# Hawaiian Electric Company, Inc.

Maui Electric Company, Inc.

Hawaiian Electric Light Company, Ltd.



# MM5 M ETEOROLOGICAL



# D ATASET 

# D EVELOPMENT FOR



# H AWAII

Prepared by

Draft December 2008MM5 Meteorological Dataset Development i Draft – December 2008 

# T ABLE OF 

# C ONTENTS

1.0 I NTRODUCTION 

### ........................................................................................................................ 1

2.0 MM5 M ETEOROLOGICAL 

### D ATASET 

### D EVELOPMENT 

### ................................................................... 2

2.1. I NPUT 

### D ATA



### .............................................................................................................................. ... 7

2.1.1. T OPOGRAPHY 

### ............................................................................................................................ 7

2.1.2. V EGETATION 

### T YPE AND 

### L AND 

### U SE 

### ........................................................................................... 7

2.1.3. G RIDDED 

### M ETEOROLOGICAL 

### D ATA 

### ........................................................................................... 7

2.1.4. S EA 

### -S URFACE 

### T EMPERATURE 

### .................................................................................................. 8

2.2. D ATA 

### P REPROCESSING 

### ............................................................................................................... 8

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

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

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

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

2.3. MM5 M ODELING 

### .......................................................................................................................... 9

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

2.3.2. L INK TO 

### CALPUFF .................................................................................................................. 10

3.0 E VALUATION OF THE 

### MM5 M ETEOROLOGICAL 

### D ATA 

### S ET 

### ....................................................... 11

3.1. S TATISTICAL 

### E VALUATION 

### M EASURES 

### ....................................................................................... 11

3.1.1. S TATISTICAL 

### E VALUATION 

### M ETHODOLOGY 

### ............................................................................. 12

3.1.2. S TATISTICAL 

### E VALUATION 

### R ESULTS 

### ....................................................................................... 12

3.2. G RAPHICAL 

### E VALUATION 

### M EASURES 

### ........................................................................................ 16

3.2.1. G RAPHICAL 

### E VALUATION 

### M ETHODOLOGY 

### .............................................................................. 16

3.2.2. G RAPHICAL 

### E VALUATION 

### R ESULTS 

### ......................................................................................... 16

4.0 R EFERENCES 

### ........................................................................................................................ 18

A PPENDIX 

### A

– TERRAIN P REPROCESSOR 

### C ONFIGURATION 

### ......................................................... 19

A PPENDIX 

### B

– PREGRID P REPROCESSOR 

### V- TABLES 

### ................................................................... 20

A PPENDIX 

### C

– INTERPF P REPROCESSOR 

### C ONFIGURATION 

### .......................................................... 21

A PPENDIX 

### D

– MM5 S ET



### - UP 

### P ARAMETERS 

### ................................................................................... 22

A PPENDIX 

### E

– NESTDOWN S ET



### - UP 

### P ARAMETERS 

### ....................................................................... 25

A PPENDIX 

### F

– MM5 G RAPHICAL 

### E VALUATION 

### P LOTS 

### .................................................................... 26



# L IST OF 

# F IGURES

F IGURE 

### 2.0-1 MM5 M ODELING 

### D OMAINS 

### ........................................................................................ 2

F IGURE 

### 2.0-2 MM5 M ODELING 

### S UITE 

### ............................................................................................. 3

F IGURE 

### 2.0-3 MM5 D OMAIN 

### 2 (S TATE 

### M ODELING 

### D OMAIN 

### ) ............................................................. 4

F IGURE 

### 2.0-4 MM5 D OMAIN 

### 3 (M AUI 

### M ODELING 

### D OMAIN 

### ) ............................................................... 5

F IGURE 

### 2.0-5 MM5 D OMAIN 

### 4 (H AWAII 

### M ODELING 

### D OMAIN 

### ) ............................................................ 6

F IGURE 

### 2.1-1 N ORTH 

### A MERICAN 

### M ESOSCALE 

### G RID 

### 243 D OMAIN 

### .................................................... 8

F IGURE 

### F-1

– M AY 

### 2, 2005 12 Z 

### S CENARIO 

### A S URFACE 

### A NALYSIS 

### .................................................. 26

F IGURE 

### F-2

– M AY 

### 2, 2005 12 Z 

### S CENARIO 

### A MM5 M ODEL 

### D OMAIN 

### 2 ............................................ 27

F IGURE 

### F-3

– J ULY 

### 7, 2005 18 Z 

### S CENARIO 

### B S URFACE 

### A NALYSIS 

### ................................................. 28

F IGURE 

### F-4

– J ULY 

### 7, 2005 18 Z 

### S CENARIO 

### B MM5 M ODEL 

### D OMAIN 

### 2 ............................................ 29

F IGURE 

### F-5

– J ULY 

### 7, 2005 12Z M AUI 

### D OMAIN 

### W IND 

### V ECTORS 

### ..................................................... 30MM5 Meteorological Dataset Development ii Draft – December 2008 

# L IST OF 

# T ABLES

T ABLE 

### 3.1-1 MM5 S TATISTICAL 

### E VALUATION 

### R ESULTS 

### ................................................................. 14

T ABLE 

### 3.1-2 MM5 S TATISTICAL 

### E VALUATION 

### I NDEX OF 

### A GREEMENT 

### R ESULTS 

### .............................. 15

T ABLE 

### A-1

- TERRAIN D OMAIN 

### D ATA 

### I NPUT 

### P ARAMETERS 

### ............................................................ 19

T ABLE 

### A-2

- TERRAIN N AMELIST 

### P ARAMETER 

### S ETTINGS 

### .............................................................. 19

T ABLE 

### B-1

– PREGRID V- TABLES 

### ................................................................................................ 20

T ABLE 

### C-1

- INTERPF N AMELIST 

### P ARAMETERS 

### ............................................................................ 21

T ABLE 

### D-1

- MM5 C ONFIGURE 

### .U SER 

### P ARAMETER 

### S ETTINGS 

### ......................................................... 22

T ABLE 

### D-2

- MM5 MM 

### 5. DECK 

### P ARAMETER 

### /O PTIONS 

### S ETTINGS 

### ...................................................... 23

T ABLE 

### D-3

- MM5 MM 

### 5. DECK 

### P ARAMETERS 

### /O PTIONS 

### S ETTINGS 

### C ONTINUED 

### ................................. 24

T ABLE 

### E-1

- N ESTDOWN 

### N AMELIST 

### P ARAMETER 

### S ETTINGS 

### /O PTIONS 

### ............................................. 25MM5 Meteorological Dataset Development 1 Draft - December 2008 

# 1.0 I NTRODUCTION

Regional haze is visibility impairment caused by the cumulative air pollutant emissions from numerous sources over a wide geographic area. Visi bility impairment is caused primarily by the scattering of light by fine particles (PM 2.5 

### ), 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 pollutants 1



### . 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 remedy ing 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, tem poral 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.htmlMM5 Meteorological Dataset Development 2 Draft - December 2008 

# 2.0 MM5 M ETEOROLOGICAL 

# D ATASET 

# D EVELOPMENT

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 atmo spheric 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 illustrat es 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 Is land, 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.

F IGURE 

### 2.0-1 MM5 M ODELING 

### D OMAINS

-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 26Latitude (degrees) Domain 1 (27 km)

Domain 2 (9 km)

Domain 3 (3 km)

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

### F IGURE 

### 2.0-2 MM5 M ODELING 

### S UITE



### 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.MM5 Meteorological Dataset Development 4 Draft - December 2008 

### F IGURE 

### 2.0-3 MM5 D OMAIN 

### 2 (S TATE 

### M ODELING 

### D OMAIN 

### )

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

Longitude (degrees)1718192021222324Latitude (degrees) State Domain (9 km)MM5 Meteorological Dataset Development 5 Draft - December 2008 

### F IGURE 

### 2.0-4 MM5 D OMAIN 

### 3 (M AUI 

### M ODELING 

### D OMAIN 

### )

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

Longitude (degrees)19.520.020.521.021.522.0Latitude (degrees) Maui Domain (3 km)MM5 Meteorological Dataset Development 6 Draft - December 2008 

### F IGURE 

### 2.0-5 MM5 D OMAIN 

### 4 (H AWAII 

### M ODELING 

### D OMAIN 

### )

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

Longitude (degrees)18.018.519.019.520.020.521.0Latitude (degrees) Hawaii Domain (3 km)MM5 Meteorological Dataset Development 7 Draft - December 2008 

## 2.1. I NPUT 

## D ATA


### 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. T OPOGRAPHY


### 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. V EGETATION 

### T YPE AND 

### L AND 

### U SE


### Vegetation type and land use information is der ived 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. G RIDDED 

### M ETEOROLOGICAL 

### D ATA


### 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 data 2



### .

2 http://www.emc.ncep.noaa.gov/mmb/namgrids/MM5 Meteorological Dataset Development 8 Draft - December 2008 

### F IGURE 

### 2.1-1 N ORTH 

### A MERICAN 

### M ESOSCALE 

### G RID 

### 243 D OMAIN



### 2.1.4. S EA 

### -S URFACE 

### T EMPERATURE


### Real-time global sea-surface temperature analyses (RTG-SST) obtained from NCEP 3



### , 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. D ATA 

## P REPROCESSING


### MM5 requires preprocessing of all input data into a specific file format. Preprocessing of the data is accomplished by 4 programs: TERRA IN, 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/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 M ODELING


### 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.MM5 Meteorological Dataset Development 10 Draft - December 2008 

### 2.3.1. NESTDOWN

The NESTDOWN program is both a preproc essor 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. L INK 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 variabl es 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.MM5 Meteorological Dataset Development 11 Draft - December 2008 

# 3.0 E VALUATION OF THE 

# MM5 M ETEOROLOGICAL 

# D ATA 

# S ET

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. S TATISTICAL 

## E VALUATION 

## M EASURES


### 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):

ൌ ܤ 1

ܬܫ ܲሺ෍෍

௝ ௜ 

### െ ܱ ௝ ௜ 

### ሻ ூ

௜ୀଵ ௃

௝ୀଵ



### Gross Error (E) : The mean absolute difference in prediction-observation pairings with valid data within a given analysis region and for a given time period (hourly or daily):

ൌ ܧ 1

ܬܫ ܲห෍෍

௝ ௜ 

### െ ܱ ௝ ௜ 

### ห ூ

௜ୀଵ ௃

௝ୀଵ



### 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 obse rvation and the observed mean:MM5 Meteorological Dataset Development 12 Draft - December 2008 

## ⎥ ⎥ ⎥ ⎥

⎦ ⎤

⎢ ⎢ ⎢ ⎢

⎣ ⎡

−+− ⋅

−= 

# ∑∑

= = J

j I

i o i
jo i
j 

## MOMP

RMSEIJ IOA

1 1 2 

## 1

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. S TATISTICAL 

### E VALUATION 

### M ETHODOLOGY


### 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. S TATISTICAL 

### E VALUATION 

### R ESULTS


### 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 ov er 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 hav e 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: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 aforementi oned 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.MM5 Meteorological Dataset Development 14 Draft - December 2008TABLE3.1-1MM5STATISTICALEVALUATIONRESULTSMM5ModelEvaluationStatisticsfortheStateModelingDomainJan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec MeanHumidityBias(g/kg)2005‐1.73‐0.77‐1.11‐1‐1.34‐1.16‐0.86‐0.56‐1.25‐1‐1.42‐0.22‐1.042006‐0.71‐0.25‐1.11‐0.45‐0.52‐0.41‐0.44‐0.84‐0.78‐0.94‐1.12‐0.22‐0.652007‐0.66‐0.62‐1.27‐0.7‐0.84‐1.04‐1.49‐1.08‐0.14‐0.03‐0.46‐0.69‐0.75HumidityError(g/kg)20051.89 1.42 1.6 1.2 1.54 1.36 1.18 1.14 1.46 1.3 1.64 0.98 1.3920061.09 1.11 1.42 0.89 1.01 1.21 1.13 1.45 1.43 1.43 1.49 1.05 1.2320071.21 1.16 1.5 1.2 1.27 1.7 2.14 1.84 0.98 0.9 1.08 0.96 1.33TemperatureBias(K)20051.22 0.96 0.9‐0.07‐0.16‐0.54‐0.43‐0.45‐0.23 0.04 0.35 0.75 0.1920060.12 0.23 0.96 0.37 0.4 0.07 0.08 0.13 0.21 0.81 0.68 0.45 0.3820070.44 0.27 0.55 0.02‐0.05‐0.11 0.14‐0.13‐0.04 0.11 0.82 0.60 0.22TemperatureError(K)20051.83 1.82 1.89 1.53 1.62 1.6 1.6 1.73 1.46 1.39 1.45 1.73 1.6420061.36 1.43 1.65 1.42 1.48 1.51 1.53 1.49 1.57 1.71 1.61 1.45 1.5220071.54 1.55 1.65 1.52 1.57 1.49 1.52 1.56 1.51 1.39 1.66 1.39 1.53WindSpeedBias(m/s)20050.02 0.17‐0.42‐0.02‐0.66‐0.37‐0.24‐0.81‐0.66‐0.18‐0.31‐0.45‐0.3320060.52‐0.23 0.26 0.04‐0.49‐0.55‐0.1‐0.31‐0.32‐0.57 0.1 0.32‐0.1120070.44 0.36‐0.1‐0.16‐0.48‐0.29‐0.13‐0.28‐0.11 0‐0.08 1.18 0.03WindSpeedError(m/s)20051.67 1.71 1.93 1.85 1.65 1.73 1.66 1.68 1.72 1.64 1.57 1.53 1.7020061.92 1.75 1.82 1.7 1.6 1.63 1.58 1.62 1.65 1.61 1.49 1.70 1.6720071.73 1.75 1.73 1.6 1.55 1.64 1.64 1.57 1.64 1.64 1.59 2.13 1.68WindDirBias(deg)20057.97 11.92 10.97 12.69 17.34 14.38 15.09 15.84 14.47 12.16 13.3 12.58 13.23200614.87 10.85 10.5 11.87 13.14 19.14 13.87 11.42 14.82 10.14 14.34 16.09 13.42200712.99 13.49 9.52 14.36 12.49 13.89 12.87 14.63 14.46 14.36 13.43 13.87 13.36WindDirError(deg)200547.93 37.04 42.21 32.18 32.91 26.28 23.15 31.64 30.7 29.35 41.22 44.55 34.93200633.62 41.12 52.65 28.44 31.12 32.33 26.78 31.77 30.1 45.03 44.44 33.65 35.92200736.37 33.31 41.68 29.12 32.63 26.63 23.75 27.1 27.52 27.81 42.71 29.72 31.53Notes:1.StatisticalarefromdailyoutputfromtheMETSTATprogram,averagedforthemonth.MM5 Meteorological Dataset Development 15 Draft - December 2008TABLE3.1-2MM5STATISTICALEVALUATIONINDEX OFAGREEMENTRESULTSMM5ModelEvaluationStatisticsfortheStateModelingDomainJan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec MeanTemperatureIOA20050.73 0.73 0.73 0.8 0.81 0.79 0.78 0.79 0.8 0.81 0.79 0.81 0.7820060.8 0.79 0.72 0.76 0.8 0.82 0.79 0.82 0.8 0.78 0.76 0.79 0.7920070.78 0.78 0.77 0.83 0.83 0.83 0.81 0.81 0.8 0.8 0.74 0.71 0.79WindSpeedIOA20050.55 0.6 0.53 0.57 0.55 0.63 0.67 0.59 0.6 0.65 0.59 0.55 0.5920060.59 0.51 0.51 0.63 0.61 0.6 0.63 0.62 0.59 0.54 0.56 0.58 0.5820070.61 0.61 0.59 0.64 0.57 0.62 0.66 0.64 0.63 0.66 0.56 0.65 0.62HumidityIOA20050.41 0.38 0.49 0.44 0.48 0.48 0.5 0.52 0.45 0.48 0.41 0.52 0.4620060.52 0.47 0.46 0.49 0.56 0.53 0.59 0.54 0.56 0.49 0.49 0.53 0.5220070.52 0.54 0.5 0.53 0.56 0.53 0.54 0.53 0.59 0.56 0.5 0.51 0.53Notes:1.StatisticalarefromdailyoutputfromtheMETSTATprogram,averagedforthemonth.MM5 Meteorological Dataset Development 16 Draft - December 2008 

## 3.2. G RAPHICAL 

## E VALUATION 

## M EASURES


### 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 (METST AT Documentation). The graphical evaluation consists of comparing the MM5 model output with publically available surface analysis

charts from NCDC 4 

### , 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. G RAPHICAL 

### E VALUATION 

### M ETHODOLOGY


### 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. G RAPHICAL 

### E VALUATION 

### R ESULTS


### 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/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.MM5 Meteorological Dataset Development 18 Draft - December 2008 

# 4.0 R EFERENCES

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.MM5 Meteorological Dataset Development 19 Draft - December 2008 

# A PPENDIX 

# A

– TERRAIN P REPROCESSOR 

# C ONFIGURATION

The below tables list the TERRAIN configurations used to develop the 27, 9 and 3 km MM5 land-use and elevation inputs.

T ABLE 

### A-1

- TERRAIN D OMAIN 

### D ATA 

### I NPUT 

### P ARAMETERS



### 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)*12 0

JTRH 800 1800 Used when downloading 30

‐ second Elevation data.

JTRH ≈ (XMAXLON

‐ XMINLON)*12 0



### T ABLE 

### A-2

- TERRAIN N AMELIST 

### P ARAMETER 

### S ETTINGS



### Parameter Default Comments

D1 D2 D3 D4

PHIC

‐‐ Coarse Domain Central Latitude

(minus for southern 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 Di rection

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

IS MTHTR 2 T w o 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 4 Category USGS 1 JCA

20.8

‐ 157. 6

.F.

.F. 360

LAMCON

60

30

.TRUE. .TRUE.

.F.

2

.F. 4

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

# A PPENDIX 

# B

– PREGRID P REPROCESSOR 

# V- TABLES

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

files.

T ABLE 

### 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 10 0

‐ 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 SKINTEMP K Sea

‐ Surface TemperatureMM5 Meteorological Dataset Development 21 Draft - December 2008 

# A PPENDIX 

# C

– INTERPF P REPROCESSOR 

# C ONFIGURATION

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

T ABLE 

### C-1

- INTERPF N AMELIST 

### P ARAMETERS

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 b a s e state isothermal strato. Temp (K)

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

usesfc .TRUE. .TRUE. Use surface data

wrth2o .TRUE. .TRUE. Specific hu midity wrt liq. H2O

psfc_method 0 0MM5 Meteorological Dataset Development 22 Draft - December 2008 

# A PPENDIX 

# D

– MM5 S ET



# - UP 

# P ARAMETERS

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

T ABLE 

### D-1

- MM5 C ONFIGURE 

### .U SER 

### P ARAMETER 

### S ETTINGS



### Parameter Default Comments

D1 D2 D3 D4

Section 4. General Commands

CC cc C

‐ compiler option, set for Portland Grou p

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 N o a h 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_SYS T

‐‐ 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_ALPHAMM5 Meteorological Dataset Development 23 Draft - December 2008 

### T ABLE 

### D-2

- MM5 MM 

### 5. DECK 

### P ARAMETER 

### /O PTIONS 

### S ETTINGS

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 m o d e l output

TAPFRQ 180 Model output frequency (minutes)

BUFFRQ 0 H o w frequently to split output files

INCTAP 1

IFSKIP .FALSE. Whether to skip input files

MASCHK 99999

User

‐ chosen Op tions 1

RADFRQ 30 Atmospheric Radiation calc frequency

IMVDIF 1 M o i s t vertical diffusion in clouds

IVQADV 1 V e r t i c a l moisture advection

IVTADV 1 V e r t i c a l temperature advection

ITHADV 1

ITPDIF 1

TDKORR 2 Temperature Gradiant correction

ICOR3D 1 3 D 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 1MM5 Meteorological Dataset Development 24 Draft - December 2008 

### T ABLE 

### D-3

- MM5 MM 

### 5. DECK 

### P ARAMETERS 

### /O PTIONS 

### S ETTINGS 

### C ONTINUED



### 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

0.0 4

0.3 3MM5 Meteorological Dataset Development 25 Draft - December 2008APPENDIXE–NESTDOWNSET-UPPARAMETERSThe below table identifies the parameter settings used for running the NESTDOWN program for Domain 4. The MM5 output datafrom Domain 2 (State Domain) is used to initialize and generate the hourly boundary conditions for the MM5 for Domain 4 (Big Islanddomain) modeling.TABLEE-1-NESTDOWNNAMELISTPARAMETERSETTINGS/OPTIONSParameterDefaultJCACommentsINTERVAL‐‐3600Timeincrementbetweenanalysistimes(sec)SIGMA_F_BU1.00,0.99,0.98,0.96,0.93,0.89,0.85,0.80,0.75,0.70,0.65,0.60,0.55,0.50,0.45,0.40,0.35,0.30,0.25,0.20,0.15,0.10,0.05,0.001.00,0.995,0.99,0.985,0.98,0.96,0.93,0.89,0.85,0.80,0.75,0.70,0.65,0.60,0.55,0.50,0.45,0.40,0.35,0.30,0.25,0.20,0.15,0.10,0.05,0.00Fullsigmalevels,bottom‐upsst_to_ice_threshold‐9999‐9999TemperatureatwhichicedevelopsinoceanisnotapplicabletoHawaii.wrth2o.TRUE..TRUE.ifdatim‐‐‐1"‐1"specifiestooutputalltimestepsfromtheMMOUT_DOMAIN#file.interp_method11Overlappingparabolicmethod(fast)print_info.TRUE..TRUE.use_mm5_lowbdy.FALSE..FALSE.BecauseISOIL=2,wearere‐calculatingthedeepsoiltemperaturefromourmodeloutputanddomain4grid‐size.MM5 Meteorological Dataset Development 26 Draft - December 2008APPENDIXF–MM5GRAPHICALEVALUATIONPLOTSFIGUREF-1–MAY2,200512ZSCENARIOASURFACEANALYSISMM5 Meteorological Dataset Development 27 Draft - December 2008FIGUREF-2–MAY2,200512ZSCENARIOAMM5MODELDOMAIN2MM5 Meteorological Dataset Development 28 Draft - December 2008FIGUREF-3–JULY7,200518ZSCENARIOBSURFACEANALYSISMM5 Meteorological Dataset Development 29 Draft - December 2008FIGUREF-4–JULY7,200518ZSCENARIOBMM5MODELDOMAIN2MM5 Meteorological Dataset Development 30 Draft - December 2008FIGUREF-5–JULY7,200512ZMAUIDOMAINWINDVECTORS