Satellite-based wave data and wave energy resource assessment for South China Sea

Renewable Energy 88 (2016) 359e371

Contents lists available at ScienceDirect

Renewable Energy journal homepage: www.elsevier.com/locate/renene

Satellite-based wave data and wave energy resource assessment for South China Sea Omar Yaakob a, Farah Ellyza Hashim a, Kamaludin Mohd Omar b, Ami Hassan Md Din b, Kho King Koh a, * a b

Marine Technology Centre, Universiti Teknologi Malaysia, 81310 UTM, Skudai, Johor Bahru, Malaysia Department of Geomatics, Faculty of Geoinformation & Real Estate, Universiti Teknologi Malaysia, 81310 UTM, Skudai, Johor Bahru, Malaysia

a r t i c l e i n f o

a b s t r a c t

Article history: Received 2 February 2015 Received in revised form 2 November 2015 Accepted 11 November 2015 Available online xxx

Wave energy has the potential to valuably contribute to the coastal states renewable energy mix. However, lack of data sources hinders the effort to deliberately assess this resource. This paper presents an assessment of wave energy resources in the South China Sea (Malaysian Exclusive Economic Zone) using satellite altimeter. Radar Altimeter Database System (RADS) provides data of significant wave height and wind speed from several satellite altimeters. The data were extracted for a space resolution of 0.25
 0.25
, and within the time range from January 2001 to December 2010 and space range of 1.5
N e 10.0
N, 95.0
E  116.0
E. For this study, fifteen 2
 2
zones were considered around the east coast of Peninsular Malaysia and the coast of East Malaysia. The 10-year-data were validated with buoy measurements and presented as the probability distribution of wave height and wave period. The results indicate that bulk of the waves had peak period between 5s and 7s and significant wave height between 0.5 m and 1.5 m. The data were then used to calculate the theoretical available wave energy and power in the study areas. The results show that the average wave energy density of Malaysian seas facing the South China Sea is in the range of 1.41 kW/m to 7.92 kW/m, while the energy storage varies from 7.10 MW h/m to 69.41 MW h/m. This study also demonstrates the ability of satellite altimeter to provide an accurate and reliable data for more comprehensive and realistic estimate of the energy potential. The ability of satellite altimeter to provide wave data for all sea zones will enable more accurate identification of potential locations for wave energy development in Malaysia. © 2015 Elsevier Ltd. All rights reserved.

Keywords: Ocean Wave energy resources Satellite altimeter Malaysia

1. Introduction Malaysia has adopted the Five-Fuel Diversification Strategy energy mix implemented since 1999. According to this strategy, five main sources, namely natural gas, coal, oil, hydro and renewable energy contributed to the energy mix in Malaysia. In 2012, the energy supply mix was roughly 46% natural gas, 32% oil, and 19% coal and coke, and 3% hydro [1]. The Malaysian Prime Minister, Najib Razak, during his keynote address at the official opening of the third International Greentech and Eco Products Exhibition and Conference Malaysia (IGEM 2012) stated that, the government targeted to have 5.5% share of renewable energy in the total energy mix in installed capacity by 2015. The potential renewable energy sources in Malaysia include

* Corresponding author. E-mail address: [email protected] (K.K. Koh). http://dx.doi.org/10.1016/j.renene.2015.11.039 0960-1481/© 2015 Elsevier Ltd. All rights reserved.

solar energy, wind energy and ocean energy. Ocean energy consists of wave energy, tidal energy, salinity gradient and Ocean Thermal Energy Conversion (OTEC). Ocean energy has a number of significant advantages includes source-predictability, abundance, high load factor and low environmental impact and availability compared to other renewable energy sources. The wave energy is rightly regarded as one of the renewable energy sources with the greatest potential to replace conventional energy sources. According to Zubaidah in Ref. [2], out of the ocean-related sources of waves, tides and currents, the one with some potential is perhaps wave energy off-shore the east coast of Malaysia. Numerous studies have established the wave energy resource assessments in various region, for example in Baltic Sea [3], Hawaiian Islands [4] and Indian shelf seas [5]. Many countries around the world also have evaluated the potential of the wave energy resource in their coastlines including China [6], Korea [7], French [8], Australia [9], England [10] and the United States [11]. In

360

O. Yaakob et al. / Renewable Energy 88 (2016) 359e371

addition, atlas of global wave energy resource assessment were also presented [12,13]. The assessments were carried out using wave climate data from buoy, satellite altimeter, numerical wave hind casts model or a combination of these sources. The wave climate data play a key role in the assessment of wave energy resources. Thus, acquiring an accurate and reliable wave climate data is one of the crucial steps in the assessment. Previously, visual observations from merchant ships and buoys have provided us with very useful information on the wave climate with better spatial distributions despite not giving full global coverage. However, several studies questioned the quality and consistency of these visual estimations [14e16]. Maulud et al. [17] and Wan Nik et al. in Ref. [18] assessed the potential of wave energy resources in Malaysia using buoy measurements data. Both studies identified that the wave energy has a potential to valuably contribute towards Malaysian renewable energy mix. Maulud et al. in Ref. [17] developed a mapping of Malaysian seas using Geographical Information System (GIS) based on wave data from Malaysian Meteorological Department (MMD) to assess the potential wave energy location in Malaysia. In their study, they identified a high potential location for wave energy off Sabah in the Northern portion of East Malaysia. On the other hand, Wan Nik et al. in Ref. [18] used field survey or in-situ measurements to assess the wave power potential along the east coast of Peninsular Malaysia. Their study based on one and two-hourly buoy datasets acquired from acoustic wave and current (AWAC) instruments deployed by Universiti Malaysia Terengganu (UMT) and Malaysian Meteorology Department (MMD). Their results indicated that Terengganu coast of Peninsular Malaysia could provide a source of low wave power varies in the range of 0.1 kW/m to 6.49 kW/m. However, a recent survey on the potential of marine renewable energy in Malaysia by Yaakob and Koh in Ref. [19] suggested that the data from Malaysian Meteorological Department (MMD) is incomplete and inaccurate with limited coverage. Thus, the mapping developed by Maulud et al. in Ref. [17] does not accurately portray the true picture of Malaysian seas. Their estimate of the wave power density between 13 kW/m to 160 kW/m is also unacceptable as it is too high for Malaysian sea conditions. The buoy data as in Ref. [18] also have limited spatial and temporal coverage [20] and only applicable to the particular area of interest. This measuring system also are not widely available and does not have a worldwide evenly distributed cover, mainly due to high costs and difficulty related to harsh sea environment [21]. Nevertheless, the buoy measurements data are usually used to validate the performance of other wave measurements such as satellite altimeter and wave model. An alternative approach of using satellite altimeter in oceanography and marine field is capable to offer more accurate and reliable data with a good comprehensive coverage. However, due to small area of the earth's surface at low and mid latitude, the wave data can only be imaged once every few days with a single satellite, hence, lack in temporal resolution. The satellite measurements are also quite intermittent compared to 3 hour in situ or buoy measurements. Krogstad and Barstow in Ref. [22] addressed that the 3 hour in-situ buoy measurements is unnecessarily dense to determine the long term distribution of significant wave height, H s . Even so, satellite altimeter is ideal for detail resource mapping due to its high spatial resolution [23]. Barstow et al. in Ref. [24] used two years of altimeter data to construct a global map of the available wave energy resources in deep water. They succeeded in generating reasonable estimates of the spatial variations of mean wave energy despite the relatively short record length [21]. Besides, satellite altimeter data provided more comprehensive data of wave height and wave period for all sea zones [25]. Numerous studies have assessed the accuracy of satellite altimeter

measurements by comparing them with oceanic in-situ measurements from buoy stations around the globe [26e29]. Aziz et al. in Ref. [30] showed that the altimeter wave height is accurate and has excellence coverage and frequency of occurrence as compare to buoy measurement data in the South China Sea. Fairly little research on validation and application of altimeter has been conducted in the South China Sea, particularly around Malaysian seas. Thus, this paper highlights the application of satellite altimeter in the assessment of wave energy resource in Malaysia. 2. Area of interest and wave data Fifteen 2
 2
zones around the east coast of peninsular Malaysia and the coast of East Malaysia (Sabah and Sarawak basin) were considered in this study, as shown in Fig. 1. All selected zones are located within the Exclusive Economic Zone (EEZ) of Malaysia. Zone A to E are located in the east coast of Peninsular Malaysia facing the South China Sea, while Zone F to O are located around the coast of East Malaysia. The South China Sea possesses great potential of wave energy. These zones are exposes to the Northeast Monsoon season in the month of November to January each year. During this monsoon season, wave heights are larger attributable to the stronger wind speed and larger wind fetch. According to Muzathik et al. in Ref. [31], the intensity of the wave energy fluctuates seasonally in this zone, with the highest energy density occur during the northeast monsoon season. 2.1. Satellite altimeter Since the launch of Geostat in 1985, satellite altimeter has been used to explore the ocean dynamics and provided global coverage of sea level [32,33], ocean current [34,35], wave height, and wind speed [20]. Satellite altimeter is a nadir-pointing instrument designed to measure precisely the time a radiated pulse takes to travel to the surface and return. Fig. 2 illustrates the schematic 0 representation of satellite measurements. The angles q and q are the antenna pointing angle and incidence angle. R is the satellite altitude above the nadir point while Rq is the slant range of radar measurement at pointing angle q and Af is the antenna footprint area [36]. Theoretically, radar altimeters on board the satellite will permanently transmit a short pulse of microwave radiation with known power towards the sea surface. Interaction between the sea surface and the microwave direction will reflect part of the signal to the satellite altimeter to precisely measure the travel time between the satellite and the sea surface. Radar Altimeter Database System (RADS) established the data from satellite altimeter in a harmonized, validated and crosscalibrated sea level database. The database is consistent with accuracy, correctness, format, and reference system parameters. It also enables users to extract the data from several present and past satellite altimeter missions such as Envisat, Topex/Poseidon (T/P), Jason-1, Jason-2 etc. In this study, the wave data were retrieved from combination of multi satellite altimeters including Envisat, ERS-2, Jason-1, Jason-2, and Topex/Poseidon (T/P). These altimeters have provided us with good quality measurements for the past several decades [27]. Multi-mission processing allows more alongtrack data for model assimilation, higher resolution and better resilience thus providing high precision altimeter data. The data were extracted from Radar Altimeter Database System (RADS) located in the Global Navigation Satellite System (GNSS) and Geodynamics Laboratory, Universiti Teknologi Malaysia. The RADS data were retrieved with each mission have been updated with most up-to-date correction [37] for a space resolution of 0.25
 0.25
, within the time range from January 2001 to December 2010 and space range of 1.5
N e 10.0
N, 95.0
E 

O. Yaakob et al. / Renewable Energy 88 (2016) 359e371

361

Fig. 1. Selected area along the coast of East Peninsular Malaysia and East Malaysia.

were used to validate the significant wave height retrieved from the satellite altimeter. The combination of data from several satellite altimeters allows for better validation and assessment of wave energy resources in Malaysia. However, the condition of collocation and concurrence of satellite overpass with in-situ observations restricted the size of the number of data points [39].

3. Validation of altimeter wave data with buoy measurement data

Fig. 2. Altimetry principles [36].

116.0
E. Table 1 shows the mission parameters of several satellite altimeters that widely used in researches and studies. Fig. 3 illustrates a sample of ground tracks of Envisat and Jason-1 altimeter passing the South China Sea while the temporal coverage of available datasets from 7 altimeters used in this study is shown in Fig. 4. Travelling exceeding 7 km every second as it traces out its orbit, Envisat covers the global oceans every 35 days (the repeat period of the satellite orbit). Envisat altimeter covers the Malaysia sea zones, hence it is reasonable to assume that the application of satellite altimeter in this study is valid and reliable. Fig. 5 shows an example of distribution of significant wave height from Envisat and Jason-1 for 10 years (2001e2010). As illustrated in the figure, the combination of the two satellite altimeters provides comprehensive data and good spatial coverage for wave energy study in Malaysian sea. However, Sabique et al. in Ref. [38] suggested that the satellite altimeter data are not suitable for applications during extreme conditions. Fig. 6 presents the distribution of collocated significant wave height, Hs from Envisat, Jason-1 and Topex in Sabah for two years (2005e2007). These data

Many studies concluded that the altimeter measurements are comparable to the accuracy of buoy measurements. According to Le Traon et al. in Ref. [40], the satellite altimeters have continued to provide precise significant wave height observations with a global coverage. Comparison of altimeter significant wave height in Ref. [22] concluded that the altimeter measurement of significant wave height have reached the accuracy of buoy measurements, while studies in Refs. [41] and [42] demonstrated that the satellite data are in good agreement with buoy and WAVEWATCH-III model. The satellite altimeter data was validated with available buoy data using collocation method to assess the performance of the satellite altimeter. A set of data from an oil and gas production company were acquired for the validation purpose. The comparisons between buoy and satellite altimeter data require a criteria for the spatial and temporal separation between such observations to be applied. A space domain ranges from 0 to 150 km and time domain varies from 0 to 1.5 hour are acceptable criteria for better comparison between the buoy and altimeter data [43]. The criteria of 50 km and 30 min are widely applied in calibration using collocation method. Monaldo in Ref. [44] stated that such time separation leads to an expected uncertainty of 0.5 m/s for wind speed and 0.3 m for significant wave height. Study by Hector et al. in Ref. [45] also reported that, a minimum time difference between the two measurements is suitable for a minimum data number to eliminate errors due to the time sea state variability. A total of 600 collocated data points were considered within the

Table 1 Mission parameters. Satellite Topex/Poseidon Jason-1 Jason-2 ERS-1 ERS-2 Envisat

Altitude (km)

Track spacing equator (km)

Repeat period (days)

1340 1336 1336 780 785 796

315 315 315 80 80 80

10 10 10 35 35 35

362

O. Yaakob et al. / Renewable Energy 88 (2016) 359e371

Fig. 3. Altimeter ground tracks from 2005 to 2007 for Envisat (grey line) and Jason-1 (black line).

GFO TOPEX ENVISAT JASON2 JASON1 CRYOSAT ERS2 Buoy (Sarawak) Buoy (Sabah) 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Fig. 4. Temporal coverage of available datasets for 2001e2010.

wave period data essential in estimating the wave energy. Since the pioneer work of Davies et al. [46], several researchers have developed algorithms relating altimeter significant wave height Hs , wind speed U and backscatter coefficient, s0 to derive the wave period from satellite altimeter, for example Hwang [28] and Gommengiger et al. [47]. Yaakob et al. in Ref. [48] compared the wave period derived from satellite altimeter data using three empirical methods with in-situ data from the National Data Buoy Centre (NDBC), Petronas Research and Scientific Services (PRSS), and Global Wave Statistic (GWS) data. Fig. 9 shows the comparison of marginal probability occurrence of wave period from Topex/Poseidon (T/P)

Fig. 5. Distribution of significant wave height (m) from Envisat and Jason-1 for 10 years.

space-time window of 50 km and ±30 min in this study. The in-situ data consist of significant wave heights and wave periods data for Sabah (5.83
N, 114.39
E, depth of 1050 m) and Sarawak waters (5.15
N, 111.82
E, depth of 120 m) as illustrated in Fig. 7. Fig. 8 presents the comparison of significant wave height, Hs retrieved from satellite altimeter with different spatial resolution. The figure indicates that the significant wave height data retrieved from small temporal resolution (less than or equal to 50 km); see Fig. 8(a) presents a very good agreement with buoy significant wave height (correlation coefficient ¼ 0.8649) and less scattered compared to the large spatial resolution (greater than 50 km); see Fig. 8(b). This signifies the importance of selecting appropriate spatial resolution to validate the satellite altimeter data. The satellite altimeter has a drawback as it did not provide the

satellite data with NDBC buoy data. They indicated that although the values of probability peak period are quite far, but it is still in the same range, between 4.5 and 5.5 s [48]. Kshatriya et al. [39], Govindan et al. [49], Quilfen et al. [50] and Mackay et al. [51] have developed new algorithms to derive the wave period from satellite altimeter data using different approaches. Each algorithm claimed to have good performance compared to previous empirical algorithms. Fig. 10 presents the comparison of probability distribution plots of the algorithms stated above using Malaysian sea data. The shape of the probability distribution by Mackay algorithm in Ref. [51] conforms to that of buoy observations much better than the other algorithms. Note that all algorithms derived to give zero-crossing period, Tz since buoy measurements data provide values for the Tz and peak period, TP .

O. Yaakob et al. / Renewable Energy 88 (2016) 359e371

363

Fig. 6. Distribution of collocated significant wave height (m) between Envisat, Jason-1 and Topex with buoy (2005e2007).

Fig. 7. Locations of buoys used in this study.

Hwang method gives value of TP , thus the assumption of relation TP ¼ 1:4Tz [52] is used for comparison purposes. However, Hwang method was applied for the calculation of wave energy as most Wave Energy Converters (WECs) developers utilized an approach that shows their WEC power production by means of a power matrix in terms of Hs and TP [53]. In Hwang method, the peak period of the wave field, T, is related to wind speed, U, and significant wave height, H, as given in equation (1):

0:67
. U=ðgTÞ ¼ 0:048 U 2 ðgHÞ

(1)

where g is the gravitational constant, T is the peak period and H is the significant wave height. In addition, the RADS data extracted only provides data of wind speed, U and significant wave height, Hs . Thus, Hwang method is the best method to apply in this study. Hwang reported that by using the Topex/Poseidon data to derive U and H, the period is found slightly less (by 6%) than the peak period of buoy measurement. The comparison of wave data from satellite altimeter with buoy

measurements can be observed in Fig. 11 and Fig. 12. It is apparent that both measurements give good correlation with the correlation coefficient between the two measurement methods is 0.885 for wave period and 0.931 for significant wave height. Straight line (Line of Best Fit) in the figures represents the line where correlation coefficient between the two measurement methods is 1.0, i.e. perfect correlation with all data points lie exactly on the straight line. Table 2 summarizes the statistical parameters of the two wave parameters. The RMSE obtained in the study between the significant wave height of the buoy and altimeter data is 0.217 m while the RMSE for the peak wave period is relatively high at 0.993 s. This agrees well with the analyses on the typical errors associated with altimeter-buoy comparisons done by Dobson [44] and Monaldo [54]. From their study, they concluded that the differences of RMSE ranges between 0.4 m and 0.5 m could be expected for significant wave height, Hs . The positive bias indicates an overestimation of the altimeter data with respect to the buoy data. Significant wave height data from satellite altimeter are comprehensive, accurate and reliable, thus is reliable to accurately estimate wave energy

364

O. Yaakob et al. / Renewable Energy 88 (2016) 359e371

6

6

N: 572 BIAS: -0.005 m RMSE: 0.21 m SI: 0.21

5

4

Hs [Altimeter] (m)

Hs [Altimeter] (m)

5

N: 572 BIAS: -0.125 m RMSE: 0.65 m SI: 0.64

3

2

4

3

2

1

1

0

0 0

1

2 Hs [Buoy] (m)

3

0

4

(a) ≤ 50 km spatial coverage

1

2 Hs [Buoy] (m)

3

4

(b) > 50 km spatial coverage

Fig. 8. Scatter plot of comparison of significant wave height retrieved from different spatial resolution (Temporal resolution is constant, ± 30 min).

T/P vs NDBC P¼

0.70 0.60

rg 2 2 H Te ¼ 0:49Hs2 Te 64p s

where P is the wave power per unit of crest length (kW/m), Hs is the significant wave height, Te is the energy period, r is the density of seawater (assumed to be 1025 kg/m3) and g is the gravitational acceleration. Te is computed as a function of spectral moments:

0.50

0.40 NDBC

P (T)

(2)

Hwang

0.30

Gommen 0.20

Davies

0.10 0.00 0

5

T (sec)

10

15

Fig. 9. Comparison of marginal probability occurrence of wave period from Topex/ Poseidon data with NDBC buoy data [48].

resources in Malaysia. The satellite altimeter underestimate the TP by more than 0.9 s. This may result from large variation when different data processing procedures were used in Hwang algorithm besides TP is an unstable quantity [28]. However, the errors in period are less significant than errors in wave height [55] and study by Yaakob [48] recommended that Hwang method provides the best fit for Malaysian ocean wave data, thus the method will be used in calculation of wave energy.

4. Wave energy potential in Malaysia The wave power can be obtained using the following expression:

Te ¼

m1 m0

(3)

However, measured sea states are often specified in terms of significant wave height, Hs and either peak period TP or zerocrossing period Tz . The energy period Te is rarely specified and must be estimated from other variables when the spectral shape or the spectral moments are unknown [53], as in this study. Since no spectral information available, the energy period cannot be computed with the equation (3). Another approach when TP is known, the energy period can be assumed as:

Te zaTP

(4)

where a is a coefficient whose value depends on the shape of the wave spectrum (e.g. 0.86 for a Pierson-Moskowitz spectrum). In this paper, we assumed that Te ¼ TP as recommend in the assessment of wave energy resources in southern New England [10]. Pastor and Liu in Ref. [53] adopted more conservative assumption of a ¼ 0:90 or Te ¼ 0:9TP . This assumption introduces uncertainty in the resulting wave power. However, the errors in period are less significant than errors in wave height since P is proportional to Te and to the square of the Hs [55]. Fig. 13 compares the relationship of wave power calculated using different coefficient of a in equation (3). Relationship of Te ¼ TP in Fig. 13(a) increased the value of wave power in Fig. 13(b) by approximately 10 kW/m. However, both relationships give same value of correlation coefficient, (R-sq ¼ 0.803). Thus, it may be reasonable to assume that this region has the same environment as

O. Yaakob et al. / Renewable Energy 88 (2016) 359e371

365

Fig. 10. Probability distribution functions of wave period calculated by the altimeter/buoy collocation data set.

15

4

correlation = 0.88549

correlation = 0.93104 3.5

Line of Best Fit

Line of Best Fit 3

Altimeter Tp (s)

Altimeter Hs (m)

10

5

2.5 2 1.5 1 0.5

0

0

5

10

15

Buoy Tp (s) Fig. 11. Scatter plots of altimeter data and in-situ data for the peak wave period using Hwang method (s).

southern New England. This figure also demonstrates that the different coefficient a used will not affect the period value significantly. Note that it is beyond the scope of this study to evaluate in detail the accuracy of the coefficients that relate peak period to energy period.

4.1. Wave scatter diagram Fifteen specific sites were selected to investigate the potential of wave power in Malaysia (see Fig. 1). The satellite altimeter data were presented in form of scatter diagrams to better visualize the composition of the wave energy resource for wave height and wave period. The results were presented based on the altimeter data covering a period of 10 years to capture the overall variability of the

0

0

0.5

1

1.5

2

2.5

3

3.5

4

Buoy Hs (m) Fig. 12. Scatter plots of altimeter data and in-situ data for significant wave height (m).

wave. Table 3 to 17 present the joint annual probability distributions of Hs and TP for 2001e2010 for all selected zones. The wave scatter tables were tabulated considering seven intervals of significant wave height and eleven peak period intervals. A ‘bins’ of typically 0.5 m of wave height by 1s of wave period is segmented to the diagram. The number within each square represents the occurrence (in number of hours per year) of sea states whose Hs and TP fall within the corresponding range. The joint probability is expressed as parts per thousand, thus a value of 78 would represent a probability of 0.078 or 7.8%. The actual figure is given for the number of occurrence less than 1 part per thousand to allow better assessment. The most dominant Hs and TP combinations can be identified to allow the derivation of the corresponding wave power, P Within the 10 years, most frequent waves recorded at significant

366

O. Yaakob et al. / Renewable Energy 88 (2016) 359e371 Table 2 Statistic of satellite altimeters comparison with buoy. Parameters Bias ¼

1 N

Significant wave height (m)

Peak wave period (s)

0.004

0.269

0.217

0.993

0.931

0.885

PN

i¼1 ðAi  Bi Þ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi P 2 RMSE ¼ N1 N i¼1 ðAi  Bi Þ PN ½ðAi AÞðBi BÞ i¼1 ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi Correlation ¼ qP N

i¼1

½ðAi AÞðBi BÞ

Ai represents The data from the altimeter; Bi represents the data obtained from the buoy; N is the number collocation data points (600 data points); the over bar represents the mean value

70

Wave power [Altimeter] (kW/m)

60

50

y = 0.3207 + 0.8768 x R-sq = 0.803

40

30

20

10

0 0

10

20

30

40

50

60

70

80

Wave power [Buoy] (kW/m)

=

(a) wave power in kW/m of buoy and satellite altimeter (

)

Wave power [Altimeter] (kW/m)

60

50

y = 0.2886 + 0.8768 x R-sq = 0.803

40

30

20

10

0 0

10

20

30

40

50

60

70

Wave power [Buoy] (kW/m)

(b) wave power in kW/m of buoy and satellite altimeter (

=0

)

Fig. 13. Comparison of wave power calculated using different coefficient of.a.

wave height of 0.5 me1.0 m with wave period of 5se6s for most of the selected zones. Zone J, K, L and M recorded the most probable wave height and wave period (1.0 me1.5 m and 6se7s, respectively). From Fig. 1, these zones are located in the open ocean of the South China Sea. In general, Malaysian seas have a low wave climate condition with peak period are in between 5s and 7s and significant wave height between 0.5 m and 1.5 m. This information

is of great practical interest especially for wave energy converters (WECs) developers when it comes to design, develop and select a device that performs best in their area of interest. The WEC is required to achieve it working requirement in order to operate efficiently. Typically, the minimum and maximum wave height of the wave is the main working requirement for the device [56]. Thus, Malaysian seas require wave energy converter that can operate in

O. Yaakob et al. / Renewable Energy 88 (2016) 359e371 Table 3 Annual wave scatter table for 2001e2010 (zone A).

(m)

0

0

0

51

287

388

Table 7 Annual wave scatter table for 2001e2010 (zone E).

204

65

5

1

0

1000

(m)

> 3.0

0

> 3.0

2.5-3.0

0

2.5-3.0

1

1

2

2.0-2.5

12

43

2

59

1.5-2.0

2.0-2.5 1.5-2.0

1

1.0-1.5

2

0.4

75

156

17

1

0.5-1.0

8

264

313

36

4

1

0-0.5

40

22

0.2

3-4

4-5

5-6

0-1

1-2

2-3

367

6-7

7-8

8-9

1

9-10

0.2

0

0

3

32

266

0

0

31

210

420

6

204

0-0.5

25

6

3-4

4-5

0-1

1-2

2-3

0

0

30

92

175

1

1

6 1

2

5

27

150

42

5

253

289

85

13

1

62

0-0.5

3

19

8

2-3

3-4

4-5

5-6

6-7

7-8

8-9

9-10

> 10

175

91

10

0

0-1

148

16

0

0

1000

(m)

0

> 3.0

1

1

2.5-3.0

11

14

25

2.0-2.5

1

27

124

152

1.5-2.0

12

255

1.0-1.5

311

14

1

536

0.5-1.0

31

0-0.5

6-7

247

62

7

134

345

16 6

7-8

8-9

9-10

0

1-2

81 235 647 31

0

0

20

212

307

185

1000 0 0

0-1

> 10

1-2

2-3

2

14

4

20

3

49

50

5

1

5

79

108

26

1

107

2

166

298

103

16

0.4

18

45

4

3-4

4-5

5-6

6-7

7-8

8-9

9-10

> 10

157

23

2

0

0.3

221 585 67

Peak Period (s)

Table 5 Annual wave scatter table for 2001e2010 (zone C).

0

4

0.5-1.0

Peak Period (s)

(m)

1000 0

2

1.0-1.5

109

5-6

2

Table 8 Annual wave scatter table for 2001e2010 (zone F).

2.0-2.5

0.5-1.0

2

Peak Period (s)

2.5-3.0

1.0-1.5

10

252

> 10

> 3.0

1.5-2.0

118

625

Table 4 Annual wave scatter table for 2001e2010 (zone B).

0

251

0

Peak Period (s)

(m)

316

Table 9 Annual wave scatter table for 2001e2010 (zone G).

233

50

2

0

1000

(m)

0

0

0

8

211

326

272

1000

> 3.0

0

> 3.0

0

2.5-3.0

0

2.5-3.0

0

6

2.0-2.5

154

1.5-2.0

2.0-2.5 1.5-2.0

1

5

1

118

34

1

1.0-1.5

2

2

16

134

101

10

1

0.5-1.0

1

82

329

112

13

1

0.2

6-7

7-8

8-9

9-10

0-0.5 0-1

1-2

2-3

26

9

0.2

3-4

4-5

5-6

0.5

267

1.0-1.5

538

0.5-1.0

35

0-0.5

> 10

0

178 0-1

1-2

2-3

0

1

42

243

325

186

175

2.5-3.0 10

1.5-2.0 1.0-1.5

1

0.5-1.0 0-0.5 0-1

1-2

9

237

1

32

6

2-3

3-4

4-5

0.4

11

125

105

9

1

314

145

23

1

6-7

7-8

8-9

9-10

> 10

144

122

45

0

8

33

2

4-5

5-6

45 0.4

252 661 42

Table 10 Annual wave scatter table for 2001e2010 (zone H).

28

0

0

> 3.0 2.0-2.5

13

Peak Period (s)

Table 6 Annual wave scatter table for 2001e2010 (zone D).

0

29

3-4

Peak Period (s)

(m)

3

1000

(m)

0

> 3.0

0.3

0

2.5-3.0

20

30

2.0-2.5

0

0

0

13

148

353

174

1000 0

3

3 89

4

57

28

14

128

6

148

1.5-2.0

12

73

53

13

150

49

129

34

0.3

213

1.0-1.5

4

6

53

122

64

13

1

262

276

43

3

1

569

0.5-1.0

3

125

299

40

3

40

0-0.5

7

18

1

3-4

4-5

5-6

6-7

7-8

5-6

6-7

Peak Period (s)

7-8

8-9

9-10

> 10

0-1

1-2

2-3

470 25

Peak Period (s)

8-9

9-10

> 10

368

O. Yaakob et al. / Renewable Energy 88 (2016) 359e371

Table 11 Annual wave scatter table for 2001e2010 (zone I).

(m)

0

0

0

14

140

368

Table 15 Annual wave scatter table for 2001e2010 (zone M).

318

125

34

1

0

1000

(m)

0

0

0

12

133

429

343

77

6

0

0

1000

> 3.0

0

> 3.0

0

2.5-3.0

0

2.5-3.0

0

7

2.0-2.5

103

1.5-2.0

2.0-2.5 1.5-2.0 1.0-1.5 0.5-1.0

0.2

0-0.5 0-1

1-2

2-3

127

14

13

3-4

4-5

1

6

13

67

22

0.3

6

0.2

51

208

50

317

96

8

5-6

6-7

7-8

8-9

9-10

315

1.0-1.5

549

0.5-1.0

27

0-0.5

> 10

0

1 0-1

1-2

2-3

11

4

3-4

4-5

Peak Period (s)

0

0

0

11

55

355

442

114

21

1

0

1000 0

2.5-3.0

0

2.0-2.5

0

1.0-1.5 0.5-1.0

48

0-0.5

11 0-1

1-2

2-3

3-4

9

34

6

45

286

66

10

310

147

14

5

48 1

408 524

8 4-5

19 5-6

4

84

126

271

22

2

303

45

2

5-6

6-7

7-8

8-9

9-10

> 10

69

6

1

0

421 480 15

Table 16 Annual wave scatter table for 2001e2010 (zone N).

> 3.0

1.5-2.0

53

Peak Period (s)

Table 12 Annual wave scatter table for 2001e2010 (zone J).

(m)

129

27

6-7

7-8

8-9

9-10

(m)

0

0

0

20

141

424

340

1000

> 3.0

0

2.5-3.0

0

2.0-2.5

0

1.5-2.0

2

16

0.2

18

1.0-1.5

2

2

80

230

47

4

1

365

0.5-1.0

4

133

344

109

6

1

0.2

597

0-0.5

15

6

3-4

4-5

5-6

6-7

7-8

8-9

9-10

36

13

0-1

> 10

1-2

2-3

21 > 10

Peak Period (s)

Peak Period (s)

Table 17 Annual wave scatter table for 2001e2010 (zone O). Table 13 Annual wave scatter table for 2001e2010 (zone K).

(m)

0

0

0

6

99

298

263

(m)

163

116

52

2

> 3.0

1000 0

2.5-3.0 2.0-2.5 1.5-2.0 1.0-1.5 0.5-1.0

1

0-0.5 0-1

1-2

2-3

95

5

4

3-4

4-5

11

22

1

35

13

79

22

1

115

7

29

109

24

103

209

41

2

195

25

5-6

6-7

170 354 317 9

7-8

8-9

9-10

0

0

0

2

102

396

332

113

6

999

> 3.0

0

2.5-3.0

0

2.0-2.5

0

1.5-2.0

1

1.0-1.5 0.5-1.0

93

0-0.5 0-1

1-2

2-3

2

9

3-4

4-5

1

4

52

36

23

11

392

280

76

13

2

5-6

6-7

7-8

8-9

9-10

6

132 856 11

> 10

Peak Period (s)

> 10

Peak Period (s)

low significant wave heights (as low as 0.5 m) and periods to harness the energy from the wave. Table 14 Annual wave scatter table for 2001e2010 (zone L).

(m)

0

0

0

12

115

340

372

4.2. Wave energy resource 123

36

1

0

> 3.0

1000 0

2.5-3.0

0

2.0-2.5 1.5-2.0 1.0-1.5 0.5-1.0

1

106

0-0.5

10

9

3-4

4-5

0-1

1-2

2-3

8

19

1

28

52

91

15

158

100

281

25

2

407

241

39

387 19

5-6

6-7

Peak Period (s)

7-8

8-9

9-10

> 10

The total wave energy resource at each zone is calculated using equation (5) which taking into account the probability occurrence of having certain H s and T P . By assuming the power density corresponding to sea state in different ranges of H s and T P weighted, with regard to its probability of occurrence, will determined the annual wave energy density in each zone, as shown in equation (5) [57].

Paverage ¼

X

P: Prob

(5)

The total storage per unit zone of wave energy E PT is calculated to study the available wave energy resources. By multiplying the

O. Yaakob et al. / Renewable Energy 88 (2016) 359e371

369

annual average wave energy P average by the number of hours in a year, approximately z8766 hours [58] yield the total wave energy storage as follows;

EPT ¼ Paverage $8766

(6)

Fig. 14 illustrates the annual average wave energy density, while Fig. 15 presents the total storage of wave energy resources per unit area for each selected zone. It appears that the annual average wave energy density in South China Sea is not greater than 7 kW/m. This is closely similar with study by Zheng et al. [59] and Zhang et al. [60]. They described that the wave energy density in South China Sea is in the range of 1 kW/m to 6 kW/m. The frequency of different energy level is an important criterion in the assessment of wave energy resources to measure degree of richness of the energy. Several studies suggest that the area with wave energy density greater than 2 kW/m is considered as the area with available wave energy, while the area with density greater than 20 kW/m is considered as rich zone, such as the North Sea in Europe [58,61,62]. Fig. 16 presents the interval plot with 95% of Confidence Interval (CI) for the mean of wave energy density (kW/m). For example, with 95% confidence, the average wave energy density (kW/m) in zone K is approximately between 7 and 9 kW/m. Zone G appears to give the narrowest confidence interval as a results of high volume of data with respect to the area of the zone. Note that we provide 95% CI since this is the most used confidence interval. Individual standard deviations are used to calculate the interval. In this study, we present the resources into 3
; high, intermediate and low energy zone as suggested by Zheng et al. in Ref. [58]. The high-energy zones are zone H, K and L which located in the open ocean of the South China Sea, with total storage of wave

Fig. 16. 95% Confidence interval plot of wave energy density (kW/m) for each zone.

energy greater than 40 MW h/m. On the contrary, the low energy zones are zone C, G, J, N and O located in the sheltered zone with wave energy storage less than 20 MW h/m. The intermediateenergy zone with wave energy ranging from 20 MW h/m to 30 MW h/m are consist of unlisted zones in the east coast of Peninsular Malaysia and several zones in East Malaysia waters. Table 18 summarizes all results from this study with the status indicate the availability of wave energy resources in all zones. The exploitable significant wave height Hs in wave energy is defined as Hs greater than 0.5 m. The definition takes into consideration the efficiency of several wave energy converters (WEC), which can absorb wave energy from wave heights as low as 0.5 m. From the study, the occurrence of exploitable Hs in South China Sea is high throughout the 10 years, point out a good indication of wave energy resource development.

Wave energy density (kW/m)

9.00 8.00

5. Conclusions

7.00 6.00 5.00 4.00 3.00 2.00 1.00 0.00 A

B

C

D

E

F

G

H I Zone

J

K

L

M N

O

Fig. 14. Average wave energy density of 2001e2010 for study areas (kW/m).

Wave energy storage (MWh/m)

80 70 60 50 40 30 20 10 0 A

B

C

D

E

F

G

H I Zone

J

K

L

M

N

O

Fig. 15. Total storages of wave energy resources in study areas (MWh/m per year).

The main purpose of the present work is to provide fundamental information on wave climate in several zones along the Malaysian coast. In this study, the performance of satellite altimeter data in oceanography studies was being evaluated. Satellite altimeter data, covering 10 years interval from 2001 to 2010 were used to analyse the wave power at different zones in the South China Sea. The comparisons of wave period and wave height data retrieved from both satellite altimeter and in-situ measurement give good agreement, with correlation coefficient for both parameters greater than 0.8. These imply that both techniques are competitive. The satellite altimeter is a convenient method, especially to Malaysian region where buoy measurement data are still limited and rare in both number and geographic distribution. The 10-year average probabilities of occurrence of all wave height-wave period combinations for each zone were presented in the form of wave scatter diagrams. The scatter diagram enables the prediction of overall variability of the zones. The bivariate scatter diagrams indicate that almost all zones have similar wave climate condition with most frequent wave recorded at the significant wave height of 0.5 me1.0 m and wave period of 5se6s. Zone K and L have slightly high wave climate; significant wave height of 1.0 me1.5 m and wave period of 6se7s. Zone B, H and K also recorded higher wave height as compared to other zones which only have wave height up to 3 m. In general, this study reveals that Malaysia has low wave climate as compared to other regions. Apart from zone C, G, J, N, and O, others zones are marked as available zone with average energy density greater than 2 kW/m. From this early assessment, Zone K shows high potential in wave farm development. It is estimated to

370

O. Yaakob et al. / Renewable Energy 88 (2016) 359e371

Table 18 Comparison of wave energy resource in different areas. Zone

Average wave energy density (kW/m)

Probability of exploitable SWH (%)

Annual wave energy storage (MWh/m)

2.65 4.23 1.82 4.14 2.08 3.06 0.81 5.85 3.53 1.41 7.92 4.88 3.57 1.76 1.41

93.80 96.90 96.50 96.00 96.90 93.30 95.80 97.50 97.30 98.10 99.10 98.10 98.50 97.90 98.90

23.18 37.10 15.98 36.32 18.25 26.83 7.10 51.32 30.92 12.36 69.41 42.75 31.31 15.42 12.58

A B C D E F G H I J K L M N O

produce up to 69.41 MW h/m of energy. Nevertheless, the probability of exploitable significant wave height indicates that 90% of all zones in South China Sea have H s greater than 0.5 m. This study also indicates that the wave energy in Malaysian waters is low. Thus, efficient and reliable devices suitable for low significant wave height are needed to enable maximum energy being harvested. The geographical position of the selected zone is one of the factors which contribute to such results. Zone H, K and L, located in the open ocean of the South China Sea encounter bigger swell wave and ocean wave with higher significant wave heights. Zone C, G, J, N and O, located near the shoreline of Peninsular Malaysia and sheltered zone in East Malaysia water encounter smaller wave with lower significant wave height, thus produce smaller wave power. The existence of multiple islands and bathymetric steepness in South China Sea may obstruct and reduce the wave height and consequently affect the wave energy density. Acknowledgements The authors would like to thank TUDelft, GNSS and Geodynamics Laboratory, Universiti Teknologi Malaysia for providing the satellite data available for the research and also Marine Technology Centre, Universiti Teknologi Malaysia for the support in computer hardware. Special thanks to Universiti Teknologi Malaysia (UTM) and the Ministry of Higher Education (MOHE) for funding this project under the Exploratory Research Grant Scheme Vote No: 4L125. References [1] Commission E, Malaysia Energy Statistics Handbook 2014, Comm Energy, 2014 (accessed 4.08.1.5), http://meih.st.gov.my/. [2] I. Zubaidah, M.D. Siti Zawiah, K. Ramlee, The Fifth Energy Option for Malaysia, JURUTERA (2006) 16e19. €stedt, M. Leijon, Wave energy resources in sheltered sea [3] H. Bernhoff, E. Sjo areas: a case study of the Baltic Sea, Renew. Energy 31 (2006) 2164e2170. [4] J.E. Stopa, J.F. Filipot, N. Li, K.F. Cheung, Y.L. Chen, L. Vega, Wave energy resources along the Hawaiian Island chain, Renew. Energy 55 (2013) 305e321. [5] V.S. Kumar, T.R. Anoop, Wave energy resource assessment for the Indian shelf seas, Renew. Energy 76 (2015) 212e219. [6] B. Liang, F. Fan, Z. Yin, H. Shi, D. Lee, Numerical modelling of the nearshore wave energy resources of Shandong peninsula, China, Renew. Energy 57 (2013) 330e338, http://dx.doi.org/10.1016/j.renene.2013.01.052. [7] G. Kim, W.M. Jeong, K.S. Lee, K. Jun, M.E. Lee, Offshore and nearshore wave energy assessment around the Korean Peninsula, Energy 36 (2011) 1460e1469. [8] M. Gonçalves, P. Martinho, C. Guedes Soares, Wave energy conditions in the western French coast, Renew. Energy 62 (2014) 155e163, http://dx.doi.org/ 10.1016/j.renene.2013.06.028. [9] M.G. Hughes, A.D. Heap, National-scale wave energy resource assessment for Australia, Renew. Energy 35 (2010) 1783e1791. [10] G. Hagerman, Southern New England wave energy resource potential, Build.

Status Available Available Poor Available Available Available Poor Available Available Poor Available Available Available Poor Poor

Energy (2001). Boston, USA. [11] Z. Defne, K.A. Haas, H.M. Fritz, Wave power potential along the Atlantic coast of the southeastern USA, Renew. Energy 34 (2009) 2197e2205. [12] R.A. Arinaga, K.F. Cheung, Atlas of global wave energy from 10 years of reanalysis and Hindcast data, Renew. Energy 39 (2012) 49e64, http:// dx.doi.org/10.1016/j.renene.2011.06.039. [13] K. Gunn, C. Stock-Williams, Quantifying the global wave power resource, Renew. Energy 44 (2012) 296e304, http://dx.doi.org/10.1016/ j.renene.2012.01.101. [14] A. Shinkai, S. Wan, Statistical characteristics of global wave data and the appraisal for long-term predictions of ship response, J. Soc. Nav. Archit. Jpn. (1995) 289e296. [15] P.G. Challenor, S. Foale, D.J. Webb, Seasonal changes in the global wave climate measured by the geosat altimeter, Int. J. Remote Sens. 11 (1990) 2205e2213. [16] S.K. Gulev, V. Grigorieva, A. Sterl, D. Woolf, Assessment of the reliability of wave observations from voluntary observing ships: insights from the validation of a global wind wave climatology based on voluntary observing ship data, J. Geophys. Res. Ocean 108 (2003) 32e36. [17] K.A. Maulud, O. Karim, K. Sopian, Z.M. Darus, E.M. Ramly, Identification a potential wave energy location in Malaysia using GIS, in: N.E. Mastorakis, M. Poulos, V. Mladenov, Z. Bojkovic, D. Simian, S. Kartalopoulos., C. Udriste (Eds.), WSEAS Int. Conf. Proc. Math. Comput. Sci. Eng., 2008, pp. 426e430. [18] W.B. Wan Nik, M.Z. Ibrahim, K.B. Samo, Wave energy potential of Peninsular Malaysia, ARPN J. Appl. Sci. 5 (2010) 11e23. [19] O. Yaakob, K.K. Koh, The promise of marine renewable energy in Malaysia: too good to be true? Malaysia J. Sci. 32 (2013) 309e316. [20] S. Zieger, J. Vinoth, I. Young, Joint calibration of multiplatform altimeter measurements of wind speed and wave height over the past 20 years, J. Atmos. Ocean Technol. 26 (2009) 2549e2564. [21] A. Angelis-Dimakis, M. Biberacher, Methods and tools to evaluate the availability of renewable energy sources, Renew. Sustain Energy Rev. 15 (2011) 1182e1200. [22] H.E. Krogstad, S.F. Barstow, Satellite wave measurements for coastal engineering applications, Coast Eng. 37 (1999) 283e307. [23] D. Doody, E.V. Mc Garrigle, K.B. Coffey, P.G. Leahy, Comparison of wind speeds from QuikSCAT scatterometry and surface buoys for wind resource estimation in the Irish Sea, Proc. Eur. Wind Energy Conf. (2012) 1e7. [24] S. Barstow, O. Haug, H. Krogstad, Satellite altimeter data in wave energy studies, Ocean Wave Meas. Anal., ASCE (1998) 339e354. [25] O. Yaakob, N. Zainudin, Y. Samian, A. Maimun, A. Malik, R.A. Palaraman, Presentation and validation of remote sensing ocean wave data, Int. J. Res. Rev. Appl. Sci. 4 (2010) 373e379. [26] Naoto Ebuchi, H. Kawamura, Validation of wind speeds and significant wave heights observed by the TOPEX altimeter around Japan, J. Oceanogr. 50 (1994) 479e487. [27] K. Park, E. Lee, K. Kim, Validation of significant wave height from satellite altimeter in the seas around Korea and error characteristics, Korean J. Remote Sens. 29 (2013) 631e644. [28] P.A. Hwang, W.J. Teague, G.A. Jacobs, D.W. Wang, A statistical comparison of wind speed, wave height, and wave period derived from satellite altimeters and ocean buoys in the Gulf of Mexico region, J. Geophys Res. 103 (1998) 10451e10468, http://dx.doi.org/10.1029/98JC00197. [29] T.H. Durrant, D.J.M. Greenslade, Validation and application of Jason-1 and envisat significant wave heights, in: Proc. 10th Int. Work. Wave Hindcasting Forecast. Coast. Hazards Symp., Hawaii, USA, 2007. [30] W.A.W.A. Aziz, O. Yaakob, K.M. Omar, A.H.M. Din, Satellite altimeter ocean wave heights data in South China Sea, Int. Conf. Mar. Technol. Kuala Teren. Malays. (2012) 20e22. [31] A.M. Muzathik, W.B.W. Nik, K.B. Samo, M.Z. Ibrahim, Ocean Wave Measurement and Wave Climate Prediction of Peninsular Malaysia 22, 2011, pp.

O. Yaakob et al. / Renewable Energy 88 (2016) 359e371 77e92. [32] R. Nerem, Measuring very low frequency sea level variations using satellite altimeter data, Glob. Planet Change (1999). [33] R. Nerem, Measuring global mean sea level variations using TOPEX/POSEIDON altimeter data, J. Geophys Res. Ocean. 100 (1995) 25135e25151. [34] C. Hammons, Using Satellite Altimetry to Detect Ocean Currents, 2003. [35] A. Joseph, Remote measurements of ocean currents using satellite-borne altimeters, Chapter 12, Meas. Ocean. Curr. (2014) 381e396, http://dx.doi.org/ 10.1016/B978-0-12-415990-7.00012-0. [36] D.B. Chelton, J.C. Ries, B.J. Haines, L.L. Fu, P.S. Callahan, Satellite ALTI$metry, Int. Geophys 69 (2001), 1 e ii. [37] R. Scharroo, E. Leuliette, RADS: Consistent multi-mission products, ESA SO710, in: Proc. Symp. 20 Years Prog. Radar Altimetry, Venice, Italy, 2012, pp. 20e28. [38] L. Sabique, T.B. Nair, Comparison of grid averaged altimeter and buoy significant wave heights in the Northern Indian Ocean, Mar. Geod. 36 (2013) 72e85. [39] J. Kshatriya, A. Sarkar, R. Kumar, Determination of ocean wave period from altimeter data using wave-age concept, Mar. Geod. 28 (2005) 71e79, http:// dx.doi.org/10.1080/01490410590884575. [40] P.-Y. Le Traon, D. Antoine, A. Bentamy, H. Bonekamp, L.A. Breivik, B. Chapron, et al., Use of satellite observations for operational oceanography: recent achievements and future prospects, J. Oper. Oceanogr. 8 (2015) s12e27, http://dx.doi.org/10.1080/1755876X.2015.1022050. [41] C. Sølvsteen, C. Hansen, Comparison of altimetry wave and wind data with model and buoy data, ESA Spec. Publ. 614 (2006) 46. [42] K. Sudheesh, P. Vethamony, M.T. Babu, S. Jayakumar, Assessment of wave modeling results with buoy and altimeter deep water waves for a summer monsoon, in: Third Indian Natl. Conf. Dock Harb. Eng., Goa, India, 2004, pp. 184e192. [43] P.R. Shanas, V. Sanil Kumar, N.K. Hithin, Comparison of gridded multi-mission and along-track mono-mission satellite altimetry wave heights with in situ near-shore buoy data, Ocean Eng. 83 (2014) 24e35. [44] E. Dobson, F. Monaldo, J. Goldhirsh, J. Wilkerson, Validation of geostat altimeter-derived wind speeds and significant wave heights using buoy data, J. Geophys Res. Ocean 92 (1987) 10719e10731. [45] H.H. Sepulveda, P. Queffeulou, F. Ardhuin, Assessment of SARAL AltiKa wave height measurements relative to Buoy, Jason-2 and Cryosat-2 data, Mar. Geod. 38 (2015) 449e465, http://dx.doi.org/10.1080/01490419.2014.1000470. [46] C.G. Davies, P.G. Challenor, P.D. Cotton, Measurement of wave period from radar altimeters, Ocean Wave Meas. Anal. (1997) 819e826. [47] C.P. Gommenginger, M.A. Srokosz, P.G. Challenor, An empirical model to retrieving ocean wave period from nadir altimeter, in: International I (Ed.), Geosci. Remote Sens. Symp. 2003, IGARSS’03. Proc., vol. 00, IEEE, 2003, pp.

371

2706e2708. [48] O. Yaakob, N. Zainudin, A. Malik, A. Maimun, Y. Samian, R.A. Palaraman, Development of a Malaysian ocean wave database and models for engineering purposes, in: 25th Asian Conf. Remote Sens. 1st Asian Sp. Conf., Chiang Mai, Thailand, 2006. [49] R. Govindan, R. Kumar, S. Basu, A. Sarkar, Altimeter-derived Ocean wave period using genetic algorithm, IEEE Geosci. Remote Sens. Lett. 8 (2011) 354e358. [50] Y. Quilfen, B. Chapron, F. Collard, M. Serre, Wave period retrieval from altimeters, Mar. Geod. 3 (2004) 2003. [51] E.B.L. Mackay, C.H. Retzler, P.G. Challenor, C.P. Gommenginger, A parametric model for ocean wave period from K u band altimeter data, J. Geophys Res. 113 (2008) C03029, http://dx.doi.org/10.1029/2007JC004438. [52] I. Fairley, Wave Resources in South Wales and a Comparison with Wave Climate at the Wave Hub, 2012, pp. 1e11. [53] J. Pastor, Y. Liu, Wave energy resource analysis for use in wave energy conversion, J. Offshore Mech. Arct. Eng. 137 (011903) (2015) 1e9, http:// dx.doi.org/10.1115/1.4028880. [54] F. Monaldo, Expected differences between buoy and radar altimeter estimates of wind speed and significant wave height and their implications on buoyaltimeter comparisons, J. Geophys Res. Ocean. 93 (1988) 2285e2302. [55] A.M. Cornett, A global wave energy resource assessment, Int. Soc. Offshore Polar Eng. (2008). [56] Siow CL. Jaswar, A. Maimun, C.G. Soares, Estimation of electrical-wave power in Merang Shore, Terengganu, Malaysia, J. Teknol. 66 (2014) 9e14. [57] J.P. Kofoed, A. Pecher, L. Margheritini, M. Antonishen, C. Bittencourt, B. Holmes, et al., A methodology for equitable performance assessment and presentation of wave energy converters based on sea trials, Renew. Energy 52 (2013) 99e110. [58] C.W. Zheng, J. Pan, J.X. Li, Assessing the China Sea wind energy and wave energy resources from 1988 to 2009, Ocean. Eng. 65 (2013) 39e48. [59] C.W. Zheng, H. Zhuang, X. Li, X.Q. Li, Wind energy and wave energy resources assessment in the East China Sea and South China Sea, Sci. China Technol. Sci. 55 (2011) 163e173. [60] D. Zhang, W. Li, Y. Lin, Wave energy in China: current status and perspectives, Renew. Energy 34 (2009) 2089e2092, http://dx.doi.org/10.1016/ j.renene.2009.03.014. [61] J.L. Ren, Y.Y. Luo, J.J. Chen, X.M. Zhang, Y.J. Zhong, Research on wave power application by the information system for ocean wave resources evaluation, Renew. Energy Resour. 3 (028) (2009). [62] C. Zheng, H. Zhuang, X. Li, X.Q. Li, Wind energy and wave energy resources assessment in the East China Sea and South China Sea, Sci. China Technol. Sci. 55 (2012) 163e173, http://dx.doi.org/10.1007/s11431-011-4646-z.