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 2008Table of Contents

Section No. Page No.

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

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

2.1 M ODEL S ELECTION AND A PPLICATION ..................................................................................................................... 2-1

2.2 E VALUATION A PPROACH

.............................................................................................................................. ............ 2-2

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

3.1 I NTRODUCTION

.............................................................................................................................. ........................... 3-1

3.2 T EMPERATURE

.............................................................................................................................. ............................ 3-2

3.3 M IXING R ATIO

.............................................................................................................................. ............................ 3-2

3.4 W INDS

.............................................................................................................................. ........................................ 3-3

3.5 P RECIPITATION

.............................................................................................................................. ........................... 3-3

3.6 M ODEL P ERFORMANCE S UMMARY ........................................................................................................................... 3- 4

4 REFERENCES

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

iList 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 temper ature (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 temp erature (deg. C) for March 2005. ............................................................................................... 3-6 Figure 3-4. Spatial mean temp erature (deg. C) for April 2005. ................................................................................................. 3-6 Figure 3-5. Spatial mean te mperature (deg. C) for May 2005. ................................................................................................... 3-7 Figure 3-6. Spatial mean temp erature (deg. C) for June 2005. ................................................................................................... 3-7 Figure 3-7. Spatial mean temp erature (deg. C) for July 2005. ................................................................................................... 3-7 Figure 3-8. Spatial mean temp erature (deg. C) for August 2005. .............................................................................................. 3-7 Figure 3-9. Spatial mean temper ature (deg. C) for September 2005. ......................................................................................... 3-8 Figure 3-10. Spatial mean temper ature (deg. C) for October 2005. ........................................................................................... 3-8 Figure 3-11. Spatial mean temper ature (deg. C) for November 2005. ....................................................................................... 3-8 Figure 3-12. Spatial mean temp erature (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

iiFigure 3-48. Accumulated Preci pitation (mm) Observations and Model Estimates for December 2005. ............................... 3-17

iiiiv 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. ............................................................................................ .................................... 3-51 INTRODUCTION
Over the past decade, emergent requirement s for direct numerical simulation of urban and regional scale photochemical and seconda ry aerosol air quality—spawned largely by particulate matter (PM 2.5 ), 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 th roughput of low-cost modern scientific

workstations has ushered in a new era of regional air quality m odeling. It is now possible, for example, to exercise sophi sticated mesoscale prognostic meteorological models and Eulerian and Lagrangian photoc hemical/aerosol models, simulating ozone, sulfate and nitrate deposition, and secondary organic aeroso ls (SOA) across quite large geographic regions.

One such model is the Pennsylvania State Un iversity/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 atmos pheric circulation. Th e model is supported by several pre- and post-processing programs wh ich 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, tw o-week long simulations that resulted in the best model performance.

Ultimately, it is believed that the he propos ed 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/CALP UFF application. However, though the MM5 results can be used in other applications (e.g., Regional Haze studies, Best Available Retrofit Technology (BART) determinations, St ate Implementation Plans to develop and test options to resolve air qua lity issues or support manage ment policy decisions), care must be taken when applying the current MM5 simulation results in such applications.

1-12 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 sensit ivity 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 se lect the final configuration for the annual

simulation.

Model Selection : The publicly available non-hydrosta tic 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, an d INTERPF (UCAR, 2003b) were used to develop model inputs.

Horizontal Doma in 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 gr id 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 C onformal Coordinates (LCC) with a grid projection pole of 23 o , -158 o with true latitudes of 30 o and 60 o

.

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 coor dinates 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 Da ta Center global data (UCAR, 2002) interpolated to the model grid using a Cressman-type objective analysis scheme (Cressman, 1959). To avoid interpolating elev ated terrain over wa ter bodies, after the

terrain databases are inter polated 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 releas ed PSU/NCAR databases provided with the MM5 distribution (UCAR, 2002). Standard MM5 surface characteri stics 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 Tropospheri c 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

2-1relatively coarse 2.5x2.5 degree resolution wherea s the NCEP Final Analysis data have much higher 1x1 degree resolution. For th e current modeling study, the NCEP Final Analysis dataset was used.

Available surface and upper-air observations we re used in the objective analyses. These data were incorporated into the analyses datasets following the procedures outlined by Stauffer and Seaman (1990) and were quali ty-inspected by MM5 pre-processors using automated gross-error check s and "buddy" checks. In addition, radiosonde soundings were subjected to vertical consistency ch ecks. The synoptic-scale data used for the initialization (and in the analysis nudging discussed later) were obtained from the

conventional National Weathe r 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 nu dging coefficients of 2.5x10 -4 on the 36km grid domain and

1.0x10 -4 on the 12km grid domain were used fo r winds and temperature. For the 4km grid domain, nudging to observed winds wa s employed with a coefficient of 1.0x10 -3

.

Physics Options : Sensitivity testing using approx imately 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 Weat her 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 st ep. Model results were output every 60 minutes and output files are split at 24 hour in tervals. 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 av ailable 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.

2-2The model performance evaluation approach was based on a combination of qualitative and quantitative analyses using comparisons of observed and predic ted 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 mo nthly total precipitation was graphically compared with the TD3240 precipitation da tabase. For the quantitative model performance evaluation, tabulati ons 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.

2-3Table 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

2-42-5 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 th e MM5 predictions can independently judge the adequacy of the model simulation. Overall comparisons are offered herein to judge the model efficacy, bu t 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 te mperature, 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 quantif ies the average of the absolu te 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 quantifie s 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 ar e measured at about two meters and wind speeds and directions that are measured at a bout 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 mode l to reproduce observed condi tions. The benchmarks are based upon the evaluation of about thirty MM 5 and RAMS meteorologi cal 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 st udies. The purpose of these benchmarks is not to give a pass or fail grade to any one partic ular meteorological model application, but rather to put the model results into the prope r context of other mode ls and meteorological data sets. That is, the benchmarks provide a contextual understanding as to how the model results compare to other model applica tions run over portions of the continental United States. Therefore, the benc hmarks must be viewed as being guideline and not bright-line numbers. These benchmarks include bi as and error in temperature and mixing ratio as well as the wind speed index of ag reement (IA) between th e model predictions

and observational data bases. The benchm arks 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 th an or equal to 2.0 g/kg

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

Of critical note for the current study – though me teorological 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 coas tal areas (Emery et al., 2001). Hence, application of these model performance benchm arks 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 th at MM5 consistently underpredicts temperatures throughout 2005. MM5’s best performance is for January where the bias is -0.47 K, and the worst perf orming 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 Nove mber and a high of 2.32 K in August.

Figures 3-1 through 3-12 gra phically compare the spatial mean observed and predicted temperatures for each month of 2005. Qualita tively, MM5 does better at predicting the nighttime low temperatures as compared to its ability to predict the daytime high temperatures. In fact, MM5 performs qu ite poorly at predicting the daytime high temperatures in that it seldom is able to ge nerate spatial mean temperatures in excess of 25 C where as the observed spatial mean te mperatures 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 laye r are critical to accur ately predict coastal meteorology. Indeed, initialization of lim ited area prognostic meteorological models over the data-sparse ocean and coastal regi ons remains a difficult problem (e.g., Leidner et al., 2001). Substantial effort is likely n ecessary to correct the inadequate temperature performance of MM5 for the current simu lation. Such efforts may include the incorporation of highly resolv ed vertical and horizontal gr id structures (Stauffer and Seaman, 1999), updating land use characteri stics, or revision to the deep soil temperatures or other force-restore pa rameterizations (Boucouvala et al., 2003).

3.3 Mixing Ratio
Table 3-1 presents the mixing ratio bias a nd 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 remaini ng months show biases of between -0.96 g/kg

3-2for 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 bi as 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. Qualitativel y, MM5 does reasonably well at predicting both the highs and lows of the spatial m ean mixing ratios, though there is a small underprediction bias. Further, MM5 does well at predicting the diur nal pattern of the spatial mean mixing ratios. Overall MM5 doe s reasonably well at predicting mixing ratio for all months of 2005 with no month being ove rtly 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 compar e 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 th e low wind speed in some months over other months. In January (Figure 3-25), for example, MM5 fo llows the diurnal trend though it does have periods of overprediction of the high wi nds (e.g., 07-08 January) and low winds (e.g., 23- 24 January). In June (Figure 3-30), MM5 ha s a general trend to underpredict the high wind speeds and overpredict the low wind speed s. However, for all months, MM5 does reasonably well at predicting th e 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 stat istics for precipitation by month for the 2005 MM5 simulation. In terms of bias, June s hows none and January shows the largest bias of -8.88 mm. In terms of error, January is the lowest perfor ming month with 10.24 mm, and December is the best performing mont h 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 precip itation performance is to examine spatial fields of accumulated observed and predicted precipitation. The graphics are prepared by plotting the modeled episode estimated precipit ation overlaid with co lor coded markers at locations where precipitation measurements were reported. Episode accumulated precipitation for each month of 2005 is pr esented 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 re vert 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 performan ce 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 hi gh precipitation throughout the year.

3.6 Model Performance Summary

A 2005 annual MM5 simulation over the Hawaii an 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 in dex of agreement. In regards to mixing ratio, MM5 does reasonably well at predicti ng 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 reasona bly well at predicting the observed wind speeds diurnal structure with slight underpredicti on and overprediction biases throughout the year de pending on the month. In te rms of precipitation, MM5 does reasonably well at predicting both the magnit udes and locations of the high precipitation throughout the year.

The surface temperature eval uation 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 we re derived from largely continental, episodic applications of MM5. For this application the model is being applied to a very challenging problem with nearly all coasta l 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 temperatur e 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/CALP UFF modeling system uses temperature primarily in buoyancy, deposition, and atmospheric stability calculations (and in some cases, nitrate chemistry), the MM5 predictions for the curr ent 2005 simulation are likely sufficient for the task intended in a CALMET/CALPUFF appli cation. Again, care must be taken if the

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

3-43-5 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 0.693-6Figure 3-1. Spatial mean temperature (deg. C) for January 2005.Figure 3-2. Spatial mean temperature (deg. C) for February 2005.Figure 3-3. Spatial mean temperature (deg. C) for March 2005.Figure 3-4. Spatial mean temperature (deg. C) for April 2005.3-7Figure 3-5. Spatial mean temperature (deg. C) for May 2005.Figure 3-6. Spatial mean temperature (deg. C) for June 2005.Figure 3-7. Spatial mean temperature (deg. C) for July 2005.Figure 3-8. Spatial mean temperature (deg. C) for August 2005.3-8Figure 3-9. Spatial mean temperature (deg. C) for September 2005.Figure 3-10. Spatial mean temperature (deg. C)for October 2005.Figure 3-11. Spatial mean temperature (deg. C) for November 2005.Figure 3-12. Spatial mean temperature (deg. C) for December 2005.3-9Figure 3-13. Spatial mean mixing ratio (g/kg) for January 2005.Figure 3-14. Spatial mean mixingratio (g/kg) for February 2005.Figure 3-15. Spatial mean mixing ratio (g/kg) for March 2005.Figure 3-16. Spatial mean mixing ratio (g/kg) for April 2005.3-10Figure 3-17. Spatial mean mixing ratio (g/kg) for May 2005.Figure 3-18. Spatial mean mixing ratio (g/kg) for June 2005.Figure 3-19. Spatial mean mixing ratio (g/kg) for July 2005.Figure 3-20. Spatial mean mixing ratio (g/kg) for August 2005.3-11Figure 3-21. Spatial mean mixingratio (g/kg) for September 2005.Figure 3-22. Spatial mean mixing ratio (g/kg) for October 2005.Figure 3-23. Spatial mean mixing ratio (g/kg) for November 2005.Figure 3-24. Spatial mean mixingratio (g/kg) for December 2005.3-12Figure 3-25. Spatial vector meanwind speed (m/s) for January 2005.Figure 3-26. Spatial vector meanwind speed (m/s) for February 2005.Figure 3-27. Spatial vector mean wind speed (m/s) for March 2005.Figure 3-28. Spatial vector meanwind speed (m/s) for April 2005.3-13Figure 3-29. Spatial vector mean wind speed (m/s) for May 2005.Figure 3-30. Spatial vector mean wind speed (m/s) for June 2005.Figure 3-31. Spatial vector mean wind speed (m/s) for July 2005.Figure 3-32. Spatial vector meanwind speed (m/s) for August 2005.3-14Figure 3-33. Spatial vector meanwind speed (m/s) for September 2005.Figure 3-34. Spatial vector meanwind speed (m/s) for October 2005.Figure 3-35. Spatial vector meanwind speed (m/s) for November 2005.Figure 3-36. Spatial vector meanwind speed (m/s) for December 20053-15Figure 3-37. Accumulated Precipitation (mm) Observations and Model Estimates for January 2005.Figure 3-38. Accumulated Precipitation (mm) Observations and Model Estimates for February 2005.Figure 3-39. Accumulated Precipitation (mm) Observations and Model Estimates for March 2005.Figure 3-40. Accumulated Precipitation (mm) Observations and Model Estimates for April 2005.3-16Figure 3-41. Accumulated Precipitation (mm) Observations and Model Estimates for May 2005.Figure 3-42. Accumulated Precipitation (mm) Observations and Model Estimates for June 2005.Figure 3-43. Accumulated Precipitation (mm) Observations and Model Estimates for July 2005.Figure 3-44. Accumulated Precipitation (mm) Observations and Model Estimates for August 2005.3-17Figure 3-45. Accumulated Precipitation (mm) Observations and Model Estimates for September 2005.Figure 3-46. Accumulated Precipitation (mm) Observations and Model Estimates for October 2005.Figure 3-47. Accumulated Precipitation (mm) Observations and Model Estimates for November 2005.Figure 3-48. Accumulated Precipitation (mm) Observations and Model Estimates for December 2005.

### 4 References

Boucouvala, D., R. Bornstein, J. Wilkinson, and D. Miller, 2003. MM5 simulation of a SCOS97-NARSTO episode. Atmos. Env. , 37(2):S95-S117.

Cressman, G. P., 1959. An Operati onal Objective Analysis System. Mon. Wea. Rev.

, 87:367-374.

Dudhia, J., 1993. "A Non-hydrostatic Versi on of the Penn State/NCAR Mesoscale

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

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

Emery, C. et al., 2001. “Enhanced Meteorologi cal Modeling and Performance Evaluation for Two Texas Ozone Episodes”, prepared for the Texas Natural Resource Conservation Commission, prepared by ENVIRON Inte rnational Corporation, Novato, CA.

Gemmill, William, Bert Katz and Xu Li, 2007. Daily Real-Time Global Sea Surface Temperature - High Resolution Analys is at NOAA/NCEP. NOAA/NWS/NCEP/MMAB Office Note Nr. 260, 39 pp.

Grell, G. A., J. Dudhia, and D. R. Stauffer. 1994. A Description of the Fifth Generation Penn State/NCAR Mesoscale Model (MM5). NCAR Tech. Note, NCAR TN-398-STR, 138 pp.

Leidner, S. Mark, D. R. Stauffer, a nd N. L. Seaman, 2001. Improving Short-Term

Numerical Weather Prediction in the Califor nia Coastal Zone by D ynamic Initialization of the Marine Boundary Layer. Mon. Wea. Rev ., 129(2): 275–294

McNally, D. E., and T W. Tesche, 2003. “Annua l Application of MM5 to the Continental United States”, prepared for the U. S. E nvironmental Protection Agency, Office of Air Quality Planning and Standards, prepared by Alpine Geophysics, LLC, Arvada, CO.

McNally, D.E. and G.J. Schewe, 2007, “Evalu ation of 4 km MM5 for Calendar Year 2003 over Colorado”, prepared for the Colorado Department of Public Health, prepared by Alpine Geophysics, LLC, Arvada, CO.

McNally, D.E., 2008,”MM5 Sensitivity to Alte rnate Configurations over the Hawaiian Islands”, prepared for the Hawaii State Department of Health, Environmental Management Division, Clean Air Branch, pr epared by Alpine Geophysics, LLC, Arvada,

CO.

NCEP, 2008. Real-time, global, sea surface temperature (RTG _SST) analysis. 10-July. polar.ncep.noaa.gov/sst/oper/Welcome.html

Stauffer, D.R., and N.L. Seaman, 1990. Use of four dimensional da ta assimilation in a

limited-area mesoscale model. Part I: E xperiments with synoptic-scale data. Mon.

4-1Wea. Rev. , 118, 1250-1277.

Stauffer, D. R. and N. L. Seaman. 1999. Intercomparison of turbulence parameterizations for simulating coastal-zone marine boundary layer structure. Preprints, Third Conf. on Coastal Atmospheric and Ocean ic Prediction Processes, New Orleans,
LA, Amer. Meteor. Soc ., 7–12.

TRC, 2008. The CALPUFF Modeling System. The Atmospheric Studies Group at TRC.

27-June. www.src.com/calpuff/calpuff1.htm

UCAR, 2002. MM5 Community Model: TERRAIN DATA. ftp.ucar.edu/mesouser/ MM5V3/TERRAIN_DATA

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

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

UCAR, 2003b. MM5 Community Model: MM5 Mode ling System Overview. 10-April. www.mmm.ucar.edu/mm5/overviewProgram.html

UCAR, 2007. MM5 Community Model: Download Datasets for MM5 from UCAR. 03-May. www.mmm.ucar.edu/mm5/mm 5v3/data/free_data.html

UCAR, 2008a. ds353.4 Home Page. NCEP ADP Global Upper Air Observation Subsets, January 1973 – continuing. CISL Research Da ta Archive. dss.ucar.edu/datasets/ds353.4.

UCAR, 2008b. ds464.0 Home Page. NCEP ADP Global Surface Observations, February 1975 – continuing. CISL Research Data Archive. dss.ucar.edu/datasets/ds464.0.

UCAR, 2008c. ds472.0 Home Page. TDL U.S. and Canada Surface Hourly Observations, daily 1976Dec – cont. CISL Research Data Archive.

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

UCAR, 2008d. ds505.0 Home Page. NCDC TD3240 U.S. Cooperative Hourly Precipitation, continuing from 1948. CISL Research Data Archive.

dss.ucar.edu/datasets/ds505.0.

Willmott, C.J. 1982. Some comments on th e evaluation of model performance. Bull.

Amer. Meteor. Soc . 63:1309-1313.

Willmott, C. J., S. G. Ackleson, R. E. Davis, J. J. Feddema, K. M. Klink, D. R. Legates, J. O’Donnell, and C. M. Rowe, 1985. Statistics for the evaluati on and comparison of

models. J. Geophys. Res ., 90, 8995–9005.

Yang, Yang, and Y.-L. Chen, 2003: High Resolu tion Simulations of the Island Induced

Circulations for the Island of Hawaii duri ng HaRP. 5th Symposium on Fire and Forest Meteorology, 16-20 November 2003, Orlando, Florida.

4-24-3 Yang, Yang, Y.-L. Chen, and F. M. Fujioka, 2005 : Numerical Simulations of the IslandInduced Circulations over the Island of Hawaii during HaRP. Mon. Wea. Rev. , 133, 3693-

3713.

Zhang, Y., Y.-L. Chen, S.-Y. Hong, K. Koda ma, and H.-M. H. Juang, 2005: Validation of the coupled NCEP Mesoscale Spectral Model and an advanced Land Surface

Model over the Hawaiian Islands. Part I: Summer trade wind conditions over Oahu and Heavy rainfall events. Wea. Forecasting. (Accepted with minor revisions).