Hawaiian Electric Company, Inc. 

Maui Electric Company, Inc. 

Hawaiian Electric Light Company, Ltd. 

MM5 METEOROLOGICAL 

DATASET DEVELOPMENT FOR 

HAWAII

Prepared by 

Draft December 2008

DRAFT
MM5 Meteorological Dataset Development i Draft – December 2008 

TABLE OF CONTENTS

1.0 INTRODUCTION ........................................................................................................................ 1

2.0 MM5 METEOROLOGICAL DATASET DEVELOPMENT ................................................................... 2

2.1. INPUT DATA ................................................................................................................................. 7

2.1.1. TOPOGRAPHY ............................................................................................................................ 7

2.1.2. VEGETATION TYPE AND LAND USE ........................................................................................... 7

2.1.3. GRIDDED METEOROLOGICAL DATA........................................................................................... 7

2.1.4. SEA-SURFACE TEMPERATURE .................................................................................................. 8

2.2. DATA PREPROCESSING ............................................................................................................... 8

2.2.1. TERRAIN .................................................................................................................................. 8

2.2.2. PREGRID ................................................................................................................................. 9

2.2.3. REGRIDDER ........................................................................................................................... 9

2.2.4. INTERPF .................................................................................................................................. 9

2.3. MM5 MODELING.......................................................................................................................... 9

2.3.1. NESTDOWN .......................................................................................................................... 10

2.3.2. LINK TO CALPUFF .................................................................................................................. 10

3.0 EVALUATION OF THE MM5 METEOROLOGICAL DATA SET ....................................................... 11

3.1. STATISTICAL EVALUATION MEASURES ....................................................................................... 11

3.1.1. STATISTICAL EVALUATION METHODOLOGY............................................................................. 12

3.1.2. STATISTICAL EVALUATION RESULTS ....................................................................................... 12

3.2. GRAPHICAL EVALUATION MEASURES ........................................................................................ 16

3.2.1. GRAPHICAL EVALUATION METHODOLOGY .............................................................................. 16

3.2.2. GRAPHICAL EVALUATION RESULTS ......................................................................................... 16

4.0 REFERENCES ........................................................................................................................ 18

APPENDIX A – TERRAIN PREPROCESSOR CONFIGURATION ......................................................... 19

APPENDIX B – PREGRID PREPROCESSOR V-TABLES ................................................................... 20

APPENDIX C – INTERPF PREPROCESSOR CONFIGURATION .......................................................... 21

APPENDIX D – MM5 SET-UP PARAMETERS ................................................................................... 22

APPENDIX E – NESTDOWN SET-UP PARAMETERS....................................................................... 25

APPENDIX F – MM5 GRAPHICAL EVALUATION PLOTS.................................................................... 26

LIST OF FIGURES

FIGURE 2.0-1 MM5 MODELING DOMAINS ........................................................................................ 2

FIGURE 2.0-2 MM5 MODELING SUITE ............................................................................................. 3

FIGURE 2.0-3 MM5 DOMAIN 2 (STATE MODELING DOMAIN) ............................................................. 4

FIGURE 2.0-4 MM5 DOMAIN 3 (MAUI MODELING DOMAIN) ............................................................... 5

FIGURE 2.0-5 MM5 DOMAIN 4 (HAWAII MODELING DOMAIN) ............................................................ 6

FIGURE 2.1-1 NORTH AMERICAN MESOSCALE GRID 243 DOMAIN .................................................... 8

FIGURE F-1 – MAY 2, 2005 12Z SCENARIO A SURFACE ANALYSIS .................................................. 26

FIGURE F-2 – MAY 2, 2005 12Z SCENARIO A MM5 MODEL DOMAIN 2 ............................................ 27

FIGURE F-3 – JULY 7, 2005 18Z SCENARIO B SURFACE ANALYSIS ................................................. 28

FIGURE F-4 – JULY 7, 2005 18Z SCENARIO B MM5 MODEL DOMAIN 2 ............................................ 29

FIGURE F-5 – JULY 7, 2005 12Z MAUI DOMAIN WIND VECTORS ..................................................... 30

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MM5 Meteorological Dataset Development ii Draft – December 2008 

LIST OF TABLES

TABLE 3.1-1 MM5 STATISTICAL EVALUATION RESULTS ................................................................. 14

TABLE 3.1-2 MM5 STATISTICAL EVALUATION INDEX OF AGREEMENT RESULTS .............................. 15

TABLE A-1 - TERRAIN DOMAIN DATA INPUT PARAMETERS ............................................................ 19

TABLE A-2 - TERRAIN NAMELIST PARAMETER SETTINGS .............................................................. 19

TABLE B-1 – PREGRID V-TABLES ................................................................................................ 20

TABLE C-1 - INTERPF NAMELIST PARAMETERS ............................................................................ 21

TABLE D-1 - MM5 CONFIGURE.USER PARAMETER SETTINGS ......................................................... 22

TABLE D-2 - MM5 MM5.DECK PARAMETER/OPTIONS SETTINGS ...................................................... 23

TABLE D-3 - MM5 MM5.DECK PARAMETERS/OPTIONS SETTINGS CONTINUED ................................. 24

TABLE E-1 - NESTDOWN NAMELIST PARAMETER SETTINGS/OPTIONS ............................................. 25

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MM5 Meteorological Dataset Development 1 Draft - December 2008 

1.0 INTRODUCTION

Regional haze is visibility impairment caused by the cumulative air pollutant emissions from 

numerous sources over a wide geographic area. Visibility impairment is caused primarily by the 

scattering of light by fine particles (PM2.5), including but not limited to organics and elemental 

carbon, ammonium nitrate, sulfates and soil. Haze-forming pollution comes from a variety of 

natural and manmade sources. Natural sources include soot from wildfires, volcanic emissions, 

and windblown dust. Manmade (anthropogenic) sources include motor vehicles, electric utility 

and industrial fuel burning, and manufacturing operations which emit particulate matter certain 

pollutants1

. 

Section 169A of the Clean Air Act (CAA) sets forth a national goal for visibility which is the 

“prevention of any future, and the remedying of any existing, impairment of visibility in Class I 

areas which impairment results from manmade air pollution”. In 1999, the Environmental 

Protection Agency (EPA) promulgated the Regional Haze Regulations which require States to 

develop programs to assure reasonable progress toward meeting the national goal of 

preventing any future and remedying any existing visibility impairment in Class I areas. The 

primary objective of the regulations is to reduce visibility impairment in Class I areas from the 

current conditions to natural conditions by 2064, where natural conditions refer to the conditions 

before the impact of mankind. 

CALPUFF is used to identify impacts within the Class I areas from a single source or group of 

sources. CALPUFF is a 3-dimensional transport and dispersion model that “advects ‘puffs’ of 

material emitted from modeled sources, simulating dispersion and transformation processes 

along the way”. The spatial and temporal distribution of meteorological data in the domain 

directly impacts the distribution of emitted material throughout the simulation period. The 

preferred meteorological input for CALPUFF is 3-dimensional gridded data. This document 

describes the data inputs, domain sizes, temporal scales and options used for generating a 3-

dimensional gridded data set using the MM5 meteorological model.

 

1

 http://www.epa.gov/air/visibility/what.html 

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MM5 Meteorological Dataset Development 2 Draft - December 2008 

2.0 MM5 METEOROLOGICAL DATASET DEVELOPMENT

MM5 is a versatile mesoscale meteorological model developed by scientists at Penn State 

University (PSU) and The National Center for Atmospheric Research (NCAR). The modeling 

system is designed to predict 3-dimensional atmospheric circulations (winds), temperature, and 

precipitation at the mesoscale level. MM5 uses a series of nested domains to produce varying 

levels of refinements. Figure 2.0-1 illustrates the boundaries of each of the modeling domains. 

CALPUFF regional haze modeling, utilizes three of the four MM5 modeling domains as 

CALMET inputs. The first, domain 2, is a 9 km resolution ‘State’ grid encompassing the 8 major 

Hawaiian Islands. The remaining two, domains 3 and 4, are 3 km resolution grids 

encompassing the islands of Maui and the Big Island, respectively. Each of the domains 

maintains at least a 100 km buffer between any island and the edge of the domain. This allows 

the complex island circulations to be fully resolved from all wind-directions. 

The MM5 modeling period extends from January 1, 2005 to January 1, 2008. Figure 2.0-2 

outlines the MM5 modeling procedure. Figure 2.0-2 shows the complexity of setting-up and 

running of the MM5 model. The process can be divided into three sections; 1) input data, 2) 

data preprocessing, and 3) MM5 modeling. The following sections discuss each of these steps. 

FIGURE 2.0-1 MM5 MODELING DOMAINS

-164 -163 -162 -161 -160 -159 -158 -157 -156 -155 -154 -153 -152 -151

Longitude (degrees)

16 17 18 19 20 21 22 23 24 25 26

Latitude (degrees)

Domain 1 (27 km)

Domain 2 (9 km)

Domain 3 (3 km)

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Domain 4 (3 km)
MM5 Meteorological Dataset Development 3 Draft - December 2008 

FIGURE 2.0-2 MM5 MODELING SUITE

Figures 2.0-3 through 2.0-5 provide a detailed view of the grid points in modeling domains 2, 3 

and 4. For more information on the exact dimensions and domain origin location, refer to Table 

A-2 in Appendix A. 

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MM5 Meteorological Dataset Development 4 Draft - December 2008 

FIGURE 2.0-3 MM5 DOMAIN 2 (STATE MODELING DOMAIN) 

-162 -161 -160 -159 -158 -157 -156 -155 -154 -153

Longitude (degrees)

17 18 19 20 21 22 23 24

Latitude (degrees)

State Domain (9 km)

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MM5 Meteorological Dataset Development 5 Draft - December 2008 

FIGURE 2.0-4 MM5 DOMAIN 3 (MAUI MODELING DOMAIN) 

-158.5 -158.0 -157.5 -157.0 -156.5 -156.0 -155.5 -155.0

Longitude (degrees)

19.5 20.0 20.5 21.0 21.5 22.0

Latitude (degrees)

Maui Domain (3 km)

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MM5 Meteorological Dataset Development 6 Draft - December 2008 

FIGURE 2.0-5 MM5 DOMAIN 4 (HAWAII MODELING DOMAIN) 

-157.0 -156.5 -156.0 -155.5 -155.0 -154.5 -154.0

Longitude (degrees)

18.0 18.5 19.0 19.5 20.0 20.5 21.0

Latitude (degrees)

Hawaii Domain (3 km)

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MM5 Meteorological Dataset Development 7 Draft - December 2008 

2.1. INPUT DATA

The initial step of the MM5 modeling procure involves establishing the number, size and 

resolution of the domains. Once the domains are established the input data used to 

initialize MM5 must be obtained and prepared. The MM5 input data is disused below. 

2.1.1. TOPOGRAPHY

Topographical information for each of the domains is developed from the NCAR terrain 

databases provided with the MM5 distribution. These databases are created at multiple 

resolutions from USGS global digital elevation model (DEM) data with a horizontal grid 

spacing of 30 arc seconds (~0.9 km). Domain 1 uses 5 min (~9 km) global data, domain 

2 uses 2 min (~4 km) global data, and domains 3 and 4 use 30 sec (~0.9 km) global data 

to determine each domain’s elevation information. 

2.1.2. VEGETATION TYPE AND LAND USE

Vegetation type and land use information is derived from NCAR databases provided with 

the MM5 distribution. Vegetation type and land use information is derived from the 25-

category USGS version 2 land cover data. The data resolution is based on the same 

categories used for the topographical data. 

2.1.3. GRIDDED METEOROLOGICAL DATA

North American Mesoscale (NAM) gridded meteorological analyses are obtained from 

the National Center for Environmental Prediction (NCEP) FTP site. NCEP grid 243 

encompasses much of the north-eastern Pacific Ocean including the entire state of 

Hawaii, at a grid resolution of 0.4 degrees Lat/Lon. The NAM model analysis data sets 

produced 4 times per day (00z, 06z, 12z, 18z) were utilized as the MM5 input. Figure 

2.1-1 identifies the domain of NCEP grid 243 NAM data2

. 

 

2

 http://www.emc.ncep.noaa.gov/mmb/namgrids/ 

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MM5 Meteorological Dataset Development 8 Draft - December 2008 

FIGURE 2.1-1 NORTH AMERICAN MESOSCALE GRID 243 DOMAIN

2.1.4. SEA-SURFACE TEMPERATURE

Real-time global sea-surface temperature analyses (RTG-SST) obtained from NCEP3

, 

are used to derive daily water temperatures for the MM5. Data resolution for the period 

of January 2005 – February 2007 is 0.5 degrees. Thereafter, the resolution is 0.083 

degrees. 

2.2. DATA PREPROCESSING

MM5 requires preprocessing of all input data into a specific file format. Preprocessing of the 

data is accomplished by 4 programs: TERRAIN, PREGRID, REGRIDDER, and INTERPF. 

2.2.1. TERRAIN 

The MM5 data preprocessing program TERRAIN (v. 3.7.0) combines the land-use and 

terrain data for input into the next step. TERRAIN processes the land-use and terrain 

data for each of the 4 modeling domains. Domain 1 (27 km resolution) ingests 9 km 

global terrain and land-use data to generate the necessary fields for the MM5 model. 

Domain 2 (9 km) ingests 4 km data, and Domains 3 and 4 ingest 0.9 km terrain and 

land-use data. The data is interpolated to the user-specified gridded domains, and is 

 

3

 http://polar.ncep.noaa.gov/sst/ 

DRAFT
MM5 Meteorological Dataset Development 9 Draft - December 2008 

used for calculating many meteorological parameters. Appendix A lists the processing 

options. 

2.2.2. PREGRID 

The PREGRID program reads in the NAM data in raw Gridded-Binary (GRIB) format, 

and formats the data into an intermediate file read in by REGRIDDER. PREGRID uses 

tables, known as “v-tables”, to identify the appropriate data fields to read from the input 

file. Each v-table used in the PREGRID program is included in Appendix B. PREGRID 

is run for a period of 1-year to generate all input meteorological data files needed for the 

REGRIDDER program. Creating all intermediate files initially allows for a more efficient 

MM5 processing routine. 

The PREGRID program generates 4 output files at the time intervals of the NAM data (6-

hr intervals). The files maintain the following naming convention: 

• FILE:YYYY-MM-DD_HH – This file contains the required MM5 meteorological 

inputs, such as wind, temperature and dewpoint. 

• SST_FILE:YYYY-MM-DD_HH – This file contains the MM5 sea surface 

temperature inputs. 

• SOIL_FILE:YYYY-MM-DD_HH – This file contains the MM5 soil moisture inputs. 

• SNOW_FILE:YYYY-MM-DD_HH – This file contains the MM5 snow cover inputs. 

2.2.3. REGRIDDER 

The REGRIDDER program combines the processed terrain and land-use data from 

TERRAIN and the 4 PREGRID output files for each 6-hour time step. REGRIDDER 

horizontally interpolates these data to the MM5 domain 1 grid size for input into 

INTERPF. 

 

2.2.4. INTERPF 

The INTERPF program reads in the output from the REGRIDDER program and 

interpolates the NAM data to the user specified MM5 vertical resolution. The NAM data 

vertical resolution is based on constant pressure levels where MM5 specifies vertical 

resolution using sigma levels. Sigma is a vertical coordinate defined as the ratio of the 

pressure at a given point in the atmosphere to the pressure on the earth’s surface 

underneath it. Only domain 1 ingests the input data. The remaining domains interpolate 

the data from domain 1 with adjustments made according to the domain’s terrain field. 

Appendix C, Table C-1 lists the options selected in the INTERPF input file 

(namelist.input). INTERPF generates the following 3 input data files needed to run the 

MM5; 1) MMINPUT_DOMAIN1, 2) LOWBDY_DOMAIN1, and 3) BDYOUT_DOMAIN1. 

2.3. MM5 MODELING

The MM5 modeling in conduced as a series of independent 132 hour (5 days plus 12 hour 

spin-up) model runs in 2 stages. The first stage consists of modeling domains 1, 2 and 3 as 

nested domains that feedback into their respective mother domain. This process enhances 

the results for the state domain (domain 2). The second stage uses the output from the 

state domain as the input data for the MM5 domain 4 modeling. Before the second MM5 

stage is conducted the state domain output must be process by NESTDOWN. For Appendix 

D, Tables D-1 through D-3 list the MM5 model configuration settings and physics options. 

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MM5 Meteorological Dataset Development 10 Draft - December 2008 

2.3.1. NESTDOWN 

The NESTDOWN program is both a preprocessor and a postprocessor. NESTDOWN 

reads in the MM5 output from the state domain and interpolates the data to the domain 4 

grid-size (i.e., from TERRAIN_DOMAIN# file) specified in the NESTDOWN input file 

(namelist.input). Appendix E, Table E-1 lists the options specified in the NESTDOWN 

input file. NESTDOWN generates 3 input data files needed to run MM5 for domain 4 

(Big Island domain). The three data files created are; 1) MMINPUT_DOMAIN4, 2) 

LOWBDY_DOMAIN4, and 3) BDYOUT_DOMAIN4. 

2.3.2. LINK TO CALPUFF 

MM5 output files contain hourly 3-dimensional meteorological data that is used for 

Regional Haze Modeling with CALPUFF. The meteorological data formatting program 

CALMM5 is used to extract the required variables at user-specified vertical levels from 

the raw MM5 output file. CALMM5 output files follow a 

YYYYMMDDHH0_YYYYMMDDHHf.DOMAIN#.MM5.dat naming convention, where the 

YYYYMMDDHH0 and YYYYMMDDHHf represent the start and end time of the file, 

respectively. 

The CALMM5 output files are the input files to the CALMET meteorological 

preprocessor. CALMET again interpolates the data to a set of horizontal and vertical 

grid points. The output file is the input meteorological data file for the CALPUFF model. 

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MM5 Meteorological Dataset Development 11 Draft - December 2008 

3.0 EVALUATION OF THE MM5 METEOROLOGICAL DATA SET

This section describes the evaluation of the resultant MM5 dataset to assess the model’s ability 

to correctly estimate wind speed and direction, temperature and mixing ratio. The evaluation 

compares the MM5 output to actual observations. Two types of evaluations are performed. 

First, a statistical analysis comparing observational NWS surface data with the MM5 results at 

specific locations. Second, a graphical comparison of each domain’s wind vectors to surface 

analysis maps for the corresponding time period. The graphical evaluation includes a 

qualitative review of MM5’s ability to predict local weather phenomenon in specific regions of the 

domains. 

3.1. STATISTICAL EVALUATION MEASURES

The statistical evaluation was performed using ENVIRON’s METSTAT MM5 program. The 

METSTAT program is a CENRAP tool of choice for quantitatively evaluating MM5 model 

performance (Ku, PowerPoint Presentation). METSTAT MM5 calculates statistics in 

absolute terms (e.g., wind speed error in m/s) rather than in relative terms (percent error) 

which can be misleading for certain meteorological fields (METSTAT Documentation). 

METSTAT compares the MM5 output to observed data collect by NWS ASOS stations. 

METSTAT was used to evaluate the surface temperature, humidity, and winds within the 

State domain for all three years of MM5 output (2005-2007). The evaluation calculated 

model’s bias and gross error for surface temperature, humidity, wind speed and wind 

direction and index of agreement with the observed temperature, humidity and winds. 

These calculations are defined below: 

Bias Error (B): The mean difference in prediction-observation pairings with valid data within 

a given analysis region and for a given time period (hourly or daily): 

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Note that the bias and gross error for winds are calculated from the predicted-observed 

residuals in speed and direction (not from vector components u and v). The direction error 

for a given prediction-observation pairing is limited to range from 0 to ±180°. 

Index of Agreement (IOA): This metric condenses all the differences between model 

estimates and observations within a given analysis region and for a given time period 

(hourly and daily) into one statistical quantity. It is the ratio of the total root mean square 

error (RMSE) to the sum of two differences. The differences are between each prediction 

and the observed mean, and each observation and the observed mean: 

DRAFT
MM5 Meteorological Dataset Development 12 Draft - December 2008 

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Viewed from another perspective, the index of agreement is a measure of the match 

between the departure of each prediction from the observed mean and the departure of 

each observation from the observed mean. Thus, the correspondence between predicted 

and observed values across the domain at a given time may be quantified in a single metric 

and displayed as a time series. The index of agreement has a range of 0 to 1, with 1 

suggesting perfect agreement. 

3.1.1. STATISTICAL EVALUATION METHODOLOGY

The statistical measures calculated by METSTAT are output as daily values for each 

variable. This is a cumulative result of the MM5 data’s representation of the conditions 

at each of the NWS surface meteorological stations. Monthly averages are then 

calculated from the METSTAT daily results. 

A few details of the data sources are important to note when analyzing the results. First, 

it is important to note that the density of the National Weather Service meteorological 

observation network is sparse in Hawaii. Therefore, the statistics are based on only a 

handful of observation stations consisting mostly of airports. The stations include 

Honolulu International Airport, Lihue WSO, Kaneohe Bay MCAS, Molokai Airport, 

Kahului Airport and Hilo International Airport. Next, the airport stations report wind 

direction to the nearest 10 degrees, whereas the MM5 reports it by degree. Lastly, due 

to the complex terrain in Hawaii, the meteorological conditions can vary dramatically 

within a few kilometers. These challenges must be kept in mind when analyzing the 

results from the MM5 model. 

3.1.2. STATISTICAL EVALUATION RESULTS

The statistical evaluation results for the state domain encompassing all of the major 

Hawaiian Islands are discussed below. Tables 3.1-1 and 3.1-2 summarize the daily 

statistical measures, averaged over the month, for temperature, mixing ratio, wind speed 

and wind direction for each of the three years. These three variables were used to 

quantify the meteorological data fields used in CENRAP’s BART Modeling Guidelines 

document prepared by Alpine Geophysics (Tesche et al., 2005). 

A set of statistical “benchmarks” were derived in 2001 from evaluation of about 30 MM5 

and RAMS air quality applications, to characterize typical meteorological model 

performance (Emery et al., 2001; Tesche et al., 2001). The purpose of the benchmarks 

is to allow a way to compare the results between different modeling applications, while 

not giving a pass/fail grade to any particular meteorological model application. Thus, 

modeling applications over the central US plains where the landscape is dominated by 

flat uniform elevation would be expected to have smaller errors than an application in the 

complex terrain of Hawaii. The 3-D meteorological dataset used for CENRAP’s BART 

modeling was analyzed using these benchmarks by Alpine Geophysics (Tesche et al., 

2005). Below is a list of the derived benchmarks: 

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MM5 Meteorological Dataset Development 13 Draft - December 2008 

 Wind Speed RMSE: ≤ 2 m/s 

 Bias: ≤ ±0.5 m/s 

 IOA: ≥ 0.6 

 Wind Direction Gross Error: ≤ 30 deg 

 Bias: ≤ ±10 deg 

 Temperature Gross Error: ≤ 2 K 

 Bias: ≤ ± 0.5 K 

 IOA: ≥ 0.8 

 Humidity Gross Error: ≤ 2 g/kg 

 Bias: ≤ ±1 g/kg 

 IOA: ≥ 0.6 

The results of the statistical analysis show the MM5 simulations for the state of Hawaii 

are in close agreement with the aforementioned benchmarks for all variables examined. 

Wind speed agreement appears very good, with typical bias below the recommended 

0.5 m/s error. The IOA for wind speed averages near the 0.6 benchmark as well. This 

alignment is very promising when considering the complex terrain features that can lead 

to regions of strong wind shear between the islands. 

The temperature analysis points out the model’s close agreement with observations 

during the summer months, with slightly larger error during the winter months. These 

results are logical, because the summer months are dominated by daytime heating and 

cooling patterns with trade-winds dominating the pattern. During the winter months, 

there is more variability in the weather patterns as “Kona” lows and frontal systems pass 

through the area. Temperature bias is very close to the benchmark level for most of the 

year, while the gross error for each month meets the benchmark criteria. 

The model predicted humidity levels meet the benchmark bias criteria for most months, 

and the benchmark for gross error for all months. The METSTAT program identifies the 

humidity levels are biased dry through the modeling simulation. This bias is likely a 

product of the limited land-use data available for the state of Hawaii, and the model’s 

ability to simulate the fine-scale orographically forced nighttime precipitation. Orographic 

clouds form on the windward side of the islands, which can be forced over the ridge of 

the mountains leading to brief rainfall on the leeward side of the island. The clouds can 

quickly dissipate due to a lack of forcing and instability at that point, which is difficult to 

predict and correctly place in a model with 9 km resolution. Even with the challenging 

climate to predict, the MM5 still maintains an IOA value near 0.5 for the simulation. 

DRAFT
MM5 Meteorological Dataset Development 14 

TABLE 3.1-1 MM5

STATISTICAL 

EVALUATION 

RESULTS

MM5 Model Evaluation Statistics for the State Modeling Domain

Jan Feb Mar Apr May Jun Jul Aug Sep Oct No

Humidity Bias (g/kg)

2005 ‐1.73 ‐0.77 ‐1.11 ‐1 ‐1.34 ‐1.16 ‐0.86 ‐0.56 ‐1.25 ‐1 

2006 ‐0.71 ‐0.25 ‐1.11 ‐0.45 ‐0.52 ‐0.41 ‐0.44 ‐0.84 ‐0.78 ‐0.94 

2007 ‐0.66 ‐0.62 ‐1.27 ‐0.7 ‐0.84 ‐1.04 ‐1.49 ‐1.08 ‐0.14 ‐0.03 

Humidity Error(g/kg)

2005 1.89 1.42 1.6 1.2 1.54 1.36 1.18 1.14 1.46 1.3 2006 1.09 1.11 1.42 0.89 1.01 1.21 1.13 1.45 1.43 1.43 2007 1.21 1.16 1.5 1.2 1.27 1.7 2.14 1.84 0.98 0.9 

Temperature Bias (K)

2005 1.22 0.96 0.9 ‐0.07 ‐0.16 ‐0.54 ‐0.43 ‐0.45 ‐0.23 0.04 

2006 0.12 0.23 0.96 0.37 0.4 0.07 0.08 0.13 0.21 0.81 2007 0.44 0.27 0.55 0.02 ‐0.05 ‐0.11 0.14 ‐0.13 ‐0.04 0.11 

Temperature Error(K)

2005 1.83 1.82 1.89 1.53 1.62 1.6 1.6 1.73 1.46 1.39 2006 1.36 1.43 1.65 1.42 1.48 1.51 1.53 1.49 1.57 1.71 2007 1.54 1.55 1.65 1.52 1.57 1.49 1.52 1.56 1.51 1.39 

Wind Speed Bias (m/s)

2005 0.02 0.17 ‐0.42 ‐0.02 ‐0.66 ‐0.37 ‐0.24 ‐0.81 ‐0.66 ‐0.18 

2006 0.52 ‐0.23 0.26 0.04 ‐0.49 ‐0.55 ‐0.1 ‐0.31 ‐0.32 ‐0.57 

2007 0.44 0.36 ‐0.1 ‐0.16 ‐0.48 ‐0.29 ‐0.13 ‐0.28 ‐0.11 0 

Wind Speed Error(m/s)

2005 1.67 1.71 1.93 1.85 1.65 1.73 1.66 1.68 1.72 1.64 2006 1.92 1.75 1.82 1.7 1.6 1.63 1.58 1.62 1.65 1.61 2007 1.73 1.75 1.73 1.6 1.55 1.64 1.64 1.57 1.64 1.64 

Wind Dir Bias (deg)

2005 7.97 11.92 10.97 12.69 17.34 14.38 15.09 15.84 14.47 12.16 2006 14.87 10.85 10.5 11.87 13.14 19.14 13.87 11.42 14.82 10.14 2007 12.99 13.49 9.52 14.36 12.49 13.89 12.87 14.63 14.46 14.36 

Wind Dir Error (deg)

2005 47.93 37.04 42.21 32.18 32.91 26.28 23.15 31.64 30.7 29.35 2006 33.62 41.12 52.65 28.44 31.12 32.33 26.78 31.77 30.1 45.03 2007 36.37 33.31 41.68 29.12 32.63 26.63 23.75 27.1 27.52 27.81 

Notes:1. Statistical are from daily output from the METSTAT

DRAFT

program, averaged for the month.
MM5 Meteorological Dataset Development 15 

TABLE 3.1-2 MM5

STATISTICAL 

EVALUATION INDEX OF 

AGREEMENT 

RESULTS

MM5 Model Evaluation Statistics for the State Modeling Domain

Jan Feb Mar Apr May Jun Jul Aug Sep Oct No

Temperature IOA

2005 0.73 0.73 0.73 0.8 0.81 0.79 0.78 0.79 0.8 0.81 2006 0.8 0.79 0.72 0.76 0.8 0.82 0.79 0.82 0.8 0.78 2007 0.78 0.78 0.77 0.83 0.83 0.83 0.81 0.81 0.8 0.8 

Wind Speed IOA

2005 0.55 0.6 0.53 0.57 0.55 0.63 0.67 0.59 0.6 0.65 2006 0.59 0.51 0.51 0.63 0.61 0.6 0.63 0.62 0.59 0.54 2007 0.61 0.61 0.59 0.64 0.57 0.62 0.66 0.64 0.63 0.66 

Humidity IOA

2005 0.41 0.38 0.49 0.44 0.48 0.48 0.5 0.52 0.45 0.48 2006 0.52 0.47 0.46 0.49 0.56 0.53 0.59 0.54 0.56 0.49 2007 0.52 0.54 0.5 0.53 0.56 0.53 0.54 0.53 0.59 0.56 

Notes:1. Statistical are from daily output from the METSTAT program, averaged for the month.

DRAFT
MM5 Meteorological Dataset Development 16 Draft - December 2008 

3.2. GRAPHICAL EVALUATION MEASURES

Graphical evaluations allow for a qualitative assessment of model performance, and 

establish a first-order acceptance/rejection of the simulation in correctly producing the 

overall weather pattern in the domain (METSTAT Documentation). The graphical evaluation 

consists of comparing the MM5 model output with publically available surface analysis 

charts from NCDC4

, identifying frequently observed surface wind features such as the “Maui 

Vortex” and the vortex that develops in the wake of the Big Island, and general agreement 

with the progression of typical weather phenomena. Thus, the graphical evaluation is a 

good way to catch any obvious flaws and errors in the model simulation. 

3.2.1. GRAPHICAL EVALUATION METHODOLOGY

It would be beyond the scope of this document to analyze every available surface 

analysis with the model results. Two 5-day MM5 modeling runs during 2005 are 

analyzed for fundamental weather features; one during a springtime period with a frontal 

passage (Scenario A), and one during the normal summer trade-wind dominated 

weather pattern (Scenario B). The same trade-wind period is chosen to analyze the 

development of the “Maui Vortex” in Domain 3. 

3.2.2. GRAPHICAL EVALUATION RESULTS

The surface analysis for May 2, 2005 at 12Z (Figure F-1) identifies a frontal zone 

extending across the state of Hawaii. This scenario corresponds with a low-pressure 

center with counterclockwise flow north of the islands, and a high pressure center to 

both the east and the west with clockwise flow. The model predicts the location of the 

front and the flow patterns within the domain very well (Figure F-2). The convergence 

line is apparent along the frontal boundary, and the clockwise and counterclockwise flow 

is correct. Channeling of the wind between the Big Island and Maui is present, a feature 

that is well known to people in the area. 

The surface analysis for July 7, 2005 at 18Z (Figure F-3) is dominated by a strong high 

pressure over the Pacific Ocean. This weather pattern is typical for Hawaii during the 

summer months, and is associated with persistent easterly trade winds. The MM5 

replicates the scenario, without any areas of discontinuity over the ocean (Figure F-4). 

Again, the channeling of winds between Maui and the Big Island (Alenuihaha Channel) 

predicted, as well as the vortex downwind of the Big Island. Volcanic emissions can be 

drawn into this vortex, leading to regions of volcanic smog or “vog” on the leeward side 

of the Big Island. Even at 9 km resolution, winds are funneled between the West Maui 

Mountains and Haleakala. The development of these well known circulations 

demonstrates the model’s ability to correctly predict the wind field in the complex terrain 

in Hawaii. 

The MM5 Maui Domain (Domain 3) has a 3 km resolution, containing 9 grid points for 

every 1 in the State domain (Domain 2). The higher resolution results in the prediction 

of fine-scale circulations between Maui, Lanai and Molokai. One key circulation is the 

“Maui Vortex” which can be seen in the MM5 analysis of July 7, 2005 at 12Z (Figure F5). Wind directions become northerly over Kahului, as they funnel between the two 

peaks on Maui. They turn cyclonically toward the western boundary of Haleakala 

National Park, and back toward the south creating a closed circulation. By correctly 

 

4

 NCDC Website http://nomads.ncdc.noaa.gov/ncep-charts/archives/ 

DRAFT
MM5 Meteorological Dataset Development 17 Draft - December 2008 

depicting fine-scale circulations, the overall flow and trajectories within the domain will 

be enhanced. 

In conclusion, the graphical analysis of the surface variables yields results that look 

accurate and correct for the region. The timing and placement of both synoptic and 

microscale features are consistent with actual recorded and analyzed weather 

conditions. 

DRAFT
MM5 Meteorological Dataset Development 18 Draft - December 2008 

4.0 REFERENCES

Emery, C.A, E. Tai, G. Yarwood. 2001. “Enhanced Meteorological Modeling and Performance 

Evaluation for Two Texas Ozone Episodes.” Prepared for the Texas Natural Resource 

Conservation Commission, by ENVIRON International Corp., Novato, CA. 

Ku, Calvin. “CENRAP Modeling Activities.” 

<www.epa.gov/air/visibility/rpo/modeling_pres/cenrap_modeling_ku.ppt>

METSTAT for MM5 Statistical Program. 2005. ENVIRON International Corp., Novato, CA. 

<http://www.camx.com/down/support.php> 

McNally and T.W. Tesche. 2002. Annual Application of MM5 to the Continental United States. 

Version 1.1. Alpine Geophysics, LLC. 

Tesche, T.W., D.E. McNally, C.A. Emery, E. Tai. 2001. “Evaluation of the MM5 Model over the 

Midwestern U.S. for Three 8-hour Oxidant Episodes.” Prepared for the Kansas City Ozone 

Technical Workgroup, by Alpine Geophysics, LLC, Ft. Wright, KY, and ENVIRON International 

Corp., Novato, CA. 

Tonneson, G and Z. Wang, 2006. CALMET/CALPUFF Protocol for BART Exemption Screening 

Analysis for Class I Areas in the Western United States. Draft Final Modeling Protocol. 

University of California at Riverside and ENVIRON. 

Tesche, T.W., D. McNally and G. Schewe, 2005. CENRAP BART Modeling Guidelines. Alpine 

Geophysics, LLC. 

DRAFT
MM5 Meteorological Dataset Development 19 Draft - December 2008 

APPENDIX A– TERRAIN PREPROCESSOR CONFIGURATION

The below tables list the TERRAIN configurations used to develop the 27, 9 and 3 km MM5 

land-use and elevation inputs. 

TABLE A-1 - TERRAIN DOMAIN DATA INPUT PARAMETERS

Parameter Default JCA Comments

IIMX 100 130 Maximum size of the domains NSIZE=IIMX*JJMX

JJMX 100 130

ITRH 700 1500

Used when downloading 30‐second Elevation data.

ITRH ≈ (XMAXLAT‐XMINLAT)*120

JTRH 800 1800

Used when downloading 30‐second Elevation data.  

JTRH ≈ (XMAXLON‐XMINLON)*120

TABLE A-2 - TERRAIN NAMELIST PARAMETER SETTINGS

Parameter Default Comments

D1 D2 D3 D4

PHIC ‐‐

Coarse Domain Central Latitude

(minus forsouthern hemisphere)

XLONC ‐‐

Coarse Domain Central longitude

(minus for western hemisphere)

IEXP .F.

Expanded coarse domain, useful for running

RAWINS/little_r (which we are not)

AEXP 360 Approx. Expansion (km)

IPROJ LAMCON Lambert‐Conformal Map Projection

MAXNES ‐‐ Number of domains to process

NESTIX ‐‐ 41 82 91 121 Grid Dimensions in the Y Direction

NESTJX ‐‐ 49 103 115 112 Grid Dimensions in the X Direction

DIS ‐‐ 27 9 3 3 Grid Distance

NUMNC ‐‐ 1 1 2 2 Mother Domain ID

NESTI ‐‐ 1 7 29 7 Lower Left I of nest in mother Domain

NESTJ ‐‐ 1 9 42 57 Lower Left J of nest in Mother Domain

RID ‐‐ 1.5 1.5 1.5 3.1 Radius of Influence in Grid Units (IFTANAL=T)

NTYPE ‐‐ 4 5 6 6 Input Data Resolution

NSTYPE ‐‐ 1 2 2 21‐‐ One Way Nest, 2 ‐‐ two way nest

IFTER .TRUE. Use Terrain Data

IFANAL ‐‐ Interpolation

ISMTHTR 2 Two pass smoother/desmoother

IFEZFUG .F. Use Landwater Mask Data

IFTFUG .F.

IFFUDG .F.

TRUELAT1 ‐‐ True Latitude 1

TRUELAT2 ‐‐ True Latitude 2

LSMDATA .TRUE. Create the data for LSM

VEGTYPE 1 2 1 4 Category USGS

JCA

20.8

‐157.6

.F.

.F.

360

LAMCON

60

30

.TRUE.

.TRUE.

.F.

2

.F.

4

DRAFT

.F.
MM5 Meteorological Dataset Development 20 Draft - December 2008 

APPENDIX B – PREGRID PREPROCESSOR V-TABLES

The below tables list the PREGRID V-tables used to extract data variables from the NAM data 

files. 

TABLE B-1 – PREGRID V-TABLES

GRIB Level Level Level REGRID REGRID REGRID

Code Code 1 2 Name Units Description

V‐table.NAM

11 100 * T K Temperature

33 100 * U m s{‐1} U

34 100 * V m s{‐1} V

51 100 * SPECHUMD kg kg{‐1}

52 100 * RH % Relative Humidity

7 100 * HGT m Height

11 105 2 T K Temperature

51 105 2 SPECHUMD kg kg{‐1}

52 105 2 RH % Relative Humidity

33 105 10 U m s{‐1} U

34 105 10 V m s{‐1} V

2 102 0 PMSL Pa Sea‐level Pressure

V‐table.NAMSNOW

65 1 0 WEASD kg m{‐2} Water Equivalent Snow Depth

V‐table.NAMSOIL

85 112 0 10 SOILT010 K T of 0‐10 cm ground layer

85 112 10 40 SOILT040 K T of 10‐40 cm ground layer

85 112 40 100 SOILT100 K T of 40‐100 cm ground layer

85 112 100 200 SOILT200 K T of 100‐200 cm ground layer

144 112 0 10 SOILM010 fraction Soil Moisture of 0‐10 cm ground layer

144 112 10 40 SOILM040 fraction Soil Moisture of 10‐40 cm ground layer

144 112 40 100 SOILM100 fraction Soil Moisture of 40‐100 cm ground layer

144 112 100 200 SOILM200 fraction Soil Moisture of 100‐200 cm ground layer

91 1 0 SEAICE 0/1 Flag Ice flag

81 1 0 LANDSEA 0/1 Flag Land/Sea flag

7 1 0 SOILHGT m Terrain field of source analysis

V‐table.RTGSST

11 1 0

DRAFT

SKINTEMP K Sea‐Surface Temperature
MM5 Meteorological Dataset Development 21 Draft - December 2008 

APPENDIX C – INTERPF PREPROCESSOR CONFIGURATION

The below tables identify the INTERPF configurations used developing the 27 km MM5 domain 

inputs 

TABLE C-1 - INTERPF NAMELIST PARAMETERS

Parameter Default JCA Comments

INTERVAL ‐‐ 21600

Time increment between analysis

times (sec)

SIGMA_F_BU

1.00,0.99,0.98,0.96,0.93,0.89,

0.85,0.80,0.75,0.70,0.65,0.60,

0.55,0.50,0.45,0.40,0.35,0.30,

0.25,0.20,0.15,0.10,0.05,0.00

1.00,0.995,0.99,0.985,0.98,

0.96,0.93,0.89,0.85,0.80,0.75,

0.70,0.65,0.60,0.55,0.50,0.45,

0.40,0.35,0.30,0.25,0.20,0.15,

0.10,0.05,0.00

Full sigma levels, bottom‐up

PTOP 10000 10000 Top pressure (Pa)

p0 1.00E+05 1.00E+05 base state sea‐level pressure (Pa)

tlp 50 50 base state lapse rate

ts0 275 298

base state sea‐level temperature (K)

[Dr. Li‐Yen Chen's MM5 runs from the

UoH use 298K for ts0]

tiso 0 0 base state isothermal strato. Temp (K)

removediv .TRUE. .TRUE. Remove integrated mean divergence.

usesfc .TRUE. .TRUE. Use surface data

wrth2o .TRUE. .TRUE. Specific humidity wrt liq. H2O

psfc_method 0 0

DRAFT
MM5 Meteorological Dataset Development 22 Draft - December 2008 

APPENDIX D – MM5 SET-UP PARAMETERS

The below tables identify the MM5 configurations used 27, 9 and 3 km MM5 modeling domains. 

TABLE D-1 - MM5 CONFIGURE.USER PARAMETER SETTINGS

Parameter Default Comments

D1 D2 D3 D4

Section 4. General Commands

CC cc C‐compiler option, set for Portland Group

Section 5. Options for making parame.incl

FDDAGD 0 FDDA gridded run (0=no, 1=yes)

FDDAOBS 0

MAXNES ‐‐ 1 Max number of domains In simulation

MIX ‐‐ Maximum Dimensions of any Domain

MJX ‐‐

MKX ‐‐ Number of half sigma levels in model

Section 6. Physics Options

IMPHYS 4 3 3 3 3 Warm‐rain Explicit Moisture Scheme (3)

MPHYSTBL 0

ICUPA 3 (coarse), 1 (Nests) 3 3 1 1 Cumulus Parameterization Scheme

IBLTYP 5 5 5 5 5 Boundary Layer

FRAD 2 2 2 2 2 Radiation Cooling of Atmos.

IPOLAR 0 Polar model

ISOIL 1 Noah Land‐surface scheme

ISHALLO 0 1 1 1 1 Shallow convection option

Section 7. MPP Options

PROCMIN_NS 1 Minimum number of processors in N/S

PROCMIN_EW 1 Minimum number of processors in E/W

RUNTIME_SYST ‐‐ Operating System

FCFLAGS

‐O2 ‐Mcray=pointer

‐tp p6 ‐pc 32

‐Mnoframe

‐byteswapio

CPPFLAGS

‐DMPI ‐Dlinux

‐DSYSTEM_CALL_OK

CFLAGS

‐DMPI

‐I$(LINUX_MPIHOME)/

include

JCA

pgcc

0

0

0

3

121

115

25

0

Default +

'‐DDEC_ALPHA'

Default +

'‐DDEC_ALPHA'

2

1

1

LINUX

‐O2 ‐Mvect

‐Mcray=pointer

‐tp p7‐64 ‐pc 32

‐Mnoframe

‐byteswapio

‐DDEC_ALPHA

 

DRAFT
MM5 Meteorological Dataset Development 23 Draft - December 2008 

TABLE D-2 - MM5 MM5.DECK PARAMETER/OPTIONS SETTINGS

Parameter Default Comments

D1 D2 D3 D4

TIMAX ‐‐

Forecast length in minutes.  Same as

that used by Alpine Geophysics MM5

modeling protocol for the continental

US.

TISTEP ‐‐ 9 coarse domain DT in model, use 3*DX

IFREST .FALSE. Is this  restart

IFTAPE 1 model output

TAPFRQ 180 Model output frequency (minutes)

BUFFRQ 0 How frequently to split output files

INCTAP 1

IFSKIP .FALSE. Whether to skip input files

MASCHK 99999

User‐chosen Options 1

RADFRQ 30 Atmospheric Radiation calc frequency

IMVDIF 1 Moist vertical diffusion in clouds

IVQADV 1 Vertical moisture advection

IVTADV 1 Vertical temperature advection

ITHADV 1

ITPDIF 1

TDKORR 2 Temperature Gradiant correction

ICOR3D 1 3D Coriolis Force

IEXSI 0

IFUPR 1

LEVSLP 9

OROSHAW 0

Orographic shadowing Not available

for MPI runs

ITADVM 0 0 0 0 0 Instability limiter not used

IQADVM 0 0000

IBOUDY 3,2,2..... 3 2 2 3 Boundary Conditions

IFDRY 0

ISSTVAR 0

IMOIAV 0 0000

IZOTOPT 0

ISFMTHD 1

IFSNOW 0 0 0 0 0 Snow cover effects

ISFFLX 1 1 1 1 1 Surface Fluxes

ITGFLG 1 1 1 1 1 Surface temperature predicted

ISFPAR 1 1 1 1 1 Surface characteristics

ICLOUD 1 1 1 1 1 Cloud effects on radiation

IEVAP 1 1 1 1 1 Evap of cloud/rain water

ISMRD 0

ISTLYR 10,40,100,200 Soil temperature layers

ISMLYR 10,40,100,200 Soil moisture layers

RDMAXALB .FALSE.

RDBRDALB .FALSE. .FALSE.

10,40,100,200

10,40,100,200

9

.FALSE.

0

1

0

0

0

0

99999

.FALSE.

1

1

1

1

2

1

0

1

1

30

JCA

7920

.FALSE.

81

1

0

60

1

DRAFT
MM5 Meteorological Dataset Development 24 Draft - December 2008 

TABLE D-3 - MM5 MM5.DECK PARAMETERS/OPTIONS SETTINGS CONTINUED

Parameter Default Comments

D1 D2 D3 D4

Nest and Moving Nest Options

LEVIDN ‐‐ 0 1 2 0 Nest level for each domain

NUMNC ‐‐ 1 1 2 1 Mother domain ID

NESTIX ‐‐ 41 82 91 121 domain size i

NESTJX ‐‐ 49 103 115 112 domain size j

NESTI ‐‐ 1 7 29 1

NESTJ 1 9 42 1

XSTNES 0 0 0 0 0 Domain initiation

IOVERW 1221

IMOVE 0 0000

IFEED 3

Miscellaneous Options

ZZLND 0.1 Roughness length over land

ZZWTR 0.0001 Roughness length over water

ALBLND 0.15 Albedo

THINLD 0.04 Surface thermal inertia

XMAVA 0.3 Moisture availability over land

CONF 1

non‐convective saturation threshold

(1 = 100%)

NO NUDGING IS PERFORMED.  All other options are default values.

1

JCA

0.1

0.0001

0.15

0.04

0.3

3

DRAFT
MM5 Meteorological Dataset Development 25 

APPENDIX 

E

– NESTDOWN

SET-UP 

PARAMETERS

The below table identifies the parameter settings used for running the NESTDOWN program for Domain 4. The from Domain 2 (State Domain) is used to initialize and generate the hourly boundary conditions for the MM5 for domain) modeling. 

TABLE E-1 - NESTDOWN 

NAMELIST 

PARAMETER 

SETTINGS/OPTIONS

Parameter Default JCA Commen

INTERVAL ‐‐ 3600

Time increment betwee

times (sec)

SIGMA_F_BU

1.00,0.99,0.98,0.96,0.93,0.89,0.85,0.80,0.75,0.70,0.65,0.60,0.55,0.50,0.45,0.40,0.35,0.30,

0.25,0.20,0.15,0.10,0.05,0.00

1.00,0.995,0.99,0.985,0.98,

0.96,0.93,0.89,0.85,0.80,0.75,0.70,0.65,0.60,0.55,0.50,0.45,0.40,0.35,0.30,0.25,0.20,0.15,

0.10,0.05,0.00

Full sigma levels, bottom

sst_to_ice_threshold ‐9999 ‐9999 Temperature at which ic

ocean is not applicable t

wrth2o .TRUE. .TRUE.ifdatim ‐‐ ‐1 "‐1" specifies to output 

from the MMOUT_DOM

interp_method 1 1 Overlapping parabolic m

print_info .TRUE. .TRUE.use_mm5_lowbdy .FALSE. .FALSE.

Because ISOIL=2, we are the deep soil temperatumodel output and doma

DRAFT
MM5 Meteorological Dataset Development 26 

APPENDIX 

F

– MM5

GRAPHICAL 

EVALUATION 

PLOTS

FIGURE F-1

–

MAY 2, 2005 12Z 

SCENARIO 

A

SURFACE 

ANALYSIS

DRAFT
MM5 Meteorological Dataset Development 27 

FIGURE F-2

–

MAY 2, 2005 12Z 

SCENARIO 

A MM5

MODEL 

DOMAIN 2 

DRAFT
MM5 Meteorological Dataset Development 28 

FIGURE F-3

–

JULY 7, 2005 18Z 

SCENARIO 

B

SURFACE 

ANALYSIS

DRAFT
MM5 Meteorological Dataset Development 29 

FIGURE F-4

–

JULY 7, 2005 18Z 

SCENARIO 

B MM5

MODEL 

DOMAIN 2 

DRAFT
MM5 Meteorological Dataset Development 30 

FIGURE F-5

–

JULY 7, 2005 12Z

MAUI 

DOMAIN 

WIND 

VECTORS

DRAFT