Topical Report 

MM5 Application for 2005 

Over the Hawaiian Islands 

Prepared for: 

Hawaii State Department of Health 

Environmental Management Division 

Clean Air Branch 

Prepared by: 

Dennis McNally 

James G. Wilkinson 

Alpine Geophysics, LLC 

7341 Poppy Way 

Arvada, Colorado 80007 

31 October 2008 

 
Table of Contents 

Section No. Page No. 

1 INTRODUCTION ............................................................................................................................ 1-1

2 METHODOLOGY ........................................................................................................................... 2-1

2.1 MODEL SELECTION AND APPLICATION ..................................................................................................................... 2-1

2.2 EVALUATION APPROACH .......................................................................................................................................... 2-2

3 MM5 PERFORMANCE EVALUATION RESULTS .................................................................. 3-1

3.1 INTRODUCTION ......................................................................................................................................................... 3-1

3.2 TEMPERATURE .......................................................................................................................................................... 3-2

3.3 MIXING RATIO .......................................................................................................................................................... 3-2

3.4 WINDS ...................................................................................................................................................................... 3-3

3.5 PRECIPITATION ......................................................................................................................................................... 3-3

3.6 MODEL PERFORMANCE SUMMARY ........................................................................................................................... 3-4

4 REFERENCES ................................................................................................................................. 4-1

 i 
List of Figures 

Figure No. Page No.

Figure 2-1. 36/12/4 km MM5 domain. ....................................................................................................................................... 2-5

Figure 2-2. Hawaii 4km MM5 domain. ..................................................................................................................................... 2-5

Figure 3-1. Spatial mean temperature (deg. C) for January 2005. ............................................................................................. 3-6

Figure 3-2. Spatial mean temperature (deg. C) for February 2005. ........................................................................................... 3-6

Figure 3-3. Spatial mean temperature (deg. C) for March 2005. ............................................................................................... 3-6

Figure 3-4. Spatial mean temperature (deg. C) for April 2005. ................................................................................................. 3-6

Figure 3-5. Spatial mean temperature (deg. C) for May 2005. ................................................................................................... 3-7

Figure 3-6. Spatial mean temperature (deg. C) for June 2005. ................................................................................................... 3-7

Figure 3-7. Spatial mean temperature (deg. C) for July 2005. ................................................................................................... 3-7

Figure 3-8. Spatial mean temperature (deg. C) for August 2005. .............................................................................................. 3-7

Figure 3-9. Spatial mean temperature (deg. C) for September 2005. ......................................................................................... 3-8

Figure 3-10. Spatial mean temperature (deg. C) for October 2005. ........................................................................................... 3-8

Figure 3-11. Spatial mean temperature (deg. C) for November 2005. ....................................................................................... 3-8

Figure 3-12. Spatial mean temperature (deg. C) for December 2005. ........................................................................................ 3-8

Figure 3-13. Spatial mean mixing ratio (g/kg) for January 2005. .............................................................................................. 3-9

Figure 3-14. Spatial mean mixing ratio (g/kg) for February 2005. ............................................................................................ 3-9

Figure 3-15. Spatial mean mixing ratio (g/kg) for March 2005. ................................................................................................ 3-9

Figure 3-16. Spatial mean mixing ratio (g/kg) for April 2005. .................................................................................................. 3-9

Figure 3-17. Spatial mean mixing ratio (g/kg) for May 2005. ................................................................................................. 3-10

Figure 3-18. Spatial mean mixing ratio (g/kg) for June 2005. ................................................................................................. 3-10

Figure 3-19. Spatial mean mixing ratio (g/kg) for July 2005. .................................................................................................. 3-10

Figure 3-20. Spatial mean mixing ratio (g/kg) for August 2005. ............................................................................................. 3-10

Figure 3-21. Spatial mean mixing ratio (g/kg) for September 2005. ........................................................................................ 3-11

Figure 3-22. Spatial mean mixing ratio (g/kg) for October 2005. ............................................................................................ 3-11

Figure 3-23. Spatial mean mixing ratio (g/kg) for November 2005. ........................................................................................ 3-11

Figure 3-24. Spatial mean mixing ratio (g/kg) for December 2005. ........................................................................................ 3-11

Figure 3-25. Spatial vector mean wind speed (m/s) for January 2005. .................................................................................... 3-12

Figure 3-26. Spatial vector mean wind speed (m/s) for February 2005. .................................................................................. 3-12

Figure 3-27. Spatial vector mean wind speed (m/s) for March 2005. ...................................................................................... 3-12

Figure 3-28. Spatial vector mean wind speed (m/s) for April 2005. ........................................................................................ 3-12

Figure 3-29. Spatial vector mean wind speed (m/s) for May 2005. ......................................................................................... 3-13

Figure 3-30. Spatial vector mean wind speed (m/s) for June 2005. ......................................................................................... 3-13

Figure 3-31. Spatial vector mean wind speed (m/s) for July 2005. .......................................................................................... 3-13

Figure 3-32. Spatial vector mean wind speed (m/s) for August 2005. ..................................................................................... 3-13

Figure 3-33. Spatial vector mean wind speed (m/s) for September 2005. ................................................................................ 3-14

Figure 3-34. Spatial vector mean wind speed (m/s) for October 2005. .................................................................................... 3-14

Figure 3-35. Spatial vector mean wind speed (m/s) for November 2005. ................................................................................ 3-14

Figure 3-36. Spatial vector mean wind speed (m/s) for December 2005. ................................................................................ 3-14

Figure 3-37. Accumulated Precipitation (mm) Observations and Model Estimates for January 2005. ................................... 3-15

Figure 3-38. Accumulated Precipitation (mm) Observations and Model Estimates for February 2005. ................................. 3-15

Figure 3-39. Accumulated Precipitation (mm) Observations and Model Estimates for March 2005. ..................................... 3-15

Figure 3-40. Accumulated Precipitation (mm) Observations and Model Estimates for April 2005. ....................................... 3-15

Figure 3-41. Accumulated Precipitation (mm) Observations and Model Estimates for May 2005. ........................................ 3-16

Figure 3-42. Accumulated Precipitation (mm) Observations and Model Estimates for June 2005. ........................................ 3-16

Figure 3-43. Accumulated Precipitation (mm) Observations and Model Estimates for July 2005. ......................................... 3-16

Figure 3-44. Accumulated Precipitation (mm) Observations and Model Estimates for August 2005. .................................... 3-16

Figure 3-45. Accumulated Precipitation (mm) Observations and Model Estimates for September 2005. ............................... 3-17

Figure 3-46. Accumulated Precipitation (mm) Observations and Model Estimates for October 2005. ................................... 3-17

Figure 3-47. Accumulated Precipitation (mm) Observations and Model Estimates for November 2005. ............................... 3-17

 ii 
Figure 3-48. Accumulated Precipitation (mm) Observations and Model Estimates for December 2005. ............................... 3-17

 iii 
 iv 

 

List of Tables 

Table No. Page No.

Table 2-1: MM5 Vertical Domain Specification. ....................................................................................................................... 2-4

Table 3-1. Bias and error for temperature, mixing ratio, and precipitation and wind speed index of agreement by month for 

2005 annual Hawaii MM5 simulation. ................................................................................................................................ 3-5
1 INTRODUCTION 

Over the past decade, emergent requirements for direct numerical simulation of urban 

and regional scale photochemical and secondary aerosol air quality—spawned largely by 

particulate matter (PM2.5), ozone and regional haze regulations—have led to intensified 

efforts to construct high-resolution emissions, meteorological and air quality data sets. 

The concomitant increase in computational throughput of low-cost modern scientific 

workstations has ushered in a new era of regional air quality modeling. It is now 

possible, for example, to exercise sophisticated mesoscale prognostic meteorological 

models and Eulerian and Lagrangian photochemical/aerosol models, simulating ozone, 

sulfate and nitrate deposition, and secondary organic aerosols (SOA) across quite large 

geographic regions. 

One such model is the Pennsylvania State University/National Center for Atmospheric 

Research (PSU/NCAR) Mesoscale Model (MM5) (Dudhia, 1993; Grell et al., 1994: 

www.mmm.ucar.edu/mm5). MM5 is a limited-area, non-hydrostatic, terrain-following 

model designed to simulate mesoscale atmospheric circulation. The model is supported 

by several pre- and post-processing programs which are referred to collectively as the 

MM5 modeling system. 

This document describes a 2005 annual MM5 simulation. The MM5 physics for the 

annual simulation were selected based on two, two-week long MM5 simulations – (1) for 

early February 2005 (a winter-time episode); and (2) for early July 2005 (a summer-time 

episode). The MM5 physics options that were selected for the annual simulation were 

based on those physics options from the two, two-week long simulations that resulted in 

the best model performance. 

Ultimately, it is believed that the he proposed annual 2005 MM5 modeling results will be 

used in CALMET/CALPUFF modeling system (TRC, 2008). With that in mind, the 

model performance evaluation results presented in this document are discussed within the 

context of being used in a CALMET/CALPUFF application. However, though the MM5 

results can be used in other applications (e.g., Regional Haze studies, Best Available 

Retrofit Technology (BART) determinations, State Implementation Plans to develop and 

test options to resolve air quality issues or support management policy decisions), care 

must be taken when applying the current MM5 simulation results in such applications. 

1-1
2 METHODOLOGY 

2.1 Model Selection and Application 

A brief summary is provided of the MM5 input data preparation procedures that were 

used for this meteorological modeling sensitivity exercise. Because of the different 

physics options available in MM5, sensitivity testing was performed for a winter (early 

February) and summer (early July) period to select the final configuration for the annual 

simulation. 

Model Selection: The publicly available non-hydrostatic version of MM5 (version 3.7.4) 

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

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

develop model inputs. 

 

Horizontal Domain Definition: The computational grid is presented in Figures 2-1 and 2-

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

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

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

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

chain with a buffer greater than 100km around the perimeter of the eight major Hawaiian 

Islands. The projection was in Lambert Conformal Coordinates (LCC) with a grid 

projection pole of 23o, -158o with true latitudes of 30o and 60o. 

 

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

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

both sigma and height coordinates in Table 2-1. 

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

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

based on the 2 min (~4 km) Geophysical Data Center global data (UCAR, 2002) 

interpolated to the model grid using a Cressman-type objective analysis scheme 

(Cressman, 1959). To avoid interpolating elevated terrain over water bodies, after the 

terrain databases are interpolated onto the MM5 grid, the NCAR graphic water body 

database was used to correct elevations over water bodies. 

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

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

MM5 distribution (UCAR, 2002). Standard MM5 surface characteristics corresponding 

to each land use category was employed. 

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

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

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

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

2-1
relatively coarse 2.5x2.5 degree resolution whereas the NCEP Final Analysis data have 

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

Analysis dataset was used. 

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

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

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

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

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

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

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

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

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

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

FDDA Data Assimilation: Both analysis and observation based nudging were used in the 

simulation. Analysis nudging coefficients of 2.5x10-4 on the 36km grid domain and 

1.0x10-4 on the 12km grid domain were used for winds and temperature. For the 4km 

grid domain, nudging to observed winds was employed with a coefficient of 1.0x10-3. 

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

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

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

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

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

al. 2005. Namely: 

• Warm Rain; 

• Grell Cumulus Parameterization on 36/12/04km Domains; 

• MRF PBL; 

• NOAH Land Surface Model; and 

• Cloud Radiation. 

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

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

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

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

and subsequent model performance evaluation. 

2.2 Evaluation Approach 

Because only limited observational data were available at the 12km and 36km grid scales, 

and because it is very likely that only the 4km domain will be used for subsequent air 

quality analyses, only MM5 predictions on the 4km grid were evaluated. 

2-2
The model performance evaluation approach was based on a combination of qualitative 

and quantitative analyses using comparisons of observed and predicted values of mixing 

ratio, temperature, precipitation and winds. The observed values for winds, temperature, 

and water mixing ratio were taken from the NOAA Techniques Development Lab (TDL) 

(UCAR, 2008c) surface observations and the National Climatic Data Center (NCDC) 

TD3240 (UCAR, 2008d) hourly precipitation databases. For the qualitative model 

performance evaluation, the model estimated monthly total precipitation was graphically 

compared with the TD3240 precipitation database. For the quantitative model 

performance evaluation, tabulations of (1) the model bias and error for temperature, 

precipitation, and mixing ratio and (2) the index of agreement for the wind fields were 

analyzed for the 4km simulations. 

2-3
Table 2-1: MM5 Vertical Domain Specification. 

k (MM5) sigma pressure (Pa) height (m) depth (m)

34 0.000 10000 15674 2004 

33 0.050 14500 13670 1585 

32 0.100 19000 12085 1321 

31 0.150 23500 10764 1139 

30 0.200 28000 9625 1004 

29 0.250 32500 8621 900 

28 0.300 37000 7720 817 

27 0.350 41500 6903 750 

26 0.400 46000 6153 693 

25 0.450 50500 5461 645 

24 0.500 55000 4816 604 

23 0.550 59500 4212 568 

22 0.600 64000 3644 536 

21 0.650 68500 3108 508 

20 0.700 73000 2600 388 

19 0.740 76600 2212 282 

18 0.770 79300 1930 274 

17 0.800 82000 1657 178 

16 0.820 83800 1478 175 

15 0.840 85600 1303 172 

14 0.860 87400 1130 169 

13 0.880 89200 961 167 

12 0.900 91000 794 82 

11 0.910 91900 712 82 

10 0.920 92800 631 81 

9 0.930 93700 550 80 

8 0.940 94600 469 80 

7 0.950 95500 389 79 

6 0.960 96400 310 78 

5 0.970 97300 232 78 

4 0.980 98200 154 39 

3 0.985 98650 115 39 

2 0.990 99100 77 38 

1 0.995 99550 38 38 

0 1.000 100000 0 0 

2-4
2-5

Figure 2-1. 36/12/4 km MM5 domain. 

Figure 2-2. Hawaii 4km MM5 domain. 
3 MM5 PERFORMANCE EVALUATION RESULTS 

3.1 Introduction 

Quantitative and qualitative model performance evaluation results for surface winds, 

temperature, mixing ratio and episode total precipitation are presented and discussed. A 

full annual model evaluation is very difficult to summarize in a single document. With 

this in mind, this section presents results so potential users of the MM5 predictions can 

independently judge the adequacy of the model simulation. Overall comparisons are 

offered herein to judge the model efficacy, but this review does not necessarily cover all 

potential user needs and applications. 

The statistics that were estimated include the mean bias (or simply bias) and mean 

absolute gross error (or simply error) for temperature, mixing ratio and precipitation as 

well as the wind speed index of agreement (Willmott, 1982; Willmott et al., 1985). Bias 

quantifies the difference between the average of a predicted value and the average of an 

observed value. If the bias is negative, the model is said to underpredict the mean 

observed value. Error quantifies the average of the absolute differences between the 

predictions and observations. Thus, even if a simulation exhibits no bias, it can still 

exhibit large error as the simulation results have offsetting under- and overpredictions. 

Index of agreement quantifies the relative average error between the predictions and 

observations. Please note that in the comparisons, vertical level one model estimates that 

are predicted at approximately nineteen meters are compared directly with the nominal, 

observed temperatures and moistures that are measured at about two meters and wind 

speeds and directions that are measured at about ten meters. As such, model performance 

must be judged with this caveat taken into context. 

Quantitative meteorological model performance benchmarks have been developed to help 

assess the ability of a model to reproduce observed conditions. The benchmarks are 

based upon the evaluation of about thirty MM5 and RAMS meteorological simulations of 

multi-day episodes in support of air quality modeling study applications performed over 

several years (Emery et al., 2001). In turn, these benchmarks have been adopted for use 

in annual meteorological modeling studies. The purpose of these benchmarks is not to 

give a pass or fail grade to any one particular meteorological model application, but 

rather to put the model results into the proper context of other models and meteorological 

data sets. That is, the benchmarks provide a contextual understanding as to how the 

model results compare to other model applications run over portions of the continental 

United States. Therefore, the benchmarks must be viewed as being guideline and not 

bright-line numbers. These benchmarks include bias and error in temperature and mixing 

ratio as well as the wind speed index of agreement (IA) between the model predictions 

and observational data bases. The benchmarks for each variable to judge whether 

predictions from a meteorological model are on par with previous meteorological 

modeling studies are as follows: 

• Temperature bias: less than or equal to +/- 0.5 K; 

3-1
• Temperature error: less than or equal to 2.0 K 

• Mixing ratio bias: less than or equal to +/- 1.0 g/kg 

• Mixing ratio error: less than or equal to 2.0 g/kg 

• Wind Speed Index of Agreement (unitless): 0 = worst, 1 = best, 0.6 = 

acceptable 

Of critical note for the current study – though meteorological model performance 

benchmarks have been developed to help assess the ability of a model to reproduce 

observed conditions, these benchmarks were derived from simulations performed 

primarily for continental and limited coastal areas (Emery et al., 2001). Hence, 

application of these model performance benchmarks is suspect for the current study as 

the MM5 simulations were performed over the Hawaiian Islands. However, it is still 

prudent to mention these benchmarks as they do provide a reference point for the model 

performance evaluation. 

3.2 Temperature 

Review of temperature bias statistics in Table 3-1 indicates that MM5 consistently 

underpredicts temperatures throughout 2005. MM5’s best performance is for January 

where the bias is -0.47 K, and the worst performing month is August where the bias is -

1.66 K. Over the entire year, MM5 has a bias of -1.07 K. Table 3-1 also shows the 

temperature error statistics to be a low of 1.75 K in November and a high of 2.32 K in 

August. 

Figures 3-1 through 3-12 graphically compare the spatial mean observed and predicted 

temperatures for each month of 2005. Qualitatively, MM5 does better at predicting the 

nighttime low temperatures as compared to its ability to predict the daytime high 

temperatures. In fact, MM5 performs quite poorly at predicting the daytime high 

temperatures in that it seldom is able to generate spatial mean temperatures in excess of 

25 C where as the observed spatial mean temperatures routinely exceed 25 C reaching 

highs of 31 C in September (Figure 3-9). 

The temperature performance though poor is not wholly unexpected as establishing such 

features as an appropriate marine boundary layer are critical to accurately predict coastal 

meteorology. Indeed, initialization of limited area prognostic meteorological models 

over the data-sparse ocean and coastal regions remains a difficult problem (e.g., Leidner 

et al., 2001). Substantial effort is likely necessary to correct the inadequate temperature 

performance of MM5 for the current simulation. Such efforts may include the 

incorporation of highly resolved vertical and horizontal grid structures (Stauffer and 

Seaman, 1999), updating land use characteristics, or revision to the deep soil 

temperatures or other force-restore parameterizations (Boucouvala et al., 2003). 

3.3 Mixing Ratio 

Table 3-1 presents the mixing ratio bias and error statistics by month for the 2005 MM5 

simulation. January is the trailing month in terms of performance with a bias of -1.23 

g/kg and an error of 1.59 g/kg. The remaining months show biases of between -0.96 g/kg 

3-2
for May and 0.18 g/kg for December and errors from 1.07 g/kg from August to1.37 g/kg 

for April. For the entire period, the bias is -0.63 g/kg, and the error is 1.22 g/kg. 

Figure 3-13 through 3-24 graphically compare the spatial mean observed and predicted 

mixing ratios by month for 2005. Qualitatively, MM5 does reasonably well at predicting 

both the highs and lows of the spatial mean mixing ratios, though there is a small 

underprediction bias. Further, MM5 does well at predicting the diurnal pattern of the 

spatial mean mixing ratios. Overall MM5 does reasonably well at predicting mixing ratio 

for all months of 2005 with no month being overtly better or worse than other months. 

3.4 Winds 

Table 3-1 presents the wind speed index of agreement by month for 2005 with May 

exhibiting the lowest performance at 0.63 and January and September exhibiting the best 

performance at 0.73. Annually, MM5 exhibits an index of agreement of 0.69. 

Figures 3-25 through 3-36 graphically compare the observed and predicted vector mean 

wind speeds by month for 2005. Qualitatively, there is mixed performance with MM5 

doing better at predicting the high wind speeds in some months than others or with MM5 

doing better at predicting the low wind speed in some months over other months. In 

January (Figure 3-25), for example, MM5 follows the diurnal trend though it does have 

periods of overprediction of the high winds (e.g., 07-08 January) and low winds (e.g., 23-

24 January). In June (Figure 3-30), MM5 has a general trend to underpredict the high 

wind speeds and overpredict the low wind speeds. However, for all months, MM5 does 

reasonably well at predicting the diurnal trend. Overall, as with mixing ratio, MM5 does 

reasonably well at predicting wind speeds. 

3.5 Precipitation 

Table 3-1 presents the bias and error statistics for precipitation by month for the 2005 

MM5 simulation. In terms of bias, June shows none and January shows the largest bias 

of -8.88 mm. In terms of error, January is the lowest performing month with 10.24 mm, 

and December is the best performing month with 2.63 mm. Annually, MM5 exhibits a 

bias 0 -4.00 mm and an error of 6.69 mm. 

Another way of examining qualitative precipitation performance is to examine spatial 

fields of accumulated observed and predicted precipitation. The graphics are prepared by 

plotting the modeled episode estimated precipitation overlaid with color coded markers at 

locations where precipitation measurements were reported. Episode accumulated 

precipitation for each month of 2005 is presented in Figures 3-37 though 3-48. 

For January (Figure 3-37), it can be seen that the poorer performance reflected in the bias 

and error statistics in Table 3-1 is due to the underpredictions of precipitation on all the 

islands except Kaua`i. MM5 precipitation predictions improve in February (Figure 3-38) 

over the islands of Hawai`i and Maui, then revert to poorer performance in March (Figure 

3-39) over the islands of Kaua`i, O`ahu, and Moloka`i. In April (Figure 3-40), May 

(Figure 3-41), June (Figure 3-42), July (Figure 3-43), and August (Figure 3-44), MM5 

performance is relatively consistent. In September (Figure 3-45) through November 

3-3
(Figure 3-47), MM5 precipitation performance degrades slightly, and then improves 

slightly for December (Figure 3-48). In general, however, MM5 does tend to predict 

both the magnitudes and locations of high precipitation throughout the year. 

3.6 Model Performance Summary 

A 2005 annual MM5 simulation over the Hawaiian Islands has been performed and a 

model performance evaluation has been conducted. The model performance lies within 

the guidelines for mixing ratio and wind field index of agreement. In regards to mixing 

ratio, MM5 does reasonably well at predicting the diurnal structure of the observed 

mixing ratios though it slightly underpredicts observed values throughout the year. As 

with its ability to predict observed mixing ratios, MM5 does reasonably well at predicting 

the observed wind speeds diurnal structure with slight underprediction and overprediction 

biases throughout the year depending on the month. In terms of precipitation, MM5 does 

reasonably well at predicting both the magnitudes and locations of the high precipitation 

throughout the year. 

The surface temperature evaluation shows that for many months and for the annual 

average, the bias and error are outside of the guideline metrics. This result is not 

unexpected since the performance metrics were derived from largely continental, 

episodic applications of MM5. For this application the model is being applied to a very 

challenging problem with nearly all coastal monitors near very steep orography. 

Therefore, a small difference in wind flow can determine if a monitor is influenced by 

land or maritime air parcels. 

Given the reasonable model performance of the MM5 predicted winds, precipitation, and 

mixing ratio, they are adequate for use in a CALMET/CALPUFF application. Though 

MM5 has less skill at replicating temperature observations, the Hawaiian Islands, have a 

relative high, stable temperature range over the year that is roughly 15 C to 30 C; and 

given that the CALMET/CALPUFF modeling system uses temperature primarily in 

buoyancy, deposition, and atmospheric stability calculations (and in some cases, nitrate 

chemistry), the MM5 predictions for the current 2005 simulation are likely sufficient for 

the task intended in a CALMET/CALPUFF application. Again, care must be taken if the 

current 2005 MM5 simulation results are to be used in other applications. 

3-4
3-5

Table 3-1. Bias and error for temperature, mixing ratio, and precipitation and wind 

speed index of agreement by month for 2005 annual Hawaii MM5 simulation. 

Month Temperature (K) Mixing Ratio (g/kg) Precipitation (mm) Wind 

Bias Error Bias Error Bias Error IA 

Jan -0.47 1.81 -1.23 1.59 -8.88 10.24 0.73 

Feb -0.76 1.82 -0.63 1.31 -5.71 7.35 0.71 

Mar -0.71 1.95 -0.67 1.20 -8.65 9.43 0.69 

Apr -1.44 2.01 -0.94 1.37 0.11 5.80 0.67 

May -1.57 2.16 -0.96 1.35 -1.81 4.22 0.63 

Jun -1.44 2.07 -0.65 1.16 0.00 6.55 0.69 

Jul -1.49 2.05 -0.47 1.06 -2.17 6.03 0.66 

Aug -1.66 2.32 -0.38 1.07 -0.77 4.57 0.70 

Sep -1.31 1.98 -0.59 1.10 -6.58 8.86 0.73 

Oct -0.81 1.90 -0.44 1.05 -6.39 8.31 0.66 

Nov -0.62 1.75 -0.78 1.21 -5.10 6.31 0.71 

Dec -0.53 2.07 0.18 1.14 -2.05 2.63 0.69 

Annual -1.07 1.99 -0.63 1.22 -4.00 6.69 0.69 
3-6

Figure 3-1. Spatial mean temperature (deg. C) for January 2005. Figure 3-2. Spatial mean temperature (deFigure 3-3. Spatial mean temperature (deg. C) for March 2005. Figure 3-4. Spatial mean temperature (de
3-7

Figure 3-5. Spatial mean temperature (deg. C) for May 2005. Figure 3-6. Spatial mean temperature (deFigure 3-7. Spatial mean temperature (deg. C) for July 2005. Figure 3-8. Spatial mean temperature (de
3-8

Figure 3-9. Spatial mean temperature (deg. C) for September 2005. Figure 3-10. Spatial mean temperature (dFigure 3-11. Spatial mean temperature (deg. C) for November 2005. Figure 3-12. Spatial mean temperature (d
3-9

Figure 3-13. Spatial mean mixing ratio (g/kg) for January 2005. Figure 3-14. Spatial mean mixing ratio (gFigure 3-15. Spatial mean mixing ratio (g/kg) for March 2005. Figure 3-16. Spatial mean mixing ratio (g
3-10

Figure 3-17. Spatial mean mixing ratio (g/kg) for May 2005. Figure 3-18. Spatial mean mixing ratio (gFigure 3-19. Spatial mean mixing ratio (g/kg) for July 2005. Figure 3-20. Spatial mean mixing ratio (g
3-11

Figure 3-21. Spatial mean mixing ratio (g/kg) for September 2005. Figure 3-22. Spatial mean mixing ratio (gFigure 3-23. Spatial mean mixing ratio (g/kg) for November 2005. Figure 3-24. Spatial mean mixing ratio (g
3-12

Figure 3-25. Spatial vector mean wind speed (m/s) for January 2005. Figure 3-26. Spatial vector mean wind spFigure 3-27. Spatial vector mean wind speed (m/s) for March 2005. Figure 3-28. Spatial vector mean wind sp
3-13

Figure 3-29. Spatial vector mean wind speed (m/s) for May 2005. Figure 3-30. Spatial vector mean wind spFigure 3-31. Spatial vector mean wind speed (m/s) for July 2005. Figure 3-32. Spatial vector mean wind sp
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Figure 3-33. Spatial vector mean wind speed (m/s) for September 2005. Figure 3-34. Spatial vector mean wind spFigure 3-35. Spatial vector mean wind speed (m/s) for November 2005. Figure 3-36. Spatial vector mean wind sp
3-15

Figure 3-37. Accumulated Precipitation (mm) Observations and Model Estimates for January 2005. Figure 3-38. Accumulated Precipitation (mm) Observations a

Figure 3-39. Accumulated Precipitation (mm) Observations and Model Estimates for March 2005. Figure 3-40. Accumulated Precipitation (mm) Observations a
3-16

Figure 3-41. Accumulated Precipitation (mm) Observations and Model Estimates for May 2005. Figure 3-42. Accumulated Precipitation (mm) Observations aFigure 3-43. Accumulated Precipitation (mm) Observations and Model Estimates for July 2005. Figure 3-44. Accumulated Precipitation (mm) Observations a
3-17

Figure 3-45. Accumulated Precipitation (mm) Observations and Model Estimates for September 2005. Figure 3-46. Accumulated Precipitation (mm) Observations aFigure 3-47. Accumulated Precipitation (mm) Observations and Model Estimates for November 2005. Figure 3-48. Accumulated Precipitation (mm) Observations a
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