GUIDE WAVE ANALYSIS AND FORECASTING - WMO

emote, data-sparse areas. However, these data are also
valuable in giving supplementary information in data-rich
areas on the spatial variability, in extending measured
series in time and for investigating their temporal representativeness
for long-term conditions. A review of
applications of satellite data is given in Barstow et al.
(1994 (b)). As further years of data from GEOSAT and
later satellites, such as ERS-1 and Topex-Poseidon,
become available, such analyses will provide invaluable
information on wave climate. Papers on estimating
extreme values from altimeter data have been published by
Tournardre and Ezraty (1990), Carter (1993) and Barstow
(1995). Figure 9.6 shows an estimate of wave height in an
area 46°–48°S, 166°–168°E based on three years of
GEOSAT data, with an estimated 50-year return value of
16.5 m. The method used and other figures of the wave
climate around New Zealand are given in Carter et al.
(1994). In addition, it is soon expected that SAR data will
become more important.
A list of available databases composed of visual
observations and measurements of waves is shown in
Table 9.2. A detailed description of wave observations
and measurements is given in Chapter 8.
While the data measured by buoys is considered to be
of excellent quality, its utility for wave climatology is
severely limited. Wave data collection is expensive and
logistically difficult. As a result, there are often insufficient
data to adequately define the wave climatology over a
region, or for deriving with confidence any climate statistic
which requires a long-time series (e.g. extremal analysis).
Directional wave data from buoys are not yet common but
have increased considerably in the last decade. Buoy data
can be used for some operability studies, as long as due
consideration is given to ensuring that the period of instrumental
record is representative of the longer term climate.
Buoy data are usually inappropriate for extremal analysis
due to short record lengths. Buoy data are particularly
useful for validating (or calibrating) numerical models and
remote sensing algorithms.
Shipborne Wave Recorders are more robust than
wave buoys and, fitted in Ocean Weather Ships and
moored Light Vessels since the 1960s, have provided
several years of data at a few locations (see, for example,
Figure 9.5). Shipborne Wave Recorder data, as well as
visual estimates, have been used to investigate an apparent
upward trend in average wave heights in parts of the
North Atlantic by one to two per cent per year over the
last few decades (Neu, 1984; Carter and Draper, 1988;
Bacon and Carter, 1991). It is likely that long-term monitoring
of wave climate trends can now be carried out by
satellite altimeters provided that the data are validated
and corrections applied to make data from different
satellites comparable.
Visual wave observations from ships have associated
problems of quality and consistency (Laing, 1985) which
reduce their utility for climatology. In addition, the fact that
most observations are from transient ships makes their use
for time-series type applications of climate virtually impos-
WAVE CLIMATE STATISTICS 111
sible. Ship data are most useful for climatological purposes
when treated as an ensemble for regional descriptive
climate applications such as “downtime” estimates for
offshore operations. Transient ship data are inappropriate
for extremal or time-series analysis.
In practice, wave climate studies usually use data
from a combination of sources for specifying offshore
conditions, and employ shallow water modelling if the
site of interest is near the coast.
9.6.2 Wave hindcasts
There is no doubt that hindcasts are playing an increasing
role in marine climatology. Most recent wave climatologies,
especially regional climatologies, are based on
hindcast data. The same applies to design criteria
produced by offshore oil and gas exploration and production
companies, and the regulatory authorities in many
countries around the world. The reason is simple: the costs
involved in implementing a measuring programme, especially
on a regional basis, and the period spent waiting for
a reasonable amount of data to be collected, are unacceptable.
Therefore, given the demonstrated ability of the
present generation of spectral ocean wave models, the
timeliness and relatively lower cost of hindcast data
becomes quite attractive. The 2nd (Canadian Climate
Center, 1989) and 3rd (Canadian Climate Center, 1992 (a))
International Workshops on Wave Hindcasting and
Forecasting identified no fewer than 12 separate recentlycompleted
regional hindcasts around the world. A number
of other such studies are also under way.
The quality of hindcast data (or at least confidence
in it) can be improved by validating the results against
available in situ measurements or satellite altimeter Hs data (for instance, this is currently being carried out in a
European Union project, WERATLAS, in which a wave
energy resource atlas is being constructed based on
ECMWF’s WAM model archive and validated by buoy
data and altimeter data, Pontes et al., 1995).
Hindcasts can be classified into two basic
categories: continuous hindcasts and discrete storm hindcasts;
a third possibility arises as a hybrid of the two.
The features of each type are described briefly in the
following paragraphs.
9.6.2.1 Continuous hindcasts
Continuous hindcasts are usually the most useful form of
hindcast data, representing a long-term, uniform distribution
in space and time of wind and wave information.
The time spacing is typically six or 12 hours. The
database is then suitable for all manner of statistical
analysis, including frequency analysis and persistence, at
a single location or over a region. If the period of the
hindcast is sufficiently long, the database can also be
used for extremal analysis to long-return periods. The
major drawback to continuous hindcasts is the time and
expense involved in their production. The generation of
up to 20 years of data at six-hour intervals requires an
enormous amount of effort, especially if it involves the