Evaluation of Prognostic Meteorological Data in 

AERMOD Applications 

EPA-454/R-15-004

July 2015

Evaluation of Prognostic Meteorological Data in AERMOD Applications

U.S. Environmental Protection Agency

Office of Air Quality Planning and Standards

Air Quality Assessment Division

Air Quality Modeling Group

Research Triangle Park, North Carolina
ii

Preface

This document provides an evaluation of prognostic meteorological data in AERMOD. Included 

in this document are descriptions of the inputs, evaluation of the meteorological data, and 

evaluation of AERMOD results using observed meteorological data and prognostic 

meteorological data. EPA will respond to specific requests for subsets of the data or for specific 

additional inputs and outputs of the modeling process, depending on the availability of the data. 

Requests for electronic copies of the air quality modeling data used for this rule should be sent to 

James Thurman (Thurman.james@epa.gov).
iii

Acknowledgements

This report was developed as part of the 2015 proposal of The Guideline on Air Quality Models, 

Appendix W with input from the meteorological data workgroup comprised of staff from EPA’s 

Office of Air Quality Planning and Standards and Regions 5, 7, and 8. WRF and MMIF 

processing for the Gibson, IN evaluation were processed by Computer Science Corporation. 

WRF and MMIF processing for Martins Creek, PA and Herculaneum, MO were performed by 

Andy Hawkins of EPA’s Region 7. Evaluations discussed in Appendix B were performed by 

Rebecca Matichuk of EPA’s Region 8. The workgroup acknowledges Kali Frost of the Indiana 

Department of Environmental Management for providing AERMOD inputs for Gibson, IN.
iv

Contents

Preface............................................................................................................................................. ii

Acknowledgements........................................................................................................................iii

Figures............................................................................................................................................ vi

Tables.............................................................................................................................................. x

1. Introduction............................................................................................................................... 12

2. Methodology............................................................................................................................. 13

2.1 Study areas.......................................................................................................................... 13

2.1.1 Gibson.......................................................................................................................... 14

2.1.2 Martins Creek............................................................................................................... 18

2.1.3 Herculaneum................................................................................................................ 21

2.2 Meteorological data evaluation........................................................................................... 25

2.3 Model evaluation methodology .......................................................................................... 26

3. Results....................................................................................................................................... 28

3.1 Gibson................................................................................................................................. 28

3.1.1 Meteorological data comparisons................................................................................ 28

3.1.2 AERMOD results......................................................................................................... 36

3.2 Martins Creek...................................................................................................................... 44

3.2.1 Meteorological data comparisons................................................................................ 44

3.2.2 AERMOD results......................................................................................................... 51

3.3 Herculaneum....................................................................................................................... 59

3.3.1 Meteorological data comparisons................................................................................ 59

3.3.2 AERMOD results......................................................................................................... 69

4. Summary and Conclusions ....................................................................................................... 70

5. References................................................................................................................................. 72

Appendix A. Meteorological data comparisons.......................................................................... A-1

A.1 Gibson .............................................................................................................................. A-1

A.2 Martins Creek................................................................................................................. A-17

A.3 Herculaneum.................................................................................................................. A-33

B.1 Introduction ...................................................................................................................... B-1

B.2 Methodology .................................................................................................................... B-1
v

B.3 Results/Summary ............................................................................................................. B-5

B.3.1 Temperature .............................................................................................................. B-5

B.3.2 Relative humidity ...................................................................................................... B-6

B.3.3 Wind speed................................................................................................................ B-7

B.3.4 Wind displacement.................................................................................................... B-8

B.3.5 Mechanical mixing heights ....................................................................................... B-9

B.3.6 Convective mixing heights...................................................................................... B-10

B.3.7 Surface friction velocity.......................................................................................... B-11

B.3.8 Convective velocity scale........................................................................................ B-12
vi

Figures

Figure 1. Locations of MMIF evaluation study areas.................................................................. 14

Figure 2. Location of Gibson facility, SO2 monitors, and meteorological tower for Gibson...... 15

Figure 3. Locations of Gibson meteorological tower (green dot), Evansville (EVV) NWS station 

(airplane) and 12 km WRF grid cells containing those stations. Panel a is a statewide view and 

panel b is a closer view of southwest Indiana............................................................................... 16

Figure 4. Location of Martins Creek study area with meteorological sites, monitors, and 

emission sources. .......................................................................................................................... 18

Figure 5. Detailed view of Martins Creek meteorological sites, monitors, and emissions sources 

with detailed terrain. ..................................................................................................................... 19

Figure 6. Location of the Doe Run lead facility, NWS station (CPS), site-specific tower 

(Herculaneum), three MMIF output locations (MMIF 4 KM, MMIF 12 KM, and MMIF 36 KM), 

and lead monitors.......................................................................................................................... 22

Figure 7. As for Figure 6, with more detail around the facility. .................................................. 23

Figure 8. Detailed view of MMIF grid cell locations, Herculaneum site specific tower, monitors, 

and Doe Run. ................................................................................................................................ 24

Figure 9. Gibson 2010 wind roses for a) GIB OBS, b) GIB MMIF, c) EVV OBS, and d) EVV 

MMIF............................................................................................................................................ 29

Figure 10. Wind displacement (km) among the Gibson meteorological scenarios. .................... 30

Figure 11. Gibson study monthly surface roughness lengths (m) by 10 degree sectors for a) 

January, b) February, c) March, and d) April. .............................................................................. 31

Figure 12. Gibson study monthly surface roughness lengths (m) by 10 degree sectors for a) May, 

b) June, c) July, and d) August. .................................................................................................... 32

Figure 13. Gibson study monthly surface roughness lengths (m) by 10 degree sectors for a) 

September, b) October, c) November, and d) December.............................................................. 33

Figure 14. Gibson hourly QQ plots. Concentrations are in g/m3. ............................................ 37

Figure 15. Gibson 3-hour screening results. ................................................................................ 38

Figure 16. Gibson 24-hour screening results. .............................................................................. 39

Figure 17. Gibson fractional biases for a) 1-hour, b) 3-hour, c) 24-hour, and d) CPM based on 

fractional biases. ........................................................................................................................... 40

Figure 18. Gibson composite performance metric values with 5th and 95th percentiles of the 

CPM values from the bootstrap results......................................................................................... 41

Figure 19. Gibson MCM differences with a) 90th percentile and b) 95th confidence intervals. .. 43

Figure 20. May 1992 – May 1993 Martins Creek wind roses for a) Martins Creek, b) ABE, c) 

MMIF 1 km, and d) MMIF 4 km.................................................................................................. 44

Figure 21. Wind displacement (km) among the Martins Creek meteorological scenarios........... 45

Figure 22. Martins Creek study monthly surface roughness lengths (m) by 10 degree sectors for 

a) January, b) February, c) March, and d) April. .......................................................................... 46

Figure 23. Martins Creek study monthly surface roughness lengths (m) by 10 degree sectors for 

a) May, b) June, c) July, and d) August. ....................................................................................... 47
vii

Figure 24. Martins Creek study monthly surface roughness lengths (m) by 10 degree sectors for 

a) September, b) October, c) November, and d) December.......................................................... 48

Figure 25. Martins Creek QQ plots for a) 1-hour, b) 3-hour, c) 24-hour, and d) annual averages. 

Concentrations are in g/m3. ........................................................................................................ 52

Figure 26. Martins Creek 3-hour screening results...................................................................... 53

Figure 27. Martins Creek 24-hour screening results.................................................................... 54

Figure 28. Martins Creek fractional biases for a) 1-hour, b) 3-hour, c) 24-hour, and d) CPM 

based on fractional biases. ............................................................................................................ 55

Figure 29. Martins Creek composite performance metric values with 5th and 95th percentiles of 

the CPM values from the bootstrap results. .................................................................................. 56

Figure 30. Martins Creek MCM differences with a) 90th percentile and b) 95th confidence 

intervals......................................................................................................................................... 58

Figure 31. 2009 wind roses for a) Herculaneum and b) CPS. ..................................................... 59

Figure 32. 2009 wind roses for a) MMIF 4 km, b) MMIF 12 km, and c) MMIF 36 km............. 60

Figure 33. Wind displacement (km) among the Herculaneum meteorological scenarios. .......... 61

Figure 34. Herculaneum study monthly surface roughness lengths (m) by 10 degree sectors for 

a) January, b) February, c) March, and d) April. .......................................................................... 62

Figure 35. Herculaneum study monthly surface roughness lengths (m) by 10 degree sectors for 

a) May, b) June, c) July, and d) August. ....................................................................................... 63

Figure 36. Herculaneum study monthly surface roughness lengths (m) by 10 degree sectors for 

a) September, b) October, c) November, and d) December.......................................................... 64

Figure 37. Herculaneum 24-hour QQ plots. Concentrations are in g/m3. ................................ 69

Figure 38. Herculaneum 24-hour screening results. .................................................................... 70

Figure A-1. Gibson wind speed (m/s): a) annual distributions and b) bias distributions. ......... A-3

Figure A-2. Gibson ambient temperature (K): a) annual distributions and b) bias distributions.A4

Figure A-3. Gibson station pressure (mb): a) annual distributions and b) bias distributions. ... A-5

Figure A-4. Gibson relative humidity (percent): a) annual distributions and b) bias distributions.

..................................................................................................................................................... A-6

Figure A-5. Gibson daytime albedo (fraction): a) annual distributions and b) bias distributions.

..................................................................................................................................................... A-7

Figure A-6. Gibson Bowen ratio: a) annual distributions and b) bias distributions. ................. A-8

Figure A-7. Gibson heat flux (W/m2): a) annual distributions and b) bias distributions........... A-9

Figure A-8. Gibson surface friction velocity, u* (m/s): a) annual distributions and b) bias 

distributions............................................................................................................................... A-10

Figure A-9. Gibson convective velocity scale, w* (m/s): a) annual distributions and b) bias 

distributions............................................................................................................................... A-11

Figure A-10. Gibson Monin-Obukhov length (m): a) annual distributions and b) bias 

distributions............................................................................................................................... A-12

Figure A-11. Gibson convective mixing height (m): a) annual distributions and b) bias 

distributions............................................................................................................................... A-13

Figure A-12. Gibson mechanical mixing height (m): a) annual distributions and b) bias 

distributions............................................................................................................................... A-14
viii

Figure A-13. Gibson potential temperature gradient (K/m) above Zic: a) annual distributions and 

b) bias distributions................................................................................................................... A-15

Figure A-14. Gibson cloud cover (tenths*10): a) annual distributions and b) bias distributions.A-

....................................................................................................................................................... 16

Figure A-15. Martins Creek wind speed (m/s): a) annual distributions and b) bias distributions.

................................................................................................................................................... A-19

Figure A-16. Martins Creek ambient temperature (K): a) annual distributions and b) bias 

distributions............................................................................................................................... A-20

Figure A-17. Martins Creek station pressure (mb): a) annual distributions and b) bias 

distributions............................................................................................................................... A-21

Figure A-18. Martins Creek relative humidity (percent): a) annual distributions and b) bias 

distributions............................................................................................................................... A-22

Figure A-19. Martins Creek daytime albedo (fraction): a) annual distributions and b) bias 

distributions............................................................................................................................... A-23

Figure A-20. Martins Creek Bowen ratio: a) annual distributions and b) bias distributions... A-24

Figure A-21. Martins Creek heat flux (W/m2): a) annual distributions and b) bias distributions.

................................................................................................................................................... A-25

Figure A-22. Martins Creek surface friction velocity, u* (m/s): a) annual distributions and b) 

bias distributions. ...................................................................................................................... A-26

Figure A-23. Martins Creek convective velocity scale, w* (m/s): a) annual distributions and b) 

bias distributions....................................................................................................................... A-27

Figure A-24. Martins Creek Monin-Obukhov length (m): a) annual distributions and b) bias 

distributions............................................................................................................................... A-28

Figure A-25. Martins Creek convective mixing height (m): a) annual distributions and b) bias 

distributions............................................................................................................................... A-29

Figure A-26. Martins Creek mechanical mixing height (m): a) annual distributions and b) bias 

distributions............................................................................................................................... A-30

Figure A-27. Martins Creek potential temperature gradient (K/m) above Zic: a) annual 

distributions and b) bias distributions....................................................................................... A-31

Figure A-28. Martins Creek cloud cover (tenths*10): a) annual distributions and b) bias 

distributions............................................................................................................................... A-32

Figure 29. Herculaneum wind speed (m/s): a) annual distributions and b) bias distributions. A-35

Figure A-30. Herculaneum ambient temperature (K): a) annual distributions and b) bias 

distributions............................................................................................................................... A-36

Figure A-31. Herculaneum station pressure (mb): a) annual distributions and b) bias 

distributions............................................................................................................................... A-37

Figure A-32. Herculaneum relative humidity (percent): a) annual distributions and b) bias 

distributions............................................................................................................................... A-38

Figure A-33. Herculaneum daytime albedo (fraction): a) annual distributions and b) bias 

distributions............................................................................................................................... A-39

Figure A-34. Herculaneum Bowen ratio: a) annual distributions and b) bias distributions. ... A-40

Figure A-35. Herculaneum heat flux (W/m2): a) annual distributions and b) bias distributions. A41
ix

Figure A-36. Herculaneum surface friction velocity, u* (m/s): a) annual distributions and b) bias 

distributions............................................................................................................................... A-42

Figure A-37. Herculaneum convective velocity scale, w* (m/s): a) annual distributions and b) 

bias distributions. ...................................................................................................................... A-43

Figure A-38. Herculaneum Monin-Obukhov length (m): a) annual distributions and b) bias 

distributions............................................................................................................................... A-44

Figure A-39. Herculaneum convective mixing height (m): a) annual distributions and b) bias 

distributions............................................................................................................................... A-45

Figure A-40. Herculaneum mechanical mixing height (m): a) annual distributions and b) bias 

distributions............................................................................................................................... A-46

Figure A-41. Herculaneum potential temperature gradient (K/m) above Zic: a) annual 

distributions and b) bias distributions....................................................................................... A-47

Figure A-42. Herculaneum cloud cover (tenths*10): a) annual distributions and b) bias 

distributions............................................................................................................................... A-48

Figure B-1. Map of sites analyzed for study............................................................................... B-2
x

Tables

Table 1. Gibson emissions........................................................................................................... 15

Table 2. Martins Creek study facility emissions (tons/year) and stack parameters..................... 20

Table 3. Herculaneum point source emission rates and stack parameters................................... 25

Table 4. Mean bias, fractional bias, root mean square error, and R2for primary meteorological 

variables. ....................................................................................................................................... 35

Table 5. Mean bias, fractional bias, root mean square error, and R2for calculated meteorological 

variables. ....................................................................................................................................... 35

Table 6. Mean bias, fractional bias, root mean square error, correlation, and index of agreement 

for calculated meteorological variables. ....................................................................................... 36

Table 7. 1-hour, 3-hour, 24-hour absolute fractional biases and composite performance measures 

for Gibson meteorological scenarios. ........................................................................................... 41

Table 8. Model comparison measures (MCM) for the four Gibson meteorological scenarios. .. 42

Table 9. Mean bias, fractional bias, root mean square error, and R2for primary meteorological 

variables. ....................................................................................................................................... 50

Table 10. Mean bias, fractional bias, root mean square error, and R2for calculated 

meteorological variables. .............................................................................................................. 50

Table 11. Mean bias, fractional bias, root mean square error, and R2for calculated 

meteorological variables. .............................................................................................................. 51

Table 12. 1-hour, 3-hour, 24-hour absolute fractional biases and composite performance 

measures for Martins Creek meteorological scenarios. ................................................................ 56

Table 13. Model comparison measures (MCM) for the four Gibson meteorological scenarios. 57

Table 14. Mean bias, fractional bias, root mean square error, and R2for primary meteorological 

variables. ....................................................................................................................................... 66

Table 15. Mean bias, fractional bias, root mean square error, and R2for calculated 

meteorological variables. .............................................................................................................. 67

Table 16. Mean bias, fractional bias, root mean square error, and R2for calculated 

meteorological variables. .............................................................................................................. 68

Table B-1. Meteorological sites analyzed for study. .................................................................. B-2

Table B-2. Description of NWS/AERMET model cases............................................................ B-3

Table B-3. Description of WRF/MMIF model cases.................................................................. B-4

Table B-4. Monthly averaged temperature (K) across all modeled cases at each site................ B-6

Table B-5. Monthly averaged relative humidity across all modeled cases at each site.............. B-7

Table B-6. Monthly averaged wind speed (m/s) across all modeled cases at each site.............. B-8

Table B-7. Monthly averaged wind displacement (km) across all modeled cases at each site. . B-9

Table B-8. Monthly averaged mechanical mixing heights (m) across all modeled cases at each 

site............................................................................................................................................. B-10

Table B-9. Monthly averaged mechanical mixing heights (m) across all modeled cases at each 

site............................................................................................................................................. B-11
xi

Table B-10. Monthly averaged surface friction velocity (m/s) across all modeled cases at each 

site............................................................................................................................................. B-12

Table B-11. Monthly averaged convective velocity scale (m/s) across all modeled cases at each 

site............................................................................................................................................. B-13
12

1. Introduction

An important part of dispersion modeling applications is the selection of meteorological data 

input into the model. The meteorological data input into models such as AERMOD should be 

adequately representative of conditions of the modeling, as discussed in Section 8.4 of the 

proposed version of EPA’s Guideline on Air Quality Models (U.S. EPA 2015). Specifically, 

Section 8.4.b states:

“The meteorological data used as input to a dispersion model should be selected on the 

basis of spatial and climatological (temporal) representativeness as well as the ability of 

the individual parameters selected to characterize the transport and dispersion conditions 

in the area of concern. The representativeness of the measured data is dependent on 

numerous factors including but not limited to: (1) The proximity of the meteorological 

monitoring site to the area under consideration; (2) the complexity of the terrain; (3) the 

exposure of the meteorological monitoring site; and (4) the period of time during which 

data are collected. The spatial representativeness of the data can be adversely affected by 

large distances between the source and receptors of interest and the complex topographic 

characteristics of the area. Temporal representativeness is a function of the year-to-year 

variations in weather conditions. Where appropriate, data representativeness should be 

viewed in terms of the appropriateness of the data for constructing realistic boundary 

layer profiles ...”

Meteorological data often comes from National Weather Service (NWS) or site-specific 

meteorological monitoring programs. In recent years, interest has grown in the use of prognostic 

meteorological data, such as the Weather Research and Forecasting (WRF) model to create 

inputs for dispersion modeling with AERMOD. This is especially true in locations where it can 

be difficult to find an adequately representative NWS station or cost-prohibitive or infeasible to 

set up a site-specific meteorological monitoring tower. As part of the 2015 proposed update to 

Appendix W, EPA has proposed the use of prognostic meteorological data for use in AERMOD 

for areas where it is cost-prohibitive or not feasible to collect site-specific data and there is no

representative NWS or comparable station nearby. EPA has developed the Mesoscale Model 

Interface Program, or MMIF, for processing prognostic meteorological data for AERMOD 

(Environ, 2014). For more information on the use of prognostic data in AERMOD see Section 

8.4.5 of the proposed version of Appendix W.

The purpose of the prognostic evaluation process was to determine if prognostic meteorological 

data are suitable for input into AERMOD. The goal of the evaluation was to determine if such

prognostic data was comparable to NWS or site-specific meteorological data and, how

AERMOD performance to observed concentrations compares based on prognostic 

meteorological data with simulations based on NWS data or site-specific data.

This report details the evaluation process used to determine the feasibility of prognostic 

meteorological data for use in AERMOD. Section 2 discusses the methodology of the three case 

studies: 1) Gibson, IN; 2) Martin’s Creek, PA; and 3) Herculaneum, MO. This includes 
13

meteorological data processing (i.e., for both observed and prognostic modeling) and 

development of AERMOD inputs. Section 3 is a review of the evaluation results, including 

meteorological data evaluation and AERMOD concentration results for the three locations, while 

Section 4 is a summary of conclusions and Section 5 provides the references. Appendix A gives 

more results on the meteorological evaluations of the three case studies and Appendix B is an 

evaluation of observed and modeled meteorological data for six sites in EPA’s Region 8.

2. Methodology

2.1 Study areas

Three case study areas were considered for evaluation (Figure 1). Gibson (Frost, 2014) and 

Martins Creek were both SO2 releases while Herculaneum is a lead release. The Gibson 

modeling presented in this document are based on data presented in Frost (2014). Martins Creek 

is one of the databases used to evaluate AERMOD during its development (U.S. EPA, 2003; 

Perry et al., 2005). All three case study area evaluations include site specific meteorological 

data, a representative NWS station, and multiple prognostic model grid cells. More details about 

each evaluation are below.
14

Figure 1. Locations of MMIF evaluation study areas.

2.1.1 Gibson

As shown in Figure 1, the Gibson facility is located in southwest Indiana. The facility is 

comprised of five units with a 3145 MW capacity (Frost, 2014). Table 1 lists the five units with 

stack parameters, average annual hourly SO2 emissions and maximum hourly SO2 emissions for 

2010 as described in Frost (2014).
15

Table 1. Gibson emissions

Unit Stack 

height (m)

Stack 

diameter 

(m)

Average 

annual 

hourly 

emissions 

(g/s)

Maximum 

hourly 

emissions 

(g/s)

Exit 

temperature 

(K)

Average 

stack exit 

velocity 

(m/s)

1 and 2 189.0 23.5 116.0 462.2 327.0 15.6

3 189.0 25.0 90.9 794.1 327.0 18.5

4 152.4 25.0 111.3 460.3 322.0 17.4

5 152.4 25.0 290.0 819.8 328.0 12.5

Figure 2 shows the AERMOD study area with the location of stacks, four monitors, and the sitespecific meteorological tower. 

Figure 2. Location of Gibson facility, SO2 monitors, and meteorological tower for Gibson.

The site-specific tower included multi-level measurements of winds, temperature, pressure, solar 

radiation, and standard deviation of the horizontal wind direction (σΘ) at 10, 25, and 60 m. More 
16

details can be found in Frost (2014). Data from the National Weather Service (NWS) station at 

Evansville, IN (EVV) were used for substitution of missing hours. Upper air data from Lincoln, 

IL (ILN) was used for the morning soundings. Figure 3 shows the relationship between the sitespecific tower and EVV. 

Figure 3. Locations of Gibson meteorological tower (green dot), Evansville (EVV) NWS 

station (airplane) and 12 km WRF grid cells containing those stations. Panel a is a 

statewide view and panel b is a closer view of southwest Indiana.
17

For the purposes of this study, 10 m pressure and insolation were input into AERMET as well as 

60 m winds and temperature. The σΘ data was not used due to suspected problems with the data 

(Frost, 2014b).

Four AERMET (version 14134) and AERMOD (version 14134) runs were performed using four 

sources of meteorological data (meteorological and model run names in parentheses):

 Site-specific data supplemented with EVV surface data (GIB OBS)

 EVV data only (EVV OBS)

 12 km WRF processed through MMIF and AERMET for the WRF grid cell containing 

the facility (GIB MMIF)

 12 km WRF processed through MMIF and AERMET for the WRF grid cell containing 

EVV (EVV MMIF)

All four scenarios utilized a 0.5 m/s wind speed threshold. In addition to showing the locations 

of the observed meteorological data sources, Figure 3 shows the locations of the two WRF grid 

cells processed through MMIF and AERMET. While MMIF can process data for AERMOD 

input directly, MMIF was run to process MMIF for AERMET in accordance with Section 8.4.2 

of Appendix W and MMIF guidance (U.S. EPA, 2015b). Evaluation of the WRF data can be 

found in U.S. EPA (2014). Note evaluations are for 2011 but are applicable to 2010 as well. 

MMIF was processed to output 27 layers using the MID interpolation option. The output heights 

corresponded to the vertical grid structure (25, 50, 75, 100, 125, 150, 175, 200, 250, 300, 350, 

400, 450, 500, 600, 700, 800, 900, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, and 5000 

m). Upper air data in the Forecast Systems Laboratory (FSL) format was output for every hour.

For GIB OBS and EVV OBS surface characteristics, AERSURFACE (U.S. EPA, 2013) was run 

to determine albedo, Bowen ratio, and surface roughness length for 12 months and 12 30o surface 

roughness sectors. Monthly Bowen ratios were adjusted using soil moisture and precipitation 

data from the National Centers for Environmental Information (NCEI)1for EVV. Adjustments 

to albedo, Bowen ratio, and surface roughness length for winter months were determined using 

snowfall data for EVV from NCEI and ratios of days with at least one inch of snow to days with 

no snow cover were also used to adjust values (Frost, 2014).For GIB MMIF and EVV MMIF, 

surface characteristics from the MMIF processor were used in accordance with guidance in 

Section 8.4.2(b) of Appendix W (U.S, EPA, 2015a) and Section 3.3 of MMIF guidance (U.S. 

EPA, 2015b). The grid cells for Gibson and Evansville were processed to output both AERMET 

ready inputs and AERMOD ready inputs. Due to a coding error in the version of MMIF used for 

this evaluation, when processing multiple grid cells for AERMET input, the surface 

characteristics were output incorrectly for the AERMET ready files for Gibson and Evansville. 

The surface characteristics were determined using the values from the AERMOD ready files. 

 

1 Formerly the National Climatic Data Center (NCDC)
18

Surface characteristics for 12 months and one 360o sector were calculated. More information 

about the meteorological data can be found in Section 3.1.1 of this document.

2.1.2 Martins Creek

As previously stated, Martins Creek was one of the evaluation databases used in AERMOD 

development (U.S, EPA, 2003; Perry et al., 2005). In those evaluations, AERMOD performed 

better than other models (ISCST3, CTDMPLUS, ISCST3, and RTDM). The Martins Creek 

Steam Electric Station is located along the Delaware River on the Pennsylvania/New Jersey 

border, approximately 30 km northeast of Allentown, PA and 95 km north of Philadelphia, PA 

(U.S. EPA, 2003). See Figure 4 for the general location of Martins Creek and meteorological 

sites. 

Figure 4. Location of Martins Creek study area with meteorological sites, monitors, and 

emission sources.

SO2 measurements were taken at seven monitors east of the facility (Figure 5) on Scott’s 

Mountain. Site-specific meteorology was recorded from May 1, 1992 to May 19, 1993. 
19

Figure 5. Detailed view of Martins Creek meteorological sites, monitors, and emissions 

sources with detailed terrain.

Data included temperature, wind speed, wind direction, and σΘ at 10 m height west of the facility 

(Figure 4). In addition, hourly multi-level wind measurements were taken by a sodar located 

southwest of the facility. Upper air data was from Albany, NY with missing soundings 

substituted from Sterling, VA.

In addition to emissions from Martins Creek, three other facilities were modeled: 1) Portland, 

Hoffman-LaRoche, and Warren (Figure 5). Hourly emissions and hourly stack parameters 

(temperature and exit velocity) at all four facilities were modeled in AERMOD. 

Table 2 lists the units at each facility with stack parameters and annual emissions.
20

Table 2. Martins Creek study facility emissions (tons/year) and stack parameters.

Facility Stack Annual 

SO2 (tpy)

Stack 

height 

(m)

Stack 

diameter 

(m)

Avg. exit 

temperature 

(K)1

Avg. exit 

velocity 

(m/s)1

Martins 

Creek

MC12 20272 182.9 5.3 400.8 17.1

MC3 2923 182.9 6.9 403.9 17.5

MC4 3395 182.9 6.9 403.0 18.7

Portland ED1 5459 121.9 3.1 395.0 33.3

ED2 12939 121.9 3.6 400.0 26.4

HoffmanLaRocheHL2 837 59.4 2.7 451.5 6.8

Warren WC1 0.2 76.2 1.9 404.6 3.3

WC2 0.2 76.2 1.9 410.3 3.4

1. Average temperature and exit velocity based on hours with non-zero emissions.

Four AERMET (version 14134) and AERMOD (version 14134) simulations were performed:

 Site-specific data supplemented with Allentown/Bethlehem (ABE) data (Martins Creek)

 ABE only data (ABE)

 1 km WRF simulation of grid cell containing Martins Creek (MMIF 1 km)

 4 km WRF simulation of grid cell containing Martins Creek (MMIF 4 km)

Figure 5 shows the locations of the 1 km and 4 km WRF grid cells processed through MMIF and 

AERMET. 

For the Martins Creek site-specific data, the surface characteristics supplied with the evaluation 

database on SCRAM were used. For the ABE surface characteristics, AERSURFACE (U.S. 

EPA, 2013) was run to determine albedo, Bowen ratio, and surface roughness length for 12 

months and three surface roughness sectors (110o-230o, 230o-330o, and 330o-110o). Winter was 

assumed to be “no snow” and moisture conditions were assumed to be dry based on 

climatological data for ABE.

For the 1 km and 4 km MMIF generated outputs, surface characteristics from the MMIF 

processor were used in accordance with guidance in Section 8.4.2(b) of Appendix W (U.S, EPA, 

2015a) and Section 3.3 of MMIF guidance (U.S. EPA, 2015b). Surface characteristics for 12 

months and one 360o sector were calculated. MMIF was run to output 10 layers (17.13, 51.46, 

85.91, 120.48, 207.7, 348.56, 564.66, 936.27, 1566.38, and 2851.29 m) using the TOP 

interpolations. More information about the meteorological data can be found in Section 3.2.1 of 

this document.
21

2.1.3 Herculaneum

The third evaluation site is focused on the Herculaneum, MO area, specifically the Doe Run lead 

facility (Figures 6 through 8). This evaluation allowed for the use of two observed datasets, sitespecific and NWS station and three MMIF outputs at three different horizontal grid resolutions, 4 

km, 12 km, and 36 km. Figures 6 through show the spatial relationship between the Doe Run 

facility, two lead monitors, and the various meteorological data locations. Table 3 lists the major 

modeled point sources with emission rates. The five scenarios are:

 Site specific data supplemented with St. Louis (CPS) meteorological data (Herculaneum)

 CPS data only (CPS)

 36 km WRF simulation of grid cell containing Doe Run (MMIF 36 km)

 12 km WRF simulation of grid cell containing Doe Run (MMIF 12 km)

 4 km WRF simulation of grid cell containing Doe Run (MMIF 4 km)

The Herculaneum site-specific tower and CPS were processed through AERMET using version 

13350 with ILX upper air data. The WRF grid cells were processed through MMIF to generate 

AERMOD and AERMET ready input files. MMIF was run using the same layer structure and 

options and FSL formatted output frequency as for GIBSON. All five scenarios were processed 

in version 13350 of AERMOD.
22

Figure 6. Location of the Doe Run lead facility, NWS station (CPS), site-specific tower 

(Herculaneum), three MMIF output locations (MMIF 4 KM, MMIF 12 KM, and MMIF 36 

KM), and lead monitors.
23

Figure 7. As for Figure 6, with more detail around the facility.
24

Figure 8. Detailed view of MMIF grid cell locations, Herculaneum site specific tower, 

monitors, and Doe Run.
25

Table 3. Herculaneum point source emission rates and stack parameters.

Stack

Emissions 

(g/s)

Stack height 

(m)

Stack 

diameter (m)

Avg. exit 

temperature 

(K)

Avg. exit 

velocity (m/s)

30001 4.17E+00 100.75 346.67 5.81 10.31

40004 8.58E-04 21.3 391.5 0.69 0.76

40005 8.58E-04 21.3 391.5 0.69 0.76

50007 4.31E-02 45.72 285.56 7.13 2.59

50008 2.97E-01 45.72 276.11 34.57 3.05

50011 1.65E-03 18.8 989.3 5.96 0.61

50012 1.65E-03 18.8 989.3 5.96 0.61

50013 1.65E-03 18.8 989.3 5.96 0.61

50014 1.65E-03 18.8 989.3 5.96 0.61

50015 1.65E-03 18.8 989.3 5.96 0.61

50016 1.65E-03 18.8 989.3 5.96 0.61

50017 1.65E-03 18.8 989.3 5.96 0.61

50018 1.65E-03 18.8 989.3 5.96 0.61

60001 1.13E-04 21.3 699.8 2.73 0.56

60002 1.13E-04 21.3 699.8 2.73 0.56

60003 5.93E-06 7.6 297 7.7 1.08

60004 1.80E-03 6.1 327.6 17.5 0.25

60005 1.17E-03 16.8 297 5 0.56

60006 1.17E-03 16.8 297 5 0.56

60007 1.17E-03 16.8 297 5 0.56

60008 1.17E-03 16.8 297 5 0.56

2.2 Meteorological data evaluation

Evaluation among the various meteorological datasets for each study encompassed comparing 

the hourly distributions of each dataset, comparing the distributions of the hourly differences 

among pairs of datasets, and calculations of several statistics, including mean bias, fractional 

bias, root mean square error, correlation, and index of agreement. For the wind direction 

difference statistics, a difference called displacement, which is the difference in the U and V 

vectors of the modeled and observed winds and was used. This was used in the assessment of 

the 2011 12km WRF simulations over the U.S. (US EPA, 2014). The displacement can be 

calculated as:

D = abs((UM − UO + VM − VO) × (1 km⁄1000 m) × (3600 s⁄hr) × 1hr) (1)
26

Where D is the displacement in km, UM and VM are the u and v components respectively of the 

modeled wind vector and UO and VO are the u and v components of the observed wind vector. 

2.3 Model evaluation methodology

AERMOD output among the different meteorological datasets was evaluated using the EPA 

Protocol for determining the best performing model, or Cox-Tikvart method (U.S. EPA, 1992; 

Cox and Tikvart, 1990). The protocol uses a two-step process for determining the better 

performing model when comparing models. The first step is a screening test that fails to perform 

at a minimal operational level. The second test applies to those models that pass the screening 

test that uses bootstrapping to generate a probability distribution of feasible outcomes (U.S. EPA, 

1992). This section will discuss the methodology using the evaluation cases as examples.

The first step is to perform a screening test based on fractional bias:

FB = 2 [

OB−PR

OB+PR] (1)

Where FB is the fractional bias, OB is the average of the highest 25 observed concentrations and 

PR is the average of the highest 25 predicted averages. The fractional bias is also calculated for 

the standard deviation where OB and PR refer to the standard deviation of the highest 25 

observed and predicted concentrations respectively. This is done across all monitors and 

modeled receptors, unpaired in time and space for the 3-hour and 24-hour averaging periods. The 

fractional bias of the means is plotted against the fractional bias of the standard deviation. Biases 

that exceed a factor-of-two under-prediction or over-prediction are considered grounds for 

excluding a model for further evaluation (U.S. EPA, 1992).

Models that pass the screening test are subjected to a more comprehensive statistical comparison 

that involves both an operational and scientific component. The operational component is to 

measure the model’s ability to estimate concentration statistics most directly used for regulatory 

purposes and the scientific component evaluates the model’s ability to perform accurately 

throughout the range of meteorological conditions and the geographic area of concern (U.S. 

EPA, 1992). The test statistic used for the comparison is the robust highest concentration 

(RHC) statistic and is given by:

RHC = Χ(N) + [Χ̅ − Χ(N)] × ln [

3N−1

2

] (2)

Where N) is the Nth largest value, X is the average of N-1 values, and N is the number of 

values exceeding the threshold value, usually 26.

The operational component of the evaluation compares performance in terms of the largest 

network-wide RHC test statistic. The RHC is calculated separately for each monitor within the 

network for observations and modeled values. The highest observed RHC is then compared to 

the highest modeled RHC using equation 1, where RHC now replaces the means of the top 25 

values of observed or modeled concentrations. Absolute fractional bias (the absolute value of 

fractional bias), AFB is calculated for 3 and 24-hour averages.
27

The scientific component of the evaluation is also based on absolute fractional bias but the bias is 

calculated using the RHC for each meteorological condition and monitor. The meteorological 

conditions are a function of atmospheric stability and wind speed. For the purposes of these 

studies, six unique conditions were defined based on two wind speed categories (below and 

above 2.0 m/s) and three stability categories: unstable, neutral, and stable. 2In this evaluation, 

only 1-hour concentrations are used and the AFB is based on RHC values paired in space and 

stability/wind speed combination. 

A composite performance measure (CPM) is calculated from the 1-hour, 3-hour, and 24-hour 

AFB’s:

CPM =

1

3

× (AFBi,j) +

2

3

× [

AFB3−AFB24

2

] (3)

Where AFBi,j is the absolute fractional bias for monitor i and meteorological condition j, AFBi,j

is the average absolute fractional bias across all monitors and meteorological conditions, AFB3 is 

the absolute fractional bias for the 3-hour average, and AFB24 is the absolute fractional bias for 

the 24-hour average. Once CPM values have been calculated for each model, a model 

comparison measure is calculated to compare the models:

MCMA,B = CPMA − CPMB (4)

Where CPMA is the CPM for model A and CPMB is the CPM for model B. When more than two 

models are being compared simultaneously, the number of MCM values is equal to the total of 

the number of unique combinations of two models. In the case of these evaluations, it is not the 

number of models but the number of meteorological scenarios. For Gibson and Martins Creek, 

there are four meteorological scenarios each, so there were six MCM comparisons for each 

location. For Herculaneum, CPM values could not be calculated because there were only 24-

hour average concentrations available from the two monitors. 

In order to determine if the difference between models was statistically significant, the standard 

error was calculated. A bootstrapping technique was used to create 1000 sample years based on 

methodology outlined in U.S. EPA (1992). The original data is divided into 3-day blocks. 

Within each season, the 3-day blocks are sampled with replacement until a total season is 

created. The process is repeated until a 1000 boot-strap years are created3. The standard error is 

calculated as the standard deviation of the bootstrap generated outcomes for the MCM.

The magnitude and sign of the MCM are indicative of relative performance of each pair of 

models. The smaller the CPM the better the overall performance of the model. This means that 

 

2

In U.S. EPA (1992), the three stability categories are related to the Pasquill-Gifford categories, unstable being A, 

B, and C, neutral being D, and stable being E and F. Since AERMOD does not use the stability categories, the 

stability class was determined using Monin-Obukhov length and surface roughness using methodology from 

AERMOD subroutine LTOPG.

3 The bootstrapping was completed using the SAS© SURVEYSELECT procedure with resampling for 1000 

replicates.
28

for two models, A and B, a negative difference between the CPM for A and CPM for B implies 

that model A is performing better (Model A has a smaller CPM) while a positive difference 

indicates that Model B is performing better.

Since more than two scenarios are being evaluated in these studies, simultaneous confidence 

intervals of 90 and 95 percent were calculated. These were calculated by finding the 90th and 

95th percentiles of the distribution across all MCM values from the bootstrapping procedure for 

all model comparisons. The confidence intervals were then found by:

CIX,A,B = MCMA,B ± cXsA,B (5)

Where CIX,A,B is the confidence interval for X percent (90 or 95th) for models A and B, MCMA,B

is as defined in Equation 4, cX is the X percentile of the MCM values from the bootstrap results 

and sA,B is the standard deviation of the bootstrap MCM results for models A and B. Note that in 

Equation 5, MCMA,B is the MCM value from the original data, not the bootstrap results.

For each pair of model comparisons, the significance of the model comparison measure 

depended on whether the confidence interval overlapped zero. If the confidence interval 

overlapped zero, then the two models were not performing at a level which was considered 

statistically different. Otherwise, if they did not overlap zero, then there was a statistically

significant difference between the two models.

3. Results

This section provides results for each of the three case study areas. Meteorological data 

comparisons among the different scenarios for each site are shown as well as the associated 

AERMOD results for each meteorological scenario.

3.1 Gibson

3.1.1 Meteorological data comparisons

Figure 9 compares the 2010 wind roses for site specific tower (GIB OBS), 12 km WRF cell of 

the site specific tower (GIB MMIF), Evansville NWS observations (EVV OBS) and the 12 km 

WRF cell of EVV (EVV MMIF). The wind roses for GIB OBS and GIB MMIF have similarities 

but GIB OBS exhibits a stronger component of winds from the southwest while GIB MMIF 

exhibits a stronger components of winds from the northwest. For Evansville, the observed winds 

(EVV OBS) exhibit dominant directions of southwest, northwest and northeast. The MMIF 

wind rose (EVV MMIF) exhibits a strong component of winds from the northwest with a 

secondary component from the southwest.
29

Figure 9. Gibson 2010 wind roses for a) GIB OBS, b) GIB MMIF, c) EVV OBS, and d) 

EVV MMIF.

Wind displacement (Figure 10), distribution for GIB MMIF – EVV MMIF shows a tighter 

distribution than the other three indicating wind directions, on an hourly basis, are in more 

agreement between the two model grid cells. The other three bias distributions are similar, with 

larger displacement values between the two observed datasets.
30

Figure 10. Wind displacement (km) among the Gibson meteorological scenarios.

Figures 11 through 13 show monthly values of surface roughness, zo (m) by 10o sectors for the 

four meteorological scenarios. For most of the winter, the EVV MMIF surface roughness is the 

highest, over 0.3 m. The plots show that GIB OBS can vary significantly depending on the wind 

direction and month. EVV OBS also vary depending on month and sector.
31

Figure 11. Gibson study monthly surface roughness lengths (m) by 10 degree sectors for a) 

January, b) February, c) March, and d) April.
32

Figure 12. Gibson study monthly surface roughness lengths (m) by 10 degree sectors for a) 

May, b) June, c) July, and d) August.
33

Figure 13. Gibson study monthly surface roughness lengths (m) by 10 degree sectors for a) 

September, b) October, c) November, and d) December.

Tables 4 through 6 show statistics for several primary variables including wind speed, 

temperature, pressure, and relative humidity. The tables also show statistics for heat flux, 

surface friction velocity (u*), convective velocity scale (w*), Monin-Obukhov length (L), 

convective mixing height (Zic), mechanical mixing height (Zim), potential temperature gradient 

(dΘ/dz), and cloud cover. In each table, the GIB OBS – EVV OBS are highlighted as they can 

be considered a “control” since in the absence of prognostic data, these would be the only 

available data sources. Box and whisker plots of the variable distributions as well as bias 

distributions can be found in Appendix A. Findings include:

 Differences between MMIF wind speeds and observed wind speeds appear to be lower 

than the differences between the two observed datasets. The GIB MMIF scenario underpredicts when compared to the GIB OBS and GIB MMIF over-predicts when compared 

to EVV MMIF.

 MMIF scenarios tend to under-predict temperatures and pressure when compared to their 

respective observed counterparts. The GIB MMIF scenario under-predicts (over-
34

predicts) when compared to EVV MMIF for temperature (pressure), but GIB OBS overpredicts when compared to EVV OBS for both variables.

 MMIF scenarios over-predict relative humidity compared to their observed counterparts. 

It should be noted that the GIBS-EVV OBS are zero because EVV observed RH is used

for the Gibson site-specific tower in AERMET.

 For heat flux, u*, and w*, the MMIF scenarios over-predict compared to their observed 

counterparts. GIB MMIF under-predicts compared EVV MMIF as does the GIB OBS 

when compared to EVV OBS.

 For the potential temperature gradient, the MMIF scenarios under-predict when 

compared to their observed counterparts and the GIB MMIF scenario under-predicts 

compared against EVV MMIF and GIB OBS under-predicts compared to EVV OBS.

 For mixing heights (both convective and mechanical), the MMIF scenarios over-predict 

compared to the observed counterparts with low R2 and IOA when compared to the GIB 

OBS – EVV OBS differences.

 Monin-Obukhov length differences show relatively low agreement between the MMIF 

and observed scenarios.

 Cloud cover differences also show relatively low agreement but this may be due to the 

calculation methodology in AERMET when cloud cover is missing.

Overall, while there are differences, the MMIF scenarios appear to show relatively good 

agreement with the observed data. Differences between the MMIF scenarios and their respective 

counterparts is usually in line with the differences between the site-specific data and NWS data 

(i.e., GIB OBS – EVV OBS).
35

Table 4. Mean bias, fractional bias, root mean square error, and R2for primary 

meteorological variables.

Variable Scenario

Mean 

bias

Fractional 

bias RMSE R2

Wind speed

GIB MMIF-EVV MMIF 0.2395 0.0130 0.6873 0.8899

GIB MMIF-GIB OBS -0.6808 -0.0309 1.7568 0.5385

EVV_MMIF-EVV OBS 0.4345 0.0564 1.1826 0.6568

GIB OBS-EVV OBS 1.3562 0.0943 2.1125 0.5348

Ambient 

temperature

GIB MMIF-EVV MMIF -0.3321 -0.0003 0.8500 0.9957

GIB MMIF-GIB OBS -0.6568 -0.0006 3.0398 0.9376

EVV MMIF-EVV OBS -0.1067 -0.0001 1.9698 0.9736

GIB OBS-EVV OBS 0.2133 0.0002 3.0920 0.9351

Pressure

GIB MMIF-EVV MMIF 0.4412 0.0001 0.7131 0.9920

GIB MMIF-GIB OBS -3.9806 -0.0010 4.1719 0.9658

EVV MMIF-EVV OBS -1.0903 -0.0003 1.5625 0.9728

GIB OBS-EVV OBS 3.3315 0.0008 3.6102 0.9557

Relative 

humidity

GIB MMIF-EVV MMIF 1.1127 0.0045 6.2611 0.9227

GIB MMIF-GIB OBS 6.5496 0.0216 14.3825 0.6555

EVV MMIF-EVV OBS 5.4416 0.0172 14.1551 0.6511

GIB OBS-EVV OBS 0.0000 0.0000 0.0000 1.0000

Table 5. Mean bias, fractional bias, root mean square error, and R2for calculated

meteorological variables.

Variable Scenario

Mean 

bias

Fractional 

bias RMSE R2

Heat flux

GIB MMIF-EVV MMIF -8.1307 -0.0409 29.7447 0.9256

GIB MMIF-GIB OBS 16.2727 0.1259 54.6334 0.6997

EVV MMIF-EVV OBS 11.4507 0.1025 53.7118 0.7413

GIB OBS-EVV OBS -13.8544 -0.0673 36.5569 0.8467

u*

GIB MMIF-EVV MMIF -0.0315 -0.0209 0.0757 0.9050

GIB MMIF-GIB OBS 0.1660 0.1440 0.2150 0.6087

EVV MMIF-EVV OBS 0.1858 0.1643 0.2271 0.6816

GIB OBS-EVV OBS -0.0159 0.0013 0.0839 0.6753

w*

GIB MMIF-EVV MMIF -0.1095 -0.0208 0.2595 0.8677

GIB MMIF-GIB OBS 0.3639 0.1077 0.5728 0.4937

EVV MMIF-EVV OBS 0.1874 0.0389 0.5022 0.5190

GIB OBS-EVV OBS -0.2887 -0.0941 0.4124 0.7618

dΘ/dz

GIB MMIF-EVV MMIF 0.0001 0.0018 0.0013 0.7727

GIB MMIF-GIB OBS -0.0016 -0.0375 0.0043 0.2171

EVV MMIF-EVV OBS -0.0018 -0.0434 0.0045 0.2009

GIB OBS-EVV OBS -0.0001 -0.0040 0.0027 0.6755
36

Table 6. Mean bias, fractional bias, root mean square error, correlation, and index of 

agreement for calculated meteorological variables.

Variable Scenario

Mean 

bias

Fractional 

bias RMSE R2

Zic

GIB MMIF-EVV MMIF -48.3405 -0.0122 244.6457 0.8405

GIB MMIF-GIB OBS 250.3559 0.1324 523.6197 0.4137

EVV MMIF-EVV OBS 66.4046 0.0431 512.7182 0.4251

GIB OBS-EVV OBS -236.2854 -0.1140 349.0821 0.8315

Zim

GIB MMIF-EVV MMIF -52.0755 -0.0172 159.9024 0.9166

GIB MMIF-GIB OBS 249.6961 0.1424 410.5375 0.5749

EVV MMIF-EVV OBS 274.1437 0.1652 439.7079 0.6514

GIB OBS-EVV OBS -35.5563 -0.0001 152.9793 0.6606

L

GIB MMIF-EVV MMIF 24.9063 -0.0197 1592.5484 0.5897

GIB MMIF-GIB OBS 572.5552 0.1899 2509.5879 0.0475

EVV MMIF-EVV OBS 336.0061 0.2854 2320.8618 0.0404

GIB OBS-EVV OBS -221.9609 0.0651 1389.7583 0.0325

Cloud 

cover

GIB MMIF-EVV MMIF 0.1830 0.0152 2.2867 0.7180

GIB MMIF-GIB OBS 1.2902 0.0981 5.7003 0.0307

EVV MMIF-EVV OBS 2.2222 0.1789 5.4528 0.1123

GIB OBS-EVV OBS 1.1128 0.1092 3.6298 0.4976

3.1.2 AERMOD results

Figure 14 shows the hourly QQ-plot among all four monitors for the four meteorological 

scenarios. The GIB OBS concentrations tend to be the highest predicting scenario. The two 

MMIF driven scenarios and the EVV OBS scenario are visually are in close agreement with each 

other and tend to under-predict at higher concentrations.
37

Figure 14. Gibson hourly QQ plots. Concentrations are in g/m3.

The modeled scenarios were evaluated using the Cox-Tikvart methodology as discussed in 

Section 2.2. Figures 29 and 30 show the initial 3-hr and 24-hour screening results. For the 3-

hour results, all four scenarios are outside the ±0.67 box that identifies good performance. The 

3-hour results correspond with the QQ-plots in Figure 14 in that GIB OBS over-predicts (bias 

less than 0) and the other scenarios tend to under-predict (bias greater than 0). However, all four 

scenarios are in close proximity to one another around a bias of zero. For the 24-hour average, 

all four scenarios fall within the ±0.67 box. For 24-hour averages, all scenarios except for the 

EVV OBS scenario have slight over-prediction. All four scenarios have a bias close to zero.
38

Figure 15. Gibson 3-hour screening results.
39

Figure 16. Gibson 24-hour screening results.

Figure 17 compares the 1-hour, 3-hour, and 24-hour fractional biases and a composite of the 

fraction biases (Equation 3 using fractional biases instead of absolute fractional biases). Shown 

are the fractional biases from the original data along with the 5th and 95th percentiles of the 

fractional biases from the bootstrap results. Figure 17.d shows the modified CPM from the 

original data with the 5th and 95th percentiles of the modified CPM from the bootstrap results. In 

the plots, positive (negative) fractional biases indicate model under-prediction (over-prediction). 

Based on the distributions, for 1-hour and 3-hour, all four scenarios tend to under-predict

compared to observations. For the 24-hour periods, the models tend to show more overprediction. The composite shows mostly under-prediction, with the four scenarios showing 

comparable distributions.
40

Figure 17. Gibson fractional biases for a) 1-hour, b) 3-hour, c) 24-hour, and d) CPM based 

on fractional biases.

Table 7 shows the AFB values for the 1-hour, 3-hour, and 24-hour periods as well as the CPM 

statistics for each meteorological scenario. Figure 18 shows the CPM values with the 5th and 

95th percentiles of the CPM’s from the bootstrap results. Based on the results shown in Table 7

and Figure 18 the EVV MMIF results were somewhat different compared to the other three 

scenarios which had similar CPM values. 
41

Table 7. 1-hour, 3-hour, 24-hour absolute fractional biases and composite performance 

measures for Gibson meteorological scenarios.

Scenario AFB CPM

1-hour 3-hour 24-hour

EVV MMIF 0.77 0.26 0.19 0.41

EVV OBS 0.75 0.14 0.04 0.31

GIB MMIF 0.77 0.17 0.09 0.34

GIB OBS 0.90 0.09 0.05 0.35

Figure 18. Gibson composite performance metric values with 5th and 95th percentiles of 

the CPM values from the bootstrap results.
42

Table 8 lists the MCM values for the six model comparisons and also lists the best performing 

model for each pair. As discussed in Section 2.2, positive (negative) values of an MCM indicate 

that the second scenario of the difference (scenario to the right of the “-“ sign) is performing 

better (worse) than the first scenario (scenario to the left of the “-“ sign). Figure 19 show the 

MCM differences among the four scenarios at the 90th and 95th confidence intervals respectively. 

. The results indicate that the EVV MMIF meteorology performs slightly worse when compared 

to the other three scenarios. The results also indicate that the GIB MMIF scenario performs 

worse against EVV OBS but is almost equal to the GIB OBS scenario and that the GIB OBS 

scenario performs worse than the EVV OBS scenario. While there is differences in performance, 

the confidence intervals for each difference overlap zero, indicating that differences in 

performance are not statistically significant. Of the two MMIF scenarios, the GIB MMIF 

appears to be the better scenario, which is not surprising given that the cell contains the facility.

Table 8. Model comparison measures (MCM) for the four Gibson meteorological 

scenarios.

MCM Scenario MCM Best performing scenario

EVV MMIF – EVV OBS 0.1 EVV OBS

EVV MMIF – GIB OBS 0.06 GIB OBS

GIB MMIF – EVV MMIF -0.06 GIB MMIF

GIB MMIF – EVV OBS 0.03 EVV OBS

GIB MMIF – GIB OBS -0.004 GIB MMIF

GIB OBS – EVV OBS 0.04 EVV OBS
43

Figure 19. Gibson MCM differences with a) 90th percentile and b) 95th confidence 

intervals.
44

3.2 Martins Creek

3.2.1 Meteorological data comparisons

The wind roses for the four meteorological scenarios are presented in Figure 20. The Martins 

Creek site-specific tower exhibits a strong southwest to northeast pattern. The two MMIF wind 

roses do exhibit a southwest to northeast pattern, they also have more hours from other 

directions. The ABE rose shows a strong westerly component to its winds with a secondary 

maximum from the northeast.

Figure 20. May 1992 – May 1993 Martins Creek wind roses for a) Martins Creek, b) ABE, 

c) MMIF 1 km, and d) MMIF 4 km.

Figure 21 shows the wind displacement distributions among the scenarios. For the most part, 

displacements between the scenarios is less than 10 km. Figure 22 through Figure 24 compare 

the surface roughness values by month and 10o sector. ABE has the lower roughness values 

throughout the year with Martins Creek spiking between 180o and 260o due to the presence of a 

stand of trees from that direction.
45

Figure 21. Wind displacement (km) among the Martins Creek meteorological scenarios.
46

Figure 22. Martins Creek study monthly surface roughness lengths (m) by 10 degree 

sectors for a) January, b) February, c) March, and d) April.
47

Figure 23. Martins Creek study monthly surface roughness lengths (m) by 10 degree 

sectors for a) May, b) June, c) July, and d) August.
48

Figure 24. Martins Creek study monthly surface roughness lengths (m) by 10 degree 

sectors for a) September, b) October, c) November, and d) December.

Tables 9 through 11 present statistics for meteorological variables for the four scenarios. The 

control difference, ABE – Martins Creek is highlighted in yellow. Box and whisker plots of the 

variable distributions as well as bias distributions can be found in Appendix A. 

 Differences between MMIF wind speeds and observed wind speeds are lower than the 

differences between the two observed datasets. Both MMIF scenarios over-predict when 

compared to the Martins Creek winds. The MMIF 4 km simulation over-predicts 

compared to the 1 km simulation. 

 The MMIF scenarios and ABE over-predict temperatures with the MMIF scenarios 

having less over-prediction. 

 For pressures, the MMIF 1 km scenario over-predicts while the MMIF 4 km overpredicts. ABE and Martins Creek do not differ, most likely due to ABE RH values being 

used at Martins Creek.
49

 MMIF scenarios over-predict relative humidity and the MMIF 4 km scenario slightly 

under-predicts compared to MMIF 1 km. ABE and Martins Creek do not differ, most 

likely due to ABE RH values being used at Martins Creek.

 For heat flux the MMIF 1 km scenario under-predicts while the MMIF 4 km scenario 

over-predicts as does ABE. 

 For u*, both MMIF scenarios and ABE over-predict and are comparable to each other.

 For w*, the MMIF 1 km scenario under-predicts while the MMIF 4 km and ABE overpredict.

 For the potential temperature gradient, the MMIF scenarios under-predict while ABE 

over-predicts but values are relatively close.

 For mixing heights (both convective and mechanical), the MMIF scenarios and ABE

over-predict.

 For Monin-Obukhov length, the MMIF 1 km scenario under-predicts while the MMIF 4 

km scenario and ABE over-predict. The MMIF 4 km biases appear to be an outlier when 

compared to the MMIF 1 km biases and ABE biases.

 Cloud cover differences also show relatively low agreement but this may be due to the 

calculation methodology in AERMET when cloud cover is missing. ABE and Martins 

Creek do not differ, most likely due to ABE cloud cover being used for Martins Creek.

Overall, while there are differences, the MMIF scenarios appear to show relatively good 

agreement with the observed data. Differences between the MMIF scenarios and Martins Creek

counterparts is usually in line with the differences in the site-specific data and NWS data (i.e., 

ABE – Martins Creek).
50

Table 9. Mean bias, fractional bias, root mean square error, and R2for primary 

meteorological variables.

Variable Scenario

Mean 

bias

Fractional 

bias RMSE R2

Wind speed

MMIF 1 km-Martins Creek 0.58962 0.05979 1.36357 0.53139

MMIF 4 km -Martins Creek 1.06006 0.09979 1.66366 0.54214

MMIF 4 km -MMIF 1 km 0.47061 0.04196 0.88542 0.83952

ABE-Martins Creek 1.44933 0.09314 2.17893 0.52709

Ambient 

temperature

MMIF 1 km -Martins Creek 0.23995 0.00021 2.59116 0.92888

MMIF 4 km -Martins Creek 0.31796 0.00028 2.42705 0.93764

MMIF 4 km -MMIF 1 km 0.07750 0.00007 0.62537 0.99582

ABE-Martins Creek 0.77481 0.00068 1.89823 0.96915

Pressure

MMIF 1 km -Martins Creek 3.24523 0.00081 3.41835 0.98217

MMIF 4 km -Martins Creek -1.83670 -0.00046 2.11053 0.98365

MMIF 4 km -MMIF 1 km -5.06872 -0.00126 5.09355 1.0

ABE-Martins Creek 0 0 0 1.0

Relative 

humidity

MMIF 1 km -Martins Creek 7.73760 0.02848 16.10607 0.55156

MMIF 4 km -Martins Creek 7.61139 0.02804 15.63945 0.57766

MMIF 4 km -MMIF 1 km -0.12598 -0.00049 3.84615 0.96119

ABE-Martins Creek 0 0 0 1.0

Table 10. Mean bias, fractional bias, root mean square error, and R2for calculated

meteorological variables.

Variable Scenario

Mean 

bias

Fractional 

bias RMSE R2

Heat flux

MMIF 1 km-Martins Creek -10.60956 0.02594 39.27118 0.48069

MMIF 4 km -Martins Creek 15.76726 0.09672 48.34657 0.75046

MMIF 4 km -MMIF 1 km 26.36619 0.07375 62.95421 0.56546

ABE-Martins Creek 19.72457 0.19548 52.67964 0.89900

u*

MMIF 1 km -Martins Creek 0.07382 0.06145 0.15689 0.62727

MMIF 4 km -Martins Creek 0.09468 0.09864 0.15717 0.62565

MMIF 4 km -MMIF 1 km 0.02140 0.03829 0.09022 0.85083

ABE-Martins Creek 0.09746 0.11097 0.16210 0.57773

w*

MMIF 1 km -Martins Creek -0.03219 -0.02176 0.41303 0.38025

MMIF 4 km -Martins Creek 0.31158 0.05245 0.52603 0.56513

MMIF 4 km -MMIF 1 km 0.33947 0.07410 0.49497 0.70120

ABE-Martins Creek 0.37888 0.08166 0.46271 0.86686

dΘ/dz

MMIF 1 km -Martins Creek -0.00149 -0.03543 0.00430 0.21148

MMIF 4 km -Martins Creek -0.00151 -0.03592 0.00430 0.21398

MMIF 4 km -MMIF 1 km -0.00003 -0.00084 0.00087 0.89759

ABE-Martins Creek 0.00009 0.00311 0.00329 0.51953
51

Table 11. Mean bias, fractional bias, root mean square error, and R2for calculated

meteorological variables.

Variable Scenario

Mean 

bias

Fractional 

bias RMSE R2

Zic

MMIF 1 km-Martins Creek 205.20000 0.05694 567.66350 0.27664

MMIF 4 km -Martins Creek 226.59382 0.06771 526.93158 0.36883

MMIF 4 km -MMIF 1 km 18.69463 0.01236 216.79283 0.87216

ABE-Martins Creek 267.46126 0.08113 393.69289 0.81594

Zim

MMIF 1 km -Martins Creek 158.29334 0.10105 371.44030 0.48209

MMIF 4 km -Martins Creek 167.37733 0.13701 353.60026 0.48169

MMIF 4 km -MMIF 1 km 10.09504 0.03481 145.49705 0.90391

ABE-Martins Creek 190.78705 0.15120 356.34586 0.55482

L

MMIF 1 km -Martins Creek -8.03602 0.10690 2087.77469 0.07088

MMIF 4 km -Martins Creek 360.81687 0.12134 2447.01436 0.04617

MMIF 4 km -MMIF 1 km 377.92163 -0.00433 1819.59481 0.51254

ABE-Martins Creek 42.92637 0.09176 667.80236 0.25921

Cloud 

cover

MMIF 1 km -Martins Creek 0.17313 -0.01996 5.05242 0.08225

MMIF 4 km -Martins Creek 0.56836 0.00469 4.97054 0.09158

MMIF 4 km -MMIF 1 km 0.39765 0.03361 3.05554 0.54030

ABE-Martins Creek 0 0 0 1.0

3.2.2 AERMOD results

QQ plots for various averaging times are shown in Figure 25. For the 1-hour averages (Figure 

25.a), ABE tends to over-predict outside of the factor of 2 line except for the highest 

concentrations. The other scenarios tend to slightly over predict below 1,000 g/m3, with the 

MMIF 4 km showing better agreement with the observed concentrations. For the highest 

concentrations, the MMIF 4 km and Martins Creek under-predict while the other two scenarios 

over-predict. All are within a factor of two of the observations.

For the 3-hour averages (Figure 25.b), all scenarios over-predict below 600 g/m3. Again, ABE 

tends to be the higher of the concentrations. As with the 1-hour concentrations, at the higher 

concentrations, the MMIF 4 km and Martins Creek under-predict. For the 24-hour averages 

(Figure 25.c), the model values under-predict between 25 and 50 g/m3 with over-prediction at 

concentrations higher than 50 g/m3. For the annual averages (Figure 25.d), all scenarios underpredict. Figure 26 and Figure 27 show the 3-hr and 24-hour screening results. Both show that 

the four scenarios over-predict at 3-hour and 24-hour averages but are within the factor of two 

limit. Figure 28 shows the fractional biases for 1-hour, 3-hour, and 24-hour as well as a CPM 

based on those values. ABE tends show over-predictions at all averages, agreeing with the QQplots. Fractional biases for Martins Creek and the two MMIF scenarios over-predict but for 1-
52

hour they do cross over zero, indicating under-prediction. All scenarios over-predict at the 24-

hour average and the CPM plot shows that.

Figure 25. Martins Creek QQ plots for a) 1-hour, b) 3-hour, c) 24-hour, and d) annual 

averages. Concentrations are in g/m3.
53

Figure 26. Martins Creek 3-hour screening results.
54

Figure 27. Martins Creek 24-hour screening results.
55

Figure 28. Martins Creek fractional biases for a) 1-hour, b) 3-hour, c) 24-hour, and d) 

CPM based on fractional biases.

Table 12 shows the absolute fractional biases and CPM for each scenario for Martins Creek and 

Figure 29 shows the CPM values with the 5th and 95th percentiles of the bootstrap results. The 

CPM values show that Martins Creek site-specific data performed better with the other three 

having comparable values. Table 13 shows the MCM values for the different model pair 

differences and Figure 30 shows the MCM values with the 90th and 95th confidence intervals. 

Based on Table 13, Martins Creek tends to be the better performing scenario. The plots in 

Figure 30 show that the differences between the MMIF simulations and Martins Creek are 

statistically significant.
56

Table 12. 1-hour, 3-hour, 24-hour absolute fractional biases and composite performance 

measures for Martins Creek meteorological scenarios.

Scenario AFB CPM

1-hour 3-hour 24-hour

ABE 0.78 0.51 0.56 0.61

Martins 

Creek

0.40 0.08 0.56 0.34

MMIF 1 km 0.56 0.64 0.77 0.66

MMIF 4 km 0.50 0.31 0.76 0.52
57

Figure 29. Martins Creek composite performance metric values with 5th and 95th 

percentiles of the CPM values from the bootstrap results.

Table 13. Model comparison measures (MCM) for the four Gibson meteorological 

scenarios.

MCM Scenario MCM Best performing scenario

ABE - Martins Creek 0.27 Martins Creek

MMIF 1 km – ABE 0.05 ABE

MMIF 1km – Martins Creek 0.31 Martins Creek

MMIF 4 km – ABE -0.09 MMIF 4 km

MMIF 4 km – Martins Creek 0.18 Martins Creek

MMIF 4 km – MMIF 1km -0.14 MMIF 1 km
58

Figure 30. Martins Creek MCM differences with a) 90th percentile and b) 95th confidence 

intervals.
59

3.3 Herculaneum

3.3.1 Meteorological data comparisons

Figure 31 and Figure 32 show the wind roses for the Herculaneum site-specific tower, CPS, and 

three MMIF grid cells. Herculaneum and the three MMIF roses appear to show dominant 

directions from the northeast, southwest, west, and northwest. CPS is dominated by a 

southeasterly flow.

Figure 31. 2009 wind roses for a) Herculaneum and b) CPS.
60

Figure 32. 2009 wind roses for a) MMIF 4 km, b) MMIF 12 km, and c) MMIF 36 km.

Figure 33 compares the wind displacement distributions among several difference among the 

Herculaneum scenarios. For the most part, displacements are less than 20 km. Figure 34

through Figure 36 compare the surface roughness values by month and wind direction. Surface 

roughness values for Herculaneum and CPS can vary dramatically by direction and month.
61

Figure 33. Wind displacement (km) among the Herculaneum meteorological scenarios.
62

Figure 34. Herculaneum study monthly surface roughness lengths (m) by 10 degree sectors 

for a) January, b) February, c) March, and d) April.
63

Figure 35. Herculaneum study monthly surface roughness lengths (m) by 10 degree sectors 

for a) May, b) June, c) July, and d) August.
64

Figure 36. Herculaneum study monthly surface roughness lengths (m) by 10 degree sectors 

for a) September, b) October, c) November, and d) December.

Table 14 through Table 16 show statistics for the meteorological variables for several scenario 

differences. Box and whisker plots of the variable distributions as well as bias distributions can 

be found in Appendix A. 

 Differences between MMIF wind speeds and observed wind speeds are lower than the 

differences between the two observed datasets but are comparable. 

 The MMIF 4 and 12 km scenarios over-predict temperatures while the MMIF 36 km and 

CPS over-predict temperatures with the MMIF scenarios having less over-prediction. 

 For pressures, the MMIF scenario under-predict. CPS and Herculaneum do not differ, 

most likely due to CPS values being used at Herculaneum in AERMET processing.

 MMIF scenarios and CPS over-predict relative humidity and heat flux.

 For u*, both the MMIF 4 km under-predicts while the MMIF 12 and 36 km scenarios

over-predict.

 MMIF scenarios and CPS over-predict w*.
65

 For the potential temperature gradient, the MMIF scenarios over-predict while the CPS 

over-prediction is essentially zero.

 For mixing heights (both convective and mechanical) and Monin-Obukhov length, the 

MMIF scenarios and CPS over-predict.

 Cloud cover differences also show relatively low agreement but this may be due to the 

calculation methodology in AERMET when cloud cover is missing. ABE and Martins 

Creek do not differ, most likely due to ABE cloud cover being used for Martins Creek.

Overall, while there are differences, the MMIF scenarios appear to show relatively good 

agreement with the observed data. Differences between the MMIF scenarios and Herculaneum 

are usually in line with the CPS – Herculaneum biases.
66

Table 14. Mean bias, fractional bias, root mean square error, and R2for primary 

meteorological variables.

Variable Scenario

Mean 

bias

Fractional 

bias RMSE R2

Wind speed

MMIF 4 km-Herculaneum 1.11391 0.10602 1.60036 0.58907

MMIF 12 km-Herculaneum 1.10712 0.10918 1.56114 0.57557

MMIF 36 km-Herculaneum 1.15359 0.11262 1.60752 0.56262

MMIF 12 km-MMIF 4 km -0.00679 0.00354 0.30025 0.97318

MMIF 36 km-MMIF 4 km 0.03969 0.00759 0.46448 0.93287

MMIF 36 km-MMIF 12 km 0.04647 0.00411 0.30716 0.96801

CPS-Herculaneum 1.37366 0.10400 1.93392 0.59851

Ambient 

temperature

MMIF 4 km-Herculaneum -0.03556 -0.00002 2.65892 0.93389

MMIF 12 km-Herculaneum 0.00787 0.00002 2.66439 0.93362

MMIF 36 km-Herculaneum -0.05384 -0.00004 2.55690 0.93889

MMIF 12 km-MMIF 4 km 0.04342 0.00004 0.34129 0.99886

MMIF 36 km-MMIF 4 km -0.01828 -0.00002 0.63005 0.99602

MMIF 36 km-MMIF 12 km -0.06170 -0.00006 0.47229 0.99780

CPS-Herculaneum -0.38928 -0.00035 2.09388 0.96207

Pressure

MMIF 4 km-Herculaneum -0.72089 -0.00018 1.42029 0.97096

MMIF 12 km-Herculaneum -2.80479 -0.00070 3.05647 0.97200

MMIF 36 km-Herculaneum -6.45605 -0.00162 6.56495 0.97386

MMIF 12 km-MMIF 4 km -2.08390 -0.00052 2.10606 0.99801

MMIF 36 km-MMIF 4 km -5.73516 -0.00144 5.75746 0.99454

MMIF 36 km-MMIF 12 km -3.65126 -0.00092 3.68374 0.99470

CPS-Herculaneum 0 0 0 1.0

Relative 

humidity

MMIF 4 km-Herculaneum 2.81507 0.00556 14.68699 0.58847

MMIF 12 km-Herculaneum 1.88505 0.00103 14.86928 0.59163

MMIF 36 km-Herculaneum 1.06450 -0.00176 14.50951 0.59984

MMIF 12 km-MMIF 4 km -0.93002 -0.00463 2.81453 0.98725

MMIF 36 km-MMIF 4 km -1.75057 -0.00746 4.48268 0.96733

MMIF 36 km-MMIF 12 km -0.82055 -0.00284 3.12158 0.98291

CPS-Herculaneum 0.81473 0.00436 9.40803 0.75540
67

Table 15. Mean bias, fractional bias, root mean square error, and R2for calculated

meteorological variables.

Variable Scenario

Mean 

bias

Fractional 

bias RMSE R2

Heat flux

MMIF 4 km-Herculaneum 18.72392 0.03237 56.91295 0.58847

MMIF 12 km-Herculaneum 30.29101 0.00573 79.39036 0.59163

MMIF 36 km-Herculaneum 31.98523 0.01935 82.15212 0.59984

MMIF 12 km-MMIF 4 km 11.55608 0.08710 35.43597 0.98725

MMIF 36 km-MMIF 4 km 13.21507 0.06863 51.23716 0.96733

MMIF 36 km-MMIF 12 km 1.65898 0.01543 38.98357 0.98291

CPS-Herculaneum 6.65078 0.04541 23.37517 0.75540

u*

MMIF 4 km-Herculaneum -0.01028 -0.02039 0.14290 0.58847

MMIF 12 km-Herculaneum 0.05683 0.03896 0.16563 0.59163

MMIF 36 km-Herculaneum 0.10084 0.07518 0.18834 0.59984

MMIF 12 km-MMIF 4 km 0.06712 0.06483 0.08534 0.98725

MMIF 36 km-MMIF 4 km 0.11108 0.10116 0.13904 0.96733

MMIF 36 km-MMIF 12 km 0.04397 0.03955 0.07255 0.98291

CPS-Herculaneum 0.01861 0.02198 0.12423 0.75540

w*

MMIF 4 km-Herculaneum 0.44585 0.11391 0.65988 0.58847

MMIF 12 km-Herculaneum 0.70574 0.15819 0.88205 0.59163

MMIF 36 km-Herculaneum 0.72036 0.16160 0.88035 0.59984

MMIF 12 km-MMIF 4 km 0.34209 0.06955 0.42287 0.98725

MMIF 36 km-MMIF 4 km 0.33219 0.06015 0.50998 0.96733

MMIF 36 km-MMIF 12 km -0.00180 -0.00490 0.30334 0.98291

CPS-Herculaneum 0.17976 0.08154 0.33074 0.75540

dΘ/dz

MMIF 4 km-Herculaneum 0.00314 0.07345 0.00647 0.58847

MMIF 12 km-Herculaneum 0.00298 0.06886 0.00649 0.59163

MMIF 36 km-Herculaneum 0.00280 0.06495 0.00620 0.59984

MMIF 12 km-MMIF 4 km 0.00004 0.00012 0.00151 0.98725

MMIF 36 km-MMIF 4 km -0.00028 -0.00648 0.00214 0.96733

MMIF 36 km-MMIF 12 km -0.00035 -0.00710 0.00201 0.98291

CPS-Herculaneum 5.6×10-7 0.00078 0.00304 0.75540
68

Table 16. Mean bias, fractional bias, root mean square error, and R2for calculated

meteorological variables.

Variable Scenario

Mean 

bias

Fractional 

bias RMSE R2

Zic

MMIF 4 km-Herculaneum 566.12886 0.21689 689.85788 0.55479

MMIF 12 km-Herculaneum 570.47601 0.21825 699.26281 0.55115

MMIF 36 km-Herculaneum 555.89452 0.21464 682.41628 0.57861

MMIF 12 km-MMIF 4 km 25.53641 0.00685 82.12590 0.98265

MMIF 36 km-MMIF 4 km 10.05379 0.00148 134.17900 0.94961

MMIF 36 km-MMIF 12 km -17.08090 -0.00619 113.15424 0.96540

CPS-Herculaneum 178.88253 0.11968 288.74963 0.79372

Zim

MMIF 4 km-Herculaneum 89.93761 0.03008 523.68956 0.20612

MMIF 12 km-Herculaneum 111.37459 0.04574 534.07238 0.20443

MMIF 36 km-Herculaneum 107.75352 0.04503 544.49645 0.18224

MMIF 12 km-MMIF 4 km 21.22066 0.01780 105.80767 0.96336

MMIF 36 km-MMIF 4 km 17.59007 0.01751 151.40997 0.92336

MMIF 36 km-MMIF 12 km -3.63059 -0.00001 122.67669 0.94935

CPS-Herculaneum 28.58368 0.02725 276.80805 0.62259

L

MMIF 4 km-Herculaneum 249.61225 -0.10265 1681.53419 0.00120

MMIF 12 km-Herculaneum 231.00914 -0.06609 1699.98627 0.00065

MMIF 36 km-Herculaneum 288.82372 -0.45512 1823.38370 0.00419

MMIF 12 km-MMIF 4 km -16.39179 0.06853 1356.42715 0.00001

MMIF 36 km-MMIF 4 km 39.35898 0.09618 1458.10173 0.01241

MMIF 36 km-MMIF 12 km 55.75078 0.07233 1570.10520 0.00005

CPS-Herculaneum 141.90371 0.01109 1276.36930 0.20768

Cloud 

cover

MMIF 4 km-Herculaneum 3.71020 0.23923 5.81028 0.13757

MMIF 12 km-Herculaneum 3.67508 0.23811 5.78924 0.13824

MMIF 36 km-Herculaneum 3.55515 0.23551 5.72211 0.14100

MMIF 12 km-MMIF 4 km -0.03505 -0.00294 0.45694 0.98193

MMIF 36 km-MMIF 4 km -0.15502 -0.00932 0.94022 0.92781

MMIF 36 km-MMIF 12 km -0.11998 -0.00655 0.88577 0.93537

CPS-Herculaneum 0 0 0 1.0
69

3.3.2 AERMOD results

Figure 37 shows the 24-hour QQ plots for Herculaneum. Since the pollutant is lead, daily 

averages are the only averaging period available at the monitors. Figure 37 shows that all five 

scenarios are under-predicting concentrations. Figure 38 shows the 24-hour screening results 

and the results concur with the QQ-plots. With the exception of CPS, the scenarios are outside 

the factor of two limit but the MMIF scenarios are comparable to the Herculaneum site specific 

scenario.

Figure 37. Herculaneum 24-hour QQ plots. Concentrations are in g/m3.
70

Figure 38. Herculaneum 24-hour screening results.

4. Summary and Conclusions

MMIF output was evaluated for three areas involving differing complexities of terrain from 

relatively flat to complex, differing resolutions of prognostic model output, and two different 

pollutants, SO2 and lead. Evaluation of the meteorological data for several variables indicated 

that while there are differences between the prognostic model output and observations, the 

prognostic model output was not unreasonable. However, it should be noted that small 

differences in meteorological variables, such as temperature, wind speed or direction, can lead to 

vastly different air quality results.
71

Statistical evaluations of the resulting AERMOD results from observed meteorological data and 

prognostic data exhibited differing behaviors. 

For Gibson, all meteorological scenarios, observed or prognostic, tended toward underprediction of SO2 concentrations at higher concentrations but the screening analyses indicated 

biases close to zero. At the 3-hour averaging time, all scenarios exceeded the factor-of-two limit 

in the screening analysis but were within a factor-of-two for the 24-hour averages. Evaluations 

of the MCM values indicated the best performing scenarios tended to be the observed 

meteorological data but that the prognostic outputs were not statistically different. Of the two 

prognostic outputs, the MMIF output of the grid cell of Gibson performed better than the grid 

cell of the Evansville NWS station.

For the Martins Creek case study, the meteorological scenarios tended to over-predict and the 

CPM and MCM analyses revealed that the Martins Creek site-specific observed data performed 

better. From the MCM analysis, the prognostic meteorological scenarios were statistically 

different from the Martins Creek site-specific scenario. Compared to the ABE NWS data, both 

prognostic scenarios were not statistically different, which was a goal of this evaluation; 

prognostic meteorological data performs no worse than a representative NWS station.

Finally, for Herculaneum, detailed statistical analyses could not be performed due to the lack of 

hourly lead observations, but a screening analysis of the outputs indicated that while the 

prognostic scenarios were not within the factor-of-two agreement for 24-hour averages, their 

performance was actually comparable to the Herculaneum site-specific tower.

While there is a need for more evaluations in more challenging environments, i.e. complex 

terrain or meteorological conditions, these results indicate promise for the use of prognostic 

meteorological data in AERMOD applications. 
72

5. References

Cox, W.M. and J.A. Tikvart, 1990: A statistical Procedure for Determining the Best Performing 

Air Quality Simulation Model. Atmos. Environ., 24A (9): 2387-2395.

Environ, 2014: The Mesoscale Model Interface Program (MMIF) Version 3.1 User’s Manual.

Frost, Kali, 2014: AERMOD Performance Evaluation for Three Coal-fired Electrical Generating 

Units in Southwest Indiana. Journal of the Air & Waste Management Association, 64:3

280-290.

Perry Steven G., Alan J. Cimorelli, Robert J. Paine, Roger W. Brode, Jeffrey C. Weil, Akula 

Venkatram, Robert B. Wilson, , Russell F. Lee., and Warren D. Peters, 2005: 

AERMOD: A Dispersion Model for Industrial Source Applications. Part II: Model 

Performance Against 17 Field Study Databases. Journal of Applied Meteorology, 44, 

694-708.

U.S. EPA, 1992: Protocol for Determining the Best Performing Model, EPA-454/R-92-025. 

U.S. Environmental Protection Agency, Research Triangle Park, NC.

U.S. EPA, 2003: AERMOD: Latest Features and Evaluation Results, EPA-454/R-03-003. U.S. 

Environmental Protection Agency, Research Triangle Park, NC.

U.S. EPA, 2013: AERSURFACE User’s Guide. EPA-454/B-08-001. U.S. Environmental 

Protection Agency, Research Triangle Park, North Carolina 27711.

U.S. EPA, 2014: Meteorological Model Performance for Annual 2011 WRF v3.4 Simulation. 

http://www.epa.gov/ttn/scram/reports/MET_TSD_2011_final_11-26-14.pdf

U.S. EPA, 2015a: Guideline on Air Quality Models. 40 CFR Part 51 Appendix W.

U.S. EPA, 2015b: Guidance on the Use of the Mesoscale Model Interface Program (MMIF) for 

AERMOD Applications. EPA-454/R-15-004. U.S. Environmental Protection Agency, 

Research Triangle Park, NC 27711.
A-1

Appendix A. Meteorological data comparisons

A.1 Gibson

Figures A-1 through A-6 compare the distributions of several meteorological variables among 

the four meteorological datasets (panel a of the figures). These variables are the input variables 

into AERMET: wind speed, temperature, pressure, relative humidity, albedo, and Bowen ratio. 

Comparisons of key meteorological variables calculated by AERMET including: heat flux, u*, 

w*, Monin-Obukhov length, and mixing heights can be found in Figures A-7 through A-14. In 

panel b of each figure are distributions of the biases of each variable among the different 

scenarios. The first bias distribution is between the two MMIF outputs, GIB MMIF and EVV 

MMIF. This bias shows how the meteorology can vary in a distance of a few grid cells. The 

next two bias distributions show the differences between MMIF and observations for the Gibson 

and Evansville grid cells respectively. The final distribution shows the bias between the two 

observed datasets and acts as a control since, in the absence of prognostic data, the two observed 

datasets would be the only ones available for consideration. If the differences between the 

MMIF outputs and the two observed datasets are similar, then from qualitative standpoint, the 

MMIF output is reasonable for use. 

Based on the figures the following can be seen:

 For wind speed (Figure A-1), EVV OBS tends to show lower wind speeds (Figure A-1.a) 

among the four datasets. Each locations’ MMIF – OBS bias tends to be more positive, 

correlating with the distributions in Figure A-1.a. The GIB MMIF – EVV MMIF bias 

trends positive as does the GIB OBS- EVV OBS, indicating that the MMIF output 

appears reasonable when compared to the observed datasets. 

 Temperature (K) distributions (Figure A-2) indicate very similar patterns for all scenarios 

with the bias distributions tending to show fairly unbiased results among the scenario 

(median bias near zero degrees).

 The GIB OBS pressure distribution (Figure A-3) shows higher pressures than over the 

other scenarios which also bears out in the bias distributions. The two MMIF scenarios 

are similar. The differences between the two observed datasets indicate higher pressures 

at Gibson over Evansville.

 Relative humidity (Figure A-4) is identical for GIB OBS and EVV OBS. This is due to 

EVV being the source of RH for GIB in AERMET. The two MMIF scenarios are very 

similar. Their bias distributions when compared to their respective OBS dataset are very 

similar.

 Daytime albedo (Figure A-5) somewhat similar distributions among the scenarios with 

GIB MMIF – EVV MMIF, GIB MMIF – GIB OBS exhibiting positive bias and the other 

two being negative.
A-2

 Bowen ratios (Figure A-6) vary widely among the scenarios. This could be due to the 

subjectivity of the average, dry, and wet selections in AERSURFACE.

 Heat flux (Figure A-7) shows GIB OBS with lower values compared to the other 

scenarios. The differences between the two MMIF sites and the differences between the 

observed heat fluxes were small compared to the differences between the respective 

MMIF and observed datasets. 

 Surface friction velocity, u* (Figure A-8) shows that the MMIF scenarios tended to have 

higher values than the observed scenarios. The GIB MMIF – EVV MMIF bias 

distribution is similar to the GIB OBS – EVV OBS bias distribution.

 Convective velocity scale, w* (Figure A-9) show a similar trend as u*, the MMIF 

scenarios exhibited higher values than the observed scenarios.

 Monin-Obukhov length, L (Figure A-10) distributions appear very similar across the 

scenarios with bias distributions being similar as well.

 Convective mixing height, Zic (Figure A-11) distributions and bias distributions indicate 

that the MMIF mixing heights were higher than their observed counterparts. Both the 

MMIF and observed bias plots indicate a tendency for Gibson mixing heights to be lower 

than the Evansville mixing heights.

 Mechanical mixing height, Zim (Figure A-12) distributions show similar behavior as the 

convective mixing heights.

 Potential temperature gradient (dΘ/dz), (Figure A-13), distributions indicate smaller lapse 

rates for the MMIF data compared to the observed data.

 Cloud cover (Figure A-14) distributions show no differences between EVV and GIB for 

the observed datasets since EVV cloud cover is used at Gibson. Cloud cover estimates 

for MMIF are calculated in AERMET. Differences between the MMIF and OBS for 

Gibson and Evansville show a large swing in biases. 

For the most part, the meteorological data processed through MMIF for Gibson and Evansville 

appear reasonable when compared to the Gibson and Evansville observations.
A-3

Figure A-1. Gibson wind speed (m/s): a) annual distributions and b) bias distributions.
A-4

Figure A-2. Gibson ambient temperature (K): a) annual distributions and b) bias 

distributions.
A-5

Figure A-3. Gibson station pressure (mb): a) annual distributions and b) bias 

distributions.
A-6

Figure A-4. Gibson relative humidity (percent): a) annual distributions and b) bias 

distributions.
A-7

Figure A-5. Gibson daytime albedo (fraction): a) annual distributions and b) bias 

distributions.
A-8

Figure A-6. Gibson Bowen ratio: a) annual distributions and b) bias distributions.
A-9

Figure A-7. Gibson heat flux (W/m2): a) annual distributions and b) bias distributions.
A-10

Figure A-8. Gibson surface friction velocity, u* (m/s): a) annual distributions and b) bias 

distributions.
A-11

Figure A-9. Gibson convective velocity scale, w* (m/s): a) annual distributions and b) bias 

distributions.
A-12

Figure A-10. Gibson Monin-Obukhov length (m): a) annual distributions and b) bias 

distributions.
A-13

Figure A-11. Gibson convective mixing height (m): a) annual distributions and b) bias 

distributions.
A-14

Figure A-12. Gibson mechanical mixing height (m): a) annual distributions and b) bias 

distributions.
A-15

Figure A-13. Gibson potential temperature gradient (K/m) above Zic: a) annual 

distributions and b) bias distributions.
A-16

Figure A-14. Gibson cloud cover (tenths*10): a) annual distributions and b) bias 

distributions.
A-17

A.2 Martins Creek

Figures A-15 through A-20 show the distributions of the AERMET input variables for the 

Martins Creek study and Figures A-21-27 show the calculated variables from AERMET. As 

with the Gibson plots, panel b of each figure shows key biases. The first two bias distributions 

show the biases of the two MMIF scenarios against the Martins Creek site-specific data. The 

third fourth distribution compares the two MMIF scenarios against each other, and the fourth 

distribution compares the two observed datasets and acts as a control. The following can be seen 

in the plots:

 For wind speed (Figure A-15), Martins creek tends to show lower wind speeds (Figure A15.a) among the four datasets. The bias distributions appear very similar between the 

MMIF scenarios and Martins Creek and ABE and Martins creek. 

 Temperature (K) distributions (Figure A-16) indicate very similar patterns for all 

scenarios with the bias distributions tending to show fairly unbiased results among the 

scenario (median bias near zero degrees).

 The pressure distribution (figure A-17) appear to be similar with the MMIF 1 km 

scenario exhibiting slightly higher values.

 Relative humidity (Figure A-18) is identical for Martins Creek and ABE are identical 

being due to ABE being the source of RH for Martins Creek in AERMET. The two 

MMIF scenarios are very similar. Their bias distributions are very similar.

 Daytime albedo (Figure A-19) show comparable values for ABE and the MMIF 

scenarios. Martins Creek has a wider distribution of values. Bias distributions for the two 

MMIF scenarios are tighter than for the ABE – Martins Creek bias distribution.

 Bowen ratios (Figure A-20) vary widely among the scenarios. This could be due to the 

subjectivity of the average, dry, and wet selections in AERSURFACE.

 Heat flux (Figure A-21) distributions show comparable distributions between Martins 

Creek and the MMIF 1 km scenario while ABE and the MMIF 4 km scenario appear 

similar. The bias distributions appear comparable to each other. 

 Surface friction velocity (Figure A-22) distributions show comparable distributions 

among the scenarios. The bias distributions appear comparable to each other. 

 Convective velocity scale (Figure A-23) distributions show comparable distributions 

between Martins Creek and the MMIF 1 km scenario while ABE and the MMIF 4 km 

scenario appear similar. The bias distribution for the MMIF 1km and Martins Creek is 

noticeably lower than the others which appear comparable to each other. 

 Monin-Obukhov length, L (Figure A-24) distributions appear very similar across the 

scenarios with bias distributions being similar as well. It is difficult to detect differences, 

which could be due to large absolute values for near neutral conditions.

 Convective mixing height, Zic (Figure A-25) distributions and bias distributions indicate 

that the MMIF mixing heights were comparable to ABE and Martins Creek

 Mechanical mixing height, Zim (Figure A-26) distributions show similar behavior as the 

convective mixing heights.
A-18

 Potential temperature gradient (dΘ/dz), (Figure A-27), distributions indicate smaller lapse 

rates for the MMIF data compared to the observed data.

 Cloud cover (Figure A-28) distributions show no differences between ABE and Martins 

Creek for the observed datasets since ABE cloud cover is used at Martins Creek. Cloud 

cover estimates for MMIF are calculated in AERMET. Differences between the MMIF 

and Martins Creek show a large swing in biases. 

For the most part, the meteorological data processed through MMIF for Martins Creek appear

reasonable when compared to Martins Creek observations.
A-19

Figure A-15. Martins Creek wind speed (m/s): a) annual distributions and b) bias 

distributions.
A-20

Figure A-16. Martins Creek ambient temperature (K): a) annual distributions and b) bias 

distributions.
A-21

Figure A-17. Martins Creek station pressure (mb): a) annual distributions and b) bias 

distributions.
A-22

Figure A-18. Martins Creek relative humidity (percent): a) annual distributions and b) 

bias distributions.
A-23

Figure A-19. Martins Creek daytime albedo (fraction): a) annual distributions and b) bias 

distributions.
A-24

Figure A-20. Martins Creek Bowen ratio: a) annual distributions and b) bias distributions.
A-25

Figure A-21. Martins Creek heat flux (W/m2): a) annual distributions and b) bias 

distributions.
A-26

Figure A-22. Martins Creek surface friction velocity, u* (m/s): a) annual distributions and 

b) bias distributions.
A-27

Figure A-23. Martins Creek convective velocity scale, w* (m/s): a) annual distributions 

and b) bias distributions
A-28

Figure A-24. Martins Creek Monin-Obukhov length (m): a) annual distributions and b) 

bias distributions.
A-29

Figure A-25. Martins Creek convective mixing height (m): a) annual distributions and b) 

bias distributions.
A-30

Figure A-26. Martins Creek mechanical mixing height (m): a) annual distributions and b) 

bias distributions.
A-31

Figure A-27. Martins Creek potential temperature gradient (K/m) above Zic: a) annual 

distributions and b) bias distributions.
A-32

Figure A-28. Martins Creek cloud cover (tenths*10): a) annual distributions and b) bias 

distributions.
A-33

A.3 Herculaneum

Figures A-29 through A-34 show the distributions of the AERMET input variables for the 

Herculaneum study and Figures A-35 through A-41 show the calculated variables from 

AERMET. As with the other evaluation plots, panel b of each figure shows key biases. The first 

three bias distributions show the biases of the three MMIF scenarios against the Herculaneum 

site-specific data. The fourth through sixth distribution compares the three MMIF scenarios 

against each other, and the final distribution compares the two observed datasets and acts as a 

control. The following can be seen in the plots:

 For wind speed (Figure A-29), Herculaneum tends to show lower wind speeds (Figure A29.a) among the datasets. The bias distributions appear very similar between the MMIF 

scenarios and Herculaneum and CPS and Herculaneum. Biases between the three MMIF 

scenarios are relatively low and comparable to each other.

 Temperature (K) distributions (Figure A-30) indicate very similar patterns for all 

scenarios with the bias distributions tending to show fairly unbiased results among the 

scenario (median bias near zero degrees).

 The pressure distribution (Figure A-31) appear to be similar among the two observed 

datasets and the MMIF 4 km scenario. Pressures tend to be lower with the 12 and 36 km 

MMIF scenarios.

 Relative humidity (Figure A-32) are comparable for Herculaneum and CPS. The three 

MMIF scenarios are very similar. Their bias distributions are also very similar and 

comparable to the CPS – Herculaneum bias distribution.

 Daytime albedo (Figure A-33) show comparable values among the scenarios. The bias 

distributions for the MMIF 4 km scenario shows a more positive bas than the other two 

MMIF scenarios.

 Bowen ratios (Figure A-34) are comparable for the two observed datasets and 

comparable among the three MMIF scenarios. MMIF bias distributions show more 

differences than the CPS –Herculaneum biases but are comparable.

 Heat flux (Figure A-35) distributions show comparable among the scenarios. The bias 

distributions appear comparable to each other. 

 Surface friction velocity (Figure A-36) distributions show comparable distributions 

among the scenarios. For the most part, the bias distributions appear comparable to each 

other with differences in the ranges of the biases. 

 Convective velocity scale (Figure A-37) distributions show comparable distributions 

between Herculaneum and CPS as well as the 12 and 36 km MMIF scenarios. The bias 

distributions for the MMIF scenarios (compared to Herculaneum) are slightly higher than 

the CPS – Herculaneum bias distribution. 

 Monin-Obukhov length, L (Figure A-38) distributions appear very similar across the 

scenarios with bias distributions being similar as well. It is difficult to detect differences, 

which could be due to large absolute values for near neutral conditions.

 Convective mixing height, Zic (Figure A-39) distributions and bias distributions indicate 

that the MMIF mixing heights were higher than the two observed datasets. The MMIF 
A-34

bias distributions (compared to Herculaneum) are noticeably higher than the CPS –

Herculaneum bias distribution.

 Mechanical mixing height, Zim (Figure A-40) distributions show comparable 

distributions and bias distributions among the scenarios.

 Potential temperature gradient (dΘ/dz), (Figure A-41), indicate lower lapse rates for the 

two observed datasets when compared to the MMIF scenarios. The bias distributions for 

the MMIF scenarios (compared to Herculaneum) show a more positive bias when 

compared to the CPS – Herculaneum bias distribution.

 Cloud cover (Figure A-42) distributions show no differences between CPS and 

Herculaneum since CPS cloud cover is used at Herculaneum. Cloud cover estimates for 

MMIF are calculated in AERMET. Differences between the MMIF and Herculaneum

show a large swing in biases with a tendency to positive bias. 

For the most part, the meteorological data processed through MMIF for Herculaneum appear 

reasonable when compared to Herculaneum and CPS observations.
A-35

Figure 29. Herculaneum wind speed (m/s): a) annual distributions and b) bias 

distributions.
A-36

Figure A-30. Herculaneum ambient temperature (K): a) annual distributions and b) bias 

distributions.
A-37

Figure A-31. Herculaneum station pressure (mb): a) annual distributions and b) bias 

distributions.
A-38

Figure A-32. Herculaneum relative humidity (percent): a) annual distributions and b) bias 

distributions.
A-39

Figure A-33. Herculaneum daytime albedo (fraction): a) annual distributions and b) bias 

distributions.
A-40

Figure A-34. Herculaneum Bowen ratio: a) annual distributions and b) bias distributions.
A-41

Figure A-35. Herculaneum heat flux (W/m2): a) annual distributions and b) bias 

distributions.
A-42

Figure A-36. Herculaneum surface friction velocity, u* (m/s): a) annual distributions and 

b) bias distributions.
A-43

Figure A-37. Herculaneum convective velocity scale, w* (m/s): a) annual distributions and 

b) bias distributions.
A-44

Figure A-38. Herculaneum Monin-Obukhov length (m): a) annual distributions and b) 

bias distributions.
A-45

Figure A-39. Herculaneum convective mixing height (m): a) annual distributions and b) 

bias distributions.
A-46

Figure A-40. Herculaneum mechanical mixing height (m): a) annual distributions and b) 

bias distributions.
A-47

Figure A-41. Herculaneum potential temperature gradient (K/m) above Zic: a) annual 

distributions and b) bias distributions.
A-48

Figure A-42. Herculaneum cloud cover (tenths*10): a) annual distributions and b) bias 

distributions.
B-1

Appendix B. NWS/AERMET and WRF/MMIF analyses for Region 8 sites.

B.1 Introduction

The AERMOD Meteorological Data workgroup selected a number of sites within EPA Region 8 

to examine and better understand the issues of utilizing various types of meteorological data and 

post-processors for air quality dispersion modeling. The workgroup focused on meteorological 

data reported through the National Weather Service (NWS) and processed through AERMET, as 

well as meteorological data generated by a the Weather Research and Forecasting (WRF) 

prognostic model and processed through the Mesoscale Model Interface Program (MMIF). This 

section summarizes the results of an analysis that evaluated the differences among 

NWS/AERMET and WRF/MMIF output for various surface meteorological parameters at sites 

within EPA Region 8. This work does not comment on the accuracy of the results because of the 

lack of observational data (i.e., monitored air quality concentrations) available for model 

validation at the selected sites.

The basic purpose of AERMET is to use meteorological measurements to compute certain 

boundary layer parameters used to estimate profiles of wind, turbulence and temperature. The 

depth of this layer and the dispersion of pollutants within it are influenced on a local scale by 

surface characteristics, such as surface roughness, reflectivity (albedo), and the availability of 

surface moisture. Surface characteristics in the form of albedo, surface roughness and Bowen 

ratio, plus standard meteorological observations (wind speed, wind direction, temperature, and 

cloud cover), are input to AERMET. AERMET then calculates the boundary layer parameters, 

including the Monin-Obukhov Length (L), surface friction velocity (u*), surface roughness 

length (zo), surface heat flux (H), and the convective scaling velocity (w*). AERMET also 

provides estimates of the convective and mechanical mixed layer heights, zic and zim, 

respectively. These parameters are then passed to AERMOD, where similar expressions (in 

conjunction with measurements) are used to calculate vertical profiles of wind speed, lateral and 

vertical turbulent fluctuations, potential temperature gradient, and potential temperature. 

Although AERMOD is capable of estimating meteorological profiles with data from as little as 

one measurement height, it will use as much data as the user can provide for defining the vertical 

structure of the boundary layer. In addition to the boundary layer parameters, AERMET passes 

all measurements of wind, temperature, and turbulence in a form AERMOD needs.

B.2 Methodology

Based on the data available during the time of this study, this work evaluated a total of five

model cases covering year 2010 and 2011 and eight meteorological parameters at six sites with 

EPA Region 8. This work selected sites based on proximity to flat terrain, valleys, and mountains 

to understand the impacts various types of terrain may have on the meteorological data. Table B1 and Figure B-1 presents information about the sites selected for this work. 
B-2

Table B-1. Meteorological sites analyzed for study.

Site Latitude Longitude NLCD 

Codes

Surface Site 

Codes

Upper Air Site Codes 

(2010/2011)

Lamar, CO 38.07 N 102.69 W 090900 KLAA (03013) KDDC (31484/17452)

Miles City, MT 46.43 N 105.89 W 050600 KMLS (24037) KGGW (31986/24880)

Minot, ND 48.26 N 101.28 W 083000 KMOT (24013) KBIS (18650/15494)

Rapid City, SD 44.05 N 103.05 W 101100 KRAP (24090) KRAP (19280/17035)

Vernal, UT 40.44 N 109.51 W 081500 KVEL (94030) KGJT 

(1005/29527)

Riverton, WY 43.06 N 108.46 W 081700 KRIW (24061) KRIW (1410/29950)

Figure B-1. Map of sites analyzed for study.

The work also focused on meteorological information generated from two meteorological 

platforms. The first platform included EPA’s preferred meteorological model programs, 

comprising of AERSURFACE, AERMINUTE, and AERMET. This platform, referenced as 

NWS/AERMET, was used to model calendar 2010 and 2011 at the selected sites. The second 
B-3

platform included an alternative meteorological model and post-processor, comprising of the 

WRF model and the MMIF post-processor. This platform, referenced as WRF/MMIF, was used 

to model calendar 2010 and 2011 using 4 kilometer (km) and 12 km grid resolutions. The details 

of the model descriptions and model assumptions for each model case are outlined in Table B-2

for NWS/AERMET and Table B-3 for the WRF/MMIF cases. 

Table B-2. Description of NWS/AERMET model cases.

Case Name Year Model 

Platforms

Model Assumptions Input Data

2010 

NWS/AERMET

2010 AERSURFACE 

v13016

Datum: NAD83 

Study radius for surface roughness: 1

Vary By Sector: Yes

Number of Sectors: 12

Temporal Resolution: Monthly

Snow Cover: Yes

Months to Seasons: No

Airport: Yes

Arid: N

Surface Moisture: Average

USGS NLCD92 Data

AERMINUTE 

v14237

Default ASOS 1-minute Data

[6405 Datasets]

AERMET

v14134

Default NWS Hourly Surface 

Data

[ISHD Format]

NWS Hourly Upper 

Air Data

[FSL Format]

2011 

NWS/AERMET

2011 AERSURFACE 

v13016

Datum: NAD83 

Study radius for surface roughness: 1

Vary By Sector: Yes

Number of Sectors: 12

Temporal Resolution: Monthly

Snow Cover: Yes

Months to Seasons: No

Airport: Yes

Arid: N

Surface Moisture: Average

USGS NLCD92 Data

AERMINUTE 

v14237

Default ASOS 1-minute Data

[6405 Datasets]

AERMET

v14134

Default NWS Hourly Surface 

Data

[ISHD Format]

NWS Hourly Upper 

Air Data

[FSL Format]
B-4

Table B-3. Description of WRF/MMIF model cases.

Case Name Year Model 

Platforms

Model Assumptions Input Data

2010 4-km 

WRF/MMIF

2010 WRF

v3.5.1

Resolution: 4km

Configuration: Details included in 

WestJumpAQMS WRF Evaluation 

Report4

Details included in 

WestJumpAQMS 

WRF Evaluation 

Report5

MMIF

v3.0

2010 12-km 

WRF/MMIF

2010 WRF

v3.5.1

Resolution: 12km

Configuration: Details included in 

WestJumpAQMS WRF Evaluation 

Report6

Details included in 

WestJumpAQMS 

WRF Evaluation 

Report7

MMIF

v3.0

2011 12-km 

WRF/MMIF

2011 WRF

v3.3.1

Resolution: 12km

Configuration: Details included in 

EPA’s Evaluation Report8

Details included in 

WestJumpAQMS 

WRF Evaluation 

Report9

MMIF

v3.0

Using these two platforms, this study analyzed eight meteorological surface parameters, 

including temperature, relative humidity, wind speed, wind displacement, mechanical mixing 

height, convective mixing height, surface friction velocity, and convective velocity. A number of 

statistical metrics and graphical displays were also generated and reviewed to evaluate the 

differences among the platforms at the selected sites for the various meteorological parameters 

and model years. The statistical metrics included hourly, monthly or annual averages, bias, 

fractional bias, error, fractional error, and coefficient of determination. Time series, scatter and 

box plots were also generated as part of the graphical displays. This work only includes a 

summary of the statistical results. However, all of the graphical displays and statistical analyses 

can be provided upon request. 

 

4 Western Regional Air Partnership (WRAP) West-wide Jump Start Air Quality Modeling Study (WestJumpAQMS) 

WRF Application/Evaluation, UNC, ENVIRON, Alpine Geophysics, February 29, 2012, 

http://www.wrapair2.org/pdf/WestJumpAQMS_2008_Annual_WRF_Final_Report_February29_2012.pdf 

5

Ibid.

6

Ibid.

7

Ibid.

8

XXX

9

XXX
B-5

Given that the NWS/AERMET platform may be more representative of the meteorological 

conditions at the selected sites, the statistical analysis were based on comparing the model cases 

that used the WRF/MMIF platform relative to the model cases that used the NWS/AERMET 

platform. For instance, the statistics were based on comparing the 2010 NWS/AERMET case to 

the 2010 4-km WRF/MMIF case or the 2010 NWS/AERMET case to the 2010 12-km 

WRF/MMIF case. Some of the analyses or graphical displays also compared the 2010 4-km 

WRF/MMIF case to the 2010 12-km WRF/MMIF case. Although the WRF model utilizes 

observational datasets, the observations used for this study were prescribed to the associated grid 

box with a resolution of 4 km or 12 km, which may not be representative of the actual conditions 

that cover the entire grid box or at the selected sites. Therefore, the meteorological information 

generated by the NWS/AERMET platform may be more representative at the selected sites than 

the WRF/MMIF platform.

B.3 Results/Summary

A summary of the results by meteorological parameter and the significant differences among the 

model cases are outlined below. In general, careful evaluation and consideration is recommended 

when selecting a meteorological model platform because of the range and variability observed in 

the results of this analysis. Although additional evaluation is needed for model validation, the 

results from the WRF/MMIF platform for temperature, relative humidity, and wind speed were 

found to be similar to the NWS/AERMET platform. However, mechanical and convective 

mixing heights and surface friction and convective velocities varied significantly among the 

platforms, with notable differences in magnitudes, temporal variability, and low correlations. 

Generally, the WRF/MMIF platform predicted significantly higher mechanical and convective 

mixing heights and convective velocities and lower surface friction velocities relative to the 

NWS/AERMET platform. The results of the wind displacement analysis also suggest that the 

WRF/MMIF 4 km model case could potentially displace the plume significantly, relative to the 

NWS/AERMET platform. These results raise concerns on the adequacy of the WRF/MMIF 

platform for generating meteorological information for the dispersion models and whether the 

information would generate conservative results. 

This work also found that the 4 km and 12 km WRF/MMIF model cases compared well to one 

another, with correlations greater than 0.70 for all meteorological, except wind speed at the 

Lamar, CO, Vernal, UT, and Riverton, WY sites and surface friction velocity at the Vernal, UT 

and Riverton, WY sites. These results suggests systematic difference among the WRF/MMIF 

and NWS/AERMET platforms. Without additional evaluation, this work did not find that the 

WRF/MMIF 4 km case out-performed the 12 km case relative to the NWS/AERMET platform.

B.3.1 Temperature

Table B-4 shows statistics for monthly average temperatures. The findings for temperature are:

 The WRF/MMIF results for both model years and all sites are similar to the 

NWS/AERMET results. In general, the WRF/MMIF platforms slightly over-estimated 

the values relative to the NWS/AERMET platform at all sites, except at the Minot, ND 

and Riverton, WY sites. Although minor, this work found that the most significant 
B-6

differences among the platforms were observed at the Lamar, CO, Vernal, UT and 

Riverton, WY sites during January and February.

 Relative to NWS/AERMET, the WRF/MMIF results show similar temporal variability 

for both model years and at all sites.

 The WRF/MMIF 4 km and 12 km are comparable to one another, with correlations 

greater than 0.98. This work did not find that the WRF/MMIF 4 km case out-performed 

the 12 km case relative to the NWS/AERMET platform. 

Table B-4. Monthly averaged temperature (K) across all modeled cases at each site.

Site Temperature 

(K) Mean Bias

Fractional 

Bias Error

Fractional 

Error R2

LAA

Minimum 267.45 -0.61 -0.20 1.67 0.56 0.76

Maximum 301.41 3.40 1.27 4.40 1.63 0.94

Average 285.93 1.43 0.52 2.48 0.88 0.88

MLS

Minimum 263.54 -0.73 -0.26 1.16 0.41 0.72

Maximum 297.35 2.24 0.78 3.88 1.49 0.95

Average 280.54 0.65 0.23 1.93 0.70 0.88

MOT

Minimum 258.97 -2.71 -1.05 1.01 0.35 0.51

Maximum 295.48 0.99 0.35 3.33 1.29 0.95

Average 278.48 -0.73 -0.27 1.82 0.66 0.86

RAP

Minimum 266.64 -0.35 -0.12 1.24 0.44 0.24

Maximum 297.69 2.39 0.81 3.22 1.22 0.97

Average 281.40 0.88 0.31 1.96 0.70 0.82

VEL

Minimum 261.15 -1.17 -0.41 1.33 0.45 0.24

Maximum 297.30 5.70 2.17 5.87 2.22 0.94

Average 281.17 0.98 0.37 2.30 0.83 0.80

RIW

Minimum 262.49 -3.65 -1.39 1.27 0.43 0.61

Maximum 296.40 1.29 0.45 4.07 1.54 0.93

Average 279.94 -0.07 -0.03 1.97 0.71 0.83

B.3.2 Relative humidity

Table B-5 shows statistics for monthly average relative humidity. Findings are:

 The WRF/MMIF results for both model years and all sites are similar to the 

NWS/AERMET results. In general, the WRF/MMIF platforms slightly over-estimated 

the values relative to the NWS/AERMET platform at the Miles City, MT, Minot, ND, 

and Riverton, WY sites, and slightly under-estimated the values at the Lamar, CO, Rapid 

City, SD, and Vernal, UT sites. Although minor, this work found that the most significant 

differences between the platforms were observed at the Miles City, Montana site during 

the summer and the Riverton, Wyoming site during January and February.
B-7

 Relative to NWS/AERMET, the WRF/MMIF results show similar temporal variability 

for both model years and at all sites.

 The WRF/MMIF 4 km and 12 km are comparable to one another, with correlations 

greater than 0.86. This work found that the WRF/MMIF 4 km case compared better to 

NWS/AERMET platform than the WRF/MMIF 12 km case at all sites. 

Table B-5. Monthly averaged relative humidity across all modeled cases at each site.

Site Temperature 

(K) Mean Bias

Fractional 

Bias Error

Fractional 

Error R2

LAA

Minimum 39.41 -12.13 -23.03 6.02 11.96 0.51

Maximum 81.49 7.37 19.72 13.23 32.68 0.88

Average 53.25 -1.43 -0.36 9.50 20.61 0.74

MLS

Minimum 39.92 -16.08 -27.27 6.90 8.94 0.10

Maximum 92.10 14.35 17.19 16.48 28.04 0.84

Average 65.58 0.88 -0.54 10.61 17.60 0.60

MOT

Minimum 55.54 -5.88 -10.53 6.28 9.89 0.26

Maximum 92.44 15.53 20.93 17.84 25.37 0.84

Average 71.98 5.72 7.28 10.02 14.62 0.62

RAP

Minimum 41.94 -14.78 -26.91 7.15 11.42 0.03

Maximum 89.09 13.54 17.18 15.40 28.27 0.88

Average 65.51 -0.44 -1.22 10.35 17.40 0.55

VEL

Minimum 26.57 -16.09 -29.53 7.35 10.60 0.03

Maximum 85.06 8.25 22.66 17.05 32.55 0.79

Average 54.70 -1.54 -1.41 10.20 20.93 0.53

RIW

Minimum 33.20 -3.85 -8.23 6.42 15.88 0.08

Maximum 93.77 20.10 25.03 20.35 27.53 0.80

Average 55.96 5.40 9.03 11.29 21.31 0.56

B.3.3 Wind speed

Table B-6 shows statistics for monthly average wind speed. Findings are:

 The WRF/MMIF results for both model years and all sites are similar to the 

NWS/AERMET results. In general, the WRF/MMIF platforms slightly under-estimated 

the values relative to the NWS/AERMET platform at all sites, except at the Vernal, UT 

site. Although minor, this work found that the most significant differences between the 

platforms were observed at the Vernal, UT site.

 Relative to NWS/AERMET, the WRF/MMIF results show similar temporal variability 

for both model years and at all sites.

 The WRF/MMIF 4 km and 12 km are comparable to one another at the Miles City, MT, 

Minot, ND, and Rapid City, SD sites, with correlations greater than 0.70. Generally, the 
B-8

WRF/MMIF 12 km case compared better to NWS/AERMET platform than the 

WRF/MMIF 4 km case at all sites except at the Vernal, UT site. 

Table B-6. Monthly averaged wind speed (m/s) across all modeled cases at each site.

Site Temperature 

(K) Mean Bias

Fractional 

Bias Error

Fractional 

Error R2

LAA

Minimum 2.75 -1.44 -32.47 0.94 33.80 0.24

Maximum 6.07 -0.12 3.83 2.06 49.37 0.75

Average 4.09 -0.75 -15.23 1.44 39.25 0.55

MLS

Minimum 2.61 -1.29 -30.36 0.98 23.99 0.30

Maximum 5.88 -0.17 -5.49 1.69 48.03 0.83

Average 4.03 -0.66 -16.70 1.28 36.80 0.55

MOT

Minimum 3.52 -1.36 -26.36 0.87 21.93 0.55

Maximum 6.17 -0.14 0.77 1.62 39.41 0.90

Average 4.58 -0.45 -6.98 1.07 27.89 0.74

RAP

Minimum 3.21 -1.04 -22.30 1.21 33.42 0.02

Maximum 5.77 0.29 14.60 1.92 50.93 0.90

Average 4.39 -0.47 -3.24 1.49 39.58 0.34

VEL

Minimum 1.35 -0.57 -18.96 0.80 41.04 0.02

Maximum 4.04 1.38 55.55 1.72 69.55 0.71

Average 2.75 0.46 15.58 1.30 51.28 0.26

RIW

Minimum 1.59 -1.59 -40.45 0.96 39.88 0.04

Maximum 5.39 0.30 11.52 2.20 57.21 0.59

Average 3.25 -0.99 -27.16 1.59 49.53 0.35

B.3.4 Wind displacement

Wind displacement, as defined in Section 2.2, is shown in Table B-7 for each site. Findings are:

 The calculation of wind displacement is important for determining how far a plume may 

be displaced relative to its actual location. Given that this work is assuming that the 

NWS/AERMET platform is more representative that the WRF/MMIF platform, this 

means that the wind displacement results will determine how far the WRF/MMIF 

platform will potentially displace the plume relative to the NWS/AERMET platform. The 

wind displacement results can also determine whether the resolution of the prognostic 

model could be sufficient for capturing the plume. For instance, when the wind 

displacement is within 4 km, these results could suggest that the WRF/MMIF 4 km 

model case could potentially capture the plume within that selected grid box. However, 

careful consideration of the adequacy of the WRF/MMIF 4 km model platform should be 

considered when the wind displacement is greater than 4 km.
B-9

 The results of the wind displacement analysis suggests that the WRF/MMIF 12 km model 

case could potentially capture the plume given the resolution and distance of less than 12 

km. However, careful consideration is needed when determining the adequacy of the 

WRF/MMIF 4 km model case because the distances are more than 4 km. Further, the 

most notable differences were found at the Vernal, UT site. 

Table B-7. Monthly averaged wind displacement (km) across all modeled cases at each site.

Site Minimum Maximum Average

LAA 6.32 13.13 9.23

MLS 5.58 8.64 7.08

MOT 6.32 11.03 9.04

RAP 7.13 11.33 8.11

VEL 5.47 14.42 10.65

RIW 6.60 10.84 9.33

B.3.5 Mechanical mixing heights

Table B-8 shows statistics for monthly average mechanical mixing heights. The findings for 

mechanical heights are:

 The WRF/MMIF results for both model years and all sites are not similar to the 

NWS/AERMET results, including notable differences in magnitudes, temporal 

variability, and low correlations. In general, the WRF/MMIF platforms significantly 

over-estimated the values relative to the NWS/AERMET platform at all sites, except at 

the Minot, ND site. 

 The WRF/MMIF 4 km and 12 km are comparable to one another, with correlations 

greater than 0.80. This work did not find that the WRF/MMIF 4 km case out-performed 

the 12 km case relative to the NWS/AERMET platform.
B-10

Table B-8. Monthly averaged mechanical mixing heights (m) across all modeled cases at 

each site.

Site Temperature 

(K) Mean Bias

Fractional 

Bias Error

Fractional 

Error R2

LAA

Minimum 166.76 -234.50 -46.96 130.18 69.77 0.03

Maximum 1125.91 360.06 60.41 844.73 92.30 0.47

Average 637.47 128.03 5.69 482.44 78.08 0.22

MLS

Minimum 114.02 -368.02 -90.02 102.72 58.59 0.06

Maximum 974.30 443.90 19.53 706.95 99.80 0.61

Average 520.02 24.49 -24.62 384.27 77.71 0.27

MOT

Minimum 112.38 -1168.20 -110.96 358.86 72.09 0.06

Maximum 1469.21 -120.02 -24.69 1200.46 117.07 0.37

Average 679.38 -506.86 -66.89 696.03 95.24 0.20

RAP

Minimum 201.56 -261.57 -35.49 150.71 63.89 0.03

Maximum 858.03 442.28 31.27 646.23 93.02 0.61

Average 490.40 23.49 -2.58 354.88 74.73 0.25

VEL

Minimum 45.52 12.35 -19.08 34.11 68.10 0.16

Maximum 988.12 661.08 92.88 761.05 102.31 0.47

Average 394.62 275.89 24.32 372.65 83.12 0.31

RIW

Minimum 31.61 -299.73 -113.72 97.79 79.51 0.05

Maximum 1036.19 379.77 -4.74 836.78 118.22 0.44

Average 539.10 7.36 -45.85 489.74 98.40 0.18

B.3.6 Convective mixing heights

Table B-9 shows statistics for monthly average convective mixing heights. The findings for 

convective heights are:

 The WRF/MMIF results for both model years and all sites are not similar to the 

NWS/AERMET results, including notable differences in magnitudes, temporal 

variability, low correlations. In general, the WRF/MMIF platforms significantly overestimated the values relative to the NWS/AERMET platform at all sites.

 The WRF/MMIF 4 km and 12 km are comparable to one another, with correlations 

greater than 0.75. Generally, the WRF/MMIF 12 km case compared slightly better to 

NWS/AERMET than the WRF/MMIF 4 km case at all sites except at the Vernal, UT site. 
B-11

Table B-9. Monthly averaged mechanical mixing heights (m) across all modeled cases at 

each site.

Site Temperature 

(K) Mean Bias

Fractional 

Bias Error

Fractional 

Error R2

LAA

Minimum 121.70 108.28 15.73 228.66 39.38 0.06

Maximum 1932.54 993.78 114.41 1035.89 114.41 0.71

Average 947.26 510.25 60.82 605.79 71.23 0.39

MLS

Minimum 68.00 -117.41 -11.38 74.57 34.06 0.00

Maximum 1576.02 619.99 147.31 672.85 151.82 0.71

Average 767.75 249.92 43.43 363.57 62.45 0.43

MOT

Minimum 94.26 -22.78 -30.46 62.00 30.96 0.03

Maximum 1227.90 420.21 118.19 491.13 123.38 0.74

Average 640.71 149.98 24.62 271.34 52.45 0.46

RAP

Minimum 132.75 -144.11 -7.36 106.70 31.95 0.00

Maximum 1548.62 479.86 95.05 628.09 95.68 0.76

Average 781.64 139.84 24.46 341.08 50.63 0.38

VEL

Minimum 96.43 -200.12 -18.94 81.84 30.55 0.00

Maximum 1981.48 493.66 98.98 711.62 103.24 0.65

Average 996.73 86.06 21.06 425.01 53.47 0.35

RIW

Minimum 68.18 -294.78 -49.76 50.88 29.24 0.02

Maximum 1919.11 311.17 75.16 698.34 86.45 0.76

Average 971.69 31.04 -1.52 401.16 51.31 0.41

B.3.7 Surface friction velocity

Table B-10 shows statistics for monthly average surface friction velocity (u*). The findings for 

u* are:

 The WRF/MMIF results for both model years and all sites not similar to the 

NWS/AERMET results, including notable differences in magnitudes, temporal 

variability, and low correlations. In general, the WRF/MMIF platforms significantly 

under-estimated the values relative to the NWS/AERMET platform at all sites, except at 

the Rapid City, SD and Vernal, UT sites.

 The WRF/MMIF 4 km and 12 km are comparable to one another, with correlations 

greater than 0.75, at all site except at the Vernal, UT and Riverton, WY sites. This work 

did not find that the WRF/MMIF 4 km case out-performed the 12 km case relative to the 

NWS/AERMET platform.
B-12

Table B-10. Monthly averaged surface friction velocity (m/s) across all modeled cases at 

each site.

Site Temperature 

(K) Mean Bias

Fractional 

Bias Error

Fractional 

Error R2

LAA

Minimum 0.15 -0.11 -29.97 0.07 39.07 0.20

Maximum 0.49 0.12 45.34 0.18 67.36 0.75

Average 0.33 -0.01 -2.21 0.13 48.56 0.51

MLS

Minimum 0.16 -0.09 -47.52 0.07 35.23 0.34

Maximum 0.48 0.07 23.88 0.15 69.38 0.78

Average 0.31 -0.01 -15.17 0.12 49.08 0.55

MOT

Minimum 0.16 -0.31 -73.39 0.14 35.89 0.26

Maximum 0.67 -0.07 -10.17 0.34 76.68 0.73

Average 0.40 -0.16 -36.58 0.20 53.49 0.51

RAP

Minimum 0.20 -0.02 -17.23 0.06 40.28 0.01

Maximum 0.43 0.09 44.04 0.14 64.88 0.84

Average 0.32 0.03 9.28 0.11 48.19 0.38

VEL

Minimum 0.06 -0.02 -29.02 0.04 47.41 0.12

Maximum 0.36 0.14 74.01 0.18 89.71 0.80

Average 0.21 0.05 19.17 0.11 62.22 0.39

RIW

Minimum 0.05 -0.19 -80.06 0.07 48.41 0.01

Maximum 0.47 -0.02 -5.00 0.20 89.44 0.46

Average 0.26 -0.10 -41.32 0.15 64.77 0.30

B.3.8 Convective velocity scale

Table B-10 shows statistics for monthly average convective velocity scale (w*). The findings 

for w* are:

 The WRF/MMIF results for both model years and all sites are not similar to the 

NWS/AERMET results, including notable differences in magnitudes, temporal 

variability, and low correlations. In general, the WRF/MMIF platforms significantly 

over-estimated the values relative to the NWS/AERMET platform at all sites. 

 The WRF/MMIF 4 km and 12 km are comparable to one another, with correlations 

greater than 0.85. This work did not find that the WRF/MMIF 4 km case out-performed

the 12 km case relative to the NWS/AERMET platform.
B-13

Table B-11. Monthly averaged convective velocity scale (m/s) across all modeled cases at 

each site.

Site Temperature 

(K) Mean Bias

Fractional 

Bias Error

Fractional 

Error R2

LAA

Minimum 0.32 -0.02 -5.37 0.25 21.82 0.07

Maximum 2.83 1.91 111.72 1.92 111.72 0.70

Average 1.50 0.88 57.65 0.95 64.59 0.42

MLS

Minimum 0.18 -0.46 -35.61 0.33 24.65 0.00

Maximum 2.68 1.58 114.61 1.58 117.97 0.61

Average 1.27 0.67 53.13 0.78 65.53 0.31

MOT

Minimum 0.19 -0.13 -13.22 0.25 26.07 0.00

Maximum 2.23 1.25 99.65 1.29 100.92 0.64

Average 1.09 0.47 40.50 0.58 56.00 0.34

RAP

Minimum 0.29 -0.45 -34.27 0.38 29.55 0.00

Maximum 2.49 1.22 86.88 1.24 98.56 0.74

Average 1.29 0.57 43.62 0.73 60.05 0.41

VEL

Minimum 0.25 -0.32 -25.71 0.27 25.65 0.00

Maximum 2.90 1.11 99.42 1.17 108.27 0.67

Average 1.46 0.50 35.60 0.66 50.93 0.39

RIW

Minimum 0.25 -0.25 -36.53 0.16 23.05 0.00

Maximum 2.92 1.16 64.27 1.20 84.81 0.74

Average 1.41 0.37 19.37 0.58 44.81 0.43

United States

Environmental Protection

Agency

Office of Air Quality Planning and Standards

Air Quality Assessment Division

Research Triangle Park, NC

Publication No. EPA-454/R-15-004

July 2015