Assessment of Wave Energy Resources in Hawaii

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Assessment of wave energy resources in Hawaii

Justin E. Stopa a, Kwok Fai Cheung a,*, Yi-Leng Chen b

a Department of Ocean and Resources Engineering, University of Hawaii at Manoa, Honolulu, HI 96822, USAb Department of Meteorology, University of Hawaii at Manoa, Honolulu, HI 96822, USA

a r t i c l e i n f o

Article history:

Received 3 November 2009

Accepted 20 July 2010

Available online 24 August 2010

Keywords:

Mesoscale model

Spectral wave model

Waves in Hawaii

Wave energy

Wave power

Ocean observing system

a b s t r a c t

Hawaii is subject to direct approach of swells from distant storms as well as seas generated by trade winds

passing through the islands. The archipelago creates a localized weather system that modi

es the waveenergy resources from the far eld. We implement a nested computational grid along the major Hawaiian

Islands in the global WaveWatch3 (WW3) model and utilize the Weather Research and Forecast (WRF)

model to provide high-resolution mesoscale wind forcing over the Hawaii region. Two hindcast case

studies representative of the year-round conditions provide a quantitative assessment of the regional wind

and wave patterns as well as the wave energy resources along the Hawaiian Island chain. These events of

approximately two weeks each have a range of wind speeds, ground swells, and wind waves for validation

of the model system with satellite and buoy measurements. The results demonstrate the wave energy

potential in Hawaii waters. While the episodic swell events have enormous power reaching 60 kW/m, the

wind waves, augmented by the local weather, provide a consistent energy resource of 15e25 kW/m

throughout the year.

2010 Elsevier Ltd. All rights reserved.

1. Introduction

Hawaiis mid-Pacic location andyear-roundaccess to the ocean

make it a center for marine research and recreational activities.

Hidden in these activities are the wave energy resources that have

only recently attracted attention.Fig. 1illustrates the wave climate

around the Hawaiian Islands. Extratropical storms near the Kuril

and Aleutian Islands generate northwest swells reaching 5 m

signicant wave height in Hawaii waters during the winter months

of NovembereMarch. The south facing shores experience more

gentle swell conditions associated with extratropical storms off

Antarctica during the summer months from May to October. In

addition, consistent trade winds generate wind waves from the

northeast to east throughout the year. Existing buoys operated bythe National Data Buoy Center (NDBC) provide point measurements

of the wave conditions either off the Hawaiian Island chain or at

nearshore locations. A numerical modeling approach can augment

the available measurements to provide spatial distributions of the

ocean wave resources for assessment[1e3].

The National Centers for Environmental Prediction (NCEP) oper-

atesWaveWatch3 (WW3) to provide7.5days of global wave forecasts

at1.251 resolution [4]. The model knownas NWW3is forcedwith

assimilated surface winds from the Global Forecast System (GFS) [5].

Because the grid does not resolve the Hawaiian Islands, NWW3

applies an obstruction coefcient [6] to reduce the wave energy

transmission through the island chain [7]. While the model provides

accurate description of large-scale wave patterns, it does not resolve

the wave conditions near the Hawaiian Islands, particularly in the

inter-island channels that are known to be treacherous by local

mariners. Furthermore, NWW3 does not account for island shad-

owing when one island blocks the swell from reaching another. This

may have profound effects along an archipelago as already shown by

Ponce de Leon and Guedes Soares [8]and Rusu et al. [9]through

nesting of a ner grid in spectral wave modeling.

The Hawaiian Islands also modify the trade-wind owand createlocalized weather patterns all year-round[10]. The diurnal thermal

forcing from the land surface drives the daytime sea breezes and

upslopeow as well as the nighttime land breezes and downslope

ow [11,12]. Accurate representations of terrain and thermal effects

from the land surface are required to simulate the island-induced

airow and weather over the Hawaiian Islands [13,14]. Skamarock

[15] describes the implementation and validation of the Weather

Research and Forecast (WRF) model with initial and boundary

conditions from NCEPs Final Analysis (FNL) data to provide high-

resolution weather description for Hawaii. The regional WRF model

captures ow deceleration on the windward side and signicant

* Corresponding author.

E-mail addresses: [email protected] (J.E. Stopa), [email protected] (K.F.

Cheung), [email protected] (Y.-L. Chen).

Contents lists available at ScienceDirect

Renewable Energy

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m/ l o c a t e / r e n e n e

0960-1481/$ e see front matter 2010 Elsevier Ltd. All rights reserved.

doi:10.1016/j.renene.2010.07.014

Renewable Energy 36 (2011) 554e567
mailto:[email protected]:[email protected]:[email protected]://www.sciencedirect.com/science/journal/09601481http://www.elsevier.com/locate/renenehttp://dx.doi.org/10.1016/j.renene.2010.07.014http://dx.doi.org/10.1016/j.renene.2010.07.014http://dx.doi.org/10.1016/j.renene.2010.07.014http://dx.doi.org/10.1016/j.renene.2010.07.014http://dx.doi.org/10.1016/j.renene.2010.07.014http://dx.doi.org/10.1016/j.renene.2010.07.014http://www.elsevier.com/locate/renenehttp://www.sciencedirect.com/science/journal/09601481mailto:[email protected]:[email protected]:[email protected]


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speed-up of the trade winds in the channels and prominent wakes

onthe leeside of islands.Theselocal modications of thetrade winds

may have strong effects on the wave elds.

The capability to quantify the complex wave conditions is

essential to the planning, deployment, and operation of wave

energy devices in the Hawaii region. The latest version of NWW3can include two-way nested grids to cover selected areas at higher

resolution and to ensure proper exchange of data with the global

model[16]. This study examines the implementation of a regional

nested grid in NWW3 with wind forcing from a Hawaii WRF model

to capture the propagation of swell and generation of local wind

waves across the Hawaiian Islands. An island-scale domain for

Oahu utilizing the Simulating WAves Nearshore (SWAN) model[6]

is nested into the Hawaii regional domain to describe the wave

conditions for comparison with nearshore buoy measurements.

Wind data derived from scatterometers aboard orbiting satellites

and wave data from satellite altimetryand buoys provide validation

of the model data. Two hindcast case studies consisting of

predominant north swell, south swell, and wind sea conditions

cover the typical wave climate to provide an assessment of thewave energy resources along the Hawaiian Island chain.

2. Methodology

2.1. Model description

The present study utilizes a suite of proven atmospheric and

ocean wave models from the research and operations communities.

Fig. 2 provides a schematic of the model and data system to provide

high-delity wind and wave data along the Hawaiian Island chain.

The system is automated through a set of scripts, which link the

model components with databases and utility programs. A front-

end preprocessor manages the data transfer and the computational

processes with standardized input and output

les[17,18]. A series

of nested computational grids capture physical processes at global

and regional scales with appropriate temporal and spatial resolu-

tion. The same system is also in operation to provide 7.5-day high-

resolution forecasts of wind and wave conditions around the

Hawaiian Islands (http://oceanforecast.org/).

Accurate description of the wind eld is the key to the entiremodeling process. The FNL dataset is composed of 25 meteoro-

logical variables derived from the Global Forecast System (GFS),

which is a spectral model with 1 1 resolution on the earth

surface and 64 layers extending to the top of the atmosphere. The

NCEP global data assimilation system (GDAS) implements QuikS-

CAT scatterometer winds at 10-m elevation into GFS on a real-time

basis[19,20]. The NCEP FNL data provides the initial and boundary

conditions to the Hawaii WRF, which in turn is coupled with the

Noah LSM (NCEP, Oregon State University, Air Force, and Hydro-

logical Research Laboratory Land Surface Model) using vegetation

data and surface properties compiled by Zhang et al. [21]. WRF is

a next-generation mesoscale model based on the non-hydrostatic,

three-dimensional Euler equation with the sigma vertical coordi-

nate[22]. The regional domain covers 194e

210

E and 16e

26

N tomodel the upstream wind ow and the modied wind eld

downstream of the Hawaiian Islands. The 6 km grid spacing is

sufcient to resolve the physical processes important to local wind

wave generation and the data is output at the standard 10-m

elevation every hour.

WW3 is a third-generation spectral model for wind wave

development and propagation from deep to intermediate water [4].

The model solves the action balance equation for evolution of the

wave spectrum under wind forcing. Source terms account for

nonlinear effects such as windewave interactions, quadruplet

waveewave interactions,and dissipation throughwhitecapping and

bottom friction. We implement a global WW3 at 1.25 1 resolu-

tion in hindcast mode and incorporate a nested Hawaii grid, which

covers a domain from 199 to 207

E and 17 to 24

N at 3-min

Fig. 1. Wave climate and buoy resources around the Hawaiian Islands.

J.E. Stopa et al. / Renewable Energy 36 (2011) 554e567 555
http://oceanforecast.org/http://oceanforecast.org/


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(w5.5km) resolution. The model utilizes the iceconcentration from

the FNL dataset and wind forcing from a combined FNL and Hawaii

WRF dataset. Tolman and Chalikov[23]concluded that the param-

eterization of the source term in WAM [24] underestimates the

transfer of energyfromwindsto wavesfor small fetches. TheHawaii

domain, which has intricate wind patterns caused by steep topo-

graphic gradient and narrow channels, would benet from the

source term of Tolman and Chalikov[23].

The Hawaii WW3 denes the boundary conditions for the Oahu

SWAN, which covers a domain of 201.65e202.40E and

21.20

e21.75

N at 9-sec (approximately 280 m) resolution toprovide data at the nearshore wave buoys for model validation.

Wind forcing is not considered due to the small geographic size of

the computational domain. The SWAN model is similar to WW3 in

that it solves the action balance equation with parameterization of

nonlinear processes [6]. However, SWAN is better suited for

shallow water processes by including additional source terms for

triad waveewave interactions and depth-induced wave breaking as

well as the JONSWAP parameterization for dissipation due to

bottom friction [25]. In addition, SWAN accounts for some effects of

diffraction by including an additional term derived from the mild-

slope equation[26].

Both WW3 and SWAN provide directional wave spectra at the

grid points that can reveal multiple wave events occurring simul-

taneously. This is especially important for Hawaii as the ocean

always contains a mix of swell and wind wave events. Since wave

energy devices have narrow operating frequency ranges, it is

important to identify the energy levels of the wave components

separately. The latest release of WW3 (v3.14) implements the

Hanson and Phillips [27] algorithm to determine the wave

parameters for each spectral partition. The wave power per unit

crest length is

P 1

8rgH2rmsCg

wherer is thedensity of water,gis theaccelerationdue togravity,Cgis

group velocity, Hrms is the root-mean square wave height of a spectral

partition. This algorithm is adequate in treating data produced by

wave models,but in some cases withdouble peak spectra, theGuedes

Soares[28]approach appears to be more accurate[29].

2.2. Satellite observations and post-processing

The hindcast regional wind and wave data needs validation with

measurements before its implementation in the resources assess-

ment. Buoys provide direct and continuous measurements of the

wave conditions for comparison with the model output at discrete

locations. Spatial data available for model validation includes wind

measurements from QuikSCAT and altimetry wave measurements

Fig. 2. Schematic of atmospheric and wave model system.

J.E. Stopa et al. / Renewable Energy 36 (2011) 554e567556


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from Jason-1 and Topex/Poseidon (T/P). We selected these platforms

because they are able to provide validated measurements across

a large spatial region under a wide range of atmospheric conditions.

QuikSCAT provides wind measurements over 90% of the Earths

ice-free ocean daily with errors less than 2 m/s in speed and 20 in

direction[30]. The polar orbiting satellite ies over Hawaii twice

daily in ascending and descending passes. The 1800-km swath has

a nominal spatial resolution of 25 km to capture mesoscale

winds. The scatterometer on QuikSCAT pulses cloud-pene-

trating microwaves in the Ku band towards the earth and

records the backscatter signal under all weather conditions.

The wind speed and direction at 10-m elevation can be esti-

mated from empirical relationships known as the Geophysical

Model Function (GMF) without taking into account second-

Fig. 3. Comparison of wind elds from Hawaii WRF and QuikSCAT for Case Study 1.



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order geophysical effects such as wave height and sea surface

temperature. A land mask of at least 25 km removes erroneous

values caused by coastal land mass and a multidimensional

rain-agging technique indicates the presence of rain, which

may alter the microwave pulse. The Direction Interval Retrieval

with Threshold Nudging (DIRTH) algorithm gives a unique

solution for the wind direction [31].

The Topex/Poseidon (T/P) and Jason-1 satellites are equipped

with a dual-frequency (C & Ku band) altimeter, which measures the

sea surface elevation. Other on board sensors correct for vapor

content in the atmosphere and the electron content in the iono-

sphere. Erroneous data near the coastlines due to contaminated

signals by the presence of land is removed. The signicant wave

height (Hs) is an intrinsic property of the sea surface measurement

Fig. 4. Comparison of wave elds from Hawaii WW3 and altimetry for Case Study 1.



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estimated from the slope of the leading edge of the returned signal

[32]. Typical errors are within 0.5 m or 10% of Hs, whichever is

larger. Caires and Sterl[33]have shown that wave heights derived

from buoys and altimeters have comparable instrumental error

variances. The signicant wave height derived from the Ku band is

considered in this study because the C band is more susceptible to

contamination by water vapor in the atmosphere. Similar to

QuikSCAT, Jason-1 and T/P are polar orbiting satellites. However,

these satellites orbit much slower and y over the same ground

track every 10 days. Along-track, gridded Hs values with resolution

of approximately 2.5 min (5.8 km) provide comparison with results

from the Hawaii WW3.

3. Waves in Hawaii

The wave climate in Hawaii can be categorized into north swell,

south swell, and northeast wind waves. The swell events always

contain a certain level of energy from the year-round northeast

trade winds resulting in a multimodal sea state. We select two

case studies of approximately two weeks long each that encom-

pass a north and a south swell with a mix of developing and

persistent wind waves to illustrate the typical conditions inHawaii. The coupled wave model consisting of the global and

Fig. 5. Scatter plot of computed signicant wave height for Case Study 1.

Fig. 6. Comparison of signicant wave height from Hawaii WW3 and buoys for Case Study 1.



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Hawaii WW3 is initialized 14 days prior to the period of interest in

each case study. Due to its small spatial coverage the Oahu SWAN

is initialized 4 days prior for each case. This assures full develop-

ment of the sea state in the global, regional, and island scales for

a detailed assessment of the wave energy resources along the

Hawaiian Island chain.

3.1. Winter season

Hawaii is exposed to north swells in the winter season, when

the subtropical high pressure weakens and variable wind patterns

develop. The rst case study from March 18 to April 2, 2005 occurs

towards the end of the winter season when the dominant direction

Fig. 7. Wave power distributions for swell and wind waves in Case Study 1.



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of the swell ranges from the northwest to north. The swell in the

rst three days is the largest and two weaker swells occur during

the remainder of the case study. The winds are relatively calm in

the beginning and transition to moderate northeast trade condi-

tions that generate waves within the Hawaii domain. This case

study allows testing of the numerical models and examination of

the wave conditions for a number of different episodes: weak

winds coupled with a swell from the North Pacic, moderate winds

coupled with a smaller swell, and locally generated wind waves

with a background swell.

The wind event can be broken into two distinct episodes with

three representative patterns as shown inFig. 3. The data is plotted

in the original resolution along with every other QuikSCAT wind

vector and every sixth WRF wind vector. The QuikSCAT data is re-

Fig. 8. Comparison of wind elds from Hawaii WRF and QuikSCAT for Case Study 2.



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gridded to t on an evenly spaced grid of 0.25 0.25 with the

white areas representing rain ags and land masks. The top panels

are representative of the rst half of the case study with calmwinds

of 2e6 m/s from variable directions. A high-pressure system

centered on Oahu results in the anti-cyclonic wind pattern in the

middle panels. There are subtle differences between the WRF and

QuikSCAT data, but the overall

ow matches every well. In the

second half of the case study, moderate northeast trade winds of

10e15 m/s prevail. The Hawaiian Islands cause deceleration of the

trade wind ow on the windward shores and acceleration down-

stream of the ocean channels, especially in the Alenuihaha Channel

between Maui and Hawaii Island, driven by channel-parallel pres-

sure gradients [13]. WRF depicts wake circulations extending

downstream of Hawaii Island and Maui with a well-de

ned

Fig. 9. Comparison of wave elds from WW3 Hawaii and altimetry for Case Study 2.



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westerly returnow off the leeward coasts. Because of the emission

contamination from the land surface and the relatively coarse

resolution, QuikSCAT does not fully capture the detailed island-

induced airow over coastal waters and the wake circulations

leeward of major islands[34,35].

Fig. 4displays the wave patterns from the Hawaii WW3 on the

same days of the wind snapshots and at the nearest time steps

when altimetry data is available. The arrows represent the peak

wave directions every 7th grid point. The data on March 20 shows

the strong northwest swell with Hs up to 3.5 m under calm wind

conditions. The northwest shores of Kauai and Oahu are exposed tothe maximum swell energy, while the Hawaii Island is in the

shadowof the northern islands receiving only a fraction of the wave

energy. The altimetry data provides good coverage of the wave

conditions behind Niihau and Kauai to validate the island shadow

phenomenon. The swell decreases to about 2.5 m on March 25

around the transition period of the winds. Local seas generated by

the anti-cyclonic winds from the east cover the shadow south of

Hawaii Island and increase the wave height to the north corrobo-

rating the altimetry data. The wave eld on April 1 shows a mix of

the northwest swell and the waves associated with the moderate

trade winds. The wind waves are coming from the east with typical

waveheights of 2.5 m. There is signicant strengthening of the local

wind speeds due to the large topographic gradient. The strength-

ened winds to the south of Hawaii Island and along the channelsincrease the wave heights to the south and southwest of the island

chain. The altimetry data provides transects of wave conditions

across the island chain to validate the Hawaii WW3 results. The

WW3 data is interpolated in time and space to match the altimetry

passes for the comparison inFig. 5. There are no instances of the

Hawaii WW3 over predicting Hs values larger than 1 m. The model

underestimates the wave height by more than 2 m in only a few

instances. Since the altimetry data is a snapshot of the wave

conditions, these outliers may represent rogue waves, which the

phase-average wave model cannot resolve.

Signicant wave heights recorded by the NDBC deepwater

buoys as well as the Mokapu and Waimea nearshore buoys at

100 m and 200 m water depth are compared with the model data in

Fig. 6. The largest northwest swell occurs in the beginning of the

case study peaking on March 19e20. The model data matches the

average measurements very well, but does not capture the uctu-

ations. As the winds in the Hawaii region are relatively calm, the

discrepancy is probably dueto the low-resolution FNL wind forcing.

The computed swell from March 23 to 25 is consistently larger than

the buoy measurements most likely due to the generation source

term in WW3. The computed swell arrives slightly before the

record at buoy 51001 on March 23. The wave model predicts the

swell to have a longer wave period, which equates to a more

energetic sea state and faster propagation speed. This error prop-

agates through Hawaii WW3 resulting in over-prediction of Hs at

51002, 51003, and Waimea. The Mokapu buoy, which is in the

shadow of Oahus northeast headland, does not register this swell.

The winds transition from calm to moderate and local waves begin

to develop on March 26. A common feature at buoys 51002, 51003,

Mokapu, and Waimea is the slight under-prediction of wave

heights during March 27e29. A possible explanation lies in the

ability of wind wave models to account for the abrupt change in

wind speed. The match is much better towards the end of the

period when the wind waves gradually develop. The high-resolu-

tion Hawaii WRF forcing in the Hawaii WW3 reproduces the small

uctuations at buoys 51002, 51003, Mokapu, and Waimea at the

end of the episode. The computed wave height at buoy 51001,which is located outside the Hawaii WW3, is smooth throughout

this last episode due to the low-resolution FNL forcing.

With the model validated, we quantify the wave power around

the Hawaiian Islands and plot the results inFig. 7at the same time

steps as those in Fig. 3. The left column of panels represents the

primary swell and the right column represents the wind waves

determined from the algorithm of Hanson and Phillips[27]. The left

panel on March 20 corresponds to the peak of the northwest swell.

The wave power, which reaches 60 kW/m on the north facing shores

of the Hawaiian Islands, is on the same order of magnitude as

average conditions found in northwest Spain[1]. There are minimal

wind wave activities consistent with the weak local wind conditions

in the beginning of the case study. On March 25, the smaller

northwest swell has wave power in the range of 25e

35 kW/m, andlocal waves begin to develop with the trade winds from the east. The

northwest swell reduces to an estimated wave power of 15 kW/m or

less on April 1. The wind waves, on the other hand, become domi-

nant and have 15e35 kW/m of wave power available. Because of the

return ow and low wind speed, the algorithm of Hanson and

Phillips [27] classies the wind waves in the wakeof the trade winds

behind Hawaii Island as swell. Other than that the estimated power

is relatively large when compared to windewave regimes around

the world. Since the northeast and east trade winds are common

throughout the year, their speed up on the south side of Hawaii

Island provides a reliable resource for wave energy development.

3.2. Summer season

Hawaii experiences gentler south swells in the summer, when

the subtropical high-pressure generates persistent trade winds

from the east and northeast. In South Pacics winter, storms in the

roaring 40s generate most of the swells toward the Americas.

Hawaii receives swells from these sources through directional

spreading of the waves. The second case study from September 4 to

18, 2005 occurs in end of the summer season, when the systems

form closer to Australia and aim more wave energy toward Hawaii.

There are multiple swells throughout the case with the largest

around September 16e17 with signicant wave heights over 2 m

and peak periods over 18 s. Typical summer winds with speeds in

the 10e15 m/s range from the east persist throughout the entire

case study.Fig. 8shows three representative patterns of the trade

wind

ow across the Hawaiian Island chain. Alternating speed-up

Fig. 10. Scatter plot of computed signicant wave height for Case Study 2.



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and wake formations are evident leeward of the island chain. The

Hawaii WRF wind elds match the QuikSCAT mean ow very well

especially over the open ocean and in the exit region of the Ale-

nuihaha Channel. QuikSCAT does not capture the island-induced

airow in coastal waters and the wake circulations off the leewardcoasts due to its spatial resolution. The rather constant trade winds

give rise to predominant wind waves throughout the period. This

case study allows testing of the numerical models and assessment

of the wave pattern in a typical summer event with moderate wind

waves and a range of south swell conditions.

Fig. 9 displays the representative wave patterns around the time

of the wind data snapshots for direct comparison. The rst and

second rows show the wind waves from the east and the last row

shows the south swell coupled with the wind waves. On September

6, the trade winds generate signicant wave heights of up to 3.5 m

in the exit region of Alenuihaha Channel. The Hawaii WW3

reproduces the shadow west of Hawaii Island, but slightly under-

estimates the wind waves to the south. On September 11, the wind

wave pattern continues. The computed wind waves compare well

with the altimetry data, with errors less than 0.5 m. On September

16, the wave eld displays the peak of the south swell coupled with

the moderate wind waves from the east. The energy from the south

swell lls the shadows of the wind waves on the leeward side of the

islands and vice versa. This results in a discontinuous pattern of thepeak wave direction in the domain. The regional model is able to

reproduce the wave heights relatively well in comparison to the

altimetry data. There are discrepancies in the southern part of the

domain where the wind waves and south swell combine to give

larger wave heights than the altimetry data. There were 10 satellite

passes from both the T/P and Jason-1 platforms during the entire

case study.Fig. 10shows the overall comparison utilizing all the

altimetry data. The over prediction to the south of Hawaii Island is

shown as the group of points in the top of the plot. The overall

prediction is good with an error of typically within 0.5 m.

Fig. 11shows the time series comparison of the computed and

measured signicant wave heights at the buoys. The overall agree-

ment is reasonably good. There are instances of underestimation of

the wind wave heights at buoys 51002, 51003, and Mokapu due to

Fig. 11. Comparison of signicant wave heights from Hawaii WW3 and buoys for Case Study 2.



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the weaker modeled winds. The Hawaii WRF does not fully account

for all of the trade wind processes because the subtropical high-

pressure system is located outside the regional domain. These

errors, however, are less than 0.5 m in signicant wave height. The

largest discrepancies occur at buoys 51002 and 51003 at the peak of

the south swell during September 15e17. The wave model is pre-

dicting larger wave heights tothe south of theHawaiianIsland chain

as already shown inFig. 9. Given the locations of the buoys at the

Hawaii WW3 boundary, the error most likely arises from the

prediction of the swell in the global WW3, but is less than 1 m

typically. The Waimea buoy,which is shelteredfrom the south swell

andto a certain extent thewind waves, gives excellent agreementof

the background wave conditions. The time series comparison

reveals the Hawaii WRF is able to account for the mesoscale wind

Fig. 12. Wave power distributions for swell and wind waves in Case Study 2.



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eld that produces perturbation in the Hawaii WW3 results as

shown at buoys 51002, 51003,Mokapu, and Waimea. Thecomputed

time series at buoy 51001 is much smoother because this location is

in the global domain forced with synoptic winds from FNL. Contrary

tothe rstcase study, theentire two-weekperiod consists of similar

environmental conditions with moderate and gradual changes in

wind speed and direction. This provides a more favorable condition

for modeling of wind wave generation that is reected in the

comparison.

This case study allows examination of the potential of the south

swell and trade wind waves as a marine energy resource during the

summer months.Fig. 12provides estimates of the available wave

power of the two components across the Hawaiian Islands. The

stronger trade winds in this case study result in more signicant

deceleration windward of Maui and Hawaii Island and more

extensive wakes leewardof the islands. The algorithmof Hanson and

Phillips [27] classiesthe wind wavesin thoseareasas swell because

of the weak and variable local winds. On September 6, a weak south

swell with estimated wave power of less than 10 KW/m covers the

entire domain. The wind waves dominate with much higher power

of 15e25 KW/m. The generation of local wind waves is evident

downwind of the Alenuihaha Channel and south of Hawaii Island.

Albeit the misclassied swell in the wake of the trade winds, theresults on September 11 shows superposition of a west swell on the

developing south swell,which is only discernable on the east side of

Hawaii Island. The west swell was created approximately 6e8 days

earlier from Super Typhoon Nabi in the Western Pacic. The wind

waves are similar to those on September 6 with available power in

the range of 15e25 kW/m. September 16 represents the peak of the

south swell with 15 kW/m of wave poweron all southfacingshores.

The west swell has turned from the northwest as the remnants of

Nabi reorganized into an extratropical low pressure system, and is

discernable in the sheltered ocean north of Maui and Hawaii Island.

Thewind wavespeak simultaneously with the south swell andhave

wavepower reaching 40 kW/m in thechannelsand southwestof the

Hawaiian Islands.

4. Conclusions and recommendations

Two case studies representative of typical winter and summer

wave conditions have illustrated the effectiveness of the nested

wind and wave model system in describing the wave climate in

Hawaii. Comparison of the Hawaii WRF and QuikSCAT wind elds

show good agreement on the key processes around the islands. The

high-resolution Hawaii WRF describes the decelerating winds on

the windward side, wake circulations off the leeside coasts, and the

accelerating airow in the channels and around the islands. The

implementation of the WRF winds into the nested wave model is

necessary to capture the wave heights leeward of Maui and Hawaii

Island. Comparison of the computed signicant wave heights with

data from Jason-1 and T/P as well as deepwater and nearshorebuoys shows good agreement, with a negligible bias and an RMSE

on the order of 0.5 m.

This study also quanties the ocean wave resources in the three

main climate patterns typical to Hawaii. Through the two case

studies, the available wave power is estimated for the north and

south swells as well as wind waves across the Hawaiian Island

chain. Northwest swells have enormous wave power reaching

60 kW/m. These events, however, do not occur throughout the year

and are most prevalent in the winter months. South swells are

more consistent in the summer months, but have a lower level of

wave power than their northwest counterparts. The large south

swell examined in this study only has 15 kW/m at its peak. The

wind waves in the Hawaii region are the most consistent and

reliable energy resource throughout the year. The topography of the

Hawaiian Islands further augments the wave energy by local

acceleration of the wind ows. These events typically have avail-

able wave power in the range of 15e25 kW/m, which is rather high

for wind waves in comparison to other parts of the world.

Theconsistency of the wave events and the proximity to shore

play an important role in the selection of optimal locations for

deployment of wave energy devices. While the north and south

facing shores would respectively capture the energy from the north

and south swells, the most favorable sites for the wind waves are in

the Alenuihaha Channel and southwest of Hawaii Island. Both

locations have steady supply of wave energy during most of the

year and are close to shore and the existing power grid for distri-

bution. The refraction-diffraction models of Chandrasekera and

Cheung [36,37] can describe transformation of thewind waves over

the steep volcanic island slope to 50 m water depth, where wave

energy devices are typically deployed. This study has laid the

groundwork for a long-term wave climate study to provide data for

planning and operation of wave energy devices in Hawaii. An effort

to provide 10 years of high-resolution hindcast wind and wave

conditions in the Hawaii region is currently underway.

Acknowledgements

The Ofce of Naval Research funded the development of the wave

modeling package through Grant No. N00014-02-1-0903 and the

NOAA Integrated Ocean Observing System Program is supporting its

operation through a cooperative agreement NA07NOS4730207 with

the University of Hawaii. The Department of Energy provided addi-

tional support for the wave climate study through Grant No. DE-

FG36-08GO18180 via the National Marine Renewable Energy Center.

SOEST Contribution Number 7928.

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Assessment of Wave Energy Resources in Hawaii

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