 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-136Table 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 fo llowing 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 fo llowing sites: Northwest Harris County C26 (HNWA) and the relocated Conroe monitor C78 (CNR2).

or the various episodes, performance measure di fferences 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 modele d wind speed and direction were compared to F
fo concentrations. The performance measure used was a metric related to the a

A-137surface observations from the monitors in the above zones fo r 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 av eraged 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 eval uati < 30 degrees by zone on June directio
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 diffe rences in the accuracy, as determined
direction between the configurations (i.e., with an zone. F r the majority of episode days and m onitor 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

anspor t wind direction measurements often have lar additio
led the CEQ staff to conclude that using the

meteoro gical modeling performance.

A-138Figure 2.12.4.3-4: Percent of averaged hourly wi nd 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

A-139Figure 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

A-140Figure 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, 20 05

A-141Figure 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

A-142The close proximity of the Houston Ship Channel (H SC), an area with a high density of industrial sources, to Galveston Bay and the Gulf of Mexi co 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 th e 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 comp ares 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 e nough to represent the multit ude 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 relati vely high density of trajectories, there is no assurance that any of the air par cels from the selected emanating locations will be advected over a particular monitor due to the complexity and numer ous sources in the HGB ar ea. In addition the density of trajectories needs to be high enoug h 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) a nd roadway intersections, as well as starting the

a lthough, 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 obser vations, 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 ar e 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

etween 6 AM and 9 AM are typically those th at are predominately associated with maximum b ozone concentrations occurring later in the day

A-143Figure 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.

A-144Also 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 accumula te 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 tr ajectories 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 configuratio ns 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 trajectori es. 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 m odeled at smaller scales. General modeling guidance support this type of model configura tion (Grell, 1993, Grell, Dudhia and Stauffer, 1994). On the other hand, grid-resolved “explicit” convection also depends upon a different set of parameterizations – the exp licit moisture physics schemes which account for the influence on precipitation due to nucleation, coagulation, fall speed, supercooling of wa ter, and various icing

processes.

Since TCEQ staff have chosen to investigate a non-standard c onfiguration, 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 exp licit and parameterized c onvection is implemented to avoid “double counting” and thus we may expect to avoid harm by trying something new, 2) scale-separation between explicit and paramete rized 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 defa ult of an explicit-only treatment is also prone
to limitations as scale increases

.

Molinari and Dudek (1990) provide an importantreview of the “partitioning” between gridresolved convection and the sub-grid convectiv e parameterization. Although these authors argue for a hybrid scheme in which water content from a cumulus parameterization is provided directly

cau se grid-scale condensation is computed at the end

partitioning by the Grell cumulus 2.12.4.4. Discussion The previous sections have presented an operationa l rationale for investigating the use of the

rell cumulus parameterization on a 4 km grid. G

c greater, and

to an explicit moisture scheme, they note, “Calculation techniques are such that modelers do not allow ‘double counting’; energy and

oisture are conserved in such situations be m 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

A-145p arameterization 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 ex ists.” (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 m odeled 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 mo isture parameterization to do most of the work.

Molinari and Dudek cite earlier work by Arakaw a 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 convec tion 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, th e fully explicit approach is clearly superior to parameterized approaches, even though a 1-km gr id spacing can fully simulate only the largest convective clouds (Lilly

u

The concern by model developers at the intermed iate 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 likeli hood of parameterized convection growing to grid
scale increases with increased resolution. In su ch 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 th is regard, the Grell scheme seems most likely
to transition from

Wang and Seaman (1 This paper investigates the various metrics describ ed earlier in this report which are of primary concern for traditional precipitation forecast validati on. They note the desirability of finding a reasonable partition between paramete rized 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 deve lopers are interested in the co rrect evolution and intensity of a mesoscale convective system, we conjecture that this parameterization can stabilize a fair weather atmosphere. More im portantly 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 sugg estive 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 cu mulus 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 precipitati on. 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, “nonh ydrostatic 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 behavi or. With sufficient forcing, the explicit moisture physics on a four kilometer grid may be adequate since these authors offer, “that 4 km resolution is sufficien t 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.”

A-1462.12.4.5 Conclusion
A ir 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 characterizati on of fields that need to be input into the photochemical model.

A-147A-148 .12.4.6 References

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