Appendix A.

These documents are also available in the DOCKET EPA-R09-OAR-2012-0345: Approval and 

Promulgation of Implementation Plans; Hawaii; Regional Haze Federal Implementation Plan., in Section 

C.3 Documents Related to Modeling to Support the Subject to BART and Reasonable Progress. 

C.3.d Subject-to-Best Available Retrofit Technology (BART) Modeling for the State of Hawaii, 

Application of the CALPUFF Modeling System, Alpine Geophysics, LLC. 2010-03-03

C.3.a Subject-to-Best Available Retrofit Technology (BART) and Reasonable Progress (RP) 

Prioritization Modeling Protocol for the State of Hawaii Application of the CALPUFF Modeling System, 

Alpine Geophysics, LLC. 2009-11-30
Subject-to-Best Available Retrofit Technology (BART) 

Modeling for the State of Hawaii

Application of the CALPUFF Modeling System

Prepared for:

Hawaii State Department of Health

Environmental Management Division

Clean Air Branch

Prepared by:

Alpine Geophysics, LLC

7341 Poppy Way

Arvada, Colorado 80007

3 March 2010
i

Table of Contents

Section No. Page No. 

PURPOSE ......................................................................................................... 1

1 INTRODUCTION AND STUDY OBJECTIVES .............................................................................1

1.1 INTRODUCTION ............................................................................................................................................................ 1

2 MODELING APPROACH .................................................................................................................2

2.1 BART-ELIGIBLE/RP-ELIGIBLE SOURCES .................................................................................................................... 2

2.2 CLASS I AREAS ............................................................................................................................................................ 3

2.3 POLLUTANTS TO CONSIDER ......................................................................................................................................... 4

2.4 EMISSIONS AND STACK DATA...................................................................................................................................... 6

2.5 NATURAL BACKGROUND ............................................................................................................................................. 6

2.6 VISIBILITY CALCULATION ........................................................................................................................................... 7

2.7 MODEL EXECUTION ..................................................................................................................................................... 8

2.7.1 Subject-to-BART Analysis..................................................................................................................................... 8

2.7.2 Implementing Subject-to-BART/RP Prioritization Modeling Analysis................................................................. 9

3 VISIBILITY MODELING SYSTEM ..............................................................................................11

3.1 CALMET.......................................................................................................................................................................... 11

3.1.1 Meteorological Data ............................................................................................................................................. 13

3.1.2 CALMET Control File Settings ........................................................................................................................... 18

3.2 CALPUFF ................................................................................................................................................................. 19

3.2.1 Emissions ............................................................................................................................................................. 19

3.2.2 Ozone Background ............................................................................................................................................... 20

3.2.3 Ammonia Background ......................................................................................................................................... 20

3.2.4 Receptor Locations............................................................................................................................................... 20

3.3 CALPOST AND VISIBILITY POST-PROCESSING.............................................................................................. 20

4. SUBJECT-TO-BART MODELING RESULTS .................................................................................21

5. CONCLUSION...................................................................................................................................26

6. REFERENCES...................................................................................................................................27

APPENDIX A CALMET PARAMETER VALUES...............................................................................29

APPENDIX B CALPUFF PARAMETER VALUES..............................................................................34

APPENDIX C CALPOST PARAMETER VALUES .............................................................................43
ii

List of Figures

Figure No. Page No.

Figure 1 BART-Eligible Facility Locations in Proximity to Class I Areas.................................................................................... 4

Figure 2. Subject-to-BART/ RP Prioritization Flowchart ........................................................................................................... 10

Figure 3: Modeled 36/12/4 km MM5 Domain. ............................................................................................................................ 17

Figure 4. Modeled 4km MM5 Domain. ....................................................................................................................................... 18
iii

List of Tables

Table No. Page No.

Table 1. BART-eligible Sources. .................................................................................................................................................. 3

Table 2. Federal Mandatory Class I Areas in the Modeling Domain. ............................................................................................ 3

Table 3. 20% Best Natural Conditions – Concentrations and Rayleigh Scattering By Class I Area.............................................. 7

Table 4. CALPUFF Modeling System........................................................................................................................................ 11

Table 5. MM5 Vertical Domain Specification. ........................................................................................................................... 16

Table 6: Top 8 visibility impacts ( dv) at Haleakala and Hawaii Volcanoes for the HC&S Puunene Operating on either 

bagasse or coal. . Visibility impacts over 0.5 dv over 98th percentile (Top 1-7) are shaded yellow. Visibility impacts 

over 0.5 dv for 98th percentile (8th high) are shaded red. ................................................................................................... 23

Table 7. Top 8 visibility impacts ( dv) at Haleakala and Hawaii Volcanoes for the Chevron Refinery. . Visibility impacts 

over 0.5 dv over 98th percentile (Top 1-7) are shaded yellow. Visibility impacts over 0.5 dv for 98th percentile (8th

high) are shaded red ............................................................................................................................................................. 23

Table 8: Top 8 visibility impacts ( dv) at Haleakala and Hawaii Volcanoes for the Tesoro Refinery. Visibility impacts over 

0.5 dv over 98th percentile (Top 1-7) are shaded yellow. Visibility impacts over 0.5 dv for 98th percentile (8th high) are 

shaded red ............................................................................................................................................................................ 24

Table 9. Top 8 visibility impacts ( dv) at Haleakala and Hawaii Volcanoes for the Hu Honua Bioenergy –Pepeekeo facility. 

Visibility impacts over 0.5 dv over 98th percentile (Top 1-7) are shaded yellow. Visibility impacts over 0.5 dv for 98th

percentile (8th high) are shaded red ...................................................................................................................................... 24

Table 10: Top 8 visibility impacts ( dv) at Haleakala and Hawaii Volcanoes for the MECO - Hahului facility. Visibility 

impacts over 0.5 dv over 98th percentile (Top 1-7) are shaded yellow. Visibility impacts over 0.5 dv for 98

th percentile 

(8th high) are shaded red....................................................................................................................................................... 24

Table 11. Top 8 visibility impacts ( dv) at Haleakala and Hawaii Volcanoes for the HELCO Kanoelehua Hill facility. 

Visibility impacts over 0.5 dv over 98th percentile (Top 1-7) are shaded yellow. Visibility impacts over 0.5 dv for 98th

percentile (8th high) are shaded red ...................................................................................................................................... 25

Table 12. Top 8 visibility impacts ( dv) at Haleakala and Hawaii Volcanoes for the HECO - Waiau facility. Visibility 

impacts over 0.5 dv over 98th percentile (Top 1-7) are shaded yellow. Visibility impacts over 0.5 dv for 98th percentile 

(8th high) are shaded red....................................................................................................................................................... 25

Table 13. Top 8 visibility impacts ( dv) at Haleakala and Hawaii Volcanoes for the HECO - Kahe facility. Visibility impacts 

over 0.5 dv over 98th percentile (Top 1-7) are shaded yellow. Visibility impacts over 0.5 dv for 98th percentile (8th

high) are shaded red ............................................................................................................................................................. 25
1

PURPOSE

This Report is prepared for the State of Hawaii and describes the application 

methodology and results of the CALPUFF Model for determining whether the Hawaiian

Best Available Retrofit Technology (BART)-eligible sources are Subject-to-BART under 

the Regional Haze (RH) Rule, 40 CFR 308. 

1 INTRODUCTION AND STUDY OBJECTIVES

1.1 Introduction

Under the federal RH Rule, 40 CFR 308, Hawaii is required to develop a plan to establish 

Reasonable Progress goals for incremental visibility improvements in achieving natural 

background conditions within its two Federal Class I areas. Natural conditions exist 

when a Class I area is absent of anthropogenic or man-made air pollution emissions. In 

establishing RP goals, Hawaii will need to consider emission reduction measures for 

specified source groups or activities including the application of BART for existing major 

stationary sources.

On 6 July 2005, the U.S. Environmental Protection Agency (EPA) published final 

amendments to its 1999 RH Rule in the Federal Register, including 40 CFR Part 51, 

Appendix Y, the final guidance for BART determinations (BART Guideline; EPA, 2005; 

70 FR 39104-39172). The rule applies to any BART-eligible source that ―emits any air 

pollutant which may reasonably be anticipated to cause or contribute to any impairment 

of visibility‖ in any mandatory Class I federal area. States retain the authority to exempt 

certain BART-eligible sources based on dispersion modeling demonstrating that the 

source cannot reasonably be anticipated to cause or contribute to visibility impairment in 

a Class I area. Hawaii has elected to use this modeling option for BART-eligible sources.

According to 40 CFR Part 51, Appendix Y, a BART-eligible source is considered to 

―contribute‖ to visibility impairment in a Class I area if the modeled 98th percentile 

change in deciviews is equal to or greater than the ―contribution threshold.‖ Any BARTeligible source, through modeling, determined to cause or contribute to visibility 

impairment above the threshold in any Class I area is subject to BART. The EPA BART 

Guidelines suggest a contribution threshold of no greater than a 0.5 change in deciview 

be used. Hawaii has chosen to use the recommended 0.5 deciview threshold for Subjectto-BART determination.

The U.S. EPA Region 9, U.S. National Park Service (NPS), and the U.S. Fish and 

Wildlife Service (FWS) were consulted during development of the Modeling Protocol 

(Alpine Geophysics, 2010) and the performance of this project, which follows very 

closely the BART Guideline. 
2

1.2 Objectives

The objectives of this study are to provide:

A consistent approach in determining which BART-eligible source is Subject-toBART, requiring the 5-factor analysis pursuant to 40 CFR Part 51, Appendix Y, 

Guidelines for BART Determinations under the RH Rule (BART Guideline);

A clearly delineated modeling methodology;

A common CALMET/CALPUFF/POSTUTIL/CALPOST modeling configuration; 

and

A common format for reporting modeling results for each BART-eligible source.

2 MODELING APPROACH

This section describes the components or information used for the CALPUFF modeling 

and the overall process in determining whether a source is anticipated to cause or 

contribute to visibility impairment to Hawaii’s federal Class I areas. Eligible source 

criteria, Class I area identification, pollutants considered, emissions data required, natural 

background conditions, visibility impairment determination, and the analysis process are 

covered here.

2.1 BART-Eligible/RP-Eligible Sources

Sources evaluated in this assessment include BART-eligible sources. BART-eligible 

sources determined to cause or contribute to visibility impairment in any Class I area will 

be subject to further air pollution control analyses.

The term ―BART-eligible emission unit‖ refers to any single emission unit that meets the 

following criteria:

1) Emits greater than 250 tons/year of a visibility-impairing pollutant;

2) Was put into place between August 7, 1962 and August 7, 1977; and

3) Falls within one of the 26 listed source categories in the guidance.

A ―BART-eligible source‖ is the total of all BART-eligible emission units at a single 

facility. If a source has several emission units, only those that meet the BART-eligible 

criteria are included in the definition of ―BART-eligible source.‖

Hawaii identified nine BART-eligible facilities shown in Table 1. 
3

Table 1. BART-eligible Sources.

Facilities

Chevron Refinery

Tesoro Refinery

HECO – Kahe

HECO – Waiau

HC&S Puunene

MECO – Kahului

HELCO – Kanoelehua Hill

Hu Honua Bioenergy, LLC -Pepeekeo 

(formerly Hilo Coast)

Kauai Utilities – Port Allen

2.2 Class I Areas

The mandatory Class I federal areas in Hawaii that could be impacted by BART-eligible 

sources, are presented in Table 2. Refer to Figure 1 below showing the location of the 

Class I areas in relation to BART-eligible facilities/sources.

Table 2. Federal Mandatory Class I Areas in the Modeling Domain.

Class I Area IMPROVE Monitor Federal Land Manager

Haleakala National Park HALE 1 National Park Service

Hawaii Volcanoes National Park HAVO 1 National Park Service

The Haleakala National Park consists of 30,183 acres in central to eastern portions of the 

island of Maui, 24,719 acres of which are designated wilderness. Park elevations range 

from 3,055 m (10,023 ft) at the summit of Haleakala to sea level at the mouth of 

Kipahulu Valley on the extreme eastern boundary. The Park thus includes a wide variety 

of climate zones, from high mountain elevations above the Marine Boundary Layer, to 

leeward lowlands that are dry relative to windward lowlands at the same elevation. 

The Haleakala National Park IMPROVE site is HALE1, located at an elevation of 1,158 

m (3,799 ft) on the northward slope of Haleakala, ~5.7 km northwest of the Park 

Boundary. 

The Hawaii Volcanoes National Park IMPROVE Site is HAVO1, located on the east to 

southeastern slopes of Mauna Loa on the island of Hawaii. More specifically, it is 

situated on the northeastern rim of the Kilauea crater. The site elevation is 1,204 m (3,949 

ft), some 124 m above the crater floor at 1080 m (3,542 ft). One of the major terrain 

feature affecting meteorology and air quality at the monitoring site, other than the 

erupting Kilauea volcano, is the 4,170 m (13,681 ft) high Mauna Loa cone, about 35 km 

(22 mi) to the west of the monitoring site.
4

The IMPROVE data is used to represent measured pollutant impacts at the two National 

Parks.

Figure 1 BART-Eligible Facility Locations in Proximity to Class I Areas.

2.3 Pollutants to Consider

The BART Guideline specifies that sulfur dioxide (SO2), oxides of nitrogen (NOx) and 

direct particulate matter (PM) emissions, including both PM10 and PM2.5 should be 

included for the Subject-to-BART modeling analyses. 

The BART Guideline also discusses the inclusion of volatile organic compound (VOC), 

ammonia and ammonia compounds as visibility impairing pollutants. These pollutants 

will be considered separately if it is determined that these emissions from a BARTeligible source are reasonably anticipated to cause or contribute to visibility impairment. 
6

2.4 Emissions and Stack Data

The BART Guideline states, ―the emission estimates used in the models are intended to 

reflect steady-state operating conditions during periods of high capacity utilization.‖ 

These emissions should not generally include start-up, shutdown, or malfunction 

emissions. The BART Guideline recommends that states use the 24-hour average actual 

emission rate from the highest emitting day of the meteorological period modeled. 

Hawaii requested source information of the highest emitting day between calendar years 

2003 to 2007 from BART-eligible sources and will similarly request the same 

information from identified RP-eligible sources.

Depending on the availability of emissions data, the following emissions information 

(listed in order of priority) were be used with CALPUFF for the Subject-to-BART 

modeling:

24-hour average actual emission rate from the highest emitting day within the 

modeling period (preferred) based on representative CEM or stack test data; or

24-hour average emissions calculated using emission factors specified in Title V 

permits.

24-hour average emissions calculated using AP-42 emission factors or other 

equivalent methods.

Allowable emissions (maximum 24-hour allowable).

DOH worked with sources to develop appropriate emission rates.

If plant-wide emissions for SO2, NOx, and PM10, from eligible sources, are less than the 

significant emission rate (SER) used for Prevention of Significant Deterioration review, 

emissions of that pollutant are not included in the Subject-to-BART modeling. However, 

if plant-wide emissions exceed the SERs for these pollutants, then all emissions of that 

pollutant from individual emission units are evaluated even if emissions are below the 

SER for an individual emission unit.

2.5 Natural Background

The BART Guideline references EPA’s ―Guidance for Estimating Natural Conditions 

under the Regional Haze Rule‖ (EPA, 2003a). This guidance lists three sets of Natural 

Conditions corresponding to Annual Average, Best 20% Days and Worst 20% Days. 

Due to limited access to available meteorological data, the 20% best natural days are

used, in lieu of annual average natural days, to represent natural visibility background in 

Class I areas. This definition of natural background is consistent with the intent of the 

BART Guideline (Federal Register Vol. 70, No. 128, pf 39125). The specific natural 

background values used in this study are drawn from the Federal Land Managers’ Air 

Quality Related Values Workgroup (FLAG) Phase I Report – Revised (FLAG, 2008). 

Class I specific background extinction coefficients are presented in Table 3. The option 
7

presented in EPA’s guidance for refining the default visibility background are not used in 

this study.

Table 3. 20% Best Natural Conditions – Concentrations and Rayleigh Scattering By 

Class I Area.

Background Extinction Coefficients (20% Best Days)

Class I Area

(NH4)2SO4

μg/m3

NH4NO3

Μg/m3

OM

μg/m3

EC

μg/m3

Soil

μg/m3

CM

μg/m3

Sea 

Salt

μg/m3

Rayleigh

Mm-1

Haleakala 

National Park 0.05 0.04 0.24 0.01 0.11 1.59 0.13 10

Hawaii 

Volcanoes 

National Park 0.03 0.04 0.27 0.01 0.06 0.64 0.11 10

2.6 Visibility Calculation

The CALPUFF modeling techniques used in this study provide ground level 

concentrations of visibility impairing pollutants. The concentration estimates from 

CALPUFF are used with the FLAG referenced IMPROVE equation to calculate the 

extinction coefficient (bext), as shown below. 

bext = 2.2 × fS(RH) × [Small Sulfate] + 4.8 × fL(RH) × [Large Sulfate] 

• 2.4 × fS(RH) × [Small Nitrate] + 5.1 × fL(RH) × [Large Nitrate] 

• 2.8 × [Small Organic Mass] + 6.1 × [Large Organic Mass] 

• 10 × [Elemental Carbon] 

• 1 × [Fine Soil] 

• 0.6 × [Coarse Mass] 

• 1.7 × fSS(RH) × [Sea Salt]

• Rayleigh Scattering (Site Specific) 

• 0.33 × [NO2 (ppb)] {or as: 0.1755 × [NO2 (μg/m3)]} 

Where: 

[ ] indicates concentrations in μg/m3 

fS(RH) = Relative humidity adjustment factor for small sulfate and nitrate 

fL(RH) = Relative humidity adjustment factor for large sulfate and nitrate 

fSS(RH) = Relative humidity adjustment factor for sea salt 

For Total Sulfate < 20 μg/m3: 

[Large Sulfate] = ([Total Sulfate] / 20 μg/m3) × [Total Sulfate] 

For Total Sulfate ≥ 20 μg/m3: 

[Large Sulfate] = [Total Sulfate] 

And: 

[Small Sulfate] = [Total Sulfate] – [Large Sulfate] 

To calculate large and small nitrate and organic mass, substitute ({Large, Small, 

Total} {Nitrate, Organic Mass}) for Sulfate. 
8

The monthly relative humidity adjustment factors from the above equation are found in 

Appendix C.

The change in visibility for the Subject-to-BART analyses are compared against the 20% 

best natural background conditions. The delta-deciview, dv, value is calculated from 

the source’s contribution to extinction, bext (source), and background extinction, bext(bkg), as 

follows:

dv = 10 ln (bext(bkg)+bext (source)) / bext(bkg) )

2.7 Model Execution

2.7.1 Subject-to-BART Analysis

The Subject-to-BART modeling is used to determine which eligible sources are

reasonably anticipated to cause or contribute to visibility impairment at any Class I area. 

This study uses Option 1 in Section III of the BART Guideline for this analysis. This 

option is the Individual Source Attribution Approach. With this approach, each BARTand RP-eligible source is modeled separately and the impact on visibility impairment in 

any Class I area is determined.

For determining the visibility threshold, the recommendations in the BART Guideline are 

followed to assess whether a BART- source is reasonably anticipated to cause or 

contribute to any visibility impairment in a federal Class I area. According to the BART 

Guideline:

“A single source that is responsible for a 1.0 deciview change or more should be 

considered to “cause” visibility impairment; a source that causes less than a 1.0 

deciview change may still contribute to visibility impairment and thus be subject to 

BART. As a general matter, any threshold that you use for determining whether a source 

“contributes” to visibility impairment should not be higher than 0.5 deciviews.

As recommended in the BART Guideline, if a single source causes a 0.5 deciview or 

greater change from natural background, then that source is determined to be reasonably 

anticipated to contribute to any visibility impairment in a Class I area and will be subject 

to BART. For this single source analysis, the Subject-to-BART modeling will not 

consider the frequency, magnitude, and duration of impairment.

Outside the work of this study, DOH has made a preliminary determination whether an 

eligible source is Subject-to-BART by following the screening methodology described in 

Section III, Option 2 of the BART Guideline. Based on a 0.5 deciview threshold, if a 

source calculated Q/D < 10, an eligible source would be exempt from further BART 

analysis at least for the first RH planning period.
9

Q = Potential Emissions (tons/year) of SO2+NOx+PM10 from all eligible units 

within a facility

D = Distance (kilometers) between the eligible source and the boundary of the 

nearest Class I area

Generally, if a source is a smaller emitter or is located far from a Class I area, and its Q/D 

is less than 10, the source is not expected to contribute to visibility impairment at a Class 

I area.

Using this approach, the DOC determined that the Kauai Utilities Port Allen facility 

should not be Subject-to-BART.

2.7.2 Implementing Subject-to-BART/RP Prioritization Modeling Analysis

Alpine Geophysics, LLC (Alpine) has performed the Subject-to-BART modeling in 

accordance with the Modeling Protocol for the BART-eligible sources. Figure 2 is a flow 

chart of how individual facilities are evaluated for its eligible emission units. 

Consideration for allowing a more refined Subject-to-BART analysis in the event a 

source is found to be Subject-to-BART is not a part of this study’s scope.
10

Figure 2. Subject-to-BART/ RP Prioritization Flowchart

Note: “Pre-BART” units = identified “RP-eligible” units.
11

3 VISIBILITY MODELING SYSTEM

In general, the Subject-to-BART modeling used the CALPUFF suite of programs and

followed the procedures and recommendations outlined in two documents: the IWAQM 

(Interagency Workgroup on Air Quality Models) and the FLAG (Federal Land Managers 

Air Quality Related Values Workgroup) reports (EPA 1998, FLAG 2008). Tables listing 

the modeling parameters for each CALPUFF module are located in the Appendices. 

The specific CALPUFF programs and their version numbers that were used in the 

Subject-to-BART/ modeling are presented in Table 4. The CALPUFF modeling system 

is the regulatory approved versions except for the CALPOST processor. CALPOST 

version 6.221 is used to enable this application to use the most up-to-date FLAG (6-29-

2008) guidance. The CALMET model was run using the August 20, 2009, ―EPA-FLM 

Recommended CALMET input File Values.‖

The CALMET meteorological domain, as described below, covers the entire Hawaiian 

Island chain. 

Table 4. CALPUFF Modeling System

Program Version Level

CALMET 5.8 060911

CALPUFF 5.8 060911

CALPOST 6.221 080724

POSTUTIL 1.56 070627

3.1 CALMET

Input requirements for the CALMET model include various meteorological and 

geophysical data sets, and a control input file with appropriate settings. Required 

meteorological data include surface, upper-air, and precipitation observations, and 

mesoscale model output data fields. Specifics of these data are provided below.

Geophysical input data include terrain elevation and land-use data. Though CALMET 

may be run with mesoscale model meteorological data, alone (i.e., no observations), the 

EPA modeling guideline recommends ―blending‖ observations with the mesoscale model 

fields. Therefore, this study included observations in a blended approach. 

The dispersion modeling used the CALMET wind fields for the calendar year 2005. 

While a minimum of three years of mesoscale meteorological model output is 

recommended for conducting the initial step in the first-level analysis, meteorological 

data sets for more than 2005 were unavailable for the modeling domain under this 

contract. The CALMET wind fields for 2005 cover the entire island chain and 
12

sufficiently encompass both Class I areas within 300km of any BART- facility included 

in this analysis. 

Land Use Processing: CALMET requires both land use/land cover and terrain height 

data for the application region. These are generated using the CALMET CTGPROC, 

TERREL and MAKEGEO as described below.

TERREL is the terrain pre-processor that averages terrain features to the 

modeling grid resolution; TERREL constructs the basic properties of the gridded 

domain and defines the coordinates upon which meteorological data are stored. 

Key parameters include specification of grid type, location, resolution and terrain 

elevation. 

CTGPROC computes the fractional land use for the modeling grid resolution. 

Land use characteristics for each grid cell are assigned using CTGPROC. The 

primary variable adjustment associated with CTGRPOC is selection of an 

appropriate land use database. Version 2.0 of the North American Land Cover 

Characteristics database was used. 

MAKEGEO is the final pre-processor that combines the terrain and land use data 

for input to CALMET. Generating the appropriate MAKEGEO.INP control file 

requires only minimal alteration of the default assignments. Key modifications 

include specifying domain attributes and ensuring input files are correctly 

referenced.

Terrain: CALMET requires both terrain height and land use/land cover for the 

application region. These are generated using the CALMET CTGPROC, TERREL and 

MAKEGEO processors. The terrain data was created using the TERREL (version 3.684 

level 070327) processor and the Shuttle Radar Topography Mission (SRTM)-GTOPO 30 

second (~1 km) resolution dataset. 

Land Use: The land use data set was created using the Composite Theme Grid 

CTGROC processor (version 2.682, level 070430) and the United States Geological 

Survey (USGS) Global Land Cover Characterization (GLCC) version 2.0 database. The 

GLCC database is available at 30 second (~1km) resolution. 

Observed Data Processing: Observation data from both available surface and upper-air 

stations in the modeling will be used. The surface data wer taken from the NOAA 

Techniques Development Lab (TDL) database and the upper-air data from the NOAA 

Forecast Systems Lab (FSL) database.

CALMET Model Options: The options used in the CALMM5 processing will generally 

follow the FLAG (FLAG, 2008) and IWAQM (EPA, 1998) guidance. Specific 

CALMET input values will be updated using the August 20, 2009, ―EPA-FLM 

Recommended Input File Values.‖ 
13

3.1.1 Meteorological Data

3.1.1.1 Mesoscale Model Data

Alpine was contracted to apply the Pennsylvania State University/National Center for 

Atmospheric Research (PSU/NCAR) Mesoscale Model (MM5) (Dudhia, 1993; Grell et 

al., 1994: UCAR, 2003a) for calendar year 2005 to provide data for the 

CALMET/CALPUFF modeling as well as for additional modeling and analytical studies 

conducted for the Hawaiian Islands. 

A complete description of the modeling approach and a statistical evaluation of the 

annual MM5 model are described in McNally and Wilkinson, 2008. A brief description 

of the modeling approach follows:

Model Selection: The publicly available non-hydrostatic version of MM5 (version 

3.7.4) was used for this modeling study. Preprocessor programs of the MM5 modeling 

system including TERRAIN, REGRID, LITTLE_R, and INTERPF (UCAR, 2003b) were 

used to develop model inputs.

Horizontal Domain Definition: The computational grid is presented in Figures 3 and 4. 

The outer 36km domain (D01) has 165 x 129 grid cells, selected to provide boundary 

conditions to the 12km domain. The mid-scale 12km domain (D02) has 226 x 226 grid 

cells, selected to maximize the coverage of the western region around the Hawaiian 

Islands. The 4km domain (D03) has 208 x 154 grid cells, selected to cover the Island 

chain with a greater than 100km buffer around the perimeter. The projection was in 

Lambert Conformal Coordinates (LCC) with a grid projection pole of 23o, -158o with true 

latitudes of 30oand 60o.

Vertical Domain Definition: The MM5 modeling was based on 34 vertical layers with 

an approximately 38 meter deep surface layer. The MM5 vertical domain is presented in 

both sigma and height coordinates in Table 5.

Topographic Inputs: Topographic information for the MM5 was developed using the 

NCAR and the United States Geological Survey (USGS) terrain databases. The grid will 

be based on the 2 min (~4 km) Geophysical Data Center global data interpolated to the 

model grid using a Cressman-type objective analysis scheme. To avoid interpolating 

elevated terrain over water bodies, after the terrain databases are interpolated onto the 

MM5 grid, the NCAR graphic water body database is used to correct elevations over 

water bodies. 

Vegetation Type and Land Use Inputs: Vegetation type and land use information were 

developed using the most recently released PSU/NCAR databases provided with the 

MM5 distribution. Standard MM5 surface characteristics corresponding to each land use 

category were employed.
14

Atmospheric Data Inputs: Two choices for the first guess meteorological fields were 

available: (1) the NCEP Global Tropospheric Final Analysis; and (2) the NCAR/NCEP 

Reanalysis Project (NNRP) data (UCAR, 2007). Though the NNRP data were used in a 

previous study of Hawaii (Yang and Chen, 2003; Yang et. al. 2005), the NNRP data have 

relatively coarse 2.5x2.5 degree resolution whereas the NCEP Final Analysis data have 

much higher 1x1 degree resolution. For the current modeling study, the NCEP Final 

Analysis dataset was used.

Available surface and upper-air observations were used in the objective analyses. These 

data were incorporated into the analyses datasets following the procedures outlined by 

Stauffer and Seaman (1990) and were quality-inspected by MM5 pre-processors using 

automated gross-error checks and ―buddy‖ checks. In addition, radiosonde soundings 

were subjected to vertical consistency checks. The synoptic-scale data used for the 

initialization (and in the analysis nudging discussed later) were obtained from the 

conventional National Weather Service (NWS) twice-daily radiosondes (UCAR, 2008a) 

and 3-hr NWS surface observations (UCAR, 2008b).

Water Temperature Inputs: The water temperature data were taken from the NCEP 

RTG global one-half degree analysis (Gemmill et al., 2007; NCEP, 2008).

FDDA Data Assimilation: This simulation used both analysis and observation based 

nudging. For the 36km and 12km grids analysis nudging coefficients of 2.5x10-4and 

1.0x10-4 were used for winds and temperature, respectively. For the 4km grid analysis 

nudging to observed winds from the NCAR DS472.0 TDL dataset (UCAR, 2008c) was 

employed with a coefficient of 1.0x10-3. 

 

Physics Options: Sensitivity testing using approximately two-week long episodes in 

early February 2005 and early July 2005 were performed to help select the best 

performing physics options (McNally, 2008). Although the performance was similar for 

the different options, the best performing configuration was the version used by the 

University of Hawaii and the National Weather Service (Yang and Chen, 2003; Yang et. 

al. 2005). Namely:

Warm Rain;

Grell Cumulus Parameterization;

MRF PBL;

NOAH Land Surface Model; and

Cloud Radiation.

Application Methodology: The MM5 model was executed in 5-day blocks initialized at 

12Z every 4 days with a 90 second time step. Model results were output every 60 

minutes and output files are split at 24 hour intervals. Twelve (12) hours of spin-up were 

included in each 4-day block before the data were used in the atmospheric simulations 

and subsequent model performance evaluation.
15

3.1.1.2 CALMM5 Processing

The final step in the meteorological processing is to reformat the MM5 data using the 

CALMM5 processor. CALMM5 was run for the 4km MM5 nest to extract the whole 

region, less a few grid cells around the perimeter that are contaminated by MM5 

boundary artifacts.
16

Table 5. MM5 Vertical Domain Specification.

k(MM5) sigma pressure.(mb) height(m) depth(m)

34 0.000 10000 15674 2004

33 0.050 14500 13670 1585

32 0.100 19000 12085 1321

31 0.150 23500 10764 1139

30 0.200 28000 9625 1004

29 0.250 32500 8621 900

28 0.300 37000 7720 817

27 0.350 41500 6903 750

26 0.400 46000 6153 693

25 0.450 50500 5461 645

24 0.500 55000 4816 604

23 0.550 59500 4212 568

22 0.600 64000 3644 536

21 0.650 68500 3108 508

20 0.700 73000 2600 388

19 0.740 76600 2212 282

18 0.770 79300 1930 274

17 0.800 82000 1657 178

16 0.820 83800 1478 175

15 0.840 85600 1303 172

14 0.860 87400 1130 169

13 0.880 89200 961 167

12 0.900 91000 794 82

11 0.910 91900 712 82

10 0.920 92800 631 81

9 0.930 93700 550 80

8 0.940 94600 469 80

7 0.950 95500 389 79

6 0.960 96400 310 78

5 0.970 97300 232 78

4 0.980 98200 154 39

3 0.985 98650 115 39

2 0.990 99100 77 38

1 0.995 99550 38 38

0 1.000 100000 0 0
17

Figure 3: Modeled 36/12/4 km MM5 Domain.
18

Figure 4. Modeled 4km MM5 Domain.

3.1.2 CALMET Control File Settings

These CALMET wind fields were used by all BART-elegible sources within the 

modeling domain. The wind fields were prepared using CALMET. Details of the 

parameter settings in CALMET are provided in Appendix A to this document; however, 

the major assumptions are summarized below. 

1) The initial-guess fields will use the 4-km MM5 outputs, forecast from hours 13 –

108 of each 5 day run segment, January – December, 2005.

2) Both the Subject-to-BART used the wind fields at 4km resolution.
19

3) There were 10 vertical layers with face heights of 0, 20, 40, 65, 120, 200, 400, 

700, 1200, 2200, and 4000 meters.

4) CALMET was run using surface, buoy and upper air observations. Upper air data 

from the LIH (WBAN 22536) site on Kauai and the ITO (WBAN 21504) site on 

Hilo were used. Buoy data collected from the National Data Buoy Center for 

sites 51001, 51002, 51003, 51004, KLIH1 and OOUHI were used and surface 

data from 14 surface stations in the TDL DS472.0 dataset were used.

3.2 CALPUFF

The CALPUFF modeling used Version 5.8. The application generally follows the 

recommendation of the IWAQM and FLAG guidance documents. Details of the 

parameter settings in CALPUFF are provided in Appendix B to this document; however, 

the major features are summarized below: 

1) The calendar year 2005 CALMET input file were developed by Alpine 

Geophysics, LLC and be provided as input-ready to CALPUFF.

2) The Subject-to-BART modeling examined the visibility impairment on Class I 

areas within 300km of each single source or facility. The computational 

modeling domain is sufficient to include all Class I areas within a 300km radius 

of a source.

3) Pasquill-Gifford Dispersion coefficients are used.

4) MESOPUFF-II chemistry algorithm are used.

5) Puff splitting is not used, following the recommendations of the FLMs.

6) Source elevations are based on the same terrain files as the receptor elevations.

7) The CALUTIL is used to perform HNO3/NO3 repartitioning using the parameters 

in Appendix B.

CALPUFF is applied separately for each Class I Area (Haleakala National Park and 

Hawaii Volcanoes National Park) on the island of Maui and Hawaii. 

3.2.1 Emissions

Section 2.4 above presents the emissions and stack data that is required from the 

facilities. This section only discusses the emissions estimates needed in CALPUFF. 

Primary emission, species include available input species of PM, SO2, SO4, NOx; and the 

additional modeled species HNO3 and NO3. SO2 emissions were reviewed to ensure 

―double-counting‖ was avoided.

The primary PM species are treated as follows:

Filterable:

Elemental Carbon (EC) (<2.5 μm)

PM Fine (PMF) (<2.5 μm)
20

PM Coarse (PMC) (2.5 – 10 μm)

Condensable:

Organic Carbon (SOA)

Inorganic Aerosol (SO4)

The condensable fraction is treated as primary emissions in the CALPUFF input file and 

assumed to be 100% in the PM2.5 fraction (see NPS Web site listed below).

Facility specific speciation emissions data are used when provided. The following size 

distributions are used in CALPUFF:

Pollutant Mean diameter Standard deviation

SO4, NO3, PMF, SOA, EC 0.48 2

PMC 2.5 5

3.2.2 Ozone Background

Background ozone will be assumed at an annual constant level of 40 ppb.

3.2.3 Ammonia Background

Because the Hawaiian Island chain does not fit easily within the three land use values 

proposed in IWAQM (10 parts per billion (ppb) for grasslands, 0.5 ppb for forest, and 1 

ppb for arid lands), ammonia background concentrations were determined in consultation 

with the participating FLMs. A review of the ammonia emissions inventory for Hawaii 

in comparison with mainland agricultural areas indicate a background of 1.5 ppb to be 

representative based on land use and agricultural and livestock activity.

3.2.4 Receptor Locations

Predicted visibility impacts are computed at receptors locations for the Class I Areas 

within the domain which are available for download from the National Park Service Web 

site http://www2.nature.nps.gov/air/Maps/Receptors/index.cfm, Source and receptor elevations 

are based on actual elevations. The elevations for the meteorological grid points were 

taken from the CALMET modeling. 

3.3 CALPOST AND VISIBILITY POST-PROCESSING

The CALPOST processing used Version 6.221 and POSTUTIL Version 1.56. Details of 

the parameter settings in CALPOST and POSTUTIL are provided in Appendix C, with 

some of the major features summarized below for calculating visibility impairment: 
21

1) For the visibility calculation, Method 8, Mode 5 (MVISBK=8, M8_MODE=5) is

employed. This method uses monthly average relative humidity and f(RH) values 

for each Class I area, which are based on data from FLAG (FLAG 2008).

2) Particulate species for the visibility analysis include SO4, NO3, EC, OC, PMF, 

and PMC, as reported in the CALPOST output files.

3) POSTUTIL was not be used to speciate modeled PM10 concentrations, as PM10 is 

speciated into its components (PMF, PMC, SOA, EC, SO4) and entered as 

primary emissions in CALPUFF. However, HNO3/NO3 partition option in 

POSTUTIL was used 

4) Natural background extinction calculations use the 20% best natural days for each 

Class I area in the modeling domain. Tables providing the monthly f(RH) and 

20% best days coefficients are provided in Appendix C, and are based on data 

from FLAG (FLAG 2008).

5) The 98th percentile value is calculated for all BART-eligible sources at each 

mandatory Class I area. 

6) The contribution threshold has the implied level of precision equal to the level of 

precision reported by CALPOST. Therefore, the 98th percentile value will be 

reported to three decimal places.

4. SUBJECT-TO-BART MODELING RESULTS

The change in visibility impairment for the Subject-to-BART/RP prioritization modeling 

is based on the increase in the haze index from a BART-eligible source relative to natural 

background, defined as the 20% best natural visibility days for each Class I area. This 

definition of natural background is consistent with the intent of the BART Guideline 

(Federal Register Vol. 70, No. 128, pf 39125). Although the use of the annual 20% 

natural visibility days was subsequently allowed (EPA 2006, Memorandum from Joseph 

W. Paisie to Kay Prince dated July 19, 2006. Subject Regional Haze Regulations and 

Guidelines for BART Determinations), the 20% best natural visibility days is being used 

due to the availability of one year’s worth of meteorological data. Three years worth of 

meteorological data is recommended by guidance.

The U.S. EPA recommends using the 98th percentile value from the distribution of values 

containing the highest modeled delta-deciview ( dv) value for each day of the simulation 

from all modeled receptors at a given Class I area. The 98th percentile dv value will be 

determined with the 8th highest value for the year modeled (2005).

This 8th highest value is compared to the contribution threshold ( dv≥0.5 dv). If there 

are more than seven (7) days with values greater than the contribution threshold in any 

single meteorological year for any of the Class I areas, then the source is considered 

Subject-to-BART in the first RH planning period. Tables for each of BART eligible 

sources are presented. For each source the top 8 visibility impacts ( dv) are presented 

for both Haleakala and Hawaii Volcanoes. Values where the visibility impacts for the 

top 1-7 impacts (higher that 98th percentile) exceed the 0.5 dv threshold are shaded 
22

yellow. Values where the visibilities for the 8th high (the 98th percentile) exceed the 

threshold, and are thus Subject-to-BART, are shaded red.

Table 6 presents the top 8 visibility impacts ( dv) for the HC&S Puunene facility. Since 

this facility can operate on both Bagasse (a fibrous residue from sugar cane processing), 

or coal, impacts were examined separately for each fuel. For both fuels at both Class 1 

areas none of the top 8 visibility impacts exceed the 0.5 dv threshold. Based on this 

modeling assessment, the HC&S Puunene facility is not Subject-to-BART.

Table 7 presents the top 8 visibility impacts ( dv) for the Chevron Refinery. At neither of 

the Class 1 areas did any of the top 8 visibility impacts exceed the 0.5 dv threshold. 

Based on this modeling assessment, the Chevron Refinery is not Subject-to-BART.

Table 8 presents the top 8 visibility impacts ( dv) for the Tesoro Refinery. At neither of 

the Class 1 areas did any of the top 8 visibility impacts exceed the 0.5 dv threshold. 

Based on this modeling assessment, the Tesoro Refinery is not Subject-to-BART.

Table 9 presents the top 8 visibility impacts ( dv) for the Ho Honua Bioenergy –

Pepeekeo facility. At the Haleakala class 1 area the impacts are all below the 0.5 dv

threshold. However, at the Hawaii Volcanoes class 1 area, the top 8 impacts all exceed 

the 0.5 dv threshold. Based on this modeling assessment, the Ho Honua Bioenergy –

Pepeekeo facility is Subject-to-BART.

Table 10 presents the top 8 visibility impacts ( dv) for the MECO-Kahului facility. The 

only impact that exceeds the 0.5 dv threshold was the highest impact at the Haleakala 

Class 1 area. No other of the top 8 visibility impacts, including the 8th high (98th

percentile value), exceed the 0.5 dv threshold. Based on this modeling assessment, the 

MECO Kahului facility is not Subject-to-BART.

Table 11 presents the top 8 visibility impacts ( dv) for the HELCO Kanoelehua Hill 

facility. For this facility the top 8 visibility impacts at both of the Class 1 areas exceed

the 0.5 dv threshold. Based on this modeling assessment, the HELCO Kanoelehua Hill

facility is Subject-to-BART.

Table 12 presents the top 8 visibility impacts ( dv) for the HECO Waiau facility. The 

only impacts that exceeds the 0.5 dv threshold were the highest two impacts at the 

Haleakala Class 1 area. None other of the top 8 visibility impacts, including the 8th high 

(98th percentile value), exceed the 0.5 dv threshold. Based on this modeling assessment, 

the HECO Waiau facility is not Subject-to-BART.

Table 13 presents the top 8 visibility impacts ( dv) for the HECO Kahe facility. The 

only impacts that exceeds the 0.5 dv threshold were the highest five impacts at the 

Haleakala Class 1 area. The rest of the top 8 visibility impacts, including the 8th high (98th

percentile value), did not exceed the 0.5 dv threshold. Based on this modeling 

assessment, the HECO Kahe facility is not Subject-to-BART.
23

Table 6: Top 8 visibility impacts ( dv) at Haleakala and Hawaii Volcanoes for the HC&S Puunene 

Operating on either bagasse or coal. . Visibility impacts over 0.5 dv over 98th percentile (Top 1-7) 

are shaded yellow. Visibility impacts over 0.5 dv for 98th percentile (8th high) are shaded red.

Source High Haleakala Hawaii Volcanoes

dv dv

HC&S Puunene (Bagasse) 1 0.119 0.029

HC&S Puunene (Bagasse) 2 0.102 0.024

HC&S Puunene (Bagasse) 3 0.101 0.017

HC&S Puunene (Bagasse) 4 0.090 0.011

HC&S Puunene (Bagasse) 5 0.061 0.010

HC&S Puunene (Bagasse) 6 0.060 0.010

HC&S Puunene (Bagasse) 7 0.059 0.009

HC&S Puunene (Bagasse) 8 0.059 0.008

HC&S Puunene (Coal) 1 0.236 0.094

HC&S Puunene (Coal) 2 0.224 0.092

HC&S Puunene (Coal) 3 0.184 0.090

HC&S Puunene (Coal) 4 0.181 0.089

HC&S Puunene (Coal) 5 0.170 0.073

HC&S Puunene (Coal) 6 0.150 0.073

HC&S Puunene (Coal) 7 0.140 0.053

HC&S Puunene (Coal) 8 0.133 0.039

Table 7. Top 8 visibility impacts ( dv) at Haleakala and Hawaii Volcanoes for the Chevron 

Refinery. . Visibility impacts over 0.5 dv over 98th percentile (Top 1-7) are shaded yellow. 

Visibility impacts over 0.5 dv for 98th percentile (8th high) are shaded red

Source High Haleakala Hawaii Volcanoes

dv dv

Chevron Refinery 1 0.208 0.055

Chevron Refinery 2 0.135 0.032

Chevron Refinery 3 0.083 0.025

Chevron Refinery 4 0.082 0.019

Chevron Refinery 5 0.060 0.019

Chevron Refinery 6 0.031 0.017

Chevron Refinery 7 0.029 0.017

Chevron Refinery 8 0.021 0.016
24

Table 8: Top 8 visibility impacts ( dv) at Haleakala and Hawaii Volcanoes for the Tesoro Refinery. 

Visibility impacts over 0.5 dv over 98th percentile (Top 1-7) are shaded yellow. Visibility impacts 

over 0.5 dv for 98th percentile (8th high) are shaded red

Source High Haleakala Hawaii Volcanoes

dv dv

Tesoro Refinery 1 0.237 0.062

Tesoro Refinery 2 0.173 0.038

Tesoro Refinery 3 0.099 0.033

Tesoro Refinery 4 0.092 0.027

Tesoro Refinery 5 0.079 0.023

Tesoro Refinery 6 0.040 0.019

Tesoro Refinery 7 0.027 0.019

Tesoro Refinery 8 0.025 0.017

Table 9. Top 8 visibility impacts ( dv) at Haleakala and Hawaii Volcanoes for the Hu Honua 

Bioenergy –Pepeekeo facility. Visibility impacts over 0.5 dv over 98th percentile (Top 1-7) are 

shaded yellow. Visibility impacts over 0.5 dv for 98th percentile (8th high) are shaded red

Source High Haleakala Hawaii Volcanoes

dv dv

Hu Honua Bioenergy –Pepeekeo 1 0.444 1.660

Hu Honua Bioenergy –Pepeekeo 2 0.444 1.102

Hu Honua Bioenergy –Pepeekeo 3 0.404 1.067

Hu Honua Bioenergy –Pepeekeo 4 0.398 0.689

Hu Honua Bioenergy –Pepeekeo 5 0.376 0.651

Hu Honua Bioenergy –Pepeekeo 6 0.373 0.552

Hu Honua Bioenergy –Pepeekeo 7 0.364 0.551

Hu Honua Bioenergy –Pepeekeo 8 0.323 0.540

Table 10: Top 8 visibility impacts ( dv) at Haleakala and Hawaii Volcanoes for the MECO -

Hahului facility. Visibility impacts over 0.5 dv over 98th percentile (Top 1-7) are shaded yellow. 

Visibility impacts over 0.5 dv for 98th percentile (8th high) are shaded red

Source High Haleakala Hawaii Volcanoes

dv dv

MECO - Hahului 1 0.566 0.250

MECO - Hahului 2 0.467 0.250

MECO - Hahului 3 0.447 0.240

MECO - Hahului 4 0.363 0.238

MECO - Hahului 5 0.345 0.183

MECO - Hahului 6 0.298 0.163

MECO - Hahului 7 0.289 0.163

MECO - Hahului 8 0.232 0.108
25

Table 11. Top 8 visibility impacts ( dv) at Haleakala and Hawaii Volcanoes for the HELCO 

Kanoelehua Hill facility. Visibility impacts over 0.5 dv over 98th percentile (Top 1-7) are shaded 

yellow. Visibility impacts over 0.5 dv for 98th percentile (8th high) are shaded red

Source High Haleakala Hawaii Volcanoes

dv dv

HELCO Kanoelehua Hill 1 1.132 4.681

HELCO Kanoelehua Hill 2 1.023 4.088

HELCO Kanoelehua Hill 3 0.872 2.967

HELCO Kanoelehua Hill 4 0.865 2.879

HELCO Kanoelehua Hill 5 0.856 2.759

HELCO Kanoelehua Hill 6 0.846 2.448

HELCO Kanoelehua Hill 7 0.812 2.403

HELCO Kanoelehua Hill 8 0.808 2.334

Table 12. Top 8 visibility impacts ( dv) at Haleakala and Hawaii Volcanoes for the HECO - Waiau 

facility. Visibility impacts over 0.5 dv over 98th percentile (Top 1-7) are shaded yellow. Visibility 

impacts over 0.5 dv for 98th percentile (8th high) are shaded red

Source High Haleakala Hawaii Volcanoes

dv dv

HECO - Waiau 1 0.573 0.211

HECO - Waiau 2 0.512 0.131

HECO - Waiau 3 0.251 0.117

HECO - Waiau 4 0.248 0.111

HECO - Waiau 5 0.211 0.092

HECO - Waiau 6 0.193 0.071

HECO - Waiau 7 0.103 0.039

HECO - Waiau 8 0.083 0.038

Table 13

Table 13. Top 8 visibility impacts ( dv) at Haleakala and Hawaii Volcanoes for the HECO - Kahe 

facility. Visibility impacts over 0.5 dv over 98th percentile (Top 1-7) are shaded yellow. Visibility 

impacts over 0.5 dv for 98th percentile (8th high) are shaded red

Source High Haleakala Hawaii Volcanoes

dv dv

HECO - Kahe 1 1.277 0.299

HECO - Kahe 2 1.153 0.286

HECO - Kahe 3 0.707 0.222

HECO - Kahe 4 0.686 0.198

HECO - Kahe 5 0.656 0.196

HECO - Kahe 6 0.381 0.162

HECO - Kahe 7 0.240 0.142

HECO - Kahe 8 0.221 0.132
26

5. CONCLUSION

A CALPUFF modeling analysis was performed to determine which BART-eligible 

sources in Hawaii are Subject-to-BART. The modeling assessment looked at the HC&S 

Puunene facility, the Chevron Refinery, the Tesoro Refinery, the Hu Honua Bioenergy –

Pepeekeo facility, the MECO – Hahului facility, the HELCO Kanoelehua Hill facility, 

the HECO – Waiau facility, and the HECO – Kahe facility. The only facilities that 

showed a 98th percentile (8th high) 24-hour average visibility impact over the 0.5 dv

impact threshold were the Hu Honua Bioenergy – Pepeekeo and the HELCO -

Kanoelehua Hill facilities. Thus, the Hu Honua Bioenergy –Pepeekeo and the HELCO -

Kanoelehua Hill facilities are Subject-to-BART. The HC&S Puunene facility, the 

Chevron Refinery, the Tesoro Refinery, the MECO – Hahului facility the HECO – Waiau 

facility, and the HECO – Kahe facility are not Subject-to-BART.
27

6. REFERENCES

40 CFR Part 51, Appendix W. Guidelines on Air Quality Models

Alpine Geophysics, 2010. ―Subject-to-Best Available Retrofit Technology (BART) and 

Reasonable Progress (RP) PrioritizationModeling Protocol for the State of Hawaii: 

Application of the CALPUFF Modeling System‖ prepared for the Hawaii State 

Department of Health, Environmental Management Division, Clean Air Branch, prepared 

by Alpine Geophysics, LLC, Arvada, CO.

Dudhia, J., 1993. ―A Non-hydrostatic Version of the Penn State/NCAR Mesoscale 

Model: Validation Tests and Simulation of an Atlantic Cyclone and Cold Front‖, Mon. 

Wea. Rev., Vol. 121. pp. 1493-1513.

EPA (U.S. Environmental Protection Agency) 1998. Interagency Workgroup on Air 

Quality Modeling (IWAQM) Phase 2 Summary Report and Recommendations for 

Modeling Long Range Transport Impacts, EPA-454/R-98-019, December 1998.

EPA 2003. Guidance for Estimating Natural Visibility Conditions under the Regional 

Haze Rule, EPA-454/B-03-005, September, 2003.

Federal Register, Vol. 70, No. 128. Regional Haze Regulations and Guidelines for Best 

Available Retrofit Technology (BART) Determinations. pp. 39104 – 30172, July 6, 2005.

FLAG 2000. Federal Land Managers’ Air Quality Related Values Workgroup (FLAG) 

2000. Phase I Report. December 2000.

FLAG 2008. Federal Land Managers’ Air Quality Related Values Workgroup (FLAG) 

2008. Phase I Report—Revised. June 27, 2008 draft.

Gemmill, William, Bert Katz and Xu Li, 2007. Daily Real-Time Global Sea Surface 

Temperature - High Resolution Analysis at NOAA/NCEP. NOAA/NWS/NCEP/MMAB 

Office Note Nr. 260, 39 pp.

Grell, G. A., J. Dudhia, and D. R. Stauffer. 1994. A Description of the Fifth Generation 

Penn State/NCAR Mesoscale Model (MM5). NCAR Tech. Note, NCAR TN-398-STR, 

138 pp.

McNally, D.E., 2008,‖MM5 Sensitivity to Alternate Configurations over the Hawaiian 

Islands‖, prepared for the Hawaii State Department of Health, Environmental 

Management Division, Clean Air Branch, prepared by Alpine Geophysics, LLC, Arvada, 

CO.

McNally, D.E, and J.G. Wilkinson 2008, ‖MM5 Application for 2005 Over the 

Hawaiian Islands‖, prepared for the Hawaii State Department of Health, Environmental 
28

Management Division, Clean Air Branch, prepared by Alpine Geophysics, LLC, Arvada, 

CO.

NCEP, 2008. Real-time, global, sea surface temperature (RTG_SST) analysis. 10-July. 

polar.ncep.noaa.gov/sst/oper/Welcome.html

NOAA, 2009. Mauna Loa Observatory. 29-April, mlo.noaa.gov/livedata/livedata.html.

UCAR, 2003a. MM5 Community Model: Current Release. 09-April. 

www.mmm.ucar.edu/mm5/overviewCurrent.html

UCAR, 2003b. MM5 Community Model: MM5 Modeling System Overview. 10-April. 

www.mmm.ucar.edu/mm5/overviewProgram.html

UCAR, 2008a. ds353.4 Home Page. NCEP ADP Global Upper Air Observation Subsets, 

January 1973 – continuing. CISL Research Data Archive. dss.ucar.edu/datasets/ds353.4.

UCAR, 2008b. ds464.0 Home Page. NCEP ADP Global Surface Observations, February 

1975 – continuing. CISL Research Data Archive. dss.ucar.edu/datasets/ds464.0.

UCAR, 2008c. ds472.0 Home Page. TDL U.S. and Canada Surface Hourly 

Observations, daily 1976Dec – cont. CISL Research Data Archive. 

dss.ucar.edu.datasets.ds472.0.
29

Appendix A CALMET Parameter Values
30

Table A-1. CALMET Parameter Values 

The CALMET model will be run using the August 20, 2009, ―EPA-FLM Recommended 

CALMET input File Values.‖

Input 

Group Variable Description Default Value Recommended Value

0 DIADAT

Input file: preprocessed surface temperature data 

(DIAG.DAT) User Defined

0 GEODAT Input file: Geophysical data (GEO.DAT) User Defined User Define

0 LCFILES Convert file name to lower case User Defined

0 METDAT Output file (CALMET.DAT) User Defined

0 METLST Output file (CALMET.LST) User Defined

0 MM4DAT Input file: MM4 data (MM4.DAT) User Defined

0 NOWSTA Input files: Names of NOWSTA overwater stations User Defined 6

0 NUSTA Number of upper air data sites User Defined 2

0 PACDAT Output file: in Mesopuff II format (PACOUT.DAT) User Defined

0 PRCDAT Input file: Precipitation data (PRECIP.DAT) User Defined

0 PRGDAT Input file: CSUMM prognostic wind data (PROG.DAT) User Defined

0 SEADAT

Input files: Names of NOWSTA overwater stations 

(SEAn.DAT) User Defined

0 SRFDAT Input file: Surface data (SURF.DAT) User Defined

0 TSTFRD Output file (TEST.FRD) User Defined

0 TSTKIN Output file (TEST.KIN) User Defined

0 TSTOUT Output file (TEST.OUT) User Defined

0 TSTPRT Output file (TEST.PRT) User Defined

0 TSTSLP Output file (TEST.SLP) User Defined

0 UPDAT Input files: Names of NUSTA upper air data files (UPn.DAT) UPn.DAT

0 WTDAT Input file: Terrain weighting factors (WT.DAT) User Defined

1 CLDDAT Input file: Cloud data (CLOUD.DAT) User Defined Not used

1 IBDY Beginning day User Defined

1 IBHR Beginning hour User Defined

1 IBMO Beginning month User Defined

1 IBTZ Base time zone User Defined 0

1 IBYR Beginning year User Defined

1 IRLG Number of hours to simulate User Defined User Define

1 IRTYPE Output file type to create (must be 1 for CALPUFF) 1 1

1 ITEST Flag to stop run after Setup Phase 2 2

1 LCALGRD Are w-components and temperature needed? T T

1 MREG Text to options to conrim they conform to regulatory options User Defined 1

2 PMAP Map Projection UTM LCC

2 FEAST False easting at the projection origin 0.0 0.0

2 FNORTH False northing at the projection origin 0.0 0.0

2 DATUM WGS-G, NWS-27, NWS-84, ESR-S,... WGS-G

2 DGRIDKM Grid spacing User Defined 4

2 UTMHEM Hemisphere for UTM projection N N

2 IUTMZN UTM Zone User Defined User Define

2 LLCONF

When using Lambert Conformal map coordinates - rotate 

winds from true north to map north? F F

2 NX Number of east-west grid cells User Defined 199

2 NY Number of north-south grid cells User Defined 145

2 NZ Number of vertical layers User Defined 10

2 RLAT0 Latitude used if LLCONF = T User Defined 23.0N

2 RLON0 Longitude used if LLCONF = T User Defined 158.0W

2 XLAT1 Latitude of 1st standard parallel User Defined 30

2 XLAT2 Latitude of 2nd standard parallel User Defined 60

2 XORIGKM Southwest grid cell X coordinate User Defined -360
31

Input 

Group Variable Description Default Value Recommended Value

2 YORIGKM Southwest grid cell Y coordinate User Defined -588

2 ZFACE Vertical cell face heights (NZ+1 values) User Defined

0,20,40,80,160,320,640,

1200,2000,3000,4000

3 IFORMO Format of unformatted file (1 for CALPUFF) 1 1

3 LSAVE Save met. data fields in an unformatted file? T T

4 ICLOUD Is cloud data to be input as gridded fields? (0 = No) 0 0

4 IFORMC Format of cloud data (2 = formatted) 2 2

4 IFORMP Format of precipitation data (2 = formatted) 2 2

4 IFORMS Format of surface data (2 = formatted) 2 2

4 NOOBS Use or non-use of surface, overwater, upper observations User Defined 0

4 NPSTA Number of stations in PRECIP.DAT User Defined 14

4 NSSTA Number of stations in SURF.DAT file User Defined 14

5 ALPHA Empirical factor triggering kinematic effects 0.1 0.1

5 BIAS Surface/upper-air weighting factors (NZ values) NZ*0 NZ*0

5 CRITFN Critical Froude number 1 1

5 DIVLIM Maximum acceptable divergence 5.00E-06 5.00E-06

5 FEXTR2

Multiplicative scaling factor for extrap surface obs to uppr 

layrs NZ*0.0 NZ*0.0

5 ICALM Extrapolate surface calms to upper layers? (0 = No) 0 0

5 IDIOPT1 Compute temperatures from observations (0 = True) 0 0

5 IDIOPT2 Compute domain-average lapse rates? (0 = True) 0 0

5 IDIOPT3 Compute internally inital guess winds? (0 = True) 0 0

5 IDIOPT4 Read surface winds from SURF.DAT? ( 0 = True) 0 0

5 IDIOPT5 Read aloft winds from UPn.DAT? (0 = True) 0 0

5 IEXTRP

Extrapolate surface winds to upper layers? (-4 = use 

similarity theory and ignore layer 1 of upper air station data) -4 -4

5 IFRADJ Adjust winds using Froude number effects? (1 = Yes) 1 1

5 IKINE Adjust winds using kinematic effects? (1 = Yes) 0 0

5 IOBR Use O’Brien procedure for vertical winds? (0 = No) 0 0

5 IPROG Using prognostic or MM-FDDA data? (0 = No) 0 14

5 ISLOPE Compute slope flows? (1 = Yes) 1 1

5 ISTEPPG Timestep (hours) of the prognostic model input data 1 1

5 ISURFT

Surface station to use for surface temperature (between 1 

and NSSTA) User Defined 9

5 IUPT Station for lapse rates (between 1 and NUSTA) User Defined 1

5 IUPWND

Upper air station for domain winds (-1 = 1/r**2 interpolation 

of all stations) -1 -1

5 IWFCOD Generate winds by diagnostic wind module? (1 = Yes) 1 1

5 KBAR Level (1 to NZ) up to which barriers apply NZ 10

5 LLBREZE Use Lake Breeze module F F

5 LVARY Use varying radius to develop surface winds? F F

5 METBXID Station IDs in the region User Defined

5 NBAR Number of Barriers to interpolation User Defined 0

5 NBOX Number of Lake Breeze regions User Defined 0

5 NINTR2 Max number of stations for interpolations (NA values) 99 99

5 NITER Max number of passes in divergence minimization 50 50

5 NLB Number of stations in region User Defined 0

5 NSMTH Number of passes in smoothing (NZ values) 2, 4*(NZ-1) 2,4*(NZ-1)

5 R1 Relative weight at surface of Step 1 field and obs User Defined 50

5 R2 Relative weight aloft of Step 1 field and obs User Defined 100

5 RMAX1 Max surface over-land extrapolation radius (km) User Defined 100

5 RMAX2 Max aloft over-land extrapolation radius (km) User Defined 200

5 RMAX3 Maximum over-water extrapolation radius (km) User Defined 200

5 RMIN Minimum extrapolation radius (km) 0.1 0.1
32

Input 

Group Variable Description Default Value Recommended Value

5 RMIN2

Distance (km) around an upper air site where vertical 

extrapolation is excluded (Set to -1 if IEXTRP = ±4) 4 -1

5 RPROG Weighting factor for CSUMM prognostic wind data User Defined 0

5 TERRAD Radius of influence of terrain features (km) User Defined 15

5 XBBAR X coordinate of Beginning of each barrier User Defined 0

5 XBCST X Point defining the coastline (staight line) User Defined 0

5 XEBAR X coordinate of Ending of each barrier User Defined 0

5 XECST X Point User Defined 0

5 XG1 X Grid line 1 defining region of interest User Defined 0

5 XG2 X Grid line 2 User Defined 0

5 YBBAR Y coordinate of Beginning of each barrier User Defined 0

5 YBCST Y Point User Defined 0

5 YEBAR Y coordinate of Ending of each barrier User Defined 0

5 YECST Y Point User Defined 0

5 YG1 Y Grid line 1 User Defined 0

5 YG2 Y Grid Line 2 User Defined 0

5 ZUPT Depth of domain-average lapse rate (m) 200 200

5 ZUPWND Bottom and top of layer for 1st guess winds (m) 1, 1000 1.,1000.

6 CONSTB Neutral mixing height B constant 1.41 1.41

6 CONSTE Convective mixing height E constant 0.15 0.15

6 CONSTN Stable mixing height N constant 2400 2400

6 CONSTW Over-water mixing height W constant 0.16 0.16

6 CUTP Minimum cut off precip rate (mm/hr) 0.01 0.01

6 DPTMIN Minimum capping potential temperature lapse rate 0.001 0.001

6 DSHELF Coastal/shallow water length scale 0 0

6 DZZI Depth for computing capping lapse rate (m) 200 200

6 FCORIOL Absolute value of Coriolis parameter 1.00E-04 1.00E-04

6 HAFANG Half-angle for looking upwind (degrees) 30 30

6 IAVET Conduct spatial averaging of temperature? (1 = True) 1 1

6 IAVEZI Spatial averaging of mixing heights? (1 = True) 1 1

6 ICOARE Overwater surface fluxes method and parameters 10 0

6 ICOOL COARE cool skin layer computation 0 0

6 ILEVZI Layer to use in upwind averaging (between 1 and NZ) 1 1

6 ILUOC3D Land use category ocean in 3D.DAT datasets 16 16

6 IMIXH Method to compute the convective mixing height -1 -1

6 IRAD Form of temperature interpolation (1 = 1/r) 1 1

6 IRHPROG

3D relative humidity from observations or from prognostic 

data 0 1

6 ITPROG 3D temps from obs or from prognostic data? 0 2

6 ITWPROG

Option for overwater lapse rates used in convective mixing 

height growth 0 0

6 IWARM COARE warm layer computation 0 0

6 JWAT1 Beginning landuse type defining water 999 55

6 JWAT2 Ending landuse type defining water 999 55

6 MNMDAV Max averaging radius (number of grid cells) 1 1

6 NFLAGP Method for precipitation interpolation (2 = 1/r**2) 2 2

6 NUMTS Max number of stations in temperature interpolations 5 5

6 SIGMAP Precip radius for interpolations (km) 100 100

6 TGDEFA Default over-water capping lapse rate (K/m) -0.0045 -0.0045

6 TGDEFB Default over-water mixed layer lapse rate (K/m) -0.0098 -0.0098

6 THRESHL

Threshold buoyancy flux required to sustain convective 

mixing height growth overland 0.05 0.0

6 THRESHW

Threshold buoyancy flux required to sustain convective 

mixing height growth overwater 0.05 0.05

6 TRADKM Radius of temperature interpolation (km) 500 500
33

Input 

Group Variable Description Default Value Recommended Value

6 ZIMAX Maximum over-land mixing height (m) 3000 3000

6 ZIMAXW Maximum over-water mixing heigh (m) 3000 3000

6 ZIMIN Minimum over-land mixing height (m) 50 50

6 ZIMINW Minimum over-water mixing height (m) 50 50
34

Appendix B CALPUFF Parameter Values
35

Table B-1. CALPUFF Parameter Values

CALPUFF input settings.

Table B-1. CALPUFF Parameter Values

Input 

Group

Group 

Description Variable Description Default Valuea Recommended Value

1 Run Control METRUN Do we run all periods (1) or a subset (0)? 0 0

1 IBYR Beginning year User Defined 2005

1 IBMO Beginning month User Defined 1

1 IBDY Beginning day User Defined 1

1 IBHR Beginning hour User Defined 1

1 IRLG Length of run (hours) User Defined 8760

1 XBTZ Base Time Zone User Defined 0

1 NSECDT Length of modeling time step (seconds) 3600 3600

1 NSPEC

Number of species modeled (for MESOPUFF II 

chemistry) 5 8

1 NSE Number of species emitted 3 5

1 ITEST Flag to stop run after Setup Phase 2 2

1 MRESTART 

Restart options (0 = no restart) allows splitting 

runs into smaller segments 0 0

1 NRESPD Number of periods in Restart 0 0

1 METFM 

Format of input meteorology (1 = CALMET, 2 = 

ISC) 1 1

1 AVET

Averaging time lateral dispersion parameters 

(minutes) 60 60

1 PGTIME PG Averaging time 60 60

2 Tech Options MGAUSS Near-field vertical distribution (1 = Gaussian) 1 1

2 MCTADJ 

Terrain adjustments to plume path (3 = Plume 

path) 3 3

2 MCTSG 

Do we have subgrid hills? (0 = No) allows 

CTDM-like treatment for subgrid scale hills 0 0

2 MSLUG Near-field puff treatment (0 = No slugs) 0 0

2 MTRANS Model transitional plume rise? (1 = Yes) 1 1

2 MTIP Treat stack tip downwash? (1 = Yes) 1 1

2 MBDW Method to simulate downwash not used
36

Table B-1. CALPUFF Parameter Values

Input 

Group

Group 

Description Variable Description Default Valuea Recommended Value

(1=ISC,2=PRIME)

2 MSHEAR Treat vertical wind shear? (0 = No) 0 0

2 MSPLIT Allow puffs to split? (0 = No) 0 0

2 MCHEM MESOPUFF-II Chemistry? (1 = Yes) 1 1

2 MAQCHEM Aqueous phase transformation 0 0

2 MWET Model wet deposition? (1 = Yes) 1 1

2 MDRY Model dry deposition? (1 = Yes) 1 1

2 MTILT Plume Tilt (gravitational settling) 0 0

2 MDISP

Method for dispersion coefficients 

(2=micromet,3 = PG) 3 3

2 MTURBVW

Turbulence characterization? (Only if MDISP = 

1 or 5) 3 3

2 MDISP2 Backup coefficients (Only if MDISP = 1 or 5) 3 3

2 MTAULY Method for Sigma y Lagrangian timescale 0 0

2 MTAUADV

Method for Advective-Decay timescale for 

Turbulence 0 0

2 MCTURB

Method to compute sigma v,w using micromet 

variables 1 1

2 MROUGH Adjust PG for surface roughness? (0 = No) 0 0

2 MPARTL Model partial plume penetration? (0 = No) 1 1

2 MTINV

Elevated inversion strength (0 = compute from 

data) 0 0

2 MPDF Use PDF for convective dispersion? (0 = No) 0 0

2 MSGTIBL

Use TIBL module? (0 = No) allows treatment of 

subgrid scale coastal areas 0 0

2 MBCON Boundary conditions modeled 0 0

2 MFOG Configure for FOG model output 0 0

2 MREG Regulatory default checks? (1 = Yes) 1 1

3 Species List CSPECn

Names of species modeled (for MESOPUFF II 

must be SO2-SO4-NOX-HNO3-NO3) User Defined

SO2,SO4,NOX,HNO3,NO3,

NH3,PM10,PM25

3

Specie 

Names

Manner species will be modeled 

Species,Modeled,Emitted,Dry Deposited,Group

User Defined SO2,1,1,1,0

SO4,1,0,2,0

NOX,1,1,1,0

HNO3,1,0,1,0

NO3,1,0,2,0
37

Table B-1. CALPUFF Parameter Values

Input 

Group

Group 

Description Variable Description Default Valuea Recommended Value

NH3,1,0,1,0

PM10,1,1,2,0

PM25,1,1,2,0

3

Specie 

Groups Grouping of species if any User Defined N/A

3 CGRUP

3 CGRUP

4 MapProjection XLAT1 Latitude of 1st standard parallel 30N

4 XLAT2 Latitude of 2nd standard parallel 60N

4 PMAP Map Projection UTM LCC

FEAST False Easting 0.0 0.0

FNORTH False Northing 0.0 0.0

4 DATUM Datum-region WGS-G WGS-G

4 NX Number of east-west grids of input meteorology User Defined 199

4 NY 

Number of north-south grids of input 

meteorology User Defined 145

4 NZ Number of vertical layers of input meteorology User Defined 10

4 DGRIDKM Meteorology grid spacing (km) User Defined 4.

4 ZFACE Vertical cell face heights of input meteorology User Defined

0.,20.,40.,80.,160.,320.,640.,

1200.,2000.,3000.,4000.

4 XORIGKM Southwest corner (east-west) of input User Defined meteorology -360.

4 YORIGIM Southwest corner (north-south) of input User Defined meteorology -588.

4 IUTMZN UTM zone User Defined N/A (17)

4 UTMHEM Hemisphere for UTM Projection N N

4 RLAT0 Latitude of Projection Origin User Defined 23.0N

4 RLON0 Longitude of Projection Origin User Defined 158.0W

4 IBCOMP Southwest X-index of computational domain User Defined 1

4 JBCOMP Southwest Y-index of computational domain User Defined 1

4 IECOMP Northeast X-index of computational domain User Defined 199

4 JECOMP Northeast Y-index of computational domain User Defined 145

4 LSAMP Use gridded receptors? (T = Yes) F F
38

Table B-1. CALPUFF Parameter Values

Input 

Group

Group 

Description Variable Description Default Valuea Recommended Value

4 IBSAMP Southwest X-index of receptor grid User Defined

4 JBSAMP Southwest Y-index of receptor grid User Defined

4 IESAMP Northeast X-index of receptor grid User Defined

4 JESAMP Northeast Y-index of receptor grid User Defined

4 MESHDN

Gridded recpetor spacing = 

DGRIDKM/MESHDN 1 1

5 Output Options ICON Output concentrations? (1 = Yes) 1 1

5 IDRY Output dry deposition flux? (1 = Yes) 1 1

5 IWET Output west deposition flux? (1 = Yes) 1 1

5 IT2D 2D Temperature 0 0

5 IRHO 2D Density 0 0

5 IVIS Output RH for visibility calculations (1 = Yes) 1 0

5 LCOMPRS Use compression option in output? (T = Yes) T T

5 ICPRT Print concentrations? (0 = No) 0 0

5 IDPRT Print dry deposition fluxes (0 = No) 0 0

5 IWPRT Print wet deposition fluxes (0 = No) 0 0

5 ICFRQ Concentration print interval (1 = hourly) 1 1

5 IDFRQ Dry deposition flux print interval (1 = hourly) 1 1

5 IWFRQ West deposition flux print interval (1 = hourly) 1 1

5 IPRTU

Print output units (1 = g/m**3; g/m**2/s; 3 = 

ug/m3, ug/m2/s) 1 3

5 IMESG Status messages to screen? (1 = Yes) 1 2

5 LDEBUG Turn on debug tracking? (F = No) F F

5 IPFDEB First puff to track 1 1

5 NPFDEB (Number of puffs to track) (1) 1

5 NN1 (Met. Period to start output) (1) 1

5 NN2 (Met. Period to end output) (10) 10

7

Dry Dep Chem Dry Gas Dep Chemical parameters of gaseous deposition

Species,Diffusivity,Alpha 

Star,Reactivity,Mesophyll,Henry’s User Defined

SO2,0.1509,1000.,8.,0.,0.04

NOX,0.1656,1.,8,.5.,3.5

HNO3,0.1628,1.,18.,0.,8E-8

NH3,0.234,0.2,4.,0.,3.59E-4
39

Table B-1. CALPUFF Parameter Values

Input 

Group

Group 

Description Variable Description Default Valuea Recommended Value

8

Dry Dep Size Dry Part. Dep

Chemical parameters of particulate deposition 

Species,Mass Mean Diameter,Geometric Std D User Defined

SO4,0.48.,2.,

NO3,0.48,2.,

PM10,10.,0.,

PM25,2.5,0.,

9

Dry Dep Misc RCUTR Reference cuticle resistance (s/cm) 

30 30

9 RGR Reference ground resistance (s/cm) 10 10

9 REACTR Reference reactivity 8 8

9 NINT Number of particle-size intervals 9 9

9 IVEG

Vegetative state (1 = active and unstressed; 

2=active and stressed) 1 1

10

Wet Dep Wet Dep Wet deposition parameters 

Species, Liquid Precip, Frozen Precip

User Defined

SO2,3.0E-05,0.

SO4,1.0E-04,3.0E-05

NOX,0.,0.

HNO3,6.0E-05,0.0

NO3,1.0E-04,3.0E-05

NH3,8.0E-05,0.0

PM10,1.0E-04,3.0E-05

PM25,1.0E-04,3.0E-05

11

Chemistry MOZ Ozone background? (0 = constant background 

value; 1 = read from ozone.dat) 1 0

11 BCKO3 Ozone default (ppb) (Use only for missing data) 80. 12*40.

11 BCKNH3 Ammonia background (ppb) 10 1.5

11 RNITE1 Nighttime SO2 loss rate (%/hr) 0.2 0.2

11 RNITE2 Nighttime NOx loss rate (%/hr) 2 2

11 RNITE3 Nighttime HNO3 loss rate (%/hr) 2 2

11 MH2O2 H2O2 data input option 1 1

11 BCKH2O2 Monthly H2O2 concentrations 1 not used

BKPMF Fine particulate concentration 12 * 1.00 not used

OFRAC Organic fraction of Fine Particulate 2*0.15, 9*0.20, 1*0.15 not used

VCNX VOC / NOX ratio 12 * 50.00 not used

12 Dispersion SYTDEP

Horizontal size (m) to switch to time 

dependence 550 550

12 MHFTSZ Use Heffter for vertical dispersion? (0 = No) 0 0
40

Table B-1. CALPUFF Parameter Values

Input 

Group

Group 

Description Variable Description Default Valuea Recommended Value

12 JSUP PG Stability class above mixed layer 5 5

12 CONK1 Stable dispersion constant (Eq 2.7-3) 0.01 0.01

12 CONK2 Neutral dispersion constant (Eq 2.7-4) 0.1 0.1

12 TBD Transition for downwash algorithms (0.5 = ISC) 0.5 0.5

12 IURB1 Beginning urban landuse type 10 10

12 IURB2 Ending urban landuse type 19 19

12 ILANDUIN

Land use type (20 = Unirrigated agricultural 

land) 20 20

12 ZOIN Roughness length (m) 0.25 0.25

12 XLAIIN Leaf area index 3.0 3.0

12 ELEVIN Met. Station elevation (m above MSL) 0.0 0.0

12 XLATIN Met. Station North latitude (degrees) -999.0 -999.0

12 XLONIN Met. Station West longitude (degrees) -999.0 -999.0

12 ANEMHT

Anemometer height of ISC meteorological data 

(m) 10.0 10.0

12 ISIGMAV

Lateral turbulence (Not used with ISC 

meteorology) 1 1

12 IMIXCTDM Mixing heights (Not used with ISC meteorology) 0 0

12 XMXLEN Maximum slug length in units of DGRIDKM 1.0 1.0

12 XSAMLEN

Maximum puff travel distance per sampling 

step (units of DGRIDKM) 1.0 1.0

12 MXNEW Maximum number of puffs per hour 99 99

12 MXSAM Maximum sampling steps per hour 99 99

12 NCOUNT

Iterations when computing Transport Wind 

(Calmet & Profile Winds) 2 2

12 SYMIN Minimum lateral dispersion of new puff (m) 1.0 1.0

12 SZMIN Minimum vertical dispersion of new puff (m) 1.0 1.0

12 SVMIN Array of minimum lateral turbulence (m/s) 6 * 0.50,6*0.37 6 * 0.50, 6*0.50

12 SWMIN Array of minimum vertical turbulence (m/s) 

2*(0.20,0.12,0.08,0.06,0.03,

0.016)

2*(0.20,0.12,0.08,0.06,0.03,

0.016)

12 CDIV (1), (2) Divergence criterion for dw/dz (1/s) 0.0,0.0 0.0,0.0

12 WSCALM Minimum non-calm wind speed (m/s) 0.5 0.5
41

Table B-1. CALPUFF Parameter Values

Input 

Group

Group 

Description Variable Description Default Valuea Recommended Value

12 XMAXZI Maximum mixing height (m) 3000 3000

12 XMINZI Minimum mixing height (m) 50 50

12 WSCAT Upper bounds 1st 5 wind speed classes (m/s) 1.54,3.09,5.14,8. 23,10.8 1.54,3.09,5.14,8. 23,10.8

12 PLX0 Wind speed power-law exponents 0.07,0.07,0.10,0.15,0.35,0.55 0.07,0.07,0.10,0.15,0.35,0.55

12 PTGO

Potential temperature gradients PG E and F 

(deg/km) 0.020,0.035 0.020,0.035 

12 PPC Plume path coefficients (only if MCTADJ = 3) 0.5,0.5,0.5,0.5,0.35,0.35 0.5,0.5,0.5,0.5,0.35,0.35

12 SL2PF Maximum Sy/puff length 10.0 10.0

12 NSPLIT Number of puffs when puffs split 3 3

12 IRESPLIT Hours when puff are eligible to split Hour 17=1 Hour17=1

12 ZISPLIT Previous hour’s mixing height(minimum)(m) 100.0 100.0

12 ROLDMAX

Previous Max mix ht/current mix ht ratio must 

be less then this value for puff to split 0.25 0.25

12 NSPLITH Number of puffs when puffs split horizontally 5 5

12 SYSPLITH

Min sigma-y (grid cell units) of puff before horiz 

split 1.0 1.0

12 12 SHSPLITH

Min puff elongation rate per hr from wind shear 

before horiz split 2.0 2.0

12 CNSPLITH Min conc g/m3 before puff may split horizontally 1.0E-07 1.0E-07

12 EPSSLUG

Convergence criterion for slug sampling 

integration 1.00E-04 1.00E-04

12 EPSAREA

Convergence criterion for area source 

integration 1.00E-06 1.00E-06

12 DSRISE Step length for rise integration 1.0 1.0

12 HTMINBC 500.0 500.0

12 RSAMPBC 10.0 10.0

12 MDEPBC 1 1

13 Point Source NPT1 Number of point sources User Defined

13 IPTU Units of emission rates (1 = g/s) 1 3
42

Table B-1. CALPUFF Parameter Values

Input 

Group

Group 

Description Variable Description Default Valuea Recommended Value

13 NSPT1 Number of point source-species combinations 0 0

13 NPT2

Number of point sources with fully variable 

emission rates 0 0

13

Point 

Sources Point sources characteristics User Defined

14 Area Source Area Sources Area sources characteristics User Defined

15 Volume Source Volume Volume sources characteristics User Defined Sources

16 Line Source Line Sources Buoyant lines source characteristics User Defined

17 Receptors NREC Number of user defined receptors User Defined

17

Receptor 

Data Location and elevation (MSL) of receptors User Defined
43

Appendix C CALPOST Parameter Values
Table C-1. CALPOST Parameter Values

The CALPOST utility will be used for computation of visibility parameters using the parameters in this 

table.

Table C-1: CALPOST Parameter Values

Input 

Group Variable Description Default Value Recommended Value

1 ASPEC Species to process VISIB VISIB

1 METRUN Option to run subset of met file 0 1

1 BTZONE Base time zone User Defined 0

1 ILAYER

Layer/deposition code (1 = CALPUFF concentrations; -3 = wet+dry 

deposition fluxes) 1 1

1 A Scaling factor scalar 0.0 0.0

1 B Additive factor 0.0 0.0

1 LBACK Add Hourly Background Concentrations/Fluxes? F F

1 NO2CALC Specify a single NO2/Nox ratio 1 1

1 RNO2NOX Ratio of NO2 to NOx 1.0 1.0

1 MSOURCE Process only total reported contribution 0 0

2 MVISCHECK Confirm conformity to FLAG 2008 Configuration 1 1

2 MFRH Particle growth curve for hygroscopic species 4 4

2 RHMAX Maximum relative humidity (%) used in particle growth curve 98 98

2 LDRING

Report results by Discrete receptor Ring, if Discrete Receptors used. 

(T = true) T T

Modeled species to be included in computing the light extinction

2 LVSO4 Include SO4? T T

2 LVNO3 Include NO3? T T

2 LVOC Include Organic Carbon? T F

2 LVPMC Include Coarse Particles? T T

2 LVPMF Include Fine Particles? T T

2 LVEC Include Elemental Carbon? T F

2 LVNO2 Include NO2 T T

2 LVBK

when ranking for TOP-N, TOP-50, and Exceedance tables Include 

BACKGROUND? T T

2 SPECPMC Species name used for particulates in MODEL.DAT file: COARSE = PMC PM10

2 SPECPMF Species name used for particulates in MODEL.DAT file: FINE = PMF PM25

Extinction Efficiencies (1/Mm per ug/m**3)

2 EEPMC PM COARSE = 0.6 0.6

2 EEPMF PM FINE = 1.0 1.0

2 EEPMCBK Background PM COARSE 0.6 0.6

2 EESO4 SO4 = 3.0 3.0

2 EENO3 NO3 = 3.0 3.0

2 EEOC Organic Carbon = 4.0 4.0

2 EESOIL Soil = 1.0 1.0

2 EEEC Elemental Carbon = 10.0 10.0

2 EENO2 NO2 Gas = 0.1755 0.1755

2 LAVER Method used for 24-hr avg % change light extinction F F

2 MVISBK

Method used for background light extinction (2 = Hourly RH 

adjustment; 6 = FLAG seasonal f(RH)) 8 8

2 RHFAC

Monthly RH adjustment factors from FLAG (unique for each Class I 

area) Yes if 6 N/A

Background monthly extinction coefficients (FLAG) unique for each 

Class I area

2 BKSO4 Sulfate See Table 5 (NH3)2SO4
Table C-1: CALPOST Parameter Values

Input 

Group Variable Description Default Value Recommended Value

2 BKNO3 Nitrate See Table 5 NH4NO3

2 BKPMC Coarse particulate See Table 5 CM

2 BKOC Particulate organic carbon See Table 5 OM

2 BKSOIL Soil See Table 5 Soil

2 BKEC Particulate elemental carbon See Table 5 EC

2 M8_MODE Method 8 Mode 5 5

2 BKSALT Background salt concentration See Table 2 Sea Salt

2 RHFSML Small ammonium sulfate and nitrate particle f(RH) See Table 7

2 RHFLRG Large ammonium sulfate and nitrate particle f(RH) See Table 6

2 RHFSEA Sea salt f(RH) See Table 8

2 BEXTRAY Extinction due to Rayleigh scattering 10.0 10.0

3 LDOC Print documentation image F F

Averaging time(s) reported

3 L1PD Averaging period of model output F F

3 L1HR 1-hr averages F F

3 L3HR 3-hr averages F F

3 L24HR 24-hr averages T T

3 LRUNL Run length (annual) F F

3 LT50 Top 50 table for each averaging time selected T F

3 LTOPN F T

3 NTOP 4 1

3 ITOP 1 1

3 LEXCD Threshold exceedance count F T

3 THRESH1 1-hour threshold -1.0 -1.0

3 THRESH3 3-hour threshold -1.0 -1.0

3 THRESH24 24-hour threshold -1.0 0.5

3 THRESHN N-hour threshold -1.0 -1.0

3 NDAY Accumulated Period (days) 0 0

3 NCOUNT Number of exceedances allowed 1 1

Table C-2: POSTUTIL (Repartition) Parameter Values

The CALUTIL will be used to perform HNO3/NO3 repartitioning using the parameters in this table.

Table C-2: POSTUTIL (Repartition) Parameter Values

Input 

Group Variable Description Default Value

Recommended 

Value

1 IBYR Beginning year User Defined 2005

1 IBMO Beginning month User Defined 1

1 IBDY Beginning day User Defined 1

1 IBHR Beginning hour User Defined 1

1 NPER Length of run (hours) User Defined 8760

1 NSPECINP Number of species to process User Defined 7

1 NSPECOUT Number of species to write User Defined 7

1 NDUPLCT Stop if duplicate species names 0 0

1 NSCALED Number of CALPUFF data files to be scales 0 0

1 MNITRATE Recompute HNO3/NO3 concentration partition 0 1

1 BKNH3 Background ammonia concentrations 10. 1.5

2 ASPECI Species to input User Defined SO2,SO4,NOX,HNO3,
Table C-2: POSTUTIL (Repartition) Parameter Values

Input 

Group Variable Description Default Value

Recommended 

Value

NO3,PM10,PM25

2 ASPECO Speceis to output User Defined

SO2,SO4,NOX,HNO3,

NO3,PM10,PM25

Table C-3. Background Extinction Coefficients (20% Best Days).

Background Extinction Coefficients (20% Best Days)

Class I Area

(NH4)2

SO4

μg/m3

NH4N

O3

Μg/m3

OM

μg/m3

EC

μg/m3

Soil

μg/m3

CM

μg/m3

Sea 

Salt

μg/m

3

Rayleig

h

Mm-1

Haleakala 

National Park 

(HALE1) 0.05 0.04 0.24 0.01 0.11 1.59 0.13 10

Hawaii 

Volcanoes 

National Park 

(HAVO1) 0.03 0.04 0.27 0.01 0.06 0.64 0.11 10

Table C-4. Monthly fL(RH) – Large (NH4)2SO4 and NH4NO3 Relative Humidity Adjustment Factor.

Class I Area Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov D

Haleakala National Park (HALE1) 2.34 2.27 2.25 2.20 2.13 2.09 2.16 2.14 2.10 2.19 2.33 2

Hawaii Volcanoes National Park 

(HAVO1)

2.56 2.45 2.47 2.47 2.47 2.45 2.53 2.59 2.55 2.57 2.78 2

Table C-5. Monthly fS(RH) – Small (NH4)2SO4 and NH4NO3 Relative Humidity Adjustment Factor.

Class I Area Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov D

Haleakala National Park (HALE1) 2.98 2.85 2.81 2.72 2.60 2.53 2.65 2.63 2.56 2.69 2.95 2

Hawaii Volcanoes National Park

(HAVO1)

3.35 3.10 3.14 3.13 3.14 3.11 3.24 3.42 3.34 3.38 3.76 3

Table C-6. Monthly fSS(RH) – Sea Salt Relative Humidity Adjustment Factor.

Class I Area Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov D

Haleakala National Park (HALE1) 3.52 3.43 3.40 3.35 3.25 3.18 3.29 3.27 3.22 3.33 3.52 3

Hawaii Volcanoes National Park 

(HAVO1)

3.80 3.68 3.71 3.72 3.72 3.69 3.79 3.83 3.79 3.83 4.08 3
Subject-to-Best Available Retrofit Technology (BART) 

and Reasonable Progress (RP) Prioritization

Modeling Protocol for the State of Hawaii

Application of the CALPUFF Modeling System

Prepared for:

Hawaii State Department of Health

Environmental Management Division

Clean Air Branch

Prepared by:

Alpine Geophysics, LLC

7341 Poppy Way

Arvada, Colorado 80007

30 November 2009
i

Table of Contents

Section No. Page No. 

1 INTRODUCTION AND PROTOCOL OBJECTIVES....................................................................1

1.1 INTRODUCTION ............................................................................................................................................................ 1

2 MODELING APPROACH .................................................................................................................2

2.1 CLASS I AREAS ............................................................................................................................................................ 3

2.2 POLLUTANTS TO CONSIDER ......................................................................................................................................... 5

2.3 EMISSIONS AND STACK DATA...................................................................................................................................... 5

2.4 NATURAL BACKGROUND ............................................................................................................................................. 6

2.5 VISIBILITY CALCULATION ........................................................................................................................................... 6

2.6 MODEL EXECUTION ..................................................................................................................................................... 7

2.6.1 Subject-to-BART and RP Prioritization Analysis.................................................................................................. 7

2.6.2 Implementing Subject-to-BART/RP Prioritization Modeling Analysis................................................................. 8

3 VISIBILITY MODELING SYSTEM ..............................................................................................10

3.1 CALMET.......................................................................................................................................................................... 10

3.1.1 Meteorological Data ............................................................................................................................................. 12

3.1.2 CALMET Control File Settings ........................................................................................................................... 17

3.2 CALPUFF ................................................................................................................................................................. 18

3.2.1 Emissions ............................................................................................................................................................. 18

3.2.2 Ozone Background ............................................................................................................................................... 19

3.2.3 Ammonia Background ......................................................................................................................................... 19

3.2.4 Receptor Locations............................................................................................................................................... 19

3.3 CALPOST AND VISIBILITY POST-PROCESSING.............................................................................................. 19

4 INTERPRETATION OF RESULTS ...............................................................................................20

5 REFERENCES...................................................................................................................................21

APPENDIX A CALMET PARAMETER VALUES...............................................................................23

APPENDIX B CALPUFF PARAMETER VALUES..............................................................................28

APPENDIX C CALPOST PARAMETER VALUES .............................................................................37
ii

List of Figures

Figure No. Page No.

Figure 1 BART-Eligible Facility Locations in Proximity to Class I Areas.................................................................................... 5

Figure 2. Subject-to-BART/ RP Prioritization Flowchart ............................................................................................................. 9

Figure 3: Modeled 36/12/4 km MM5 Domain. ............................................................................................................................ 16

Figure 4. Modeled 4km MM5 Domain. ....................................................................................................................................... 17
iii

List of Tables

Table No. Page No.

Table 1. BART-eligible Sources. .................................................................................................................................................. 3

Table 2. Federal Mandatory Class I Areas in the Modeling Domain. ........................................................................................... 3

Table 3. 20% Best Natural Conditions – Concentrations and Rayleigh Scattering By Class I Area.............................................. 6

Table 4. CALPUFF Modeling System........................................................................................................................................ 10

Table 5. MM5 Vertical Domain Specification. ........................................................................................................................... 15
1

PURPOSE

This Protocol is prepared for the State of Hawaii and describes the application of the 

CALPUFF Model for determining whether a Best Available Retrofit Technology 

(BART)-eligible source is deemed Subject-to-BART under the Regional Haze (RH) 

Rule, 40 CFR 308. It may also be used to prioritize other significant emitting sources to 

be considered for the Reasonable Progress (RP) control evaluation.

1 INTRODUCTION AND PROTOCOL OBJECTIVES

1.1 INTRODUCTION

Under the federal RH Rule, 40 CFR 308, Hawaii is required to develop a plan to establish 

Reasonable Progress goals for incremental visibility improvements in achieving natural 

background conditions within its two Federal Class I areas. Natural conditions exist 

when a Class I area is absent of anthropogenic or man-made air pollution emissions. In 

establishing RP goals, Hawaii will need to consider emission reduction measures for 

specified source groups or activities including the application of BART for existing major 

stationary sources.

On 6 July 2005, the U.S. Environmental Protection Agency (EPA) published final 

amendments to its 1999 RH Rule in the Federal Register, including 40 CFR Part 51, 

Appendix Y, the final guidance for BART determinations (BART Guideline; EPA, 2005; 

70 FR 39104-39172). The rule applies to any BART-eligible source that ―emits any air 

pollutant which may reasonably be anticipated to cause or contribute to any impairment 

of visibility‖ in any mandatory Class I federal area. States retain the authority to exempt 

certain BART-eligible sources based on dispersion modeling demonstrating that the 

source cannot reasonably be anticipated to cause or contribute to visibility impairment in 

a Class I area. Hawaii has elected to use this modeling option for BART-eligible sources, 

and reserves this modeling option to prioritize evaluation of other potential sources 

contributing to visibility impairment in addressing RP goals.

According to 40 CFR Part 51, Appendix Y, a BART-eligible source is considered to 

―contribute‖ to visibility impairment in a Class I area if the modeled 98th percentile 

change in deciviews is equal to or greater than the ―contribution threshold.‖ Any BARTeligible source, through modeling, determined to cause or contribute to visibility 

impairment above the threshold in any Class I area is deemed subject to BART. The 

EPA BART Guidelines suggest a contribution threshold of no greater than a 0.5 change 

in deciview be used. Hawaii has chosen to use the recommended 0.5 deciview threshold 

for Subject-to-BART determination and RP prioritization.

The U.S. EPA Region 9, U.S. National Park Service (NPS), and the U.S. Fish and 

Wildlife Service (FWS) were consulted during the development of this Protocol, which 

follows very closely the BART Guideline. 
2

1.2 OBJECTIVES

The objectives of this Protocol are to provide:

A consistent approach in determining which BART-eligible source is deemed

Subject-to-BART, requiring the 5-factor analysis pursuant to 40 CFR Part 51, 

Appendix Y, Guidelines for BART Determinations under the RH Rule (BART 

Guideline);

A consistent approach which may be used in prioritizing identified RP-eligible 

sources to be evaluated under the 4-factor analysis pursuant to EPA’s Guidance, 

Setting Reasonable Progress Goals Under the Regional Haze Program, June 1, 2007;

A clearly delineated modeling methodology;

A common CALMET/CALPUFF/POSTUTIL/CALPOST modeling configuration; 

and

A common expectation for reporting modeling results.

The Protocol is not intended to define the engineering analyses required by the US EPA’s 

BART Guidance, nor address model alternatives to the CALPUFF model.

2 MODELING APPROACH

This section describes the components or information required for the CALPUFF 

modeling and the overall process in determining whether a source is anticipated to cause 

or contribute to visibility impairment to Hawaii’s federal Class I areas. Eligible source 

criteria, Class I area identification, pollutants considered, emissions data required, natural 

background conditions, visibility impairment determination, and the analysis process are 

covered here.

1.1 BART-ELIGIBLE/RP-ELIGIBLE SOURCES

Sources to be evaluated in this protocol include BART-eligible sources and identified 

RP-Eligible sources. BART-eligible sources determined to cause or contribute to 

visibility impairment in any Class I area will be subject to further air pollution control 

analyses, similarly with RP-eligible sources.

The term ―BART-eligible emission unit‖ refers to any single emission unit that meets the 

following criteria:

1) Emits greater than 250 tons/year of a visibility-impairing pollutant;

2) Was put into place between August 7, 1962 and August 7, 1977; and

3) Falls within one of the 26 listed source categories in the guidance.
3

A ―BART-eligible source‖ is the total of all BART-eligible emission units at a single 

facility. If a source has several emission units, only those that meet the BART-eligible 

criteria are included in the definition of ―BART-eligible source.‖

A ―RP-eligible source‖ is the total of all significant emitting emission units/process at a 

single facility that are suspected of causing or contributing to visibility impairment as 

identified by the Hawaii State Department of Health (DOH)/EPA.

Hawaii identified nine BART-eligible facilities shown in Table 1. RP-eligible sources to 

be considered for the first RH planning period are under evaluation. Priority will initially 

be placed on major emitting emission units within similar BART categories, located 

within a BART-eligible source or near a Federal Class I area.

Table 14. BART-eligible Sources.

Facilities

Chevron Refinery

Tesoro Refinery

HECO – Kahe

HECO – Waiau

HC&S Puunene

MECO – Kahului

HELCO – Kanoelehua Hill

Hu Honua Bioenergy, LLC -Pepeekeo 

(formerly Hilo Coast)

Kauai Utilities – Port Allen

For purposes of this modeling protocol, references made to a BART-eligible source will 

apply to an identified RP-eligible source if not explicitly stated. The modeling 

methodology to be used will essentially be the same for determining Subject-to-BART 

sources and prioritizing sources for RP evaluation. This protocol follows the BART 

Guideline, and may extend the evaluation to identified RP-eligible sources, except that 

the modeling analysis serves to prioritize RP-eligible sources for review, rather than 

exempting these sources from further review as with BART-eligible sources.

2.8 Class I Areas

The mandatory Class I federal areas in Hawaii that could be impacted by BART-eligible 

sources, are presented in Table 2. Refer to Figure 1 below showing the location of the 

Class I areas in relation to BART-eligible facilities/sources.

Table 15. Federal Mandatory Class I Areas in the Modeling Domain.

Class I Area IMPROVE Monitor Federal Land Manager

Haleakala National Park HALE 1 National Park Service

Hawaii Volcanoes National Park HAVO 1 National Park Service
4

The Haleakala National Park consists of 30,183 acres in central to eastern portions of the 

island of Maui, 24,719 acres of which are designated wilderness. Park elevations range 

from 3,055 m (10,023 ft) at the summit of Haleakala to sea level at the mouth of 

Kipahulu Valley on the extreme eastern boundary. The Park thus includes a wide variety 

of climate zones, from high mountain elevations above the Marine Boundary Layer, to 

leeward lowlands that are dry relative to windward lowlands at the same elevation. 

The Haleakala National Park IMPROVE site is HALE1, located at an elevation of 1,158 

m (3,799 ft) on the northward slope of Haleakala, ~5.7 km northwest of the Park 

Boundary. 

The Hawaii Volcanoes National Park IMPROVE Site is HAVO1, located on the east to 

southeastern slopes of Mauna Loa on the island of Hawaii. More specifically, it is 

situated on the northeastern rim of the Kilauea crater. The site elevation is 1,204 m (3,949 

ft), some 124 m above the crater floor at 1080 m (3,542 ft). One of the major terrain 

feature affecting meteorology and air quality at the monitoring site, other than the 

erupting Kilauea volcano, is the 4,170 m (13,681 ft) high Mauna Loa cone, about 35 km 

(22 mi) to the west of the monitoring site.

The IMPROVE data is used to represent measured pollutant impacts at the two National 

Parks.
5

Figure 5 BART-Eligible Facility Locations in Proximity to Class I Areas.
5

2.9 Pollutants to Consider

The BART Guideline specifies that sulfur dioxide (SO2), oxides of nitrogen (NOx) and 

direct particulate matter (PM) emissions, including both PM10 and PM2.5 should be 

included for the Subject-to-BART modeling analyses. 

The BART Guideline also discusses the inclusion of volatile organic compound (VOC), 

ammonia and ammonia compounds as visibility impairing pollutants. These pollutants 

will be considered separately if it is determined that these emissions from a BARTeligible source are reasonably anticipated to cause or contribute to visibility impairment. 

2.10 Emissions and Stack Data

The BART Guideline states, ―the emission estimates used in the models are intended to 

reflect steady-state operating conditions during periods of high capacity utilization.‖ 

These emissions should not generally include start-up, shutdown, or malfunction 

emissions. The BART Guideline recommends that states use the 24-hour average actual 

emission rate from the highest emitting day of the meteorological period modeled. 

Hawaii requested source information of the highest emitting day between calendar years 

2003 to 2007 from BART-eligible sources and will similarly request the same 

information from identified RP-eligible sources.

Depending on the availability of emissions data, the following emissions information 

(listed in order of priority) will be used with CALPUFF for the Subject-to-BART and RP 

prioritization modeling:

24-hour average actual emission rate from the highest emitting day within the 

modeling period (preferred) based on representative CEM or stack test data; or

24-hour average emissions calculated using emission factors specified in Title V 

permits.

24-hour average emissions calculated using AP-42 emission factors or other 

equivalent methods.

Allowable emissions (maximum 24-hour allowable).

DOH will work with sources to develop appropriate emission rates.

If plant-wide emissions for SO2, NOx, and PM10, from eligible sources, are less than the 

significant emission rate (SER) used for Prevention of Significant Deterioration review, 

emissions of that pollutant will not be included in the Subject-to-BART and RP 

prioritization modeling. However, if plant-wide emissions exceed the SERs for these 

pollutants, then all emissions of that pollutant from individual emission units will be 

evaluated even if emissions are below the SER for an individual emission unit.
6

2.11 Natural Background

The BART Guideline references EPA’s ―Guidance for Estimating Natural Conditions 

under the Regional Haze Rule‖ (EPA, 2003a). This guidance lists three sets of Natural 

Conditions corresponding to Annual Average, Best 20% Days and Worst 20% Days. 

Due to limited access to available meteorological data, the 20% best natural days will be 

used, in lieu of annual average natural days, to represent natural visibility background in 

Class I areas. This definition of natural background is consistent with the intent of the 

BART Guideline (Federal Register Vol. 70, No. 128, pf 39125). The specific natural 

background values used in this protocol are drawn from the Federal Land Managers’ Air 

Quality Related Values Workgroup (FLAG) Phase I Report – Revised (FLAG, 2008). 

Class I specific background extinction coefficients are presented in Table 3. The option 

presented in EPA’s guidance for refining the default visibility background will not be 

used in this protocol.

Table 16. 20% Best Natural Conditions – Concentrations and Rayleigh Scattering 

By Class I Area.

Background Extinction Coefficients (20% Best Days)

Class I Area

(NH4)2SO4

μg/m3

NH4NO3

Μg/m3

OM

μg/m3

EC

μg/m3

Soil

μg/m3

CM

μg/m3

Sea 

Salt

μg/m3

Rayleigh

Mm-1

Haleakala 

National Park 0.05 0.04 0.24 0.01 0.11 1.59 0.13 10

Hawaii 

Volcanoes 

National Park 0.03 0.04 0.27 0.01 0.06 0.64 0.11 10

2.12 Visibility Calculation

The CALPUFF modeling techniques presented in this protocol will provide ground level 

concentrations of visibility impairing pollutants. The concentration estimates from 

CALPUFF are used with the FLAG referenced IMPROVE equation to calculate the 

extinction coefficient (bext), as shown below. 

bext = 2.2 × fS(RH) × [Small Sulfate] + 4.8 × fL(RH) × [Large Sulfate] 

• 2.4 × fS(RH) × [Small Nitrate] + 5.1 × fL(RH) × [Large Nitrate] 

• 2.8 × [Small Organic Mass] + 6.1 × [Large Organic Mass] 

• 10 × [Elemental Carbon] 

• 1 × [Fine Soil] 

• 0.6 × [Coarse Mass] 

• 1.7 × fSS(RH) × [Sea Salt]

• Rayleigh Scattering (Site Specific) 

• 0.33 × [NO2 (ppb)] {or as: 0.1755 × [NO2 (μg/m3)]} 

Where: 

[ ] indicates concentrations in μg/m3 
7

fS(RH) = Relative humidity adjustment factor for small sulfate and nitrate 

fL(RH) = Relative humidity adjustment factor for large sulfate and nitrate 

fSS(RH) = Relative humidity adjustment factor for sea salt 

For Total Sulfate < 20 μg/m3: 

[Large Sulfate] = ([Total Sulfate] / 20 μg/m3) × [Total Sulfate] 

For Total Sulfate ≥ 20 μg/m3: 

[Large Sulfate] = [Total Sulfate] 

And: 

[Small Sulfate] = [Total Sulfate] – [Large Sulfate] 

To calculate large and small nitrate and organic mass, substitute ({Large, Small, 

Total} {Nitrate, Organic Mass}) for Sulfate. 

The monthly relative humidity adjustment factors from the above equation are found in 

Appendix C.

The change in visibility for the Subject-to-BART and RP prioritization analyses will be 

compared against the 20% best natural background conditions. The delta-deciview, dv, 

value is calculated from the source’s contribution to extinction, bext (source), and 

background extinction, bext(bkg), as follows:

dv = 10 ln (bext(bkg)+bext (source)) / bext(bkg) )

2.13 Model Execution

2.13.1 Subject-to-BART and RP Prioritization Analysis

The Subject-to-BART/RP prioritization modeling will be used to determine which 

eligible sources are reasonably anticipated to cause or contribute to visibility impairment 

at any Class I area. This protocol uses Option 1 in Section III of the BART Guideline for 

this analysis. This option is the Individual Source Attribution Approach. With this 

approach, each BART- and RP-eligible source is modeled separately and the impact on 

visibility impairment in any Class I area is determined.

For determining the visibility threshold, the recommendations in the BART Guideline are 

followed to assess whether a BART- or RP-eligible source is reasonably anticipated to 

cause or contribute to any visibility impairment in a federal Class I area. According to 

the BART Guideline:

“A single source that is responsible for a 1.0 deciview change or more should be 

considered to “cause” visibility impairment; a source that causes less than a 1.0 

deciview change may still contribute to visibility impairment and thus be subject to 

BART. As a general matter, any threshold that you use for determining whether a source 

“contributes” to visibility impairment should not be higher than 0.5 deciviews.
8

As recommended in the BART Guideline, if a single source causes a 0.5 deciview or 

greater change from natural background, then that source is determined to be reasonably 

anticipated to contribute to any visibility impairment in a Class I area and will be subject 

to BART. For this single source analysis, the Subject-to-BART or RP prioritization 

modeling will not consider the frequency, magnitude, and duration of impairment.

Outside the work of this protocol, DOH will make a preliminary determination whether 

an eligible source is Subject-to-BART or should be prioritized for RP evaluation by 

following the screening methodology described in Section III, Option 2 of the BART 

Guideline. Based on a 0.5 deciview threshold, if a source calculated Q/D < 10, an 

eligible source would be exempt from further BART analysis or will not be prioritized for 

RP evaluation at least for the first RH planning period.

Q = Potential Emissions (tons/year) of SO2+NOx+PM10 from all eligible units 

within a facility

D = Distance (kilometers) between the eligible source and the boundary of the 

nearest Class I area

Generally, if a source is a smaller emitter or is located far from a Class I area, and its Q/D 

is less than 10, the source is not expected to contribute to visibility impairment at a Class 

I area.

2.13.2 Implementing Subject-to-BART/RP Prioritization Modeling Analysis

Alpine Geophysics, LLC (Alpine) will perform the Subject-to-BART modeling in 

accordance with this modeling protocol for the BART-eligible sources and the initial set 

of identified RP-eligible sources. Figure 2 is a flow chart of how individual facilities will 

be evaluated for its eligible emission units. There could be cases where a facility could 

have both BART-eligible and RP-eligible emission units.

Consideration for allowing a more refined Subject-to-BART analysis in the event a 

source is found to be Subject-to-BART or RP evaluation is not a part of this protocol’s 

scope.
9

Figure 6. Subject-to-BART/ RP Prioritization Flowchart

Note: “Pre-BART” units = identified “RP-eligible” units.
10

3 VISIBILITY MODELING SYSTEM

In general, the Subject-to-BART/RP prioritization modeling using the CALPUFF suite of 

programs will follow the procedures and recommendations outlined in two documents: 

the IWAQM (Interagency Workgroup on Air Quality Models) and the FLAG (Federal 

Land Managers Air Quality Related Values Workgroup) reports (EPA 1998, FLAG 

2008). Tables listing the modeling parameters for each CALPUFF module are located in 

the Appendices. 

The specific CALPUFF programs and their version numbers that will be used in both the 

Subject-to-BART/RP prioritization modeling are presented in Table 4. The CALPUFF 

modeling system is the regulatory approved versions except for the CALPOST processor. 

CALPOST version 6.221 will be used to enable this application to use the most up-todate FLAG (6-29-2008) guidance. The CALMET model will be run using the August 20, 

2009, ―EPA-FLM Recommended CALMET input File Values.‖

The CALMET meteorological domain, as described below, covers the entire Hawaiian 

Island chain. 

Table 17. CALPUFF Modeling System

Program Version Level

CALMET 5.8 060911

CALPUFF 5.8 060911

CALPOST 6.221 080724

POSTUTIL 1.56 070627

3.1 CALMET

Input requirements for the CALMET model include various meteorological and 

geophysical data sets, and a control input file with appropriate settings. Required 

meteorological data include surface, upper-air, and precipitation observations, and 

mesoscale model output data fields. Specifics of these data are provided below.

Geophysical input data include terrain elevation and land-use data. Though CALMET 

may be run with mesoscale model meteorological data, alone (i.e., no observations), the 

EPA modeling guideline recommends ―blending‖ observations with the mesoscale model 

fields. Therefore, Hawaii will include observations in a blended approach. 

The dispersion modeling will use CALMET wind fields for the calendar year 2005. 

While a minimum of three years of mesoscale meteorological model output is 

recommended for conducting the initial step in the first-level analysis, meteorological 

data sets for more than 2005 were unavailable for the modeling domain under this 

contract. The CALMET wind fields for 2005 will cover the entire island chain and 
11

sufficiently encompass both Class I areas within 300km of any BART- and RP- eligible 

facility included in this analysis. 

Land Use Processing: CALMET requires both land use/land cover and terrain height 

data for the application region. These are generated using the CALMET CTGPROC, 

TERREL and MAKEGEO as described below.

TERREL is the terrain pre-processor that averages terrain features to the 

modeling grid resolution; TERREL constructs the basic properties of the gridded 

domain and defines the coordinates upon which meteorological data are stored. 

Key parameters include specification of grid type, location, resolution and terrain 

elevation. 

CTGPROC computes the fractional land use for the modeling grid resolution. 

Land use characteristics for each grid cell are assigned using CTGPROC. The 

primary variable adjustment associated with CTGRPOC is selection of an 

appropriate land use database. Version 2.0 of the North American Land Cover 

Characteristics database will be used. 

MAKEGEO is the final pre-processor that combines the terrain and land use data 

for input to CALMET. Generating the appropriate MAKEGEO.INP control file 

requires only minimal alteration of the default assignments. Key modifications 

include specifying domain attributes and ensuring input files are correctly 

referenced.

Terrain: CALMET requires both terrain height and land use/land cover for the 

application region. These are generated using the CALMET CTGPROC, TERREL and 

MAKEGEO processors. The terrain data will be created using the TERREL (version 

3.684 level 070327) processor and the Shuttle Radar Topography Mission (SRTM)-

GTOPO 30 second (~1 km) resolution dataset. 

Land Use: The land use data set was created using the Composite Theme Grid 

CTGROC processor (version 2.682, level 070430) and the United States Geological 

Survey (USGS) Global Land Cover Characterization (GLCC) version 2.0 database. The 

GLCC database is available at 30 second (~1km) resolution. 

Observed Data Processing: Observation data from both available surface and upper-air 

stations in the modeling will be used. The surface data will be taken from the NOAA 

Techniques Development Lab (TDL) database and the upper-air data from the NOAA 

Forecast Systems Lab (FSL) database.

CALMET Model Options: The options used in the CALMM5 processing will generally 

follow the FLAG (FLAG, 2008) and IWAQM (EPA, 1998) guidance. Specific 

CALMET input values will be updated using the August 20, 2009, ―EPA-FLM 

Recommended Input File Values.‖ 
12

3.3.1 Meteorological Data

3.3.1.1 Mesoscale Model Data

Alpine was contracted to apply the Pennsylvania State University/National Center for 

Atmospheric Research (PSU/NCAR) Mesoscale Model (MM5) (Dudhia, 1993; Grell et 

al., 1994: UCAR, 2003a) for calendar year 2005 to provide data for the 

CALMET/CALPUFF modeling as well as for additional modeling and analytical studies 

conducted for the Hawaiian Islands. 

A complete description of the modeling approach and a statistical evaluation of the 

annual MM5 model are described in McNally and Wilkinson, 2008. A brief description 

of the modeling approach follows:

Model Selection: The publicly available non-hydrostatic version of MM5 (version 

3.7.4) was used for this modeling study. Preprocessor programs of the MM5 modeling 

system including TERRAIN, REGRID, LITTLE_R, and INTERPF (UCAR, 2003b) were 

used to develop model inputs.

Horizontal Domain Definition: The computational grid is presented in Figures 3 and 4. 

The outer 36km domain (D01) has 165 x 129 grid cells, selected to provide boundary 

conditions to the 12km domain. The mid-scale 12km domain (D02) has 226 x 226 grid 

cells, selected to maximize the coverage of the western region around the Hawaiian 

Islands. The 4km domain (D03) has 208 x 154 grid cells, selected to cover the Island 

chain with a greater than 100km buffer around the perimeter. The projection was in 

Lambert Conformal Coordinates (LCC) with a grid projection pole of 23o, -158o with true 

latitudes of 30oand 60o.

Vertical Domain Definition: The MM5 modeling was based on 34 vertical layers with 

an approximately 38 meter deep surface layer. The MM5 vertical domain is presented in 

both sigma and height coordinates in Table 5.

Topographic Inputs: Topographic information for the MM5 was developed using the 

NCAR and the United States Geological Survey (USGS) terrain databases. The grid will 

be based on the 2 min (~4 km) Geophysical Data Center global data interpolated to the 

model grid using a Cressman-type objective analysis scheme. To avoid interpolating 

elevated terrain over water bodies, after the terrain databases are interpolated onto the 

MM5 grid, the NCAR graphic water body database is used to correct elevations over 

water bodies. 

Vegetation Type and Land Use Inputs: Vegetation type and land use information were 

developed using the most recently released PSU/NCAR databases provided with the 

MM5 distribution. Standard MM5 surface characteristics corresponding to each land use 

category were employed.
13

Atmospheric Data Inputs: Two choices for the first guess meteorological fields were 

available: (1) the NCEP Global Tropospheric Final Analysis; and (2) the NCAR/NCEP 

Reanalysis Project (NNRP) data (UCAR, 2007). Though the NNRP data were used in a 

previous study of Hawaii (Yang and Chen, 2003; Yang et. al. 2005), the NNRP data have 

relatively coarse 2.5x2.5 degree resolution whereas the NCEP Final Analysis data have 

much higher 1x1 degree resolution. For the current modeling study, the NCEP Final 

Analysis dataset was used.

Available surface and upper-air observations were used in the objective analyses. These 

data were incorporated into the analyses datasets following the procedures outlined by 

Stauffer and Seaman (1990) and were quality-inspected by MM5 pre-processors using 

automated gross-error checks and ―buddy‖ checks. In addition, radiosonde soundings 

were subjected to vertical consistency checks. The synoptic-scale data used for the 

initialization (and in the analysis nudging discussed later) were obtained from the 

conventional National Weather Service (NWS) twice-daily radiosondes (UCAR, 2008a) 

and 3-hr NWS surface observations (UCAR, 2008b).

Water Temperature Inputs: The water temperature data will be taken from the NCEP 

RTG global one-half degree analysis (Gemmill et al., 2007; NCEP, 2008).

FDDA Data Assimilation: This simulation will use both analysis and observation based 

nudging. For the 36km and 12km grids analysis nudging coefficients of 2.5x10-4and 

1.0x10-4 were used for winds and temperature, respectively. For the 4km grid analysis 

nudging to observed winds from the NCAR DS472.0 TDL dataset (UCAR, 2008c) was 

employed with a coefficient of 1.0x10-3. 

 

Physics Options: Sensitivity testing using approximately two-week long episodes in 

early February 2005 and early July 2005 were performed to help select the best 

performing physics options (McNally, 2008). Although the performance was similar for 

the different options, the best performing configuration was the version used by the 

University of Hawaii and the National Weather Service (Yang and Chen, 2003; Yang et. 

al. 2005. Namely:

Warm Rain;

Grell Cumulus Parameterization;

MRF PBL;

NOAH Land Surface Model; and

Cloud Radiation.

Application Methodology: The MM5 model was executed in 5-day blocks initialized at 

12Z every 4 days with a 90 second time step. Model results were output every 60 

minutes and output files are split at 24 hour intervals. Twelve (12) hours of spin-up were 

included in each 4-day block before the data were used in the atmospheric simulations 

and subsequent model performance evaluation.
14

3.3.1.2 CALMM5 Processing

The final step in the meteorological processing is to reformat the MM5 data using the 

CALMM5 processor. CALMM5 will be run for the 4km MM5 nest to extract the whole 

region, less a few grid cells around the perimeter that are contaminated by MM5 

boundary artifacts.
15

Table 18. MM5 Vertical Domain Specification.

k(MM5) sigma pressure.(mb) height(m) depth(m)

34 0.000 10000 15674 2004

33 0.050 14500 13670 1585

32 0.100 19000 12085 1321

31 0.150 23500 10764 1139

30 0.200 28000 9625 1004

29 0.250 32500 8621 900

28 0.300 37000 7720 817

27 0.350 41500 6903 750

26 0.400 46000 6153 693

25 0.450 50500 5461 645

24 0.500 55000 4816 604

23 0.550 59500 4212 568

22 0.600 64000 3644 536

21 0.650 68500 3108 508

20 0.700 73000 2600 388

19 0.740 76600 2212 282

18 0.770 79300 1930 274

17 0.800 82000 1657 178

16 0.820 83800 1478 175

15 0.840 85600 1303 172

14 0.860 87400 1130 169

13 0.880 89200 961 167

12 0.900 91000 794 82

11 0.910 91900 712 82

10 0.920 92800 631 81

9 0.930 93700 550 80

8 0.940 94600 469 80

7 0.950 95500 389 79

6 0.960 96400 310 78

5 0.970 97300 232 78

4 0.980 98200 154 39

3 0.985 98650 115 39

2 0.990 99100 77 38

1 0.995 99550 38 38

0 1.000 100000 0 0
16

Figure 7: Modeled 36/12/4 km MM5 Domain.
17

Figure 8. Modeled 4km MM5 Domain.

3.3.2 CALMET Control File Settings

These CALMET wind fields will be used by all BART- and RP-eligible sources within 

the modeling domain for both the Subject-to-BART and RP prioritization modeling. The 

wind fields will be provided by Alpine using CALMET. Details of the parameter settings 

in CALMET are provided in Appendix A to this document; however, the major 

assumptions are summarized below. 

5) The initial-guess fields will use the 4-km MM5 outputs, forecast from hours 13 –

108 of each 5 day run segment, January – December, 2005.

6) Both the Subject-to-BART and RP prioritization modeling will utilize the wind 

fields at 4km resolution.
18

7) There will be 10 vertical layers with face heights of 0, 20, 40, 65, 120, 200, 400, 

700, 1200, 2200, and 4000 meters.

8) CALMET will be run using surface, buoy and upper air observations. Upper air 

data from the LIH (WBAN 22536) site on Kauai and the ITO (WBAN 21504) site 

on Hilo will be used. Buoy data collected from the National Data Buoy Center 

for sites 51001, 51002, 51003, 51004, KLIH1 and OOUHI will be used and 

surface data from 14 surface stations in the TDL DS472.0 dataset will be used.

3.4 CALPUFF

The CALPUFF modeling will use Version 5.8. This protocol generally follows the 

recommendation of the IWAQM and FLAG guidance documents. Details of the 

parameter settings in CALPUFF are provided in Appendix B to this document; however, 

the major features are summarized below: 

8) The calendar year 2005 CALMET input file will be developed by Alpine 

Geophysics, LLC and be provided as input-ready to CALPUFF.

9) The Subject-to-BART and RP prioritization modeling will examine the visibility 

impairment on Class I areas within 300km of each single source or facility. The 

computational modeling domain will be sufficient to include all Class I areas 

within a 300km radius of a source.

10) Pasquill-Gifford Dispersion coefficients will be used.

11) MESOPUFF-II chemistry algorithm will be used.

12) Puff splitting will not be used, following the recommendations of the FLMs.

13) Source elevations will be based on the same terrain files as the receptor 

elevations.

14) The CALUTIL will be used to perform HNO3/NO3 repartitioning using the 

parameters in Appendix B.

CALPUFF will be applied separately for each Class I Area (Haleakala National Park and 

Hawaii Volcanoes National Park) on the island of Maui and Hawaii. 

3.4.1 Emissions

Section 2.4 above presents the emissions and stack data that is required from the 

facilities. This section only discusses the emissions estimates needed in CALPUFF. 

Primary emission, species will include available input species of PM, SO2, SO4, and NOx; 

and the additional modeled species HNO3 and NO3. SO2 emissions will be reviewed to 

ensure ―double-counting‖ is avoided.

The primary PM species will be treated as follows:

Filterable:

Elemental Carbon (EC) (<2.5 μm)
19

PM Fine (PMF) (<2.5 μm)

PM Coarse (PMC) (2.5 – 10 μm)

Condensable:

Organic Carbon (SOA)

Inorganic Aerosol (SO4)

The condensable fraction will be treated as primary emissions in the CALPUFF input file 

and assumed to be 100% in the PM2.5 fraction (see NPS Web site listed below).

Facility specific speciation emissions data will be used when provided. The following 

size distributions will be used in CALPUFF:

Pollutant Mean diameter Standard deviation

SO4, NO3, PMF, SOA, EC 0.48 2

PMC 2.5 5

3.4.2 Ozone Background

Background ozone will be assumed at an annual constant level of 40 ppb.

3.4.3 Ammonia Background

Because the Hawaiian Island chain does not fit easily within the three land use values 

proposed in IWAQM (10 parts per billion (ppb) for grasslands, 0.5 ppb for forest, and 1 

ppb for arid lands), ammonia background concentrations will be determined in 

consultation with the participating FLMs. A review of the ammonia emissions inventory 

for Hawaii in comparison with mainland agricultural areas indicate a background of 1.5 

ppb to be representative based on agricultural and livestock activity.

3.2.4 Receptor Locations

Predicted visibility impacts will be computed at two sets of receptors; a set of receptor 

locations for the Class I Areas within the domain which are available for download from

the National Park Service Web site http://www2.nature.nps.gov/air/Maps/Receptors/index.cfm, 

Source and receptor elevations will be based on actual elevations. The elevations for the 

grid points will be taken from the CALMET modeling. 

3.5 CALPOST AND VISIBILITY POST-PROCESSING

The CALPOST processing will use Version 6.221 and POSTUTIL Version 1.56. Details 

of the parameter settings in CALPOST and POSTUTIL are provided in Appendix C, with 

some of the major features summarized below for calculating visibility impairment: 
20

7) For the visibility calculation, Method 8, Mode 5 (MVISBK=8, M8_MODE=5) 

will be employed. This method uses monthly average relative humidity and 

f(RH) values for each Class I area, which are based on data from FLAG (FLAG 

2008).

8) Particulate species for the visibility analysis will include SO4, NO3, EC, OC, 

PMF, and PMC, as reported in the CALPOST output files.

9) POSTUTIL will not be used to speciate modeled PM10 concentrations, as PM10 is 

speciated into its components (PMF, PMC, SOA, EC, SO4) and entered as 

primary emissions in CALPUFF. However, HNO3/NO3 partition option in

POSTUTIL will be used 

10) Natural background extinction calculations will use the 20% best natural days for 

each Class I area in the modeling domain. Tables providing the monthly f(RH) 

and 20% best days coefficients are provided in Appendix C, and are based on data 

from FLAG (FLAG 2008).

1. The 98th percentile value will be calculated for all BART- and RP-eligible 

sources at each mandatory Class I area. 

11) The contribution threshold has the implied level of precision equal to the level of 

precision reported by CALPOST. Therefore, the 98th percentile value will be 

reported to three decimal places.

4 INTERPRETATION OF RESULTS

The change in visibility impairment for the Subject-to-BART/RP prioritization modeling 

is based on the increase in the haze index from a BART- or RP-eligible source relative to 

natural background, defined as the 20% best natural visibility days for each Class I area. 

This definition of natural background is consistent with the intent of the BART Guideline 

(Federal Register Vol. 70, No. 128, pf 39125). Although the use of the average 20% 

natural visibility days was subsequently allowed (EPA 2006, Memorandum from Joseph 

W. Paisie to Kay Prince dated July 19, 2006. Subject Regional Haze Regulations and 

Guidelines for BART Determinations), the 20% best natural visibility days is being used 

due to the availability of one year’s worth of meteorological data. Three years worth of 

meteorological data is recommended by guidance.

The U.S. EPA recommends using the 98th percentile value from the distribution of values 

containing the highest modeled delta-deciview ( dv) value for each day of the simulation 

from all modeled receptors at a given Class I area. The 98th percentile dv value will be 

determined with the 8th highest value for the year modeled (2005).

This 8th highest value will be compared to the contribution threshold ( dv≥0.5 dv). If 

there are more than seven (7) days with values greater than the contribution threshold in 

any single meteorological year for any of the Class I areas, then the source is considered 

Subject-to-BART or will be considered for RP control evaluation in the first RH planning 

period.
21

5 REFERENCES

40 CFR Part 51, Appendix W. Guidelines on Air Quality Models

Dudhia, J., 1993. ―A Non-hydrostatic Version of the Penn State/NCAR Mesoscale 

Model: Validation Tests and Simulation of an Atlantic Cyclone and Cold Front‖, Mon. 

Wea. Rev., Vol. 121. pp. 1493-1513.

EPA (U.S. Environmental Protection Agency) 1998. Interagency Workgroup on Air 

Quality Modeling (IWAQM) Phase 2 Summary Report and Recommendations for 

Modeling Long Range Transport Impacts, EPA-454/R-98-019, December 1998.

EPA 2003. Guidance for Estimating Natural Visibility Conditions under the Regional 

Haze Rule, EPA-454/B-03-005, September, 2003.

Federal Register, Vol. 70, No. 128. Regional Haze Regulations and Guidelines for Best 

Available Retrofit Technology (BART) Determinations. pp. 39104 – 30172, July 6, 2005.

FLAG 2000. Federal Land Managers’ Air Quality Related Values Workgroup (FLAG) 

2000. Phase I Report. December 2000.

FLAG 2008. Federal Land Managers’ Air Quality Related Values Workgroup (FLAG) 

2008. Phase I Report—Revised. June 27, 2008 draft.

Gemmill, William, Bert Katz and Xu Li, 2007. Daily Real-Time Global Sea Surface 

Temperature - High Resolution Analysis at NOAA/NCEP. NOAA/NWS/NCEP/MMAB 

Office Note Nr. 260, 39 pp.

Grell, G. A., J. Dudhia, and D. R. Stauffer. 1994. A Description of the Fifth Generation 

Penn State/NCAR Mesoscale Model (MM5). NCAR Tech. Note, NCAR TN-398-STR, 

138 pp.

McNally, D.E., 2008,‖MM5 Sensitivity to Alternate Configurations over the Hawaiian 

Islands‖, prepared for the Hawaii State Department of Health, Environmental 

Management Division, Clean Air Branch, prepared by Alpine Geophysics, LLC, Arvada, 

CO.

McNally, D.E, and J.G. Wilkinson 2008, ‖MM5 Application for 2005 Over the 

Hawaiian Islands‖, prepared for the Hawaii State Department of Health, Environmental 

Management Division, Clean Air Branch, prepared by Alpine Geophysics, LLC, Arvada, 

CO.

NCEP, 2008. Real-time, global, sea surface temperature (RTG_SST) analysis. 10-July. 

polar.ncep.noaa.gov/sst/oper/Welcome.html
22

NOAA, 2009. Mauna Loa Observatory. 29-April, mlo.noaa.gov/livedata/livedata.html.

UCAR, 2003a. MM5 Community Model: Current Release. 09-April. 

www.mmm.ucar.edu/mm5/overviewCurrent.html

UCAR, 2003b. MM5 Community Model: MM5 Modeling System Overview. 10-April. 

www.mmm.ucar.edu/mm5/overviewProgram.html

UCAR, 2008a. ds353.4 Home Page. NCEP ADP Global Upper Air Observation Subsets, 

January 1973 – continuing. CISL Research Data Archive. dss.ucar.edu/datasets/ds353.4.

UCAR, 2008b. ds464.0 Home Page. NCEP ADP Global Surface Observations, February 

1975 – continuing. CISL Research Data Archive. dss.ucar.edu/datasets/ds464.0.

UCAR, 2008c. ds472.0 Home Page. TDL U.S. and Canada Surface Hourly 

Observations, daily 1976Dec – cont. CISL Research Data Archive. 

dss.ucar.edu.datasets.ds472.0.
23

Appendix A CALMET Parameter Values
24

Table A-1. CALMET Parameter Values 

The CALMET model will be run using the August 20, 2009, ―EPA-FLM Recommended 

CALMET input File Values.‖

Input 

Group Variable Description Default Value Recommended Value

0 DIADAT

Input file: preprocessed surface temperature data 

(DIAG.DAT) User Defined

0 GEODAT Input file: Geophysical data (GEO.DAT) User Defined User Define

0 LCFILES Convert file name to lower case User Defined

0 METDAT Output file (CALMET.DAT) User Defined

0 METLST Output file (CALMET.LST) User Defined

0 MM4DAT Input file: MM4 data (MM4.DAT) User Defined

0 NOWSTA Input files: Names of NOWSTA overwater stations User Defined 6

0 NUSTA Number of upper air data sites User Defined 2

0 PACDAT Output file: in Mesopuff II format (PACOUT.DAT) User Defined

0 PRCDAT Input file: Precipitation data (PRECIP.DAT) User Defined

0 PRGDAT Input file: CSUMM prognostic wind data (PROG.DAT) User Defined

0 SEADAT

Input files: Names of NOWSTA overwater stations 

(SEAn.DAT) User Defined

0 SRFDAT Input file: Surface data (SURF.DAT) User Defined

0 TSTFRD Output file (TEST.FRD) User Defined

0 TSTKIN Output file (TEST.KIN) User Defined

0 TSTOUT Output file (TEST.OUT) User Defined

0 TSTPRT Output file (TEST.PRT) User Defined

0 TSTSLP Output file (TEST.SLP) User Defined

0 UPDAT Input files: Names of NUSTA upper air data files (UPn.DAT) UPn.DAT

0 WTDAT Input file: Terrain weighting factors (WT.DAT) User Defined

1 CLDDAT Input file: Cloud data (CLOUD.DAT) User Defined Not used

1 IBDY Beginning day User Defined

1 IBHR Beginning hour User Defined

1 IBMO Beginning month User Defined

1 IBTZ Base time zone User Defined 0

1 IBYR Beginning year User Defined

1 IRLG Number of hours to simulate User Defined User Define

1 IRTYPE Output file type to create (must be 1 for CALPUFF) 1 1

1 ITEST Flag to stop run after Setup Phase 2 2

1 LCALGRD Are w-components and temperature needed? T T

1 MREG Text to options to conrim they conform to regulatory options User Defined 1

2 PMAP Map Projection UTM LCC

2 FEAST False easting at the projection origin 0.0 0.0

2 FNORTH False northing at the projection origin 0.0 0.0

2 DATUM WGS-G, NWS-27, NWS-84, ESR-S,... WGS-G

2 DGRIDKM Grid spacing User Defined 4

2 UTMHEM Hemisphere for UTM projection N N

2 IUTMZN UTM Zone User Defined User Define

2 LLCONF

When using Lambert Conformal map coordinates - rotate 

winds from true north to map north? F F

2 NX Number of east-west grid cells User Defined 199

2 NY Number of north-south grid cells User Defined 145

2 NZ Number of vertical layers User Defined 10

2 RLAT0 Latitude used if LLCONF = T User Defined 23.0N

2 RLON0 Longitude used if LLCONF = T User Defined 158.0W

2 XLAT1 Latitude of 1st standard parallel User Defined 30

2 XLAT2 Latitude of 2nd standard parallel User Defined 60

2 XORIGKM Southwest grid cell X coordinate User Defined -360
25

Input 

Group Variable Description Default Value Recommended Value

2 YORIGKM Southwest grid cell Y coordinate User Defined -588

2 ZFACE Vertical cell face heights (NZ+1 values) User Defined

0,20,40,80,160,320,640,

1200,2000,3000,4000

3 IFORMO Format of unformatted file (1 for CALPUFF) 1 1

3 LSAVE Save met. data fields in an unformatted file? T T

4 ICLOUD Is cloud data to be input as gridded fields? (0 = No) 0 0

4 IFORMC Format of cloud data (2 = formatted) 2 2

4 IFORMP Format of precipitation data (2 = formatted) 2 2

4 IFORMS Format of surface data (2 = formatted) 2 2

4 NOOBS Use or non-use of surface, overwater, upper observations User Defined 0

4 NPSTA Number of stations in PRECIP.DAT User Defined 14

4 NSSTA Number of stations in SURF.DAT file User Defined 14

5 ALPHA Empirical factor triggering kinematic effects 0.1 0.1

5 BIAS Surface/upper-air weighting factors (NZ values) NZ*0 NZ*0

5 CRITFN Critical Froude number 1 1

5 DIVLIM Maximum acceptable divergence 5.00E-06 5.00E-06

5 FEXTR2

Multiplicative scaling factor for extrap surface obs to uppr 

layrs NZ*0.0 NZ*0.0

5 ICALM Extrapolate surface calms to upper layers? (0 = No) 0 0

5 IDIOPT1 Compute temperatures from observations (0 = True) 0 0

5 IDIOPT2 Compute domain-average lapse rates? (0 = True) 0 0

5 IDIOPT3 Compute internally inital guess winds? (0 = True) 0 0

5 IDIOPT4 Read surface winds from SURF.DAT? ( 0 = True) 0 0

5 IDIOPT5 Read aloft winds from UPn.DAT? (0 = True) 0 0

5 IEXTRP

Extrapolate surface winds to upper layers? (-4 = use 

similarity theory and ignore layer 1 of upper air station data) -4 -4

5 IFRADJ Adjust winds using Froude number effects? (1 = Yes) 1 1

5 IKINE Adjust winds using kinematic effects? (1 = Yes) 0 0

5 IOBR Use O’Brien procedure for vertical winds? (0 = No) 0 0

5 IPROG Using prognostic or MM-FDDA data? (0 = No) 0 14

5 ISLOPE Compute slope flows? (1 = Yes) 1 1

5 ISTEPPG Timestep (hours) of the prognostic model input data 1 1

5 ISURFT

Surface station to use for surface temperature (between 1 

and NSSTA) User Defined 9

5 IUPT Station for lapse rates (between 1 and NUSTA) User Defined 1

5 IUPWND

Upper air station for domain winds (-1 = 1/r**2 interpolation 

of all stations) -1 -1

5 IWFCOD Generate winds by diagnostic wind module? (1 = Yes) 1 1

5 KBAR Level (1 to NZ) up to which barriers apply NZ 10

5 LLBREZE Use Lake Breeze module F F

5 LVARY Use varying radius to develop surface winds? F F

5 METBXID Station IDs in the region User Defined

5 NBAR Number of Barriers to interpolation User Defined 0

5 NBOX Number of Lake Breeze regions User Defined 0

5 NINTR2 Max number of stations for interpolations (NA values) 99 99

5 NITER Max number of passes in divergence minimization 50 50

5 NLB Number of stations in region User Defined 0

5 NSMTH Number of passes in smoothing (NZ values) 2, 4*(NZ-1) 2,4*(NZ-1)

5 R1 Relative weight at surface of Step 1 field and obs User Defined 50

5 R2 Relative weight aloft of Step 1 field and obs User Defined 100

5 RMAX1 Max surface over-land extrapolation radius (km) User Defined 100

5 RMAX2 Max aloft over-land extrapolation radius (km) User Defined 200

5 RMAX3 Maximum over-water extrapolation radius (km) User Defined 200

5 RMIN Minimum extrapolation radius (km) 0.1 0.1
26

Input 

Group Variable Description Default Value Recommended Value

5 RMIN2

Distance (km) around an upper air site where vertical 

extrapolation is excluded (Set to -1 if IEXTRP = ±4) 4 -1

5 RPROG Weighting factor for CSUMM prognostic wind data User Defined 0

5 TERRAD Radius of influence of terrain features (km) User Defined 15

5 XBBAR X coordinate of Beginning of each barrier User Defined 0

5 XBCST X Point defining the coastline (staight line) User Defined 0

5 XEBAR X coordinate of Ending of each barrier User Defined 0

5 XECST X Point User Defined 0

5 XG1 X Grid line 1 defining region of interest User Defined 0

5 XG2 X Grid line 2 User Defined 0

5 YBBAR Y coordinate of Beginning of each barrier User Defined 0

5 YBCST Y Point User Defined 0

5 YEBAR Y coordinate of Ending of each barrier User Defined 0

5 YECST Y Point User Defined 0

5 YG1 Y Grid line 1 User Defined 0

5 YG2 Y Grid Line 2 User Defined 0

5 ZUPT Depth of domain-average lapse rate (m) 200 200

5 ZUPWND Bottom and top of layer for 1st guess winds (m) 1, 1000 1.,1000.

6 CONSTB Neutral mixing height B constant 1.41 1.41

6 CONSTE Convective mixing height E constant 0.15 0.15

6 CONSTN Stable mixing height N constant 2400 2400

6 CONSTW Over-water mixing height W constant 0.16 0.16

6 CUTP Minimum cut off precip rate (mm/hr) 0.01 0.01

6 DPTMIN Minimum capping potential temperature lapse rate 0.001 0.001

6 DSHELF Coastal/shallow water length scale 0 0

6 DZZI Depth for computing capping lapse rate (m) 200 200

6 FCORIOL Absolute value of Coriolis parameter 1.00E-04 1.00E-04

6 HAFANG Half-angle for looking upwind (degrees) 30 30

6 IAVET Conduct spatial averaging of temperature? (1 = True) 1 1

6 IAVEZI Spatial averaging of mixing heights? (1 = True) 1 1

6 ICOARE Overwater surface fluxes method and parameters 10 0

6 ICOOL COARE cool skin layer computation 0 0

6 ILEVZI Layer to use in upwind averaging (between 1 and NZ) 1 1

6 ILUOC3D Land use category ocean in 3D.DAT datasets 16 16

6 IMIXH Method to compute the convective mixing height -1 -1

6 IRAD Form of temperature interpolation (1 = 1/r) 1 1

6 IRHPROG

3D relative humidity from observations or from prognostic 

data 0 1

6 ITPROG 3D temps from obs or from prognostic data? 0 2

6 ITWPROG

Option for overwater lapse rates used in convective mixing 

height growth 0 0

6 IWARM COARE warm layer computation 0 0

6 JWAT1 Beginning landuse type defining water 999 55

6 JWAT2 Ending landuse type defining water 999 55

6 MNMDAV Max averaging radius (number of grid cells) 1 1

6 NFLAGP Method for precipitation interpolation (2 = 1/r**2) 2 2

6 NUMTS Max number of stations in temperature interpolations 5 5

6 SIGMAP Precip radius for interpolations (km) 100 100

6 TGDEFA Default over-water capping lapse rate (K/m) -0.0045 -0.0045

6 TGDEFB Default over-water mixed layer lapse rate (K/m) -0.0098 -0.0098

6 THRESHL

Threshold buoyancy flux required to sustain convective 

mixing height growth overland 0.05 0.0

6 THRESHW

Threshold buoyancy flux required to sustain convective 

mixing height growth overwater 0.05 0.05

6 TRADKM Radius of temperature interpolation (km) 500 500
27

Input 

Group Variable Description Default Value Recommended Value

6 ZIMAX Maximum over-land mixing height (m) 3000 3000

6 ZIMAXW Maximum over-water mixing heigh (m) 3000 3000

6 ZIMIN Minimum over-land mixing height (m) 50 50

6 ZIMINW Minimum over-water mixing height (m) 50 50
28

Appendix B CALPUFF Parameter Values
29

Table B-1. CALPUFF Parameter Values

CALPUFF input settings.

Table B-1. CALPUFF Parameter Values

Input 

Group

Group 

Description Variable Description Default Valuea Recommended Value

1 Run Control METRUN Do we run all periods (1) or a subset (0)? 0 0

1 IBYR Beginning year User Defined 2005

1 IBMO Beginning month User Defined 1

1 IBDY Beginning day User Defined 1

1 IBHR Beginning hour User Defined 1

1 IRLG Length of run (hours) User Defined 8760

1 XBTZ Base Time Zone User Defined 0

1 NSECDT Length of modeling time step (seconds) 3600 3600

1 NSPEC

Number of species modeled (for MESOPUFF II 

chemistry) 5 8

1 NSE Number of species emitted 3 5

1 ITEST Flag to stop run after Setup Phase 2 2

1 MRESTART 

Restart options (0 = no restart) allows splitting 

runs into smaller segments 0 0

1 NRESPD Number of periods in Restart 0 0

1 METFM 

Format of input meteorology (1 = CALMET, 2 = 

ISC) 1 1

1 AVET

Averaging time lateral dispersion parameters 

(minutes) 60 60

1 PGTIME PG Averaging time 60 60

2 Tech Options MGAUSS Near-field vertical distribution (1 = Gaussian) 1 1

2 MCTADJ 

Terrain adjustments to plume path (3 = Plume 

path) 3 3

2 MCTSG 

Do we have subgrid hills? (0 = No) allows 

CTDM-like treatment for subgrid scale hills 0 0

2 MSLUG Near-field puff treatment (0 = No slugs) 0 0

2 MTRANS Model transitional plume rise? (1 = Yes) 1 1

2 MTIP Treat stack tip downwash? (1 = Yes) 1 1

2 MBDW Method to simulate downwash not used
30

Table B-1. CALPUFF Parameter Values

Input 

Group

Group 

Description Variable Description Default Valuea Recommended Value

(1=ISC,2=PRIME)

2 MSHEAR Treat vertical wind shear? (0 = No) 0 0

2 MSPLIT Allow puffs to split? (0 = No) 0 0

2 MCHEM MESOPUFF-II Chemistry? (1 = Yes) 1 1

2 MAQCHEM Aqueous phase transformation 0 0

2 MWET Model wet deposition? (1 = Yes) 1 1

2 MDRY Model dry deposition? (1 = Yes) 1 1

2 MTILT Plume Tilt (gravitational settling) 0 0

2 MDISP

Method for dispersion coefficients 

(2=micromet,3 = PG) 3 3

2 MTURBVW

Turbulence characterization? (Only if MDISP = 

1 or 5) 3 3

2 MDISP2 Backup coefficients (Only if MDISP = 1 or 5) 3 3

2 MTAULY Method for Sigma y Lagrangian timescale 0 0

2 MTAUADV

Method for Advective-Decay timescale for 

Turbulence 0 0

2 MCTURB

Method to compute sigma v,w using micromet 

variables 1 1

2 MROUGH Adjust PG for surface roughness? (0 = No) 0 0

2 MPARTL Model partial plume penetration? (0 = No) 1 1

2 MTINV

Elevated inversion strength (0 = compute from 

data) 0 0

2 MPDF Use PDF for convective dispersion? (0 = No) 0 0

2 MSGTIBL

Use TIBL module? (0 = No) allows treatment of 

subgrid scale coastal areas 0 0

2 MBCON Boundary conditions modeled 0 0

2 MFOG Configure for FOG model output 0 0

2 MREG Regulatory default checks? (1 = Yes) 1 1

3 Species List CSPECn

Names of species modeled (for MESOPUFF II 

must be SO2-SO4-NOX-HNO3-NO3) User Defined

SO2,SO4,NOX,HNO3,NO3,

NH3,PM10,PM25

3

Specie 

Names

Manner species will be modeled 

Species,Modeled,Emitted,Dry Deposited,Group

User Defined SO2,1,1,1,0

SO4,1,0,2,0

NOX,1,1,1,0

HNO3,1,0,1,0

NO3,1,0,2,0
31

Table B-1. CALPUFF Parameter Values

Input 

Group

Group 

Description Variable Description Default Valuea Recommended Value

NH3,1,0,1,0

PM10,1,1,2,0

PM25,1,1,2,0

3

Specie 

Groups Grouping of species if any User Defined N/A

3 CGRUP

3 CGRUP

4 MapProjection XLAT1 Latitude of 1st standard parallel 30N

4 XLAT2 Latitude of 2nd standard parallel 60N

4 PMAP Map Projection UTM LCC

FEAST False Easting 0.0 0.0

FNORTH False Northing 0.0 0.0

4 DATUM Datum-region WGS-G WGS-G

4 NX Number of east-west grids of input meteorology User Defined 199

4 NY 

Number of north-south grids of input 

meteorology User Defined 145

4 NZ Number of vertical layers of input meteorology User Defined 10

4 DGRIDKM Meteorology grid spacing (km) User Defined 4.

4 ZFACE Vertical cell face heights of input meteorology User Defined

0.,20.,40.,80.,160.,320.,640.,

1200.,2000.,3000.,4000.

4 XORIGKM Southwest corner (east-west) of input User Defined meteorology -360.

4 YORIGIM Southwest corner (north-south) of input User Defined meteorology -588.

4 IUTMZN UTM zone User Defined N/A (17)

4 UTMHEM Hemisphere for UTM Projection N N

4 RLAT0 Latitude of Projection Origin User Defined 23.0N

4 RLON0 Longitude of Projection Origin User Defined 158.0W

4 IBCOMP Southwest X-index of computational domain User Defined 1

4 JBCOMP Southwest Y-index of computational domain User Defined 1

4 IECOMP Northeast X-index of computational domain User Defined 199

4 JECOMP Northeast Y-index of computational domain User Defined 145

4 LSAMP Use gridded receptors? (T = Yes) F F
32

Table B-1. CALPUFF Parameter Values

Input 

Group

Group 

Description Variable Description Default Valuea Recommended Value

4 IBSAMP Southwest X-index of receptor grid User Defined

4 JBSAMP Southwest Y-index of receptor grid User Defined

4 IESAMP Northeast X-index of receptor grid User Defined

4 JESAMP Northeast Y-index of receptor grid User Defined

4 MESHDN

Gridded recpetor spacing = 

DGRIDKM/MESHDN 1 1

5 Output Options ICON Output concentrations? (1 = Yes) 1 1

5 IDRY Output dry deposition flux? (1 = Yes) 1 1

5 IWET Output west deposition flux? (1 = Yes) 1 1

5 IT2D 2D Temperature 0 0

5 IRHO 2D Density 0 0

5 IVIS Output RH for visibility calculations (1 = Yes) 1 0

5 LCOMPRS Use compression option in output? (T = Yes) T T

5 ICPRT Print concentrations? (0 = No) 0 0

5 IDPRT Print dry deposition fluxes (0 = No) 0 0

5 IWPRT Print wet deposition fluxes (0 = No) 0 0

5 ICFRQ Concentration print interval (1 = hourly) 1 1

5 IDFRQ Dry deposition flux print interval (1 = hourly) 1 1

5 IWFRQ West deposition flux print interval (1 = hourly) 1 1

5 IPRTU

Print output units (1 = g/m**3; g/m**2/s; 3 = 

ug/m3, ug/m2/s) 1 3

5 IMESG Status messages to screen? (1 = Yes) 1 2

5 LDEBUG Turn on debug tracking? (F = No) F F

5 IPFDEB First puff to track 1 1

5 NPFDEB (Number of puffs to track) (1) 1

5 NN1 (Met. Period to start output) (1) 1

5 NN2 (Met. Period to end output) (10) 10

7

Dry Dep Chem Dry Gas Dep Chemical parameters of gaseous deposition

Species,Diffusivity,Alpha 

Star,Reactivity,Mesophyll,Henry’s User Defined

SO2,0.1509,1000.,8.,0.,0.04

NOX,0.1656,1.,8,.5.,3.5

HNO3,0.1628,1.,18.,0.,8E-8

NH3,0.234,0.2,4.,0.,3.59E-4
33

Table B-1. CALPUFF Parameter Values

Input 

Group

Group 

Description Variable Description Default Valuea Recommended Value

8

Dry Dep Size Dry Part. Dep

Chemical parameters of particulate deposition 

Species,Mass Mean Diameter,Geometric Std D User Defined

SO4,0.48.,2.,

NO3,0.48,2.,

PM10,10.,0.,

PM25,2.5,0.,

9

Dry Dep Misc RCUTR Reference cuticle resistance (s/cm) 

30 30

9 RGR Reference ground resistance (s/cm) 10 10

9 REACTR Reference reactivity 8 8

9 NINT Number of particle-size intervals 9 9

9 IVEG

Vegetative state (1 = active and unstressed; 

2=active and stressed) 1 1

10

Wet Dep Wet Dep Wet deposition parameters 

Species, Liquid Precip, Frozen Precip

User Defined

SO2,3.0E-05,0.

SO4,1.0E-04,3.0E-05

NOX,0.,0.

HNO3,6.0E-05,0.0

NO3,1.0E-04,3.0E-05

NH3,8.0E-05,0.0

PM10,1.0E-04,3.0E-05

PM25,1.0E-04,3.0E-05

11

Chemistry MOZ Ozone background? (0 = constant background 

value; 1 = read from ozone.dat) 1 0

11 BCKO3 Ozone default (ppb) (Use only for missing data) 80. 12*40.

11 BCKNH3 Ammonia background (ppb) 10 1.5

11 RNITE1 Nighttime SO2 loss rate (%/hr) 0.2 0.2

11 RNITE2 Nighttime NOx loss rate (%/hr) 2 2

11 RNITE3 Nighttime HNO3 loss rate (%/hr) 2 2

11 MH2O2 H2O2 data input option 1 1

11 BCKH2O2 Monthly H2O2 concentrations 1 not used

BKPMF Fine particulate concentration 12 * 1.00 not used

OFRAC Organic fraction of Fine Particulate 2*0.15, 9*0.20, 1*0.15 not used

VCNX VOC / NOX ratio 12 * 50.00 not used

12 Dispersion SYTDEP

Horizontal size (m) to switch to time 

dependence 550 550

12 MHFTSZ Use Heffter for vertical dispersion? (0 = No) 0 0
34

Table B-1. CALPUFF Parameter Values

Input 

Group

Group 

Description Variable Description Default Valuea Recommended Value

12 JSUP PG Stability class above mixed layer 5 5

12 CONK1 Stable dispersion constant (Eq 2.7-3) 0.01 0.01

12 CONK2 Neutral dispersion constant (Eq 2.7-4) 0.1 0.1

12 TBD Transition for downwash algorithms (0.5 = ISC) 0.5 0.5

12 IURB1 Beginning urban landuse type 10 10

12 IURB2 Ending urban landuse type 19 19

12 ILANDUIN

Land use type (20 = Unirrigated agricultural 

land) 20 20

12 ZOIN Roughness length (m) 0.25 0.25

12 XLAIIN Leaf area index 3.0 3.0

12 ELEVIN Met. Station elevation (m above MSL) 0.0 0.0

12 XLATIN Met. Station North latitude (degrees) -999.0 -999.0

12 XLONIN Met. Station West longitude (degrees) -999.0 -999.0

12 ANEMHT

Anemometer height of ISC meteorological data 

(m) 10.0 10.0

12 ISIGMAV

Lateral turbulence (Not used with ISC 

meteorology) 1 1

12 IMIXCTDM Mixing heights (Not used with ISC meteorology) 0 0

12 XMXLEN Maximum slug length in units of DGRIDKM 1.0 1.0

12 XSAMLEN

Maximum puff travel distance per sampling 

step (units of DGRIDKM) 1.0 1.0

12 MXNEW Maximum number of puffs per hour 99 99

12 MXSAM Maximum sampling steps per hour 99 99

12 NCOUNT

Iterations when computing Transport Wind 

(Calmet & Profile Winds) 2 2

12 SYMIN Minimum lateral dispersion of new puff (m) 1.0 1.0

12 SZMIN Minimum vertical dispersion of new puff (m) 1.0 1.0

12 SVMIN Array of minimum lateral turbulence (m/s) 6 * 0.50,6*0.37 6 * 0.50, 6*0.50

12 SWMIN Array of minimum vertical turbulence (m/s) 

2*(0.20,0.12,0.08,0.06,0.03,

0.016)

2*(0.20,0.12,0.08,0.06,0.03,

0.016)

12 CDIV (1), (2) Divergence criterion for dw/dz (1/s) 0.0,0.0 0.0,0.0

12 WSCALM Minimum non-calm wind speed (m/s) 0.5 0.5
35

Table B-1. CALPUFF Parameter Values

Input 

Group

Group 

Description Variable Description Default Valuea Recommended Value

12 XMAXZI Maximum mixing height (m) 3000 3000

12 XMINZI Minimum mixing height (m) 50 50

12 WSCAT Upper bounds 1st 5 wind speed classes (m/s) 1.54,3.09,5.14,8. 23,10.8 1.54,3.09,5.14,8. 23,10.8

12 PLX0 Wind speed power-law exponents 0.07,0.07,0.10,0.15,0.35,0.55 0.07,0.07,0.10,0.15,0.35,0.55

12 PTGO

Potential temperature gradients PG E and F 

(deg/km) 0.020,0.035 0.020,0.035 

12 PPC Plume path coefficients (only if MCTADJ = 3) 0.5,0.5,0.5,0.5,0.35,0.35 0.5,0.5,0.5,0.5,0.35,0.35

12 SL2PF Maximum Sy/puff length 10.0 10.0

12 NSPLIT Number of puffs when puffs split 3 3

12 IRESPLIT Hours when puff are eligible to split Hour 17=1 Hour17=1

12 ZISPLIT Previous hour’s mixing height(minimum)(m) 100.0 100.0

12 ROLDMAX

Previous Max mix ht/current mix ht ratio must 

be less then this value for puff to split 0.25 0.25

12 NSPLITH Number of puffs when puffs split horizontally 5 5

12 SYSPLITH

Min sigma-y (grid cell units) of puff before horiz 

split 1.0 1.0

12 12 SHSPLITH

Min puff elongation rate per hr from wind shear 

before horiz split 2.0 2.0

12 CNSPLITH Min conc g/m3 before puff may split horizontally 1.0E-07 1.0E-07

12 EPSSLUG

Convergence criterion for slug sampling 

integration 1.00E-04 1.00E-04

12 EPSAREA

Convergence criterion for area source 

integration 1.00E-06 1.00E-06

12 DSRISE Step length for rise integration 1.0 1.0

12 HTMINBC 500.0 500.0

12 RSAMPBC 10.0 10.0

12 MDEPBC 1 1

13 Point Source NPT1 Number of point sources User Defined

13 IPTU Units of emission rates (1 = g/s) 1 3
36

Table B-1. CALPUFF Parameter Values

Input 

Group

Group 

Description Variable Description Default Valuea Recommended Value

13 NSPT1 Number of point source-species combinations 0 0

13 NPT2

Number of point sources with fully variable 

emission rates 0 0

13

Point 

Sources Point sources characteristics User Defined

14 Area Source Area Sources Area sources characteristics User Defined

15 Volume Source Volume Volume sources characteristics User Defined Sources

16 Line Source Line Sources Buoyant lines source characteristics User Defined

17 Receptors NREC Number of user defined receptors User Defined

17

Receptor 

Data Location and elevation (MSL) of receptors User Defined
37

Appendix C CALPOST Parameter Values
Table C-1. CALPOST Parameter Values

The CALPOST utility will be used for computation of visibility parameters using the parameters in this 

table.

Table C-1: CALPOST Parameter Values

Input 

Group Variable Description Default Value Recommended Value

1 ASPEC Species to process VISIB VISIB

1 METRUN Option to run subset of met file 0 1

1 BTZONE Base time zone User Defined 0

1 ILAYER

Layer/deposition code (1 = CALPUFF concentrations; -3 = wet+dry 

deposition fluxes) 1 1

1 A Scaling factor scalar 0.0 0.0

1 B Additive factor 0.0 0.0

1 LBACK Add Hourly Background Concentrations/Fluxes? F F

1 NO2CALC Specify a single NO2/Nox ratio 1 1

1 RNO2NOX Ratio of NO2 to NOx 1.0 1.0

1 MSOURCE Process only total reported contribution 0 0

2 MVISCHECK Confirm conformity to FLAG 2008 Configuration 1 1

2 MFRH Particle growth curve for hygroscopic species 4 4

2 RHMAX Maximum relative humidity (%) used in particle growth curve 98 98

2 LDRING

Report results by Discrete receptor Ring, if Discrete Receptors used. 

(T = true) T T

Modeled species to be included in computing the light extinction

2 LVSO4 Include SO4? T T

2 LVNO3 Include NO3? T T

2 LVOC Include Organic Carbon? T F

2 LVPMC Include Coarse Particles? T T

2 LVPMF Include Fine Particles? T T

2 LVEC Include Elemental Carbon? T F

2 LVNO2 Include NO2 T T

2 LVBK

when ranking for TOP-N, TOP-50, and Exceedance tables Include 

BACKGROUND? T T

2 SPECPMC Species name used for particulates in MODEL.DAT file: COARSE = PMC PM10

2 SPECPMF Species name used for particulates in MODEL.DAT file: FINE = PMF PM25

Extinction Efficiencies (1/Mm per ug/m**3)

2 EEPMC PM COARSE = 0.6 0.6

2 EEPMF PM FINE = 1.0 1.0

2 EEPMCBK Background PM COARSE 0.6 0.6

2 EESO4 SO4 = 3.0 3.0

2 EENO3 NO3 = 3.0 3.0

2 EEOC Organic Carbon = 4.0 4.0

2 EESOIL Soil = 1.0 1.0

2 EEEC Elemental Carbon = 10.0 10.0

2 EENO2 NO2 Gas = 0.1755 0.1755

2 LAVER Method used for 24-hr avg % change light extinction F F

2 MVISBK

Method used for background light extinction (2 = Hourly RH 

adjustment; 6 = FLAG seasonal f(RH)) 8 8

2 RHFAC

Monthly RH adjustment factors from FLAG (unique for each Class I 

area) Yes if 6 N/A

Background monthly extinction coefficients (FLAG) unique for each 
Table C-1: CALPOST Parameter Values

Input 

Group Variable Description Default Value Recommended Value

Class I area

2 BKSO4 Sulfate See Table 5 (NH3)2SO4

2 BKNO3 Nitrate See Table 5 NH4NO3

2 BKPMC Coarse particulate See Table 5 CM

2 BKOC Particulate organic carbon See Table 5 OM

2 BKSOIL Soil See Table 5 Soil

2 BKEC Particulate elemental carbon See Table 5 EC

2 M8_MODE Method 8 Mode 5 5

2 BKSALT Background salt concentration See Table 2 Sea Salt

2 RHFSML Small ammonium sulfate and nitrate particle f(RH) See Table 7

2 RHFLRG Large ammonium sulfate and nitrate particle f(RH) See Table 6

2 RHFSEA Sea salt f(RH) See Table 8

2 BEXTRAY Extinction due to Rayleigh scattering 10.0 10.0

3 LDOC Print documentation image F F

Averaging time(s) reported

3 L1PD Averaging period of model output F F

3 L1HR 1-hr averages F F

3 L3HR 3-hr averages F F

3 L24HR 24-hr averages T T

3 LRUNL Run length (annual) F F

3 LT50 Top 50 table for each averaging time selected T F

3 LTOPN F T

3 NTOP 4 1

3 ITOP 1 1

3 LEXCD Threshold exceedance count F T

3 THRESH1 1-hour threshold -1.0 -1.0

3 THRESH3 3-hour threshold -1.0 -1.0

3 THRESH24 24-hour threshold -1.0 0.5

3 THRESHN N-hour threshold -1.0 -1.0

3 NDAY Accumulated Period (days) 0 0

3 NCOUNT Number of exceedances allowed 1 1

Table C-2: POSTUTIL (Repartition) Parameter Values

The CALUTIL will be used to perform HNO3/NO3 repartitioning using the parameters in this table.

Table C-2: POSTUTIL (Repartition) Parameter Values

Input 

Group Variable Description Default Value

Recommended 

Value

1 IBYR Beginning year User Defined 2005

1 IBMO Beginning month User Defined 1

1 IBDY Beginning day User Defined 1

1 IBHR Beginning hour User Defined 1

1 NPER Length of run (hours) User Defined 8760

1 NSPECINP Number of species to process User Defined 7

1 NSPECOUT Number of species to write User Defined 7

1 NDUPLCT Stop if duplicate species names 0 0
Table C-2: POSTUTIL (Repartition) Parameter Values

Input 

Group Variable Description Default Value

Recommended 

Value

1 NSCALED Number of CALPUFF data files to be scales 0 0

1 MNITRATE Recompute HNO3/NO3 concentration partition 0 1

1 BKNH3 Background ammonia concentrations 10. 1.5

2 ASPECI Species to input User Defined

SO2,SO4,NOX,HNO3,

NO3,PM10,PM25

2 ASPECO Speceis to output User Defined

SO2,SO4,NOX,HNO3,

NO3,PM10,PM25

Table C-3. Background Extinction Coefficients (20% Best Days).

Background Extinction Coefficients (20% Best Days)

Class I Area

(NH4)2

SO4

μg/m3

NH4N

O3

Μg/m3

OM

μg/m3

EC

μg/m3

Soil

μg/m3

CM

μg/m3

Sea 

Salt

μg/m3

Raylei

gh

Mm-1

Haleakala National Park 

(HALE1) 0.05 0.04 0.24 0.01 0.11 1.59 0.13 10

Hawaii Volcanoes 

National Park (HAVO1) 0.03 0.04 0.27 0.01 0.06 0.64 0.11 10

Table C-4. Monthly fL(RH) – Large (NH4)2SO4 and NH4NO3 Relative Humidity Adjustment Factor.

Class I Area Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov D

Haleakala National Park (HALE1) 2.34 2.27 2.25 2.20 2.13 2.09 2.16 2.14 2.10 2.19 2.33 2

Hawaii Volcanoes National Park 

(HAVO1)

2.56 2.45 2.47 2.47 2.47 2.45 2.53 2.59 2.55 2.57 2.78 2

Table C-5. Monthly fS(RH) – Small (NH4)2SO4 and NH4NO3 Relative Humidity Adjustment Factor.

Class I Area Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov De

Haleakala National Park (HALE1) 2.98 2.85 2.81 2.72 2.60 2.53 2.65 2.63 2.56 2.69 2.95 2.8

Hawaii Volcanoes National Park

(HAVO1)

3.35 3.10 3.14 3.13 3.14 3.11 3.24 3.42 3.34 3.38 3.76 3.3

Table C-6. Monthly fSS(RH) – Sea Salt Relative Humidity Adjustment Factor.

Class I Area Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov D

Haleakala National Park (HALE1) 3.52 3.43 3.40 3.35 3.25 3.18 3.29 3.27 3.22 3.33 3.52 3.4

Hawaii Volcanoes National Park 3.80 3.68 3.71 3.72 3.72 3.69 3.79 3.83 3.79 3.83 4.08 3.8
(HAVO1)
Appendix B.

This document is also available in the DOCKET EPA-R09-OAR-2012-0345: Approval and Promulgation 

of Implementation Plans; Hawaii; Regional Haze Federal Implementation Plan.in Section C.3 Documents 

Related to Modeling to Support the Subject to BART and Reasonable Progress.

C.3.b MM5 Application for 2005 Over the Hawaiian Islands, prepared for Hawaii State Department 

of Health, Environmental Management Division, Clean Air Branch Prepared by: Alpine Geophysics, 

LLC., 2008

Topical Report

MM5 Application for 2005 

Over the Hawaiian Islands

Prepared for:

Hawaii State Department of Health

Environmental Management Division

Clean Air Branch

Prepared by:

Dennis McNally

James G. Wilkinson

Alpine Geophysics, LLC

7341 Poppy Way

Arvada, Colorado 80007

31 October 2008
i

Table of Contents

Section No. Page No. 

1 INTRODUCTION............................................................................................................................ 1-1

2 METHODOLOGY........................................................................................................................... 2-1

2.1 MODEL SELECTION AND APPLICATION .....................................................................................................................2-1

2.2 EVALUATION APPROACH ..........................................................................................................................................2-2

3 MM5 PERFORMANCE EVALUATION RESULTS .................................................................. 3-1

3.1 INTRODUCTION .........................................................................................................................................................3-1

3.2 TEMPERATURE..........................................................................................................................................................3-2

3.3 MIXING RATIO ..........................................................................................................................................................3-2

3.4 WINDS ......................................................................................................................................................................3-3

3.5 PRECIPITATION .........................................................................................................................................................3-3

3.6 MODEL PERFORMANCE SUMMARY ...........................................................................................................................3-4

4 REFERENCES...................................................................................................................................18
ii

List of Figures

Figure No. Page No.

Figure 2-1. 36/12/4 km MM5 domain........................................................................................................................................2-5

Figure 2-2. Hawaii 4km MM5 domain. .....................................................................................................................................2-5

Figure 3-1. Spatial mean temperature (deg. C) for January 2005. .............................................................................................3-6

Figure 3-2. Spatial mean temperature (deg. C) for February 2005. ...........................................................................................3-6

Figure 3-3. Spatial mean temperature (deg. C) for March 2005. ...............................................................................................3-6

Figure 3-4. Spatial mean temperature (deg. C) for April 2005. .................................................................................................3-6

Figure 3-5. Spatial mean temperature (deg. C) for May 2005....................................................................................................3-7

Figure 3-6. Spatial mean temperature (deg. C) for June 2005....................................................................................................3-7

Figure 3-7. Spatial mean temperature (deg. C) for July 2005. ...................................................................................................3-7

Figure 3-8. Spatial mean temperature (deg. C) for August 2005. ..............................................................................................3-7

Figure 3-9. Spatial mean temperature (deg. C) for September 2005..........................................................................................3-8

Figure 3-10. Spatial mean temperature (deg. C) for October 2005. ...........................................................................................3-8

Figure 3-11. Spatial mean temperature (deg. C) for November 2005. .......................................................................................3-8

Figure 3-12. Spatial mean temperature (deg. C) for December 2005.........................................................................................3-8

Figure 3-13. Spatial mean mixing ratio (g/kg) for January 2005. ..............................................................................................3-9

Figure 3-14. Spatial mean mixing ratio (g/kg) for February 2005. ............................................................................................3-9

Figure 3-15. Spatial mean mixing ratio (g/kg) for March 2005. ................................................................................................3-9

Figure 3-16. Spatial mean mixing ratio (g/kg) for April 2005. ..................................................................................................3-9

Figure 3-17. Spatial mean mixing ratio (g/kg) for May 2005. .................................................................................................3-10

Figure 3-18. Spatial mean mixing ratio (g/kg) for June 2005. .................................................................................................3-10

Figure 3-19. Spatial mean mixing ratio (g/kg) for July 2005. ..................................................................................................3-10

Figure 3-20. Spatial mean mixing ratio (g/kg) for August 2005. .............................................................................................3-10

Figure 3-21. Spatial mean mixing ratio (g/kg) for September 2005.........................................................................................3-11

Figure 3-22. Spatial mean mixing ratio (g/kg) for October 2005.............................................................................................3-11

Figure 3-23. Spatial mean mixing ratio (g/kg) for November 2005.........................................................................................3-11

Figure 3-24. Spatial mean mixing ratio (g/kg) for December 2005. ........................................................................................3-11

Figure 3-25. Spatial vector mean wind speed (m/s) for January 2005. ....................................................................................3-12

Figure 3-26. Spatial vector mean wind speed (m/s) for February 2005. ..................................................................................3-12

Figure 3-27. Spatial vector mean wind speed (m/s) for March 2005. ......................................................................................3-12

Figure 3-28. Spatial vector mean wind speed (m/s) for April 2005. ........................................................................................3-12

Figure 3-29. Spatial vector mean wind speed (m/s) for May 2005. .........................................................................................3-13

Figure 3-30. Spatial vector mean wind speed (m/s) for June 2005. .........................................................................................3-13

Figure 3-31. Spatial vector mean wind speed (m/s) for July 2005. ..........................................................................................3-13

Figure 3-32. Spatial vector mean wind speed (m/s) for August 2005. .....................................................................................3-13

Figure 3-33. Spatial vector mean wind speed (m/s) for September 2005.................................................................................3-14

Figure 3-34. Spatial vector mean wind speed (m/s) for October 2005.....................................................................................3-14

Figure 3-35. Spatial vector mean wind speed (m/s) for November 2005.................................................................................3-14

Figure 3-36. Spatial vector mean wind speed (m/s) for December 2005. ................................................................................3-14

Figure 3-37. Accumulated Precipitation (mm) Observations and Model Estimates for January 2005. ...................................3-15

Figure 3-38. Accumulated Precipitation (mm) Observations and Model Estimates for February 2005. .................................3-15

Figure 3-39. Accumulated Precipitation (mm) Observations and Model Estimates for March 2005. .....................................3-15

Figure 3-40. Accumulated Precipitation (mm) Observations and Model Estimates for April 2005. .......................................3-15

Figure 3-41. Accumulated Precipitation (mm) Observations and Model Estimates for May 2005. ........................................3-16

Figure 3-42. Accumulated Precipitation (mm) Observations and Model Estimates for June 2005. ........................................3-16

Figure 3-43. Accumulated Precipitation (mm) Observations and Model Estimates for July 2005. .........................................3-16

Figure 3-44. Accumulated Precipitation (mm) Observations and Model Estimates for August 2005. ....................................3-16

Figure 3-45. Accumulated Precipitation (mm) Observations and Model Estimates for September 2005................................3-17

Figure 3-46. Accumulated Precipitation (mm) Observations and Model Estimates for October 2005. ...................................3-17

Figure 3-47. Accumulated Precipitation (mm) Observations and Model Estimates for November 2005................................3-17
iii

Figure 3-48. Accumulated Precipitation (mm) Observations and Model Estimates for December 2005. ...............................3-17
iv

List of Tables

Table No. Page No.

Table 2-1: MM5 Vertical Domain Specification........................................................................................................................2-4

Table 3-1. Bias and error for temperature, mixing ratio, and precipitation and wind speed index of agreement by month for 

2005 annual Hawaii MM5 simulation.................................................................................................................................3-5
1-1

1. INTRODUCTION

Over the past decade, emergent requirements for direct numerical simulation of urban 

and regional scale photochemical and secondary aerosol air quality—spawned largely by 

particulate matter (PM2.5), ozone and regional haze regulations—have led to intensified 

efforts to construct high-resolution emissions, meteorological and air quality data sets. 

The concomitant increase in computational throughput of low-cost modern scientific 

workstations has ushered in a new era of regional air quality modeling. It is now 

possible, for example, to exercise sophisticated mesoscale prognostic meteorological 

models and Eulerian and Lagrangian photochemical/aerosol models, simulating ozone, 

sulfate and nitrate deposition, and secondary organic aerosols (SOA) across quite large 

geographic regions. 

One such model is the Pennsylvania State University/National Center for Atmospheric 

Research (PSU/NCAR) Mesoscale Model (MM5) (Dudhia, 1993; Grell et al., 1994: 

www.mmm.ucar.edu/mm5). MM5 is a limited-area, non-hydrostatic, terrain-following 

model designed to simulate mesoscale atmospheric circulation. The model is supported 

by several pre- and post-processing programs which are referred to collectively as the 

MM5 modeling system.

This document describes a 2005 annual MM5 simulation. The MM5 physics for the 

annual simulation were selected based on two, two-week long MM5 simulations – (1) for 

early February 2005 (a winter-time episode); and (2) for early July 2005 (a summer-time 

episode). The MM5 physics options that were selected for the annual simulation were 

based on those physics options from the two, two-week long simulations that resulted in 

the best model performance.

Ultimately, it is believed that the he proposed annual 2005 MM5 modeling results will be 

used in CALMET/CALPUFF modeling system (TRC, 2008). With that in mind, the 

model performance evaluation results presented in this document are discussed within the 

context of being used in a CALMET/CALPUFF application. However, though the MM5 

results can be used in other applications (e.g., Regional Haze studies, Best Available 

Retrofit Technology (BART) determinations, State Implementation Plans to develop and 

test options to resolve air quality issues or support management policy decisions), care 

must be taken when applying the current MM5 simulation results in such applications.
2-1

2. METHODOLOGY

2.1. Model Selection and Application

A brief summary is provided of the MM5 input data preparation procedures that were 

used for this meteorological modeling sensitivity exercise. Because of the different 

physics options available in MM5, sensitivity testing was performed for a winter (early 

February) and summer (early July) period to select the final configuration for the annual 

simulation.

Model Selection: The publicly available non-hydrostatic version of MM5 (version 3.7.4) 

was used for this modeling study. Preprocessor programs of the MM5 modeling system 

including TERRAIN, REGRID, LITTLE_R, and INTERPF (UCAR, 2003b) were used to 

develop model inputs.

Horizontal Domain Definition: The computational grid is presented in Figures 2-1 and 2-

2. The outer 36km domain (D01) has 165 x 129 grid cells, selected to provide boundary 

conditions to the 12km domain. The mid-scale 12km domain (D02) has 226 x 226 grid 

cells, selected to maximize the coverage of the western region around the Hawaiian 

Islands. The 4km domain (D03) has 208 x 154 grid cells, selected to cover the island 

chain with a buffer greater than 100km around the perimeter of the eight major Hawaiian 

Islands. The projection was in Lambert Conformal Coordinates (LCC) with a grid 

projection pole of 23o, -158o with true latitudes of 30oand 60o.

 

Vertical Domain Definition: The MM5 modeling was based on 34 vertical layers with an 

approximately 38 meter deep surface layer. The MM5 vertical domain is presented in 

both sigma and height coordinates in Table 2-1.

Topographic Inputs: Topographic information for the MM5 was developed using the 

NCAR and the United States Geological Survey (USGS) terrain databases. The grid was 

based on the 2 min (~4 km) Geophysical Data Center global data (UCAR, 2002) 

interpolated to the model grid using a Cressman-type objective analysis scheme 

(Cressman, 1959). To avoid interpolating elevated terrain over water bodies, after the 

terrain databases are interpolated onto the MM5 grid, the NCAR graphic water body 

database was used to correct elevations over water bodies. 

Vegetation Type and Land Use Inputs: Vegetation type and land use information was 

developed using the most recently released PSU/NCAR databases provided with the 

MM5 distribution (UCAR, 2002). Standard MM5 surface characteristics corresponding 

to each land use category was employed. 

Atmospheric Data Inputs: Two choices for the first guess meteorological fields were 

available: (1) the NCEP Global Tropospheric Final Analysis; and (2) the NCAR/NCEP 

Reanalysis Project (NNRP) data (UCAR, 2007). Though the NNRP data were used in a 

previous study of Hawaii (Yang and Chen, 2003; Yang et. al. 2005), the NNRP data have 
2-2

relatively coarse 2.5x2.5 degree resolution whereas the NCEP Final Analysis data have 

much higher 1x1 degree resolution. For the current modeling study, the NCEP Final 

Analysis dataset was used.

Available surface and upper-air observations were used in the objective analyses. These 

data were incorporated into the analyses datasets following the procedures outlined by 

Stauffer and Seaman (1990) and were quality-inspected by MM5 pre-processors using 

automated gross-error checks and "buddy" checks. In addition, radiosonde soundings 

were subjected to vertical consistency checks. The synoptic-scale data used for the 

initialization (and in the analysis nudging discussed later) were obtained from the 

conventional National Weather Service (NWS) twice-daily radiosondes (UCAR, 2008a) 

and 3-hr NWS surface observations (UCAR, 2008b).

Water Temperature Inputs: The water temperature data were taken from the NCEP RTG 

global one-half degree analysis (Gemmill et al., 2007; NCEP, 2008).

FDDA Data Assimilation: Both analysis and observation based nudging were used in the 

simulation. Analysis nudging coefficients of 2.5x10-4on the 36km grid domain and 

1.0x10-4on the 12km grid domain were used for winds and temperature. For the 4km 

grid domain, nudging to observed winds was employed with a coefficient of 1.0x10-3. 

Physics Options: Sensitivity testing using approximately two-week long episodes in 

early February 2005 and early July 2005 were performed to help select the best 

performing physics options (McNally, 2008). Although the performance was similar for 

the different options, the best performing configuration was the version used by the 

University of Hawaii and the National Weather Service (Yang and Chen, 2003; Yang et. 

al. 2005. Namely:

Warm Rain;

Grell Cumulus Parameterization on 36/12/04km Domains;

MRF PBL;

NOAH Land Surface Model; and

Cloud Radiation.

Application Methodology: The MM5 model was executed in 5-day blocks initialized at 

12Z every 4 days with a 90 second time step. Model results were output every 60 

minutes and output files are split at 24 hour intervals. Twelve (12) hours of spin-up were 

included in each 4-day block before the data were used in the atmospheric simulations 

and subsequent model performance evaluation. 

2.2. Evaluation Approach

Because only limited observational data were available at the 12km and 36km grid scales, 

and because it is very likely that only the 4km domain will be used for subsequent air 

quality analyses, only MM5 predictions on the 4km grid were evaluated. 
2-3

The model performance evaluation approach was based on a combination of qualitative 

and quantitative analyses using comparisons of observed and predicted values of mixing 

ratio, temperature, precipitation and winds. The observed values for winds, temperature, 

and water mixing ratio were taken from the NOAA Techniques Development Lab (TDL) 

(UCAR, 2008c) surface observations and the National Climatic Data Center (NCDC) 

TD3240 (UCAR, 2008d) hourly precipitation databases. For the qualitative model 

performance evaluation, the model estimated monthly total precipitation was graphically 

compared with the TD3240 precipitation database. For the quantitative model 

performance evaluation, tabulations of (1) the model bias and error for temperature, 

precipitation, and mixing ratio and (2) the index of agreement for the wind fields were 

analyzed for the 4km simulations. 
2-4

Table 2-1: MM5 Vertical Domain Specification.

k (MM5) sigma pressure (Pa) height (m) depth (m)

34 0.000 10000 15674 2004

33 0.050 14500 13670 1585

32 0.100 19000 12085 1321

31 0.150 23500 10764 1139

30 0.200 28000 9625 1004

29 0.250 32500 8621 900

28 0.300 37000 7720 817

27 0.350 41500 6903 750

26 0.400 46000 6153 693

25 0.450 50500 5461 645

24 0.500 55000 4816 604

23 0.550 59500 4212 568

22 0.600 64000 3644 536

21 0.650 68500 3108 508

20 0.700 73000 2600 388

19 0.740 76600 2212 282

18 0.770 79300 1930 274

17 0.800 82000 1657 178

16 0.820 83800 1478 175

15 0.840 85600 1303 172

14 0.860 87400 1130 169

13 0.880 89200 961 167

12 0.900 91000 794 82

11 0.910 91900 712 82

10 0.920 92800 631 81

9 0.930 93700 550 80

8 0.940 94600 469 80

7 0.950 95500 389 79

6 0.960 96400 310 78

5 0.970 97300 232 78

4 0.980 98200 154 39

3 0.985 98650 115 39

2 0.990 99100 77 38

1 0.995 99550 38 38

0 1.000 100000 0 0
2-5

Figure 2-1. 36/12/4 km MM5 domain.

Figure 2-2. Hawaii 4km MM5 domain.
3-1

3. MM5 PERFORMANCE EVALUATION RESULTS

3.1. Introduction

Quantitative and qualitative model performance evaluation results for surface winds, 

temperature, mixing ratio and episode total precipitation are presented and discussed. A 

full annual model evaluation is very difficult to summarize in a single document. With 

this in mind, this section presents results so potential users of the MM5 predictions can 

independently judge the adequacy of the model simulation. Overall comparisons are 

offered herein to judge the model efficacy, but this review does not necessarily cover all 

potential user needs and applications. 

The statistics that were estimated include the mean bias (or simply bias) and mean 

absolute gross error (or simply error) for temperature, mixing ratio and precipitation as 

well as the wind speed index of agreement (Willmott, 1982; Willmott et al., 1985). Bias 

quantifies the difference between the average of a predicted value and the average of an 

observed value. If the bias is negative, the model is said to underpredict the mean 

observed value. Error quantifies the average of the absolute differences between the 

predictions and observations. Thus, even if a simulation exhibits no bias, it can still 

exhibit large error as the simulation results have offsetting under- and overpredictions. 

Index of agreement quantifies the relative average error between the predictions and 

observations. Please note that in the comparisons, vertical level one model estimates that 

are predicted at approximately nineteen meters are compared directly with the nominal, 

observed temperatures and moistures that are measured at about two meters and wind 

speeds and directions that are measured at about ten meters. As such, model performance 

must be judged with this caveat taken into context.

Quantitative meteorological model performance benchmarks have been developed to help 

assess the ability of a model to reproduce observed conditions. The benchmarks are 

based upon the evaluation of about thirty MM5 and RAMS meteorological simulations of 

multi-day episodes in support of air quality modeling study applications performed over 

several years (Emery et al., 2001). In turn, these benchmarks have been adopted for use 

in annual meteorological modeling studies. The purpose of these benchmarks is not to 

give a pass or fail grade to any one particular meteorological model application, but 

rather to put the model results into the proper context of other models and meteorological 

data sets. That is, the benchmarks provide a contextual understanding as to how the 

model results compare to other model applications run over portions of the continental 

United States. Therefore, the benchmarks must be viewed as being guideline and not 

bright-line numbers. These benchmarks include bias and error in temperature and mixing 

ratio as well as the wind speed index of agreement (IA) between the model predictions 

and observational data bases. The benchmarks for each variable to judge whether 

predictions from a meteorological model are on par with previous meteorological 

modeling studies are as follows:

Temperature bias: less than or equal to +/- 0.5 K;
3-2

Temperature error: less than or equal to 2.0 K

Mixing ratio bias: less than or equal to +/- 1.0 g/kg

Mixing ratio error: less than or equal to 2.0 g/kg

Wind Speed Index of Agreement (unitless): 0 = worst, 1 = best, 0.6 = 

acceptable

Of critical note for the current study – though meteorological model performance 

benchmarks have been developed to help assess the ability of a model to reproduce 

observed conditions, these benchmarks were derived from simulations performed 

primarily for continental and limited coastal areas (Emery et al., 2001). Hence, 

application of these model performance benchmarks is suspect for the current study as 

the MM5 simulations were performed over the Hawaiian Islands. However, it is still 

prudent to mention these benchmarks as they do provide a reference point for the model 

performance evaluation. 

3.2. Temperature 

Review of temperature bias statistics in Table 3-1 indicates that MM5 consistently 

underpredicts temperatures throughout 2005. MM5’s best performance is for January 

where the bias is -0.47 K, and the worst performing month is August where the bias is -

1.66 K. Over the entire year, MM5 has a bias of -1.07 K. Table 3-1 also shows the 

temperature error statistics to be a low of 1.75 K in November and a high of 2.32 K in 

August.

Figures 3-1 through 3-12 graphically compare the spatial mean observed and predicted 

temperatures for each month of 2005. Qualitatively, MM5 does better at predicting the 

nighttime low temperatures as compared to its ability to predict the daytime high 

temperatures. In fact, MM5 performs quite poorly at predicting the daytime high 

temperatures in that it seldom is able to generate spatial mean temperatures in excess of 

25 C where as the observed spatial mean temperatures routinely exceed 25 C reaching 

highs of 31 C in September (Figure 3-9).

The temperature performance though poor is not wholly unexpected as establishing such 

features as an appropriate marine boundary layer are critical to accurately predict coastal 

meteorology. Indeed, initialization of limited area prognostic meteorological models 

over the data-sparse ocean and coastal regions remains a difficult problem (e.g., Leidner 

et al., 2001). Substantial effort is likely necessary to correct the inadequate temperature 

performance of MM5 for the current simulation. Such efforts may include the 

incorporation of highly resolved vertical and horizontal grid structures (Stauffer and 

Seaman, 1999), updating land use characteristics, or revision to the deep soil 

temperatures or other force-restore parameterizations (Boucouvala et al., 2003).

3.3. Mixing Ratio 

Table 3-1 presents the mixing ratio bias and error statistics by month for the 2005 MM5 

simulation. January is the trailing month in terms of performance with a bias of -1.23 

g/kg and an error of 1.59 g/kg. The remaining months show biases of between -0.96 g/kg 
3-3

for May and 0.18 g/kg for December and errors from 1.07 g/kg from August to1.37 g/kg 

for April. For the entire period, the bias is -0.63 g/kg, and the error is 1.22 g/kg.

Figure 3-13 through 3-24 graphically compare the spatial mean observed and predicted 

mixing ratios by month for 2005. Qualitatively, MM5 does reasonably well at predicting 

both the highs and lows of the spatial mean mixing ratios, though there is a small 

underprediction bias. Further, MM5 does well at predicting the diurnal pattern of the 

spatial mean mixing ratios. Overall MM5 does reasonably well at predicting mixing ratio 

for all months of 2005 with no month being overtly better or worse than other months.

3.4. Winds

Table 3-1 presents the wind speed index of agreement by month for 2005 with May 

exhibiting the lowest performance at 0.63 and January and September exhibiting the best 

performance at 0.73. Annually, MM5 exhibits an index of agreement of 0.69.

Figures 3-25 through 3-36 graphically compare the observed and predicted vector mean 

wind speeds by month for 2005. Qualitatively, there is mixed performance with MM5 

doing better at predicting the high wind speeds in some months than others or with MM5 

doing better at predicting the low wind speed in some months over other months. In 

January (Figure 3-25), for example, MM5 follows the diurnal trend though it does have 

periods of overprediction of the high winds (e.g., 07-08 January) and low winds (e.g., 23-

24 January). In June (Figure 3-30), MM5 has a general trend to underpredict the high 

wind speeds and overpredict the low wind speeds. However, for all months, MM5 does 

reasonably well at predicting the diurnal trend. Overall, as with mixing ratio, MM5 does 

reasonably well at predicting wind speeds.

3.5. Precipitation

Table 3-1 presents the bias and error statistics for precipitation by month for the 2005 

MM5 simulation. In terms of bias, June shows none and January shows the largest bias 

of -8.88 mm. In terms of error, January is the lowest performing month with 10.24 mm, 

and December is the best performing month with 2.63 mm. Annually, MM5 exhibits a 

bias 0 -4.00 mm and an error of 6.69 mm.

Another way of examining qualitative precipitation performance is to examine spatial 

fields of accumulated observed and predicted precipitation. The graphics are prepared by 

plotting the modeled episode estimated precipitation overlaid with color coded markers at 

locations where precipitation measurements were reported. Episode accumulated 

precipitation for each month of 2005 is presented in Figures 3-37 though 3-48. 

For January (Figure 3-37), it can be seen that the poorer performance reflected in the bias 

and error statistics in Table 3-1 is due to the underpredictions of precipitation on all the 

islands except Kaua`i. MM5 precipitation predictions improve in February (Figure 3-38) 

over the islands of Hawai`i and Maui, then revert to poorer performance in March (Figure 

3-39) over the islands of Kaua`i, O`ahu, and Moloka`i. In April (Figure 3-40), May 

(Figure 3-41), June (Figure 3-42), July (Figure 3-43), and August (Figure 3-44), MM5 

performance is relatively consistent. In September (Figure 3-45) through November 
3-4

(Figure 3-47), MM5 precipitation performance degrades slightly, and then improves 

slightly for December (Figure 3-48). In general, however, MM5 does tend to predict 

both the magnitudes and locations of high precipitation throughout the year.

3.6. Model Performance Summary

A 2005 annual MM5 simulation over the Hawaiian Islands has been performed and a 

model performance evaluation has been conducted. The model performance lies within 

the guidelines for mixing ratio and wind field index of agreement. In regards to mixing 

ratio, MM5 does reasonably well at predicting the diurnal structure of the observed 

mixing ratios though it slightly underpredicts observed values throughout the year. As 

with its ability to predict observed mixing ratios, MM5 does reasonably well at predicting 

the observed wind speeds diurnal structure with slight underprediction and overprediction 

biases throughout the year depending on the month. In terms of precipitation, MM5 does 

reasonably well at predicting both the magnitudes and locations of the high precipitation 

throughout the year.

The surface temperature evaluation shows that for many months and for the annual 

average, the bias and error are outside of the guideline metrics. This result is not 

unexpected since the performance metrics were derived from largely continental, 

episodic applications of MM5. For this application the model is being applied to a very 

challenging problem with nearly all coastal monitors near very steep orography. 

Therefore, a small difference in wind flow can determine if a monitor is influenced by 

land or maritime air parcels.

Given the reasonable model performance of the MM5 predicted winds, precipitation, and 

mixing ratio, they are adequate for use in a CALMET/CALPUFF application. Though 

MM5 has less skill at replicating temperature observations, the Hawaiian Islands, have a 

relative high, stable temperature range over the year that is roughly 15 C to 30 C; and 

given that the CALMET/CALPUFF modeling system uses temperature primarily in 

buoyancy, deposition, and atmospheric stability calculations (and in some cases, nitrate 

chemistry), the MM5 predictions for the current 2005 simulation are likely sufficient for 

the task intended in a CALMET/CALPUFF application. Again, care must be taken if the 

current 2005 MM5 simulation results are to be used in other applications.
3-5

Table 3-1. Bias and error for temperature, mixing ratio, and precipitation and wind speed index of 

agreement by month for 2005 annual Hawaii MM5 simulation.

Month Temperature (K) Mixing Ratio (g/kg) Precipitation (mm) Wind

Bias Error Bias Error Bias Error IA

Jan -0.47 1.81 -1.23 1.59 -8.88 10.24 0.73

Feb -0.76 1.82 -0.63 1.31 -5.71 7.35 0.71

Mar -0.71 1.95 -0.67 1.20 -8.65 9.43 0.69

Apr -1.44 2.01 -0.94 1.37 0.11 5.80 0.67

May -1.57 2.16 -0.96 1.35 -1.81 4.22 0.63

Jun -1.44 2.07 -0.65 1.16 0.00 6.55 0.69

Jul -1.49 2.05 -0.47 1.06 -2.17 6.03 0.66

Aug -1.66 2.32 -0.38 1.07 -0.77 4.57 0.70

Sep -1.31 1.98 -0.59 1.10 -6.58 8.86 0.73

Oct -0.81 1.90 -0.44 1.05 -6.39 8.31 0.66

Nov -0.62 1.75 -0.78 1.21 -5.10 6.31 0.71

Dec -0.53 2.07 0.18 1.14 -2.05 2.63 0.69

Annual -1.07 1.99 -0.63 1.22 -4.00 6.69 0.69
3-6

Figure 3-1. Spatial mean temperature (deg. C) for January 2005. Figure 3-2. Spatial mean temperature (deg. C) for February 2005.

Figure 3-3. Spatial mean temperature (deg. C) for March 2005. Figure 3-4. Spatial mean temperature (deg. C) for April 2005.
3-7

Figure 3-5. Spatial mean temperature (deg. C) for May 2005. Figure 3-6. Spatial mean temperature (deg. C) for June 2005.

Figure 3-7. Spatial mean temperature (deg. C) for July 2005. Figure 3-8. Spatial mean temperature (deg. C) for August 2005.
3-8

Figure 3-9. Spatial mean temperature (deg. C) for September 2005. Figure 3-10. Spatial mean temperature (deg. C) for October 2005.

Figure 3-11. Spatial mean temperature (deg. C) for November 2005. Figure 3-12. Spatial mean temperature (deg. C) for December 2005.
3-9

Figure 3-13. Spatial mean mixing ratio (g/kg) for January 2005. Figure 3-14. Spatial mean mixing ratio (g/kg) for February 2005.

Figure 3-15. Spatial mean mixing ratio (g/kg) for March 2005. Figure 3-16. Spatial mean mixing ratio (g/kg) for April 2005.
3-10

Figure 3-17. Spatial mean mixing ratio (g/kg) for May 2005. Figure 3-18. Spatial mean mixing ratio (g/kg) for June 2005.

Figure 3-19. Spatial mean mixing ratio (g/kg) for July 2005. Figure 3-20. Spatial mean mixing ratio (g/kg) for August 2005.
3-11

Figure 3-21. Spatial mean mixing ratio (g/kg) for September 2005. Figure 3-22. Spatial mean mixing ratio (g/kg) for October 2005.

Figure 3-23. Spatial mean mixing ratio (g/kg) for November 2005. Figure 3-24. Spatial mean mixing ratio (g/kg) for December 2005.
3-12

Figure 3-25. Spatial vector mean wind speed (m/s) for January 2005. Figure 3-26. Spatial vector mean wind speed (m/s) for February 2005.

Figure 3-27. Spatial vector mean wind speed (m/s) for March 2005. Figure 3-28. Spatial vector mean wind speed (m/s) for April 2005.
3-13

Figure 3-29. Spatial vector mean wind speed (m/s) for May 2005. Figure 3-30. Spatial vector mean wind speed (m/s) for June 2005.

Figure 3-31. Spatial vector mean wind speed (m/s) for July 2005. Figure 3-32. Spatial vector mean wind speed (m/s) for August 2005.
3-14

Figure 3-33. Spatial vector mean wind speed (m/s) for September 2005. Figure 3-34. Spatial vector mean wind speed (m/s) for October 2005.

Figure 3-35. Spatial vector mean wind speed (m/s) for November 2005. Figure 3-36. Spatial vector mean wind speed (m/s) for December 2005.
3-15

Figure 3-37. Accumulated Precipitation (mm) Observations and Model Estimates for January 2005. Figure 3-38. Accumulated Precipitation (mm) Observations and Model Estimates for February 2005.

Figure 3-39. Accumulated Precipitation (mm) Observations and Model Estimates for March 2005. Figure 3-40. Accumulated Precipitation (mm) Observations and Model Estimates for April 2005.
3-16

Figure 3-41. Accumulated Precipitation (mm) Observations and Model Estimates for May 2005. Figure 3-42. Accumulated Precipitation (mm) Observations and Model Estimates for June 2005.

Figure 3-43. Accumulated Precipitation (mm) Observations and Model Estimates for July 2005. Figure 3-44. Accumulated Precipitation (mm) Observations and Model Estimates for August 2005.
3-17

Figure 3-45. Accumulated Precipitation (mm) Observations and Model Estimates for September 2005. Figure 3-46. Accumulated Precipitation (mm) Observations and Model Estimates for October 2005.

Figure 3-47. Accumulated Precipitation (mm) Observations and Model Estimates for November 2005. Figure 3-48. Accumulated Precipitation (mm) Observations and Model Estimates for December 2005.
18

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