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L. Liberti et al. / Renewable Energy 50 (2013) 938e949
Fig. 1. Model computational domain and bathymetry.
seas where land obstructions deeply influence wave generation and
propagation [10]. In these regions wave models represent the most
important tool to assess wave energy distribution. One of the most
extensive study of the wave climate in the Mediterranean sea was
carried out in the framework of the WW-MEDATLAS project [11]. In
this project, climatology maps of wave height, period and direction
were produced using 900 points extracted from a 0.5 resolution
WAM operational model run at the European Center for Medium-
Range Weather Forecast (ECMWF). The authors presented a meth-
odology to correct the underestimation of wave heights found in
the ECMWF operational model by calibrating the model output
with corrective factors obtained from satellite and buoy measure-
ments [12]. The MEDATLAS project represent one of the first large
scale attempts to provide wave climate analysis for the Mediter-
ranean region. Another notable initiative is EU HIPOCAS project
which produced a 44 years hindcast of the wave climate in the
Mediterranean at a maximum resolution of 0.1 forced by a 0.5
resolution atmospheric model [13] [14]. More recently, a 20 years
hindcast study of the Mediterranean Sea was carried out using
a higher resolution atmospheric model and results of wave model
simulation at 0.1 resolution were presented [15]. All these works
were not explicitly devoted to wave energy potential evaluation
and do not directly provide wave energy data. Currently a high
resolution study of wave energy distribution in the Mediterranean
appears to be lacking. Filling this gap is the main purpose of the this model bathymetry. The Mediterranean is considered as a closed
paper where we describe a wave energy atlas of the Mediterranean
Sea obtained by running for the 10 years period 2001e2010 a 1/16
resolution wave model forced by the wind fields provided by the
ECMWF. The results of the model were analyzed to define the
average wave energy availability and to address the problem of its
temporal variability both in terms of seasonal cycle and inter-
annual fluctuations as suggested by some authors (see for
instance [16] or [4]). This paper is organized as follows: model set-
up and validation is described in Section 2, wave energy potential in
the Mediterranean and, more in detail, around the Italian coast, is
analyzed and discussed in Section 3, the conclusions of our study
are outlined in Section 4.
2. Model description and validation
2.1. Model set-up
Wave simulations were performed using a parallel version of the
WAM wave model Cycle 4.5.3 [17]. The model domain covers the
entire Mediterranean Sea, from 5.50W to 36.125E of longitude
939
and from 30.2N to 45.825N of latitude. The domain was dis-
cretized with a regular grid of 667 Â 251 nodes in spherical coor-
dinates with a uniform resolution of 1/16 in each direction,
corresponding to a linear mesh size of 5e7 km. By extending the
computational domain over the entire Mediterranean, we were
able to describe the wave climate along the Italian coast at rela-
tively high spatial resolution taking into account both local wave
generation and propagation from distant areas, avoiding errors
introduced by nesting procedures. Model bathymetry was calcu-
lated from the General Bathymetric Chart of the Oceans (GEBCO) 30
arc-second gridded data set [18] by averaging the depths of data
points falling in each computational cell. Cells were classified as
land if the data point nearest to the cell centroid was found to lay
onshore. This automatic procedure generally provided a good
approximation of the sea-land boundaries requiring only minimal
manual adjustments. The directional wave energy density spec-
trum function was discretized using 36 directional bins and 32
frequency bins starting from 0.06 Hz with relative size increments
of 0.1 between one frequency bin and the next. The model was
forced with six-hourly wind fields obtained from ECMWF opera-
tional analysis at 1/4 spatial resolution [19]. Further details on the
ECMWF data set can be found in [20] and [21]. The effects of
currents and variations in sea surface elevation were not taken into
account in the model. Fig. 1 shows computational domain and
basin neglecting any wave propagation from neighboring seas.
Swell propagation from the Atlantic might not be negligible in the
proximity of the Gibraltar Strait, however a detailed description of
the wave energy in this area is beyond the scope of the present
work. Wave climate simulations were performed for the period
2001e2010 extracting the integral wave parameters significant
wave height (Hs), mean wave period (Tm), significant wave period
(Te) and mean direction (qm) from the entire model domain every
3 h. Model results were validated against satellite and buoy
measurements.
Table 1
Characteristics of satellites used for model Hs validation.
Satellite Repeat Used period Track separation
cycle (days) at equator (km)
Topex-Poseidon 10 Jan. 2001eOct. 2005 315
Jason-1 10 Jan. 2002eDec. 2010 315
Jason-2 10 Jun. 2008eDec. 2010 315
Envisat 35 Oct. 2002eOct. 2010
ERS-2 35 Jan. 2001eDec. 2006 80
Jan. 2008eDec. 2010 80