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Review of Met-Ocean Data for Offshore Wind Energy
Anthony Kettle
Geophysical Institute, University of Bergen, Norway
NORCOWE project
Version 3, July 06, 2015
1. Introduction
The development of offshore wind power needs information about meteorological and oceanographic
conditions in ocean areas, particularly in coastal areas. There is a large amount of information about the
physics of atmospheric and ocean boundary layers, but only a subset is important for the offshore wind
energy community. Currently, offshore wind farms in northwest Europe and the Baltic areas are located
within ~50km of land and within the 50 m depth contour (Breton and Moe, 2009; Bilgili et al., 2011;
Wilkes et al, 2012), and this roughly delimits the focus area of the met-ocean conditions. Figure 1 shows
the location of installed wind farms in the North Sea region from the Wiki online encyclopedia (May
2015).
The use of met-ocean data in the offshore wind industry has two main goals. First, there is the
consideration of the mean wind at the hub height of the wind turbine, which is the basis of the amount of
power that can be extracted from a given wind field. Here, the ambient wind speed is important, and the
offshore wind industry typically employs a summary statistic in the form of a Weibull distribution and
wind rose. Second, there are operational meteorological considerations that affect how the turbine
structure is designed, how the components of the turbine structure accumulate fatigue damage during
operation, and how maintenance operations are scheduled and carried out. Here, an important statistic
used by offshore wind energy is turbulence intensity, which is defined as the ratio of the standard
deviation to the mean wind speed over time intervals 10–60 minutes. Gust characteristics and extreme
met-ocean conditions are important for operations. Most of the available information on met-ocean
conditions is adapted from products developed for other purposes: weather forecasting, geophysical
research, and ship and offshore rig design and operations. This information has been used to assess
ambient wind fields for offshore wind energy applications. However, atmospheric turbulence assessment
is specialized parameter that has typically been used in building and aircraft design, and for offshore wind
energy it is usually derived from meteorological tower measurements. The textbook of Emeis (2013)
gives information about the meteorological issues of wind energy development on- and offshore. Quarton
(2005) presents summary of the key meteorological parameters for the design of offshore wind turbines,
talking account of the turbulence and gust characteristics.
2. Wind Survey for Estimating Extractable Wind Power
A wind survey at an offshore location to assess extractable wind power is initially estimated by using
archives of existing data and model products (Sempreviva et al., 2008). Available data includes point
measurements of offshore and coastal weather conditions from a number of sources, and also satellite
information (Pontes et al., 2013). Model products include the output of numerical weather prediction
(NWP) simulations, and also blended products like reanalysis and gridded atlas fields (Sempreviva et al.,
2008; Woolf and Mcilvenny, 2011). After a regional distribution of a wind resource is established, a site
evaluation of wind speed is conducted for statistical variation and seasonal changes. This is typically
conducted by the construction of a high meteorological tower to collect high quality measurements over a
one year period (Furevik and Espedale, 2002; Sempreviva et al, 2008; Woolf and Mcilvenny, 2011),
although newer studies may also employ ground-based remote sensing methods.
Fig1
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2.1. Measurements of Opportunity
Measurements of opportunity include information from the existing and historic coastal weather stations,
as well as data collected from ships, oil platforms, and buoys (Sempreviva et al., 2008; Woolf and
Mcilvenny, 2011). In situ ocean monitoring programs often have problems, including record duration,
changes in instrumentation with time, flow distortion, and changes in instrument height. For some
monitoring networks, there is focus on offshore areas to assess meteorological and climatological
conditions across large tracts of ocean. On the other hand, the modern oceanographic observation
networks have been structured to deliver high quality information at good temporal and spatial resolution
(Ravichandran, 2011) required by national meteorological services. Using Sempreviva et al. (2008) as a
departure point, the available information is briefly reviewed in the following sections, together with
identified problems.
2.1.1. National Meteorological Networks
National meteorological networks typically give an overview of wind speed across a country, usually at a
standard measurement height of 10m. The stations are typically not at very high spatial density, and
Woolf and Mcilvenny (2011) outline the high costs involved with setting up a weather station to provide
information in real time. A subset of these stations along the coast can be extrapolated the further
offshore to give an estimate of available the extractable wind power. Although the measurement height is
much lower than the hub height of contemporary wind turbines, the networks have the advantage that the
historical record extends back years or decades so that the climatological variation of the wind farm site
can be characterized. Eidsvik (1985) highlights that the national meteorological networks are the only
source of information that extends back long enough to give a good estimate of extreme wind and wave
conditions with the 100 year return period that is used for designing offshore structures. Table 1 shows
an overview of the national meteorological agencies for northwest Europe together with their Internet
links. A general survey of the information reveals that climatological summaries are freely available, but
complete station data is generally available only for a charge. On the other hand, Norway has made
historic station data available from the Eklima database, and Woolf and Mcilvenny (2011) report on UK
historic data available through the MIDAS website.
Separate from national meteorological networks, internet archives of METAR and TAFF reports from
airports and national meteorological stations (http://www.ogimet.com/home.phtml.en from 2005 onward)
provide basic information from which to construct wind energy statistics. Likewise, amateur stations
provide another meteorological resource with high spatial density in Europe and North America, which is
based mostly standardized equipment (http://www.wunderground.com, http://wow.metoffice.gov.uk,
http://weatherlink.com/map.php, http://wxqa.com, http://globe.gov/). Wunderground also offers a useful
facility for the download of meteorological archives extending back at least until 2002, and Figure 2
shows the interactive Wundermap tool to access data from individual stations. This is based mostly on a
dense network of amateur stations (with potential problems of uneven quality), but it also includes
METAR records for commercial air transport. Table 2 presents a list of non-governmental organizations
with meteorological archives.
Several case studies have been identified where the wind energy potential at planned wind farm sites has
employed station data from national meteorological networks. Nielsen (2012) presents the wind energy
potential of the proposed offshore wind farm at Siragrunnen, Norway, and this is based on detailed the
analysis of the meteorological station at Lista Fyr. Furevik and Espedale (2002) focused on the
meteorological station at the Hellisøy, near Bergen in western Norway, in composing a SAR satellite
survey of offshore wind resources (see also Furevik et al., 2003, 2004). Windsim (2003) developed an
atlas of offshore winds in Norway based on a review of 80 coastal stations within the Norwegian national
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meteorological network. The wind resource assessments at the first offshore Danish wind farms at Horns
Rev and Laeso Syd were accomplished with in situ tower measurements in conjunction with nearby
meteorological stations onshore (Sommer, 2002).
The main difficulties in using point meteorological station data to assess offshore wind energy potential
are measurement biases associated with complicated topography and coastal effects. For example, in
assessing offshore winds in western Norway from the single measurement mast at Hellisøy, Furevik and
Espedale (2002) noted that the mast measurements were biased by nearby buildings and complicated
surrounding topography. Jonassen et al. (2012) also reports on topographic effects on wind fields in this
mountainous region. The Wasp program is intended to correct measured wind speed for local roughness
and topography effects (Petersen et al., 1998), but it was only partially successful when compared results
from the synthetic aperture radar in the mountainous coastal region of western Norway. The more
extensive reviews of coastal meteorology in Windsim (2003) noted that topographic flow effects were a
general feature of wind patterns along the Norwegian coast with speed-up effects over hills and around
islands. The wind rose for coastal stations typically has a bimodal distribution corresponding to winds
oriented parallel to the coast.
2.1.2. Ship observations (Voluntary Observing Ships)
The Voluntary Observing Ship (VOS) program is based on a network of ships of opportunity on which
manual and sometimes automated measurements are made at regular 3 hour intervals and transmitted to
the national meteorological centres for the incorporation into numerical weather prediction (NWP)
schemes. The observation logbooks from the VOS scheme are also sent to national meteorological
centers for digitizing and processing as part of a separate procedure. In its present configuration, over 20
national meteorological agencies contribute to the international VOS scheme, organizing observing ships
and contributing the meteorological equipment for observations. The information chain linking
commercial ship observers with the port meteorological officers and national meteorological networks is
outlined by North (2006).
The value of the VOS scheme for NWP is that it offers in situ observations (atmospheric pressure,
air/temperature, relative humidity, precipitation, and wave information) whose quality can only be
partially met with the ocean data buoys and remote sensing information (Kent and Ingleby, 2010). The
additional value of the data is that it extends centuries back in time, and can therefore be used to assess
long-term climate. While the quality and distribution of certain parameters (e.g., waves and air
temperature) may be met by buoys and remote sensing products in modern observational systems, VOS
records are still the best source of information for other parameters (e.g., relative humidity) that can not
be attained by satellites. Uncertainties and errors in VOS observations are overcome by buddy checks,
taking advantage of density of the observations, and duplication within the observation system. The VOS
in situ data is considered a standard against which to compare and calibrate remote sensing products. In
spite this, the number of VOS ships have declined in response to the volume traffic from satellites (Kent
et al., 2009a,b). Much of the data from the modern VOS program are presently available in the ICOADS
archive (Woodruff et al., 2005). The UKMO Main Marine Database is a semi-independent archive
(Shearman, 1983, 1984; http://www.global_wavestatisticsonline.com/Help/marine_databank.htm,
https://www.bodc.ac.uk/data/information_and_inventories/edmed/report/1399/) that has been used within
the larger COADS project (Woodruff et al., 2005). A related resource from NOAA allows for an online
search of digitized ship records, which can be used to extract subsets of the measurements on shorter,
user-defined time scales (http://www.nodc.noaa.gov/OC5/SELECT/dbsearch/dbsearch.html ).
For the maritime industry (mainly transport, offshore petroleum and construction), the importance of
database of shipboard meteorological observations has been recognized in the online product Global
Wave Statistics Online (http://www.globalwavestatisticsonline.com). Based on the main marine databank
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of the UKMO, this presents statistics summaries of the observational database in regional oceanic boxes
around globe and is patterned on the earlier atlas of global wave statistics by Hogben et al. (1986). Key
met-ocean parameters for this community are the statistical histogram distribution of wind speed, joint
probability distribution of wave height and direction, extreme wave height, and persistence of storms and
calms. Graham (1982) explains the background of some of these diagnostics, why they are important for
the maritime industry, and also gives the state of met-ocean knowledge at a relative early stage of North
Sea offshore energy resource development. Woolf and Mcilvenny (2011) indicate that the Global Wave
Statistics database is a good low-cost solution to obtain general information on wave climate in different
parts of the global ocean.
For the present objectives of offshore wind industry, these shipboard measurements have mainly not been
emphasized in recent literature, and tend to be used mainly in earlier investigations. In an early study to
assess offshore wind energy in western Europe, meteorological data from voluntary observing ships was
used as a departure point to assess wind fields near the German coast (Garrad et al., 1993). The
investigators were warned of potential quality issues in ship observations where the platform and
observation procedures could not be assured to the same level as the land-based national meteorological
networks. In the end, the existence of 10’s-100’s thousands of measurement measurements meant that
rigorous statistical summaries could be generated, and the same conclusion was reached by Kent et al.
(2009a,b). For the offshore wind industry, VOS reports may be particularly appropriate as the
observation heights of modern commercial vessels is rather high (20-30m above sea level, Kent et al.,
2007) and thus more comparable with wind conditions at 80–100m hub height than national
meteorological networks, for example. Reviewing the state of offshore data for UK wind farms, Grainger
et al. (1998) state that the Main Marine Data Bank of the UKMO are one of few sources of offshore
meteorological data available. In a more recent review, Pontes et al. (2013) confirms the value of ship
observation database for wind resource mapping, but that site evaluations need to be made with mast
measurements.
One related resource that has become available for the offshore wind energy community is the CLIWOC
project (http://pendientedemigracion.ucm.es/info/cliwoc/; Garcia-Herrera et al., 2005). These are
meteorological data from digitized and interpreted logbooks from the British, Dutch, French, and Spanish
sailing fleets from 1750-1850. The database originates from the colonial period and documents
meteorological conditions on the sea lanes between western Europe and colonies in America, Asia, and
Africa. The database has the disadvantage that reporting terminology and many scientific units were not
standardized in this earlier period. On the other hand, it is most the objective measurement record
available in an era where geophysical records mostly did not exist. The value of the database is
highlighted in documenting first order climate variations, and is of potential interest to the offshore wind
industry where the lifetime of a typical wind farm is long enough to be affected by climate change events
(Barthelmie et al., 2005).
Table 3 summarizes a list of Internet resources for met-ocean data from observing ships, and a map of
ICOADS wind speed data (VOS and fixed and drifting buoys) from Kent and Ingleby (2009) is shown in
Figure 3.
2.1.3. Oil/gas production and other offshore platforms
Many oil platforms are instrumented to record met-ocean data. The platforms offer a source of
meteorological data in remote locations where it is otherwise difficult to obtain data, but Berge et al.
(2009) note that oil rigs may be more susceptible to flow distortion effects than purpose-built
meteorological masts. Four countries have been identified with semi-permanent oil platforms in the
North Sea: UK, Netherlands, Denmark, and Norway. The data reporting requirements are ambiguous,
and the data and metadata reports appear in different locations. The instrumentation is usually operated
Fig3
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by the commercial company that owns the platform, and information about the data and metadata may be
passed on to government departments, depending on the country. In Norway, the data from oil platforms
in the North Sea and Norwegian Sea is passed on to the Norwegian meteorological office (met.no) and
archived in the Eklima database. The information from the largest and best-instrumented platform –
Ekofisk – is described in annual reports prepared by Miros AS (e.g., Miros, 2012) which also presents
time series of historical information. There has additionally been some work at met.no to evaluate and
compare platform data (Iden, 2010, 2013). For the Netherlands, meteorological data at hourly resolution
is available from the KNMI website
http://www.knmi.nl/klimatologie/uurgegevens/index_zeestations_cgi#no, and the time series for the
commercial gas platforms start mostly in the interval 2005-2010. Ocean temperature data from the
platforms is not available freely on this website, but can be purchased from KNMI. Some of the offshore
monitoring stations within the Dutch monitoring network are research platforms not associated with gas
production, and their measurement time series extend back further in time (KNMI, 2005). For wave
information, metadata for eight platforms in the Dutch economic sector is archived by the government
department Rijkswaterstaat, which also has access to recent datasets. KNMI (2005) gives information
about water depths and wave monitoring instrument types in the Netherlands wave monitoring network.
For Denmark, metadata for the automatic METAR stations on four Maersk Oil platforms is held by the
Danish Meteorological Institute. However, there is no information if wave data are also recorded using
radars or buoys, and no Internet information contact point with Maersk Oil. For the UK, Legget et al.
(1997) describe the installation and operation of automatic meteorological monitoring system to allow
information exchange among the UK Shell platforms in the North Sea, and this information is relayed
additionally to the UKMO and Dutch coastal authority. Enquiries revealed that the UKMO can make this
offshore information available to third parties as a chargeable service. The BODC holds wave
information for only 4 oil platforms from the 1970s and early 1980s. Data for many fixed offshore
locations (platforms and buoy) is available from the US NOAA website http://www.ncdc.noaa.gov/cdoweb/ (Figure 4), which presents the most comprehensive source of met-ocean data in the North Sea. The
data is from a variety of organizations and countries and is available at good time resolution (one hour
resolution in many instances) through an interactive map. Many historical time series are also held.
In the commercial sector, the SIMORC project shows metadata for met-ocean data collected around the
world and especially the North Sea (http://www.simorcorg/). Once the metadata are assessed, application
for access to the dataset can be submitted to the commercial concern. Table 4 summarizes information
about met-ocean data from the oil platforms in northwestern Europe.
2.1.4. Buoys
Metadata for met-ocean information from buoys is reported in multiple overlapping Internet networks that
are maintained mostly by government authorities. The WMO-IOC Joint Technical Commission for
Oceanography and Marine Meteorology holds the largest list of the buoy information, and Table 5 has
been compiled based on this information. The listed buoy networks are mostly maintained by
government agencies, but there are a few commercial organizations (e.g., WaMoS). In most cases for the
JCOMM listings, the buoy data is presented in a format that is fully integrated with the Internet, with
interactive maps and archives of real time data that extend back days or weeks. The wave instrument
information is mostly presented with a statistical summary of information from a higher instrument
sampling frequency. The summary information usually includes significant wave height and period, but
other statistical parameters are sometimes given. Mostly, there is no information to identify the type of
measuring instrument. In the North Sea, wave buoys and radar sea level gauges are commonly employed
to assess wave characteristics. Most of the listings in Table 5 are based on surface buoys, and the
Datawell Waverider types are prominent. Most of the listings have information about the buoy operator
and data holders, but there are sometimes errors. Short segments of wave data from government-operated
coastal networks in the UK (CEFAS), Germany (BSH), and Denmark (Kystdirektoriet) are have made
FIG4
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accessible by emailing their government operators. KNMI (2005) outlines the structure of the
Netherlands government wave monitoring network, which is made up of combination of Waverider buoys
and wave radars.
The Internet listings of the online wave information have cases of information duplication and sometimes
errors in the information from replicate sites. For example, Emodnet and the North West European Shelf
Operational Oceanographic System (NOOS) are intended as overarching websites to incorporate the
metadata from other wave-monitoring networks. In their reports of Netherlands wave monitoring
networks, there is a sizeable network in the southern North Sea that is attributed to the Deltares
organization, but which is actually maintained by oil platform operators. The Wavenet site that is
maintained by CEFAS is also an amalgamation of information from government agencies and private
companies. Information requests are therefore sometimes directed to other organizations that are not on
the initial website.
In addition to data presented in real time on the Internet, Table 5 also shows older archives of wave
information. This includes older wave buoy data, but also information from ocean weather stations and
coastal lightships extending back to the 19th century. In northwest Europe, lightship data archives have
been identified for the UK, Sweden, Denmark, and Germany, and these generated wave information
before the advent of automatic buoys.
2.1.5. Radiosondes
Archived radiosonde data have been used in for activities relevant to offshore wind energy development.
Furevik and Haakenstad (2012) conducted an investigation of the vertical wind profile over the
Norwegian Sea at the location of ocean weather station Polarfront based on archived radiosonde data
between 1999 and 2009. The results from this mid-ocean station were surprising and indicated a high
proportion of profiles that showed decreasing wind speed with height. Chan and Wood (2013) used
radiosonde data from 6 radiosonde stations at islands and isolated locations to verify planetary boundary
layer heights obtained from satellite radio occultation studies. In addition to the scientific results, these
studies reveal some of the practical issues associated with radiosonde data, including Internet resources
where it is archives and data sampling issues that determine the vertical resolution starting altitude.
2.1.6. Ground-Based Remote Sensing
Ground-based remote sensing of wind speed and precipitation includes Sodar, Lidar, and weather radar.
Sodar and Lidar retrieve wind profiles based on reflected sound and light waves, respectively, and the
instruments are typically placed on the ground to obtain vertical wind profiles. There has been a recent
focus in offshore wind to place Lidar instruments on offshore platforms and buoys as a less expensive
means to characterize the wind field compared with measurement masts. This technology is relatively
new, and there have been some instrument problems during extended offshore deployments. Woolf and
Mcilvenny (2011) report on the recent European Norsewind project, an important component of which is
to acquire vertical profiles of wind speed offshore using report sensing methods.
Weather radar has mostly not been exploited by the offshore wind industry, and the review paper of
Trombe et al. (2014) outlines the capabilities of radar to assist in forecasting weather and wind conditions
in the operation of the Horns Rev wind farm. The publication outlines the different types of weather
radar in use with drawbacks, typical weather patterns encountered at the Horns Rev wind farm, and gives
perspectives of future research. The study highlights the advantage of radar over Lidar techniques in
giving high density weather information in time and space, and this is important at the North Sea offshore
site where problematic wind conditions are associated with passing fronts and squall lines.
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2.2. Satellite Projects
Overviews of satellite information useful for the offshore wind energy appear in several publications
(Srokosz et al., 1995; Johannessen et al., 2000; Nielsen et al., 2004; Robinson, 2004; Hasager et al., 2006;
Sempreviva et al., 2008; Le Traon, 2010; Brower, 2011; Woolf and Mcilvenny, 2011; Lehner et al.,
2013), and a summary of satellite sensors recognized for operational oceanography is given in Tables 6-8.
The practical application of remote sensing for operational oceanography is given special focus in
Srokosz et al. (1995) and Johannessen et al. (2000). Nielsen et al. (2004), Sempreviva et al. (2008), and
Brower (2011) emphasize remote sensing products for the wind industry specifically.
The recognition of the potential importance of satellite information for operational oceanography has
been recognized since at least the early 1990’s, even though the modern analysis tools to extract the full
information from satellite images were still to the developed (Srokosz et al., 1995). Important advantages
of ocean remote sensing platforms were the ability to obtain high quality met-ocean data over large space
scales in adverse weather conditions (i.e., for microwave techniques). Also the multi-annual continuity of
satellite measurements was identified as an important advantage over in situ oceanic instruments that
typically have short deployment intervals (Srokosz et al., 1995, Sempreviva et al., 2008). For extreme
wind conditions, there are reports of the data quality of satellite products exceeding in situ measurements
(Srokosz et al., 1995). On the other hand, active microwave sensors are known to have biases at high
wind speeds >25m/s due to rainfall and spray effects (PODAAC, 2013). There are limitations of remote
sensing instruments in comparison with traditional instruments, and in particular the sampling of texture
features at the air-sea interface means that there is limited information about geophysical phenomena far
above or below the sea surface (Srokosz et al., 1995). Within the offshore wind industry, satellite remote
sensing products are perceived to have worse accuracy than in situ measurements (GL Garrad Hassan,
2013). Brower (2011) clarifies that satellite methods provide an indirect wind speed assessment based on
sea surface properties. Because this represents an area average, it is not considered as good as a point
mast measurement.
As highlighted by Johannessen et al. (2000) there are a large number satellite instruments and data
available, and these differ broadly according to if visible, infrared, or microwave radiation is considered.
Within the field of microwave remote sensing, there is a distinction between the passive radiometers and
active imaging sensors (Srokosz et al., 1995; Le Traon, 2010). For offshore wind energy, the
characteristics of the wind are most important, and active microwave satellite data are of particular
interest (Nielsen et al., 2004; Sempreviva et al., 2008; Brower, 2011). In the initial installation of wind
farms and the assessment of the structural loads from waves and currents, the offshore wind industry will
be concerned with general considerations of operational oceanography (Srokosz et al., 1995; Johannessen
et al., 2000) and hence of a number of different satellite sensors are important, in particular those dealing
with the quantification of the wave field. In the following sections, there are brief descriptions of satellite
sensors dealing mostly with winds and waves (Table 6a-d) and sea surface temperature (Table 7). Radio
occultation data from networks of GPS satellites have appeared from the mid-1990s, and provide special
information about atmospheric vertical property profiles that is not available from other satellites. Other
parameters of importance for operational oceanography (ocean currents, air temperature, biofouling) are
imperfectly met by satellite remote sensing (Srokosz et al., 1995; Johannessen et al., 2000), and are not
treated in detail in this review. Shipping operations, offshore oil platforms, and certain offshore wind
farms in high latitudes are at potential risk from sea ice, and Sandven (2008) reviews the state of the art in
sea ice remote sensing. Coastal ship traffic represents a potential risk of offshore wind farms, and recent
satellite products have included assessments of shipping traffic and density (Tournadre, 2014).
2.2.1. Scatterometer
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In the recent literature, the most important instruments for wind speed assessments over the ocean are
active microwave instruments: scatterometers and synthetic aperture radar (SAR). Scatterometers like
Quikscat work by scanning a radar beam (C-band ~5GHz or Ku-band ~13GHz) in swaths at an oblique
angle to the direction of travel, and recording strength of the backscattered signal (see Hoffman and
Leidner (2004), Hasager et al. (2006), or Le Traon (2010) for an outline of the measuring principles). A
two-dimensional image of the surface winds are built up as the satellite propagates along its orbit. The
centimeter-scale radar waves interact most strongly with the capillary waves that form on the sea surface
in response to wind stress, although the backscattered signal also depends on the angle of the incident
beam and wind direction. The wind direction is resolved by using irradiation beams at different angles to
the direction of travel with the same sea surface area viewed at different looks angles across a flight time
interval of less than <5minutes. The most important advantages of scatterometers are that surface wind
estimates are obtained for most of the global ocean surface on a daily basis, and this is independent of
many weather conditions except for heavy precipitation. Quikscat was one of the most successful and
longest scatterometer missions. Its extended deployment from 1999 to 2009 supported near real time
operational weather forecasts (Hoffman and Leidner, 2005) and provided the basis of global
climatological wind speed assessments. It had a high accuracy of wind speed determination with ±2m/s
in the measurement range 3-20m/s, and its wind direction measurement was accurate to ±20°. For the
offshore wind industry, this means that scatterometer information gives regional information about wind
conditions (Furevik et al., 2011) and can be used to derive regional wind atlases. Its main limitations are
the relative low spatial resolution (~25km), the fact that it could not be used in coastal areas, and
underestimates at high wind speeds (>20m/s). Nielsen et al. (2004) pointed that the low resolution of the
scatterometer was an advantage in that it enabled a larger set of images to be taken at an offshore location
that could be used to formulate statistics. However, the low spatial resolution means that it cannot be
used to micro-site a wind farm. Also, the low temporal data density (Nielsen et al., 2004) means that it
cannot replace the in situ meteorological mast that is used to accurately assess the energy potential of a
wind farm site.
On the other hand, with overpasses of most points on the Earth’s surface twice per day, Quikscat has been
able to give detailed overview information for an analysis of marine storms, and it was incorporated into
operation weather forecast systems soon after its launch (Hoffman and Leidner, 2004). The density of
information was so high that it could be used for a post-analysis of storms and offshore weather-related
damage. The quarterly North Alantic storm review report of Bancroft (2007) shows Quikscat image to
illustrate marine wind conditions for a North Sea storm that had a high tidal surge and caused damage to
ships and platforms offshore (see Figure 5). Bertotti and Cavaleri (2008) use Quikscat to assess wind
conditions during a rogue wave strike on a passenger ship in the western Mediterranean Sea. Other
scatterometers include ERS/AMI and Envisat/ASCAT from the European space programs and SASS,
NSCAT, and Midori-2 from the US and Japanese space programs (Hasager et al., 2006). Table 6a
presents a list of satellite scatterometer platforms providing wind speed.
2.2.2. Synthetic Aperture Radar (SAR)
SAR is an active radar imaging technique that has come to prominence for applications in the offshore
industry since the late 1990s. Its capabilities were recognized from its first satellite deployment on Seasat
in 1978 (Fu and Holt, 1982). Its ability to characterize surface features and textures has been exploited in
different ways in operational oceanography, and for offshore wind energy surface wind speed retrievals
are important. It uses an image of sea surface texture to obtain information about near-surface wind speed
and direction, and also to get information on the surface wave field through a geophysical function (Fu
and Holt, 1982; Lehner, 2005; Lehner et al., 2013). Several spaceborne SAR products are available
(ERS, ENVISAT, Radarsat), and the instruments are operated in one of several modes with different
characteristic viewing angles and ground resolution (Lehner, 2005). Table 6b presents a list of satellite
FIG5
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SAR products. Most of the listed instruments operate at C- and X-band frequencies, and wind retrievals
based on L-band SAR are less common (Shimada et al., 2004).
The SAR on the ERS satellite platforms have been used by the offshore wind community in Europe
mainly to get information on wind speed statistics. The initial 30m resolution of ERS-1 was sufficient to
resolve individual swell features, although with significant levels of noise (‘speckle’) in individual pixel
recordings. The retrieval of wind speed information from SAR was developed through a number of
studies from the late 1990’s, with major challenges to derive optimal sampling and processing strategies
of the single image to obtain estimates of wind speed and direction with low uncertainty. For ERS SAR,
the initial data grids were reduced in resolution to 400m to obtain wind direction information with a
statistical Fourier technique, and the final wind speeds were assessed with ±2m/s accuracy over the range
2-24m/s (Nielsen et al., 2004). The high resolution of the retrieved wind field has allowed for studies to
micro-site wind farms in coastal areas with complicated topography in a way that was previously not
possible with a sparse network of masts on land (Furevik and Espedale, 2002). The high spatial
resolution of the wind speed retrievals also permits an assessment of the wake effects of wind farms.
Wind retrievals from a series of SAR images from the same area can be used to build up a spatial map of
the wind speed histogram Weibull parameters to identify the wind climate at an offshore wind farm site
(Nielsen et al., 2004). The development of wind speed statistics from SAR benefits from daily satellite
overpasses (Hasager et al., 2006), although problems of low-number statistics was identified as an
important issue by Nielsen et al. (2004). Potential problems with diurnal biases associated with the
satellite overpass occurring at the same time each day are mitigated by using scatterometer data from
several platforms (Furevik and Espedale, 2002; Nielsen et al., 2004; Liu et al., 2010; Brower, 2011).
Nielsen et al. (2004) present information about the equatorial crossing times of a number of microwave
radiometer satellites. The WEMSAR Project from early 2000s sought to develop SAR case studies to the
point where the technique could be used to provide a climatological estimate of offshore winds (Nielsen
et al., 2004; Sempreviva et al., 2008). Early wind speed retrievals from SAR required a lot of manual
processing, and part of this involved quality control of the images for anomalies associated with fronts,
rain cells, sea currents and slicks (Nielsen et al., 2004). SAR wind-retrieval investigations were limited to
10’s of images (Nielsen et al., 2004). However, this has now been developed to the point where wind
speed retrievals are the performed to generate wind speed operational products (B. Furevik, personal
communication, Metvind).
The ability of SAR to operate in a high resolution ‘wave mode’ and resolve individual surface the waves
has provided an opportunity to derive large scale information on the spatial distribution of the maximum
wave height, which is useful to assess wave loads on offshore structures and potential ship damage
(Lehner, 2005). Lehner et al. (2002) present an atlas summary of the maximum wave height derived from
the automatic processing of 34000 imagettes for 27days of data collection in 1996 and 1997 from the ERS
SAR instrument. The high volume of information provided the first view of the global spatial of rogue
waves that had previously been limited to isolate ship eye-witness accounts and buoy time series. Of all
existing information on oceanic rogue waves, the value of this analysis was highlighted by Freeze (2006)
as providing the first global overview of a phenomenon that may contributes to important losses in the
global merchant fleet amounting to ~2 large ships per week. Lehner (2005) indicated that archived raw
SAR data exists from November 1991 so that a greatly expanded atlas product is possible, although no
global SAR rogue wave studies appeared after the Maxwave project.
Recent SAR developments for offshore applications have included the X-band TerraSAR-X sensor,
which has much higher spatial resolution (1m) than the C-band SAR from the 1990s (Li and Lehner,
2013; Lehner et al., 2013). Lehner et al. (2013) lists the capabilities of new X-band SAR. These include:
tracking morphological changes in coastal and estuarine topographic features, using surface wave
refraction patterns to retrieve bathymetry, estimating significant wave heights, augmenting shallow water
wave forecast models, quantification of shoreline wave breaking, investigations of wave groups and rogue
Fig6
10
waves, surface slicks, and ship detection. Li and Lehner (2013) additionally describe an analysis of wind
farm wakes in the German Bight (see Figure 6 from Lehner and Pleskachevsky, 2013), and Brower
(2011) notes that SAR is particularly well-suited for wind farm wake investigations. The opportunity for
information retrieval from the X-band images is very good because of its high spatial resolution, and
reflection speckle is less of a problem.
SAR has also used gain information about sea ice cover and also terrestrial land use. Fu and Holt (1982)
note that the Seasat SAR from 1978 gave all-weather images of ice cover in northern regions that are of
interest to operational oceanography, and particularly ship routing. This research has been developed
through the ERS and ENVISAT instruments to retrieve different ice classifications with approximate
associated thickness (Sandven et al., 1999; Sandven, 2008; Zakhvatkina et al., 2013). This important for
shipping and navigation in northern regions, but is also relevant for offshore wind energy as more wind
farms are the placed in northern regions that are susceptible to ice cover. For terrestrial applications, the
Palsar L-band SAR has been used generate a global forest cover atlas at 25m resolution, which is useful
for onshore wind energy applications to estimate roughness lengths and hence infer information about the
vertical wind speed profile.
2.2.3. Altimeter
This is an active radar technique to measure the distance between satellite and the sea surface and is used
to derive information about the wave field and surface height. The narrow downward-directed radar
beam has a limited footprint area (~7km) at the sea surface, and is reflected by the surface wave elements
(Johannessen et al., 2000). The backscattered signal that is received by the satellite is spread out in time
based on the height of the longer period ocean waves. The average travel time of the radar signal is
linked with altimeter height above sea level (after correction from the moisture content of the
atmosphere). The significant wave height can be inferred from the statistics of the returned radar signal
from different height elements of the surface wave field (Woolf and Mcilvenny, 2011). The technique
has the advantage that it gives a more direct measure of the significant wave height than the
scatterometer, which responds to centimeter-scale capillary waves. The main disadvantage of the satellite
altimeter is that it gives information on only a small area of the ocean surface along the satellite track.
Also, the density of information is low with an orbital repeat period of 3-35 days for most platforms, an
intentional design characteristic that optimizes investigations of large scale ocean phenomenon (Nielsen
et al., 2004). However, the ERS was designed to be sun-synchronous and hence its altimeter provides a
greater density of information that can be used to support North Sea storm surge studies (Høyer and
Anderson, 2003).
Altimeters were among the first satellite products to be evaluated and employed to calculate extreme
wave heights and return periods based on sizeable databases of satellite information (Tournadre and
Ezraty, 1990; Carter, 1993; Alves and Young, 2003). Case studies of freak waves in the open ocean have
used altimeter data to verify field observations and model results (Holliday et al., 2006; Bertotti and
Cavalli, 2008), and this has included databases of ship accidents (Toffoli et al., 2003; Toffoli et al., 2005).
Applications to infer coastal storm surges were deemed feasible in early studies but judged to be difficult
because of the long repeat period and large distance between overpass tracks (Srokosz et al., 1995).
However, the recent studies of Høyer and Anderson (2003) and Madsen et al (2007) have demonstrated
how long time series data from satellite altimeters can be used to infer the spatial characteristics of North
Sea storm surges by correspondence with in situ tide gauges. Satellite radar altimeter missions have
included GEOS-3, Seasat, Geosat, ERS-1/2, Topex/Poseidon, Envisat, and Jason-1 (see Table 6c). The
earlier missions were characterized by large uncertainties on the order of 60–100cm in the retrieved sea
surface heights, and the extraction of the geophysical-relevant fields started with Geosat (Tournadre and
Ezraty, 1990; Carter, 1993). Subsequent investigations combined information from the several altimeter
satellites (Alves and Young, 2003; Vinoth and Young, 2011), and the intercalibrated product of Zieger et
11
al. (2009) was important for the formulating long-term wave height statistics. In spite of the high value
attached to the altimeter technique to infer the ocean wave field in earlier review articles (Srokosz et al.,
1995; Johannessen et al., 2000), the number of modern publications describing applications of the
technique for offshore wind energy is low. In the recent publication of review of methodologies for
offshore wind resource assessment, the technique was not mentioned (Sempreviva et al., 2008).
However, Furevik et al. (2002) makes reference to the ERS altimeter to estimate wind speed in near the
ice edge in the Nordic Seas, and Nielsen et al. (2004) present a list of altimeter products obtained from
ARGOSS for the maritime industry.
In a recent review, Cipollini et al. (2010) has clarified that the difficulties previously associated with
altimetry near coastlines were being overcome with new reprocessing techniques and new instruments
with greater spatial resolution. This has led to the new opportunities of reprocessing the legacy altimetry
datasets that had been collected starting from the early 1990s and formulating the statistical results into a
global atlas. Harwood et al. (2013) gives more information about state of reprocessing coastal altimetry
data as part of the E-surge project with reprocessing of legacy data focused on the 10 year ENVISAT
project. A number of coastal oceanography applications for altimetry are outlined by Cipollini et al.
(2010). Of direct interest for the offshore wind industry is the assessment of ocean currents that are a
problem for operational safety and forecasts of sea level and storm surge from time availability of
altimetry data (Cipollini et al. 2010; Harwood et al., 2013). Tournadre (2014) presents a database of ship
traffic based on 7 satellite altimeters from 1992-2012, noting a 4-fold increase in ship density over period
with greater increases in the Indian Ocean and China seas. The implications for atmospheric chemistry in
the remote ocean were specifically identified in the report, although increases shipping traffic also
represents a potential collision risk of with offshore wind farms in coastal areas.
2.2.4. Passive Radiometer for Wind Speed
Passive microwave techniques have been used to retrieve ocean near-surface wind speeds, and the time
series of available data extend over a longer time period than the active microwave techniques (Brower,
2011). The potential of passive microwave radiometry to assess surface wind speed was recognized at a
relatively early stage with the short-lived 1978 Seasat mission (Srokosz et al., 1995). The longest time
series is available from the SSM/I instrument on the Defense Meteorology Satellite Program (DMSP)
from 1987. The instrument monitors ocean surface brightness temperature at four microwave frequencies
(19.35, 22.24, 37.00, 85.80GHz) and uses an empirical calibration based on buoys and radiosondes to
derive the ocean surface wind field over the range 3-25m/s with a root mean square accuracy of 1.3m/s.
Other satellites with slightly different operating frequencies have included AMSR-E and Windsat
(Hasager et al., 2006). The SSM/I wind speed data is retrieved with a similar resolution (25km) as the
Quikscat wind speeds. One disadvantage of the passive microwave wind speeds is that there is relatively
wide 100km strip near the coast where the wind speed retrieval cannot be used (Hasager et al., 2006), and
this is larger than Quikscat. On the hand, with six overpasses per day and a long time series extending
back to 1987, the SSM/I wind speed dataset is more extensive than Quikscat and can be used for
climatological assessments. Table 6d presents a list of satellite passive radiometers that have been used
for wind speed assessments.
2.2.5. Sea Surface Temperature
Information for global sea surface temperature is available from satellite infrared radiometers and satellite
microwave radiometers (Table 7). The infrared radiometer AVHRR has been carried on a number of
polar-orbiting satellites in slightly different configurations since 1978. The raw data are collected at 1km
spatial resolution and have an accuracy of 0.5K. The sensors exploit the infrared transparency windows
of the atmosphere, and can only retrieve sea surface temperatures under cloud-free conditions. Additional
satellite infrared radiometers for sea surface temperature have included ERS/ATSR from 1991 and
12
ENVISAT/AATSR from 2002. These have similar spatial resolution as AVHRR, but have increased
temperature accuracy of 0.1K as the result of a dual-view radiometer. The sea surface temperature
microwave radiometers have a somewhat coarser resolution (~25km) and have less accuracy (0.6-0.7K)
compared with the infrared radiometer instruments. However, they have the important advantage that sea
surface temperature assessments are not obstructed by cloud cover.
2.2.6. Visible Imagery
There some applications of the visible satellite imagery for offshore wind energy applications, and a list
of satellite sensors is given in Table 8. With properties similar to traditional optical cameras, the visible
imagery satellites have a range of applications of relevance to offshore wind energy and marine
operations: biological productivity, bathymetry assessment, wave field, sediment resuspension. Lists of
high and medium resolution optical satellite sensors are presented by Nielsen et al. (2004), and Kaab and
Leprince (2014) present a series of applications taking advantage of high resolution time offsets in
modern visible satellite data acquisition systems.
An important application of visible satellite imagery is using ocean color data to assess biological
productivity and the risk of biofouling of ocean structures (Srokosz et al., 1995). The CZCS was the first
ocean color sensor from 1979-1985, and this was followed by SeaWIFS in 1997-2010. These satellites
typically measure light from the ocean surface through the atmosphere at 4–9 visible wavelengths. The
basic near-surface chlorophyll concentration is calculated from the ratio of light intensities in the green
and blue channels with the additional wavelength channels providing information on phytoplankton
functionality and inherent optical properties. Because only 10% of the top-of-atmosphere light recorded
by the satellite has actually interacted with the ocean water column, atmospheric corrections remain an
important open research question, and ongoing ocean color calibration program also involve state-of-theart research on atmospheric aerosol characterization. Antoine et al. (2008a,b) present of an overview of
state of the art ocean color sensors (Seawifs, MODIS-A, MERIS) together with their calibration programs
in the Pacific and Atlantic Oceans and the Mediterranean Sea.
Another application of visible imagery involves exploiting how visible light reflects off the ocean bottom
to assess water depths in shallow offshore areas to approximately 20m depth. The application is
especially pertinent in areas where the sea bottom consists of unconsolidated sediments, and shoals shift
on short time scales in response to wind and wave action. Although ocean color satellites are the
specifically designed to assess subtle changes in the intensity and wavelength of light that is the upwelled
from the ocean, Srokosz et al. (1995) noted that Landsat imagery is better for bathymetric assessments
because of its higher spatial resolution. The recent review of Lehner et al. (2013) described an
application that utilized high resolution visible imagery from the Quickbird satellite to assess coastal
bathymetry at 0-20m depth off the west coast of Australia.
Other applications of visible satellite image have been to assess the degree of bottom sediment suspension
in coastal areas during storms, and also to get characteristics of the ocean internal wave field and surface
swell characteristics. Recent applications have taken advantage of the high resolution time offsets in
acquisition systems on the order to seconds to get information rapid dynamical processes at the earth’s
surface. This includes tracking river flow fields, ship movements, internal waves, and surface swell. The
remote sensing products that have been used for these projects are ASTER, PRISM, SPOT5, RapidEye,
and MISR, which are generally characterized by high spatial resolution on the order of meters, and time
offsets down to a couple of seconds (Matthew, 2005; Kaab and Leprince, 2014). The ability to track
surface swell and wind wave features is important for the offshore wind energy.
2.2.7. Radio Occultation: Vertical Property Profiles and Planetary Boundary Layer Height (PBL)
13
Atmospheric vertical property profiles are provided by radio occultation (RO) information from networks
of GPS satellites. The information is based on the bending of the L-Band radar waves by the atmosphere
as they are being transmitted by one GPS satellite received by another near the Earth’s horizon (Ao et al,
2008). The refractivity information of the atmospheric is equivalent to a limb sounding. Vertical
property profiles of refractivity can be retrieved with a horizontal resolution of 50-200km and a vertical
resolution of 100m (Mannucci et al., 2014). This can be used directly or converted to humidity and used
to derive the height of the planetary boundary layer, which is useful for the offshore wind industry as
some recent boundary layer parameterizations of vertical wind speed incorporate the depth of the
atmospheric mixed layer (Peña et al., 2008). The techniques of RO limb profiling started from the
MICROLAB 1 mission from the mid-1990s (Chan and Wood, 2013), and research programs to derive
height of the planetary boundary layer date from about 2005 (Ao et al., 2008). The method benefits from
the fact that the L-band radar used for profiling is not sensitive to the presence of rain or clouds (Ao et al.,
2008). The limb-sounding sounding technique gives higher resolution information than downwardlooking atmospheric infrared and microwave profilers, which rely on radiative transfer retrievals with
poor vertical resolution (Chan and Wood, 2013; Mannucci et al, 2014). The GPS platforms have global
coverage with profiles at a wide range of local times and a short revisit time (Mannucci et al., 2014). The
RO calibration procedure is simpler and more robust than vertical profiling satellites, and RO data can be
compared across data gaps and between satellite platforms (Mannucci et al., 2014). The RO method
should theoretically be able to derive property profiles of temperature and humidity through the
atmosphere to the Earth’s surface (Ao et al., 2008). In the present state of progress, RO has some
disadvantages and weaknesses. At its core, the fundamental vertical property that is retrieved is
refractivity, which depends on pressure, temperature, and humidity. The geophysical parameters must be
independently derived from other information. The retrieval process is complicated by some elements of
the physical structure of the atmosphere including the presence of elevated radio ducts and multiple path
transmission, which are linked to humidity and temperature stratification (Ao et al., 2008). This is
particularly a problem in the tropics. This limits how far down the RO technique can be used to get
information in the atmosphere, and it is a problem particularly in the high humidity conditions at low
latitudes. In spite of the potential difficulties, the work of Chan and Wood (2013) highlights the
usefulness of the method to derive planetary boundary height. Working with COSMIC RO data from
2007-2011, they took used 1500 refractivity profiles per day to derive a monthly gridded PBL height set
at 5 degree resolution over most of the globe. Only about half of the limb soundings had to be cut from
the analysis because they did not reach down to 500m altitude above the surface, and only a few areas
showed difficulties for PBL retrieval: low latitudes, polar latitudes, and mountainous regions. Mannucci
et al. (2014) presents other geophysical applications of the RO technique and future projects to retrieve
ocean surface processes.
2.2.8. Synergistic Use of Satellite Platforms
The number of different types of satellite platforms has been steadily increasing, and any given locations
at the Earth’s surface are imaged several times per day by different satellite sensors recording radiation at
different wavelengths. This presents an opportunity of synergistic used of different platforms in
investigate geophysical boundary layer phenomena on short time scales. The combined use of
information is effective where there is ambiguity interpreting the information from a single image, and
multiple images from different platforms places constrains on the interpretations of geophysical
phenomena. The synergistic approach is used in large scale oceanography to infer ocean currents by
combining remotely sensed information of sea surface height and gravity (Cipollini et al., 2010). Brusch
et al. (2008) and Pleskachevsky et al. (2012) have also combined information from different radar and
visible satellite images to interpret unusual sea states and boundary layer phenomena during a severe
North Sea storm (Figure 7). For ice detection and classification, several studies highlight the importance
of a multi-sensor approach with scatterometers, passive microwave, SAR, IR and optical sensors
(Bogdanov et al., 2005; Sandven, 2008), and image interpretation/fusion software is an ongoing research
FIG7
14
topic. Sandven (2008) outlines operational ice products and research using different high resolution
sensors. Stoffelen et al. (2013) highlight the important advantages of multiple wind scatterometers in sun
synchronous orbits with different equatorial crossing times to resolve diurnally varying surface wind
speed features.
2.3. Synthesis Products
A number of synthesis products are available for evaluation of offshore wind fields and near surface
atmospheric stability. These are broadly based on data interpolation schemes, data assimilation in model
reanalysis, or (mesoscale) model integrations with initial and boundary conditions provided by reanalysis
products. The products are generally identified as ‘interpolated fields’ or ‘atlases’. The geophysical
analysis involved is intended to merge multiple data or model products to give an integrated picture of a
given location on the Earth’s surface that is more accurate than from a single data set. Often, the
interpolation scheme is characterized by a high degree of smoothing in the horizontal, vertical, or
temporal dimensions, so that high frequency statistics and extremes are lost to give a more accurate view
of the average geophysical state. For offshore wind energy, this may mean that there are difficulties to
evaluate anomalous wind conditions at a particular wind farm site from a mesoscale model in comparison
with the detail available in a typical SAR image or other high resolution information source.
2.3.1. Reanalysis Products
There are several global reanalysis products available, representing long-time integrations of numerical
weather prediction models with assimilation of available in situ measurements and satellite remote
sensing data (Table 9). The main reanalysis products are ERA-40 (~40km resolution) and NCEP-NCAR
(~250km resolution) that give extended meteorological conditions starting from the middle of the 20th
century (Woolf and Mcilvenny, 2011). There are the experimental studies to extend reanalysis schemes
to the 19th century (Compo et al., 2006). The quality and accuracy the reanalysis products depends on the
availability of measurements, which generally decreases for most areas going back in time. In particular,
the accuracy and skill of reanalysis products increased dramatically in the late 1970s as satellite
information became available for assimilation into numerical models (Woolf and Mcilvenny, 2011).
2.3.2. Other Atlases and Gridded Fields
Table 10 presents a list of selected global and regional atlas products that are differentiated from the longterm numerical integrations that underpin reanalysis schemes. The table presents a series of the offshore
wind atlases that have been developed for the ocean around Europe. It also shows a number of global sea
surface temperature products, some at high temporal and spatial resolution. In particular, the GHRSST
Level 4 OSTIA product should be highlighted as potentially the best blended sea surface temperature
field, which is used by the UK Metoffice in its numerical weather prediction schemes.
3. Site specific evaluation
Sempreviva et al. (2008) distinguish between archives of existing met-ocean data for assessing
approximate resource assessments on a regional scale from the procedure followed for site-specific
evaluation. For the wind farm site evaluation, the aim is to assess the wind speed at turbine hub height
for at least a year. The usual way of achieving this has been the erection of a high instrumented mast.
Most of these masts have been 50-100m in height. Because this is lower than the hub height of modern
wind turbines, there are vertical extrapolation issues to be addressed, and this is complicated by the
frequent observation of the low-level internal boundary layers in the stable atmospheric flow regimes in
the near offshore region (Lange, 2004, 2007). Other instruments for evaluating the vertical wind profile
in the lower atmospheric boundary layer have included ground based remote sensing techniques: LIDAR
15
and SODAR. Interannual variability is an issue for assessing the wind power availability over the 25 year
lifetime of a wind turbine (Barthelmie et al., 2005). This is achieved by comparing the 1 year of data at
the measurement mast site with long-term wind measurements in the region (Sempreviva et al., 2008).
Because of commercial confidentiality concerns, the existence and locations of the high meteorological
masts evaluate the wind resource at a wind farm site is typically not advertised or published. For
example, in evaluating an offshore wind atlas for Norway, Byrkjedal and Akervik (2009) utilize data from
22 high (50m) masts that are mostly associated with Norwegian onshore coastal wind farm sites.
However, the locations of the masts and representative time series are not given. Commercial
confidentiality issues have also been cited for the United Kingdom (Carter and Huthnance, 2010).
A number of organizations have made meteorological data available from high offshore masts.
Winddata.com, based at Technical University of Denmark, provides (mostly older) measurements for a
series of onshore and offshore masts in Europe and overseas (http://winddata.com). The website shows
site information and summary meteorological data, and original data can be ordered from the
administrator. The Danish government-sponsored wind research facility, DTU, has provided an online
information source for many of its past and ongoing high tower meteorological measurements in
northwest Europe (http://veaonline.risoe.dk/Rodeo/ProjectListText.aspx?&Rnd=178347). The
Netherlands has also published information for its offshore wind measurement program (Brand et al.,
2012). Information from the German FINO project is available to the academic research community for
three towers operating in the Baltic Sea and North Sea. Woolf and Mcilvenny (2011) report on data
availability for a series of UK masts operated by Crown Estates. Shimada et al. (2004) uses data from the
Hiratsuka tower, 1 km offshore, near Tokyo, Japan.
The data acquired from the site specific towers is typically presented as summary statistics in the form of
a wind rose and Weibull distribution of wind speeds. The Weibull distribution presents the wind speed
information in a format that can readily be used in engineering fatigue studies based on rainflow counting
Monte Carlo iteration techniques (Eliassen et al., 2012). However, closer examination of the high
offshore research tower data has documented the presence of internal boundary layers (Sathe et al., 2011;
Kettle, 2014) and low level jets. The features are important for offshore wind energy because they mean
that wind speed at turbine hub height cannot automatically be assessed from surface measurements.
Also, unexpected wind speed behavior across swept area of the turbine has implications for structure
fatigue and maintenance. Knowledge of these features is still at an early stage with numerical models
representing an important resource to give insight into dynamic features higher in the boundary layer
(Beran et al., 2005; Nunalee and Basu, 2012, 2013).
4. Miscellaneous
The successful establishment of offshore wind energy requires information in addition to wind speed
characteristics. Operational considerations include information about land surface characterization,
lighting, tides and storm surges and other extreme events.
4.1. Surface Roughness, Orography/Bathymetry, Coastlines
In addition to the data for wind, wave, and sea surface temperature that is of direct relevance for
developing offshore wind energy, Furevik et al. (2004) identified important types of ancillary data
important for the wind energy industry including terrain roughness and orography/bathymetry. Terrain
roughness gives information for the roughness length for land-based wind measuring sites. This is
relevant to the offshore wind industry where coastal winds are initially evaluated from land-based coastal
masts whose the site-specific characteristics must be identified and compensated. Orography is important
for understanding possible topographic forcing mechanisms for the coastal wind circulations, and this is
16
important for Norway. Bathymetry is important for the operation issues of siting and constructing
offshore projects. There are several internet sources for this that vary according to spatial resolution.
High resolution coastline information is available from the NOAA website
http://shoreline.noaa.gov/data/datasets/wvs.htm, and this shows important differences from the
bathymetric/topographic fields in coastal regions that are below sea level (e.g., Netherlands). Table 11
presents a table of datasets for assessing roughness and Table 12 for topography/bathymetry. The
National Center for the Ecological Analysis and Synthesis presents information for the location of small
islands, oil rigs, and lighthouses, which was used by Tournadre (2014) in an assessment of global ship
from density from different satellite altimeters from 1992-2012.
4.2. Lightning
Lightning is an issue of operational concern that may be important for the offshore wind industry, but it is
not highly advertised (Sorensen et al., 2001). The German research platform FINO1 employs a lightning
cage to protect instrumentation, and it has a lightning strike counter, but the data has not been made
public (F. Kinder at DEWI, personal communication, 2013). For the nearby Danish wind farm at Horns
Rev, there was a major refit in 2004 that included the repair of lightning damage to some of the turbine
blades (ModernPowerSystems, 2004). There are a number of long-range and short-range lightning
monitoring networks around the globe and in northwest Europe (Table 13), and commercial users of this
product include power companies. Peesapati and Cotton (2009) outline the state of the art in detection
systems for the monitoring lightning and highlight some of the deficiencies in the present techniques.
4.3. Tide gauges, Sea Level Monitoring, Tidal Currents
Tide gauge stations (Table 14) represent a met-ocean resource for the offshore wind energy community
that offers similar information and is used in conjunction with radar altimeters. Tide gauges give
information on tidal heights and storm surges, and respond to alongshore current strength for some
geostrophic currents. Tidal fluctuations have a direct bearing on the feasibility of offshore construction
operations. For basic oceanographic research, tide gauge information is also important for calibrating and
validating satellite radar altimeters used in operational physical oceanography (Le Traon, 2010). The
North Sea is a special area where storm surges and tides are linked through the common mechanism of
Kelvin waves. Pugh (1987), Høyer and Anderson (2003), and Madsen et al. (2007) describe issues
involved along with accessible descriptions of the regional monitoring networks based on tide gauges and
satellite altimeters. Woolf and Mcilvenny (2011) give sources of information for tidal currents in the
north of the UK for the development of tidal energy.
4.4. Extreme events: Water spouts and rogue waves
Tables 15 and 16 shows references relating the most serious extreme events that may impact wind farms
in the North Sea: rogue waves and waterspouts. Both have been documented in the southern part of the
North Sea at the FINO1 meteorological tower (Dotzek et al., 2010; Pleskachevsky et al., 2012). For water
spouts, Dotzek et al. (2010) uses a database (Figure 8) of waterspout sightings near the German coast of
North Sea, Baltic Sea, and Lake of Constance to assess basic risk to German offshore wind energy
infrastructure. At least one waterspout will occur every two years within inside the boundary of planned
German wind farms, with potential serious consequences for wind turbine failure in the event of an
encounter.
Pleskachevsky et al. (2012) focuses attention on rogue waves in the southern North Sea as the result of a
resonant coupling of atmospheric downdrafts with ocean waves systems along storm tracks propagating
from the northwest. Rogue waves are relatively rare events, and Dysthe et al. (2008) provides a summary
of documented cases along with current understanding of the physical underpinning of the phenomenon.
FIG8
17
Lists of rogue wave encounters are additionally presented in Liu (2007), Maclean (2008), and Nikolkina
and Didenkulova (2012). Both water spouts and rogue waves are important elements of the met-ocean
database because they represent events of high risk that are difficult to parameterize when assessing
return periods in relation to the anticipated lifetime of wind power infrastructure.
4.5. Sea Ice and icing of marine structures
Operation of wind turbines in cold climates has a number of associated problems. Ice formation on the
turbine nacelle and blade structures from freezing environments is a chief concern of currently operating
wind farms in cold climates (Davis et al., 2014). This causes the problems of ice throw, reduced power
production, and reduced turbine lifetime. This has mainly been a problem of the onshore environment in
cold climates like Sweden. The understanding ice formation on large wind turbines benefits from earlier
investigations of ship icing. Vessel icing has been a serious problem at high latitudes and was heightened
from the 1960s with the extension of shipping and fishing activities into Arctic regions. Research
programs to address vessel icing problems developed between the late 1960s-1980s in many countries
with marine activities extending into the North Pacific, North Atlantic, and Arctic Oceans (Zakzewski et
al., 1989). Panov (1978) reviews the Soviet experience of ship icing, and highlights risk areas and
seasons from the North Atlantic, Arctic, and North Pacific from a large database of icing cases (see
Figure 9). Overland et al. (1986) and Overland (1990) present comprehensive analyses of the met-ocean
conditions for vessel icing based on a series of cases studies from Alaska with supplementary information
from the Labrador Sea. This forecasting scheme was used as the basis of an icing forecast scheme by the
NOAA National Meteorological Center (Feit, 1987). Zakrzewski et al. (1989) presents an overall
summary of research from several countries near the end of intensive ice investigation period at the end of
the 1980s. Guest and Luke (2005) presents a more recent summary of work by U.S. and some USSR
researchers, highlighting risk areas within 200km of pack ice edge where the wave field is high and air
temperatures much colder than ocean temperatures.
Only a few offshore wind farms in northern Europe are located in areas subject to regular sea ice cover.
Holttinen et al. (1999) reviews the issues of developing offshore wind in the Gulf of Bothnia in Finland,
potentially one of the most extreme climate environments where wind energy has been developed. Sea
ice loading is a chief consideration for the integrity of the foundation in during winter months, with ice
thickness in the Gulf of Bothnia ranging from 50 to 110cm depending on the severity of winter.
Meteorology plays an important role on the ice loading, presenting a stress that acts over large ice slab
areas with associated ice rafting and ridge effects. The threat of ice loads requires the construction of
additional structures to protect turbine foundations: poles to hold back the ice, artificial reefs to break up
advancing sea ice, and ice cone structures extending 5-10m above sea level to protect the turbine
foundation. Additional cold weather considerations are the effect of freezing and thawing of water in
porous concrete, as well as the weight ice frozen to the turbine the structure. An analysis of the economic
feasibility revealed that small turbines <1MW could not be economically set up in offshore areas subject
to sea ice. There was also an upper limit to turbine size where the cost of foundation protection became
prohibitive. In final the analysis, it was assessed that wind farms could only be set up in certain areas
where the ice thickness was less than 40cm.
As offshore wind turbines are placed in more extreme environments, ice engineering factors will become
more important, similar to shipping industry. The state of research info sea ice forecasts and climatology
for the Nordic Seas is outlined by Sandven (2008). The European storm review of Lamb (1992) reveals
that many of the wind farm sites in northern Europe have been subject to sea ice cover at least once since
1500.
4.6. Infrastructure damage
FIG9
18
Records of extreme events and disasters associated with offshore (and some onshore) structures are
shown in the Table 17. The table lists events for offshore turbines, ships, and platforms, and also includes
unusual records of waves and other physical the phenomena. Most of the reports document a single
incident, although Maclean (2008) presents a list of reports of all weather related military ship-damage
from 1945-2005, including hurricanes, rogue waves, and superstructure icing. The offshore wind energy
industry is relatively new, with recognition of the potential of offshore wind farms from the second half of
the 1980s (Wills and Cole, 1990). The early offshore the wind farms, like Horns Rev, revealed design
problems that were partly due to the placement of untested technological structures in a hostile offshore
environment. Corrosion issues were highlighted but lightning strike damage was also identified
(ModernPowerSystems, 2004). Other industries have incorporated improvements and modifications in a
stepwise process as weaknesses in infrastructure were revealed during routine operations. This includes
the evolution of ship safety design through the 20th century
(http://www.imo.org/about/conventions/listofconventions/pages/international-convention-for-the-safetyof-life-at-sea-(solas),-1974.aspx) as well the development of the British rail transportation network in the
19th century as the result of high-profile accidents (Wolmar, 2007).
Many of the reported structural failures in the seas off northwestern Europe have taken place during
storms in the late autumn and winter. For many or most of the ship accident losses linked to bad weather,
rogue waves are suspected to be direct cause. However, this this has been difficult to proof
unambiguously as geophysical records of sea state have poor resolution and are not optimized for
investigations of single extreme wave events (Toffoli et al., 2005). The offshore wind energy industry
makes a distinction between failure on a new structure from an unforeseen meteorological event, as
opposed to fatigue damage that accumulates over time and makes an older structure more susceptible to
failure at conditions lower than its original rated strength. In addition to immediate impact of high wind
and wave conditions causing maritime damage, there are also cumulative effects of met-ocean conditions
on shifting sediments on the sea bed that undermine offshore structures.
The issue of understanding historic storms was faced by the offshore petroleum industry in Europe at the
start of the development of petroleum resources in the North Sea. Shell Exploration Ltd. funded a longterm study of storm events in northern Europe by H.H. Lamb and colleagues starting from 1975, and this
culminated in the comprehensive storm catalog of Lamb and Frydendahl (1991). The work is remarkable
for its compilation and meteorological reconstruction of very severe storms during historical periods
when modern instrumentation did not exist. A careful reading of the work indicates that the worst storms
from past centuries have no analogs in modern times, and that the coldest winters were associated with
sea ice at the location of present day wind farms in the North Sea. Shifting sands have been recognized as
a navigation problem in the North Sea for centuries (Lamb and Frydendahl, 1991), and even over the past
century there are cases where offshore engineering structures have been undermined from shifting
sediments.
Hurricane force winds have mostly not been a problem for offshore wind energy in northern Europe. The
cyclone in Gujarat India in June, 1998 caused severe damage and turbine collapse in several near-shore
wind farms, and this was flagged to the European wind energy community by Danish scientists (WintherJensen and Jorgensen, 1999; Munich Re, 2010). Early in the development of European offshore wind
energy, Storm Anatol damaged the offshore meteorological mast at Horns Rev during its first year of
operation on Dec. 3, 1999 (Neckelmann and Petersen, 2000; Sommer, 2002). The event also caused the
collapse of 13 older onshore turbines in Denmark (Munich Re, 2010). There are not many storm analyses
based on offshore meterological tower data in the North Sea. Argyriadis et al. (2005) presents an
analysis of storms from an early time series data record from the FINO1 platform, and this platform
subsequent sustained wave-related data in 2006, 2007, and 2013. Summarizing recent weather-related
damage on coastal and offshore wind turbines, Munich Re (2010) notes that the planned offshore wind
19
farms in the North Sea are located in an area where storms like the 1999 Anatol event have a high
recurrence.
4.7. Information for Ship Collision Risk: Ship Traffic and Ship Registers
Ship traffic density represents a potential collision risk to offshore wind farms, and the Middelgrunden
offshore meteorological mast was lost in a ship collision in Denmark (Neckelmann and Petersen, 2000).
Tournadre (2014) presents information to assess ship density from 1992-2012. The UNCTAD register
presents information on international world annual seaborne trade and deadweight of the world merchant
fleet.
4.8. Textbooks of Boundary Layer and Wind Energy Meteorology
Table 18 presents a list of meteorological textbooks that focus on boundary layer and wind energy issues.
The textbook of Emeis (2013) focusses offshore wind energy in northern Europe draws information from
recent offshore experience in the North Sea. Hsu (1988) is a general meteorological text, but presents
sections important for operational meteorology, focussing on the US coast of the Gulf of Mexico. It
includes a section on offshore internal boundary layers, and provides is a good introduction for the
previous and subsequent research of its author. Roll (1965) summarizes the state of research in the
marine atmospheric boundary layer up to the mid-1960s. It opens with a summary of research from the
network of ocean weather ships, illuminating a valuable archive of met-ocean data that is now almost
forgotten. The open ocean weather ships were operated as fixed met-ocean research platforms for an
extended period after World War II, and the published information is still relevant for the offshore
structures being erected today. Stull (1988) is a useful reference for the boundary layer meteorology with
helpful sections on the computer analysis of meteorological instrument data, and there are worked
examples to clarify spectral analysis concepts. Garratt (1992) presents a review of the issue of internal
boundary layers offshore that there first noted during field investigations in World War II to understand
role of atmospheric ducts in radar propagation. Plate (1982) gives a review of different aspects of
engineering meteorology by different contributors. Within this volume, I.G. Davenport presents an
excellent overview of the effect of wind loading buildings together with background understand the
historical context of the problem.
5. Met-ocean contacts
Table 20 presents a list of people in universities, government agencies, and companies who have provided
information related to this report. A brief summary of the correspondence and contact information is
given. The subject is vast, and the people contacted have been helpful with advice and information, as
much as is possible within an evolving commercial activity.
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Shimada, T., H. Kawamura, M. Shimada, Evaluation of JERS-1 SAR images from a coastal wind
retrieval point of view, IEEE Transactions on Geoscience and Remote Sensing, 42, 491–500, 2004.
Sommer, A., Off shore measurements of wind and waves at Horns Rev and Laesoe, Denmark, European
Seminar OWEMES 2003, 10-12 April, 2003, Naples, Campania (Italy).
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Srokosz, M.A., P.G. Challenor, T.H. Guymer, Satellite Remote Sensing of Metocean Parameters: Present
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1995.
Stoffelen, A., A. Verhoef, J. Verspeek, J. Vogelzang, G.-J. Marseille, T. Driesenaar, Y. Risheng, G. de
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Data Summary Tables
Table 1: National meteorological agencies
Country
Agency
Website
Norway
DNMI
http://met.no,
http://sharki.oslo.dnmi.no
Iceland
Icelandic Met
http://vedur.is
Office
Denmark
DMI
Sweden
SMHI
UK
UKMO
Ireland
Met Éirean
http://www.dmi.dk/en/vejr , wave
specific information:
http://ocean.dmi.dk/validations/w
aves/index.uk.php
http://smhi.se , ocean
observations:
http://smhi.se/en/Weather/Sweden
-weather/Oceanobservations/havobs_en.htm
http://www.metoffice.gov.uk/educ
ation/teachers/weather-data-ukarchive,
http://metoffice.gov.uk/climate/uk
/stationdata/ ,
http://www.metoffice.gov.uk/clim
ate/uk/summaries
http://www.met.ie/marine/marine
Notes
Separate services for operational oceanography and
online the met-ocean archives.
Email: [email protected], [email protected] (Trausti
Jonsson); monthly weather statistics for ~100 stations
around Iceland with hourly wind, temperature, and
relative humidity for the preceding 14hours.
Email contact: [email protected] ; list of wave
buoys operated by other agencies, used by the DMI for
validation of its wave forecast model
Meteorological stations, tide level, wave buoys, ocean
temperature and currents; lightships from 1800’s &
1900’s; downloadable ASCII data of significant wave
height from ~50 past and current wave buoys
(http://opendata-download-ocobs.smhi.se/explore/#);
downloadable ASCII data salinity, temperature, sea
level, currents.
Email contact: [email protected]; different
products: 30 day archive of hourly meteorology from
~50 stations in the UK; monthly statistics from selected
(~20) selected stations; regional summaries of average
and extreme meteorological data; other products
available for a fee.
Online information of present met-ocean conditions
26
_map_asp
France
Meteofrance
Germany
DWD
http://
publiotheque.meteo.fr/okapi/acce
uil/okapiWebPubli/index.jsp,
https://donneespubliques.meteofra
nce.fr/?fond=produit&id_produit=
95&id_rubrique=32
http://www.dwd.de
Netherlands
KMNI
http://www.kmni.nl
Belgium
RMI
http://www.meteo.be
from 5 oceanographic buoys; daily average data from
~30 meteorological stations from the previous year;
other data available for a fee
Email contact: [email protected] (Virginie
Gorjux); different webpages: Internet facility to
identify met stations across France; screen facility to
access hourly data from ~100 oceanographic buoys on
a day-by-day basis for the current month
Email: [email protected], Historic daily average
weather for the 78 stations in Germany; wind speed
maximum values for Germany stations; monthly
average weather statistics (not wind) for selected global
stations, starting from ~2005.
Hourly data from ~30 stations across the Netherlands
extending back various times to the early 1900’s;
surface ocean wind fields from the ASCAT
scatterometer at 2h resolution and spatial resolution
12.5-50km.
Descriptions of weather radar at three locations with
access to European weather radar networks for other
countries. Description of the Belgium Lightning
Location System (BELLS)
Table 2: Organizations holding meteorological archives outside of government organizations: METAR, TAF, and amateur stations
Name
Description
Website
METAR & TAF
Interactive map showing current metar reports for 4000
http://no.allmetsat.com/metar-taf/
global stations including northwest Europe; most recent
listing only
METAR
Archive of METAR reports back to 2005; downloadable
http://www.ogimet.com/home.phtml.e
in1month chunks
n
METAR
Rolling 3 week METAR archive with all USA?/world?
http://www.met.fsu.edu/index.pl/wxda
Reports in single day file
ta/reports
Wunderground amateur
Real time data and archive from 2002 of amateur and
http://www.wunderground.com/
stations
national weather service stations.
Weatherlink Amateur
Real time data without archives hosted by Davis
http://www.weatherlink.com/map.php
stations
Table 3: Resources associated with meteorological data from the ships
Name
Website
Note
ICOADS
http://www.ncdc.noaa.gov/oa/clima Mainly deck logs from merchant vessels and others extending back to
te/coads
1800.
CLIWOC
http://pendientedemigracion.ucm.es Digitized and interpreted deck logs from English, French, Dutch,
/info/cliwoc/
Spanish, and Argentinian sailing vessels from 1750-1850.
MDB
https://www.bodc.ac.uk/data/infor
UK Meteorological Office marine data bank of ship observations from
mation_and_inventories/edmed/rep
1854.
ort/1399/
Global Wave
http://www.globalwavestatisticsonl
Statistical summaries of the wind and wave information from the UKMO
Statistics
ine.com
Main Marine Data Bank with a focus on parameters of special interest for
Online
the marine industry (hosted by BMT)
SAMOS
http://samos.coaps.fsu.edu/html/dat
Online database for US research ships since 2005.
a_availability.php
World Ocean
http://www.nodc.noaa.gov/OC5/SE Digitized information from ships based on ship code or cruise date
Database
LECT/dbsearch/dbsearch.html
Table 4: Resources associated with offshore oil platforms
Country/
Platforms
Information source
Source
Norway
Ekofisk, Gullfaks C, Heimdal,
Eklima
Sleipner A, Troll A, Draugen,
Heidrun, Norne, Ormen Lange
Denmark
Tyra East A, Harald B, Halfdan
B, Gorm C
[email protected]
(Kirsten Erhardi)
Netherlands
A12, D15, F16, L9, K14, Q1, J6,
AWG
[email protected]
(Martijn van Dijk)
Notes
Miros AS (2012) presents an annual report of
met-ocean data from the Ekofisk platform,
together with some historical information; Iden
(2013) presents a description and comparison of
wave information from Norwegian platforms
The Maesk Oil platform generate automatic
METAR reports; no information could found of
wave records at the platforms
Instrumented platforms in the Netherlands
economic sector. The platforms have wave-
27
United
Kingdom
United States
Anasuria; BODC archives:
Forties (June, 1974-May, 1980),
Brent (Dec., 1975-May, 1980),
Beryl/Frigg (Jan. 1979-Dec.
1982), Foula (Jan. 1977-Nov.
1978 and May, 1979-Oct.,
1979); other archives: North
Cormorant Sep. 1983-Aug.
1987.
Global listing of (hourly)
meteorological data from
offshore platforms operated by
different organizations
Anasuria metadata at
http://nwsportal.bsh.de/;
older archives for
selected platforms
(except North
Cormorant) are archived
at
http://www.bodc.ac.uk
http://www.ncdc.noaa.g
ov/cdo-web
monitoring radar and a subset generate
automatic METAR reports
Anasuria is a floating storage production ship
that is instrumented to give wave information
(NOOS website at BSH); older wave
information from four oil platforms has been
identified in the BODC archives as part of
United Kingdom Offshore Operators
Association (UKOOA).
Interactive map-based resource that includes
archives of many historic and discontinued
deployments. The first source of information
was William Brown, NOAA
([email protected])
Commercial
Various
http://www.simorc.org
Notes:
Iden, K.A., Some comparisons of wave measurements from the Norwegian continental shelf
(ftp://www.wmo.int/Documents/PublicWeb/amp/.../02.5_Iden.doc)
MIROS AS, Met-Ocean data. Ekofisk Field. Annual Report 2012, Doc. No. ND/1024/12/13, Sept. 12, 2012.
Table 5: Wave information websites (Real time data, jcomm, older archives, and associated links)
Information
Information location
National repositories of wave data
http://www.jcomm.info/index.php?option=co
m_content&task=view&id=131&Itemid=37]
http://www.jcomm.info/index.php?option=co
m_content&view=article&id=132&Itemid=3
7
US: NODC, historical archive of wave measurements from
http://www.nodc.noaa.gov/BUOY/
buoys & shore-based instruments from NDBC for US Pacific
& Atlantic coasts with eastern tropical Pacific network
GLOBAL: interactive map of global buoy network
http://www.ndbc.noaa.gov/
GLOBAL: lists of wave measuring instruments, separated by http://polar.ncep.noaa.gov/waves/templates/b
region and including geographic coordinates
uoys/
EUROPE: NOOS Northwest Shelf Data Portal: interactive
http://nwsportal.bsh.de/
map of buoy measuring waves, temperature, and salinity and
very limited data of oceanographic profiles
EUROPE: interactive map of waves, tides & meteorology for http://emodnet-physics.eu
a series of global sources but focusing more on Europe
UK: Wavenet-DEFRA listing CEFAS, UKMO and other UK
buoys & lightships online
UK: MAWS UKMO weather buoy network to west of
British Isles: 11 moored buoy & 7 systems on lighthouses &
islands
UK: UKDMOS
UK: BODC archived wave data from buoys & ships 1950’s–
1990’s
GERMANY: BSH interactive map moorings, lightships, &
fixed platforms in the North and Baltic Sea
COMMERCIAL: WaMoS radars, imap for sites at Ekofisk,
Helgoland, FINO1, Azores, & FA Platform; only FINO1 &
FA Platform running
BELGIUM: interactive map of Flemish coast showing buoy
data for ~10 buoys & fixed platforms
NETHERLANDS: interactive map of North Sea & inland
waterways showing current buoy data for ~30stations
NETHERLANDS: Deltares network near Dutch coast: L91,
Q11, F161, J61, D151, A121, AWG, zdv8-2d-MMND;
buoys initially identified with Deltares are privately operated
http://www.cefas.defra.gov.uk/ourscience/observing-andmodelling/monitoringprogrammes/wavenet.aspx
http://www.metoffice.gov.uk/weather/marine
/observations/ ,
http://www.metoffice.gov.uk/weather/marine
/observations/gathering_data/MAWS.html,
http://www.ukdmos.org/
https://www.bodc.ac.uk/data/online_request/
waves/
http://www.bsh.de/en/Marine_data/Observati
ons/MARNET_monitoring_network/MARN
ET_en.jsp
http://www.oceanwaves.de/start.html
http://www.meetnetvlaamsebanken.be/Defaul
t.aspx?Page=Map&L=en
http://www.rijkswaterstaat.nl/geotool/golve
n.aspx?cookieload=true
Buoys identified in: http://emodnetphysics.eu; metadata for the buoy network
was provided by Martijn van Dijk of RWS
Primary source
Jenkins_waveinfo (Bidlot,
2013)
Link from JCOMM website
Link from JCOMM website
No link
Information from Antonio
Novellino that Tobias Gies,
BSH responsible
Link from the JCOMM
website; basic information
from the Antonio Novellino
(emodnet); no referring link to
website
Link from JCOMM website
Carter et al. (2010)
Carter et al. (2010)
Alastair Jenkins (2013);
metadata verified.
Link from JCOMM website.
Thorger Bruning is the main
BSH person in charge of
supplying data from the
Waverider buoys network.
Jean Bidlot email 18/07/2013
Link from JCOMM website
Link from JCOMM website
Info from Antonio Novellino
that Deltares network
responsibility of Martin
28
and associated with oil platforms; the RWS government
authority hold metadata and archived wave data since 2009.
NORWAY: Meteo France real time data website to view
Norwegian oil platform data (16days): Heidrun, Troll A,
Sleipner, Heimdal, Norne FPSO
NORWAY: eklima website with metadata listing of oil
platforms in North Sea (Ekofisk, Gullfaks C, Heimdal,
Sleipner, Troll A) & Norwegian Sea (Draugen, Heidrun,
Mike, Norne, Ormen Lange)
SWEDEN: online ASCII data of significant wave height for
~50 past and ongoing buoy deployments since 1978
SWEDEN: lightship data from the late 1800s–1970s
DENMARK: lightship data archives
DENMARK: Network of near-shore coastal Waverider
buoys (including Hirtshals, Hansholm, Fjaltring,
Nymindegab, Fano Bay)
ICELAND: online data for land weather stations & 8 buoys
near coast
IRELAND: 24h of hourly meteorology & wave information
for 6 ocean buoy, 2 with wave direction sensors
FINLAND: 10 day graph archive of highest wave &
significant wave height for 4 stations in the Baltic Sea
FRANCE: real time data for ~20 stations on along the
Atlantic & Mediterranean coasts of France, presented at ½h
intervals for the previous 24 hours
FRANCE: real time data for 6 buoys operated by Meteo
France
SPAIN: interactive map of data for waves, meteorology, and
ocean temperature/salinity for ~30 locations on the Spanish
Atlantic & Mediterranean coasts; hourly wave data available
for preceding 48h
CATALONIA
PORTUGAL: interactive map of wave buoys on the
Portuguese coast, Madieras and Azores; up-to-date data
online
AZORES:
http://www.meteo.shom.fr/qctools/dataplotsu
rfmar.htm
http://sharki.oslo.dnmi.no/portal/page?_pagei
d=73,39035,73_39049&_dad=portal&_sche
ma=PORTAL
http://www.smhi.se/vadret/hav-ochkust/havobservationer; http://opendatadownload-ocobs.smhi.se/explore/
http://www.smhi.se/kunskapsbanken/lightshi
ps-1.32902/; http://www.seadatanet.org/DataAccess
Unknown Internet source; references to
lightship data in Sparre (1981) and Cappelen
et al. (2007)
www.kyst.dk
http://vs.en.sigling.is/
Verlaan; Verlaan sent contact
for Martijn van Dijk of RWS
Jean Bidlot email 18/07/2013
Alastair Jenkins (2013);
metadata verified. The main
met.no working with this data
is Knut Iden.
SMHI website
SMHI website and external
online data archive;
information provided by
[email protected]
e
DMI
This is an offline data resource.
Soeren Bjerre Knudsen
(Soeren.BjerreKnudsen@kyst.
dk) is a contact point for
information
Link from JCOMM website
http://www.marine.ie/home/publicationsdata/
data/buoys/
http://www.itameriportaali.fi/en/itamerinyt/e
n_GB/aallonkorkeus/ ;
http://www.itameriportaali.fi/wave
http://candhis.cetmef.developpementdurable.gouv.fr/
Link from JCOMM website
http://www.meteo.shom.fr/real-time/
Link from JCOMM website
http://www.puertos.es/oceanografia_y_meteo
rologia/redes_de_medida/index.html
Link from JCOMM website
Not accessible
http://www.hidrografico.pt/boiasondografo.php
GREECE: interactive map of three buoys (E1M3A, Pylos,
http://www.poseidon.hcmr.gr/onlinedata.php
Saronikos) with meteorology, wave height & current with
graph information for previous month
ITALY: interactive map with ~30 stations with facility to
http://www.idromare.it/
plot mostly older data archives.
ISRAEL: single station: Hadera meteomarine monitoring
http://isramar.ocean.org.il/isramar2009/Hade
station. Website not totally functional
ra/default.aspx/
EUROPE: reference to quality controlled wave/met buoy
Journal reference only: Saetra and Bidlot
data held at ECMWF
(2004)
INDIA: National Institute of Ocean Technology (NOIT):
http://www.niot.res.in./publi/ar/annualreports
~12-18 open ocean buoys across the Arabian Sea and Bay of
.php
Bengal
JAPAN: Nationwide Ocean Wave information network for
http://nowphas.mlit.go.jp/index_eng.html
ports and harbours (NOWPHAS): ~50 datasets of wave
information from coastal areas in 4 districts across Japan
with significant wave height and period information
available from an interactive map
KOREA: Korea Ocean Research and Development Institute
Web page not accessible
CHINA (PRC): National Marine Environmental Forecasting
Center – NMEFC
References:
Bidlot, J., copy of email from Jean Bidlot to Alistair Jenkins Feb. 18, 2013
Link from JCOMM website
Link from JCOMM website
Link from JCOMM website
Link from JCOMM website
Link from JCOMM website
Link from JCOMM website
Reference to ECMWF database
in Reistad et al. (2011)
Link from JCOMM website to
NIOT website; buoy network
outlined in annual reports
Link from the JCOMM website
(buoy network maintained by
the Port and Airport Research
Institute PARI)
Link from JCOMM
29
Cappelen, J., C. Kern-Hansen, and Kim Sarup, Guide to climate data and information from the Danish Meteorological Institute, Technical Report
06-12, DMI, Ministry o Transport and Energy, 2007.
Carter, D., J. Huthnance, with T. Mason, J. Rees*, J. Siddorn, J. Wolf, M. Yelland (2010 ) In: Charting Progress 2: Ocean Processes Evidence
Group Feeder Report. UKMMAS (2010). Ed. J. Huthnance. Defra. 22pp downloaded July 23/2013
Jenkins, A., Data source – ocean, report emailed from Alastair Jenkins 03/12/2013
Saetra, O. and J.-R. Bidlot, Potential benefits of using probabilistic forecasts for waves and marine winds based on the ECMWF ensemble
prediction system, Weather and Forecasting, 19, 673-689, 2004.
Sparre, A., The climate of Denmark: summaries of observations from light vessels. I. Wind, visibility, air temperature, cloud amount, and
weather, Kobenhavn, LVI, 171pp, 1981.
Table 6a: Satellite scatterometers and wind speed
WaveSatellite/
Website
length
Sensor
SeaSat
ERS-1,
ERS2/ESCAT
AMI
ADEOS-1
NSCAT
QuikSCAT/
Seawinds
(Ku band,
13.6GHz)
Ku
(13.515
GHz)
Midori-2
SeaWinds
EUMETSA
T
METOP/A
SCAT
Oceansat-2
http://manati.star.nesdis.no
aa.gov/products/QuikSCA
T.php ,
http://podaac.jpl.nasa.gov/
QuikSCAT
Time
Interval
06/1978–
10/1978
1991-2000,
1995-2011
Orbital and sensor information
Nielsen et al. (2004)
1996–1997
Nielsen et al. (2004)
06/199911/2009
Nielsen et al. (2004)
Brusch et al. (2008),
Sempreviva et al.
(2008), Brower (2011),
Furevik et al. (2011),
Reistad et al. (2011)
Nielsen et al. (2004)
2003–
(2004+?)
2006
http://www.isro.org/satellit
es/oceansat-2.aspx ,
http://podaac.jpl.nasa.gov/
dataset/OS2_OSCAT_LE
VEL_2B_OWV_COMP_1
2_V2
Haiyang-2
Publications referencing
satellite
Logan et al. (2014)
03/09/2009
-present
Brower (2011)
Altitude: 720–738 km
Inclination: 98.33
Period: 99.382min
Local time of pass: 12:00
Repeat cycle: 2 days
(sun synchronous)
Resolution: 50×50km
Polar: HH (inner), VV (outer)
08/2011present
ISRO (2007, 2010), Liu
et al. (2010), Stoffelen
et al. (2013)
Liu et al. (2010)
References:
Brower, M., Chapter 14. Offshore resource assessment. Wind Resource Assessment: A practical guide to developing a wind project, Wiley,
2011.
Brusch, S., S. Lehner, and J. Stellenfleth, Synergetic use of radar and optical satellite images to support sever storm prediction of offshore wind
farming, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 1, 57–66, 2008.
Furevik, B.R., A.M. Sempreviva, L. Cavaleri, J.-M. Lefevre, C. Transerici, Eight years of wind measurements from scatterometer for wind
resource mapping in the Mediterranean Sea, Wind Energy, 14, 355–372, 2011.
ISRO (Indian Space Research Organization), Oceansat-2, Publications and Public Relations, ISRO Headquarters, Bangalore, India, Sept., 2007.
ISRO (Indian Space Research Organization), Oceansat-2 Mission, 47th Session of STSC-UNCOPUOS, Vienna, Feb.8-19, 2010.
Logan, T., B. Holt, and L. Drew, The newest oldest data from Seasat’s synthetic aperture radar, EOS, 95, 93–94, 2014.
Nielsen, M., P. Astrup, C.B. Hasager, R. Barthelmie, S. Pryor, Satellite wind information for wind energy applications, RISO-R-1479, Riso
National Laboratory, Roskilde Denmark, Nov. 2004.
Reistad, M., O. Breivik, H. Haakenstad, O.J. Aarnes, B.R. Furevik, J.R. Bidlot, A high-resolution hindcast of wind and waves for the North Sea,
the Norwegian Sea, and the Barents Sea, Journal of Geophysical Research, 116, C05019, 2011.
Sempreviva, A.M., R.J. Barthelmie, S.C. Pryor, Review of methodologies for offshore the wind resource assessment in the European seas, Surv.
Geophys. 29, 471–497, 2008.
Stoffelen, A., A. Verhoef, J. Verspeek, J. Vogelzang, G.-J. Marseille, T. Driesenaar, Y. Risheng, G. de Chiara, C. Payan, J. Cotton, A. Bentamy,
M. Portabella, Research and development in Europe on global application of the OceanSat-2 scatterometer winds. Final report of
OceanSat-2 Cal/Val AO project, NWPSAF-KN-TR-022, SAF/OSI/CDOP2/KNMI/TEC/RP/196, March 27, 2013.
Table 6b: Synthetic Aperture Radar (SAR)
WaveSatellite/
Website
length
Sensor
C-band
AMI on
https://earth.esa.int/web/gu
(5.3GHz, ERS-1,
est/missions/esa5.66cm)
ERS-2
operational-eo-
Time
Interval
ERS-1:
1991-2000;
ERS-2
Orbital and sensor information
Altitude: 782–785 km
Inclination: 98.54°
Period: ~100min
Publications referencing
satellite
Furevik and Espedal
(2002), Furevik et al.
(2004), Nielsen et al.
30
SAR
missions/ers/instruments/sa
r;
https://eoportal.org/web/eo
portal/satellitemissions/e/ers-1
http://www.asccsa.gc.ca/eng/satellites/rad
arsat1/ ; http://www.asccsa.gc.ca/eng/satellites/rad
arsat/radarsat-tableau.asp
http://www.asccsa.gc.ca/eng/satellites/rad
arsat1/ ; http://www.asccsa.gc.ca/eng/satellites/rad
arsat/radarsat-tableau.asp
https://directory.eoportal.or
g/web/eoportal/satellitemissions/e/envisat/
1995-2011
Orbits/day: 14.3
Repeat cycle: 3 & 35 days
Polarization: V
Resolution: 10–30m
(2004), Brower (2011),
Calaudi et al. (2013)
1995-2013
Altitude: 793-821km
Inclination: 98.6°
Period: 100.7min
(Sun-synchronous)
Resolution: 8×8m
Altitude: 798km;
Inclination: 98.6°
Period: 100.7min
(Sun-synchronous)
Resolution:1×3m
Altitude: 800km
Inclination: 98.55°
Period: 100.6min
Repeat cycle: 35days
Polarization: HH, VV, HV, VH
Resolution: 28×28m to
950×980m
(same ground track as ERS-2
but 30min ahead)
Altitude: 693km
Inclination: polar orbit
Revisit time: 6 days
(Sun-synchronous)
Resolution: 5×5m & 20×40m
Choisnard et al. (2003),
Nielsen et al. (2004)
C-band
Radarsat-1,
Radarsat-2
C-band
Radarsat-2
C-band
(5.331
GHz)
ASAR on
ENVISAT
C-band
(5.405
GHz)
Sentinel-1
http://www.esa.int/Our_Ac
tivities/Observing
_the_Earth/Copernicus/Sen
tinel-1
Launch,
April 3,
2014
X-band
9.6GHz
X-band
(9.65GH
z, 3.1
cm)
TerraSARX
Tandem-X
http://www.astriumgeo.com/terrasar-x/
http://directory.eoportal.or
g/web/eoportal/satellitemissions/t/tandem-x
01/2008present
2010present
L-band
(1.275G
Hz,
23.5cm)
SeaSat
http://earth.esa.int/web/gue
st/data-access/browse-dataproducts/-/article/seasatsar-precision-image
June 28,
1978–Oct.
10, 1978
L-band
JERS-1
(Japanese
Earth
Resources
Satellite)
http://earth.esa.int/web/gue
st/missions/3rd-partymissions/historicalmissions/jers-1 ;
http://directory.eoportal.or
g/web/eoportal/satellitemissions/j/jers
Feb, 1992 –
Oct, 1998
L-band
(1.270G
Hz)
PALSAR
on ALOS
satellite
http://www.eorc.jaxa.jp/A
LOS/en/about/palsar.htm
2006–2011
L-band
PALSAR-2
on ALOS-2
satellite
2007present
03/2002present
May, 2014
–present
Brower (2011), Calaudi
et al. (2013)
Nielsen et al. (2004),
Brusch et al. (2008),
Brower (2011), Calaudi
et al. (2013)
Calaudi et al. (2013)
Li and Lehner (2013)
Altitude: 514km
Inclination: 97.4km
Resolution: 12m
Revisit period: 11days
(sun synchronous)
Altitude: 800km
Inclination: 108°
Orbits per day: 14
Resolution: 25×25m
Polarization: HH
Altitude: 568km
Inclination: 97.7°
Period: 96min
Spatial resolution: 18×18m
Swath width=75km
Polarization: HH
(note: instrument aliasing
problem flagged by Shimada et
al., 2004)
Range resolution: 7–100m
Swath: 40-75km or 250-350km
Polarization: HH, VV, HV, VH
(different modes)
Altitude: 628km
Inclination: 97.9°
Revisit cycle: 14days
(sun-synchronous sub-recurrent
orbit)
Li and Lehner (2013)
Fu and Holt (1982),
Logan et al. (2014)
Shimada et al. (2004);
Logan et al. (2014)
Logan et al. (2014),
Brower (2011)
References:
Brower, M., Chapter 14. Offshore resource assessment. Wind Resource Assessment: A practical guide to developing a wind project, Wiley,
2011.
Brusch, S., S. Lehner, and J. Stellenfleth, Synergetic use of radar and optical satellite images to support sever storm prediction of offshore wind
farming, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 1, 57–66, 2008.
Calaudi, R., F. Arena, M. Badger, A.M. Sempreviva, Offshore wind mapping Mediterranean area using SAR, Energy Procedia, 40, 38–47, 2013.
Choisnard, J., M. Bernier, G. Lafrance, RADARSAT-1 SAR scenes for wind power mapping in coastal area: Gulf of St. Lawrence case, in
proceedings of the International Geoscience and Remote Sensing Symposium, July 21-25, 2003, Toulouse, Francen, 3pp (distribution on
CD), 1983.
Fu, L.-L., B. Holt, Seasat views ocean and sea ice with synthetic-aperture radar, JPL Publication 81-120, Feb.15, 1982.
31
Furevik, B.R. and H.A. Espedal, Wind energy mapping using synthetic aperture radar, Can. J. Remote Sensing, 28, 196-204, 2002.
Furevik, B.R., C.B. Hasager, M. Nielsen, T. Hamre, B.H. Jorgensen, O. Rathman, O.M. Johannesen, Using satellite SAR in offshore resource
assessment, earth.esa.int/workshops/cmasar_2003/papers/EO3fure.pdf, Proceedings (CD-ROM), pp. 33-38, Nordwijk, ESA Publications
Division, 2004.
Li, X. and S. Lehner, Observation of TerraSAR-X for studies on offshore wind turbine wake in near and far fields, IEEE Journal of Selected
Topics in Applied Earth Observations and Remote Sensing, 6, 1757-1768, 2013.
Logan, T., B. Holt, and L. Drew, The newest oldest data from Seasat’s synthetic aperture radar, EOS, 95, 93–94, 2014.
Nielsen, M., P. Astrup, C.B. Hasager, R. Barthelmie, S. Pryor, Satellite wind information for wind energy applications, RISO-R-1479, Riso
National Laboratory, Roskilde Denmark, Nov. 2004.
Shimada, T., H. Kawamura, M. Shimada, Evaluation of JERS-1 SAR images from a coastal wind retrieval point of view, IEEE Transactions on
Geoscience and Remote Sensing, 42, 491–500, 2004.
Table 6c: Satellite altimetry – wind speed, sea surface height, sea state
Satellite/
Website
Time
Sensor
Interval
Skylab
http://www.altimetry.info/html/missio
05/1973
ns/skylab_en.html
GEOS-3
http://www.altimetry.info/html/missio
09/04/1975ns/geos3_en.html ,
12/1978
http://podaac.jpl.nasa.gov/dataset/GE
OS3_ALT_GDR?ids=Sensor&values=G
EOS-3 ALTIMETER ,
Seasat
http://altimetry.info/html/missions/sea
26/06/1978sat_en.html
Geosat
http://science.nasa.gov/missions/geosa 12/03/1985t/ ,
03/1989
http://altimetry.info/html/missions/geo
sat_en.html
ERS-1/2
Radar
Altimeter
(RA)
Topex/
Poseidon
GFO
ENVISAT
Jason1
Poseidon 2
altimeter
http://www.altimetry.info/html/missio
ns/ers1/welcome_en.html ,
https://earth.esa.int/web/guest/mission
s/esa-operational-eomissions/ers/instruments/ra
http://podaac.jpl.nasa.gov/dataset/TOP
EX%20GDR
http://altimetry.info/html/missions/env
isat/gfo/welcome_en.html
https://earth.esa.int/web/guest/mission
s/esa-operational-eo-missions/envisat ,
http://altimetry.info/html/missions/env
isat/envisat/welcome_en.html
https://sealevel.jpl.nasa.gov/missions/j
ason1/
17/07/199106/1996
(ERS-1),
1995-2011
(ERS-2)
10/08/199210/2005
(18/01/2006
)
02/199811/2008
01/03/200204/2012
200107/2013
Cryosat-2
Ku band
13.575GHz
https://earth.esa.int/web/guest/mission
s/esa-operational-eomissions/cryosat/overview
04/2010–
present
Jason-2
(OSTM)
https://www.aviso.altimetry.fr/en/miss
ions/present-missions/jason-2.html
20/06/2008present
SARAL/
AltiKa
https://www.aviso.altimetry.fr/en/miss
ions/present-missions/saral.html
25/02/2013present
Orbit information
Altitude: 435km
Inclination: 50°
Altitude: 845km
Inclination: 115°
Altitude: 800km
Inclination: 108°
Altitude: 800km
Inclination: 108°
Repeat Period: 17 day
(non-sun-synchronous)
Altitude: 785km
Inclination: 98.52°
Repeat period: 3, 35, 168 day
Altitude: 1336 km
Inclination: 66°
Repeat period: 10 day (nonsun-synchronous)
880 km, 108°, 17 day peat,
non-sun-synchronous
800km, 98.55°, 35 day repeat
(sun-synchronous); orbit
changed 22/10/2010 with 30
day repeat cycle
Altitude: 1336 km
Inclination: 66°
Repeat period: 10 day
Altitude: 717km
Inclination: 92°
Period: 30 day subcycle;
369day repeat
(non-sun synchronous)
Altitude: 1336 km
Inclination: 66°
Period: 9.9156 day repeat
Altitude: 790-791 km,
Inclination: 98.54°
Period: 35 day repeat
(sun-synchronous)
Publications referencing
satellite
Marsh et al. (1976)
Mognard and Lago
(1979)
Queffeulou (1983)
Tournadre and Ezraty
(1990), Carter (1993),
Alves and Young
(2003), Nielsen et al.
(2004)
Alves and Young
(2003), Nielsen et al.
(2004)
Alves and Young
(2003), Nielsen et al.
(2004)
Nielsen et al. (2004)
Vinoth and Young
(2011)
Nielsen et al. (2004),
Vinoth and Young
(2011), Tournadre et al.
(2012)
Labroue et al. (2012);
Aouf (2014), Cipollini
et al. (2014), Cotton et
al. (2014), Madsen et al.
(2014)
Cole et al. (2008)
Bronner and Carrere
(2011)
References:
Alves, J.M.G.M., and I.R. Young, On estimating extreme wave heights using combined Geosat, Topex/Poseidon and ERS-1 altimeter data,
Applied Ocean Research, 25, 167-186, 2003.
Aouf, L., The impact of the assimilation of altimeters and Sentinel-1 wave data in the wave model MFWAM, The 2014 ocean surface topography
science team meeting, Lake Constance, Germany, Oct. 28–31, 2014.
Bronner, E. and L. Carrere, SARAL/AltiKa products handbook, Dec. 12, 2011.
Carter, D.J.T., Estimating extreme wave heights in the NE Atlantic from Geosat data, Health and Safety Executive – Offshore Technology
Report, OTH 93 396, 1993.
32
Cipollini, P., L. West, H. Snaith, P. Harwood, C. Donlon, New altimetry products over shelf and coastal zone from the eSurge processor, The
2014 ocean surface topography science team meeting, Lake Constance, Germany, Oct. 28–31, 2014.
Cole, S., A. Buis, J. Leslie, E. Moreaux, C. Ritzert-Clark, G. Diller, Ocean surface topography mission/Jason 2 launch, NASA Press kit, June,
2008.
Cotton, D., F. Boy, M. Cancet, S. Dinardo, C. Gommenginger, A. Egido, J. Fernandes, P.N. Garcia, B. Lucas, T. Moreau, M. Naeije, R. Scharoo,
L. Stenseng, J. Benviniste, CryoSat Plus for oceans: an ESA project for Cryosat-2 data exploitation ocean ocean – Summary of results and
scientific roadmap, The 2014 ocean surface topography science team meeting, Lake Constance, Germany, Oct. 28–31, 2014.
Labroue, S., F. Boy, N. Picot, M. Urvoy, M. Ablain, First quality assessment of the Cryosat-2 altimetric system over ocean, Advances in Space
Research, 50, 1030–1045, 2012.
Madsen, K.S., J.L. Hoyer, L.J. West, P. Cipollini, P. Harwood, Real time altimetry measurements of the storm surge Bodil, Denmark –
comparison with model and in situ observations, The 2014 ocean surface topography science team meeting, Lake Constance, Germany, Oct.
28–31, 2014.
Marsh, JG., B.C. Douglas, S. Vincent, D.M. Walls, Tests and comparison of satellite-derived geoids with Skylab altimeter data, Journal of
Geophysical Research, 81, 3594-3598, 1976.
Mognard, N., and B. Logo, The computation of wind speed and wave heights from Geos 3 data, Journal of Geophysical Research, 84, 3979-3986,
1979.
Nielsen, M., P. Astrup, C.B. Hasager, R. Barthelmie, S. Pryor, Satellite wind information for wind energy applications, RISO-R-1479, Riso
National Laboratory, Roskilde Denmark, Nov. 2004.
Queffeulou, P., SEASAT wave height measurement: A comparison with sea truth data and a wave forecasting model – Application to the
geographic distribution of strong sea states in storms, Journal of Geophysical Research, 88, 1779-1788, 1983.
Tournadre, J., R. Ezraty, Local climatology of wind and sea state by means of satellite radar altimeter measurements, Journal of Geophysical
Research, 95, 18255-18268, 1990.
Tournadre, J., F. Girard-Ardhuin, B. Legresy, Antarctic icebergs distributions, 2002-2010, Journal of Geophysical Research, 117, C05004, 2012.
Vinoth, J. and I.R. Young, Global estimates of extreme wind speed and wave height, Journal of Climate, 24, 1647-1665, 2011.
Table 6d: Passive microwave remote sensing and wind speed
Instrument Type Satellite/
Website
Sensor
Passive
Seasat/
http://www.jpl.nasa.gov/missions/seas
Microwave
SMMR
at/ ,
Radiometer
http://nssdc.gsfc.nasa.gov/nmc/spacec
raftDisplay.do?id=1978-064A ,
DMSP/SSMI http://www.ncdc.noaa.gov/oa/rsad/ss
mi/swath/index.html ,
http://coastwatch.noaa.gov/cwn/search
/cwn_most_recent.php?sensor=SSMI
&product=wind ,
http://www.ospo.noaa.gov/Products/la
nd/spp/sharedprocessing.html#WS
TMI
Aqua
AMSR-E
Coriolis/Win
dsat
http://wwwghcc.msfc.nasa.gov/AMS
R/data_products.html
Time Interval
1978
Publications referencing
satellite
Srokosz et al. (1995),
Logan et al. (2014)
1987-present
Nielsen et al. (2004),
Hasager et al. (2006),
Brower (2011)
1997
Brower (2011)
05/2002-10/2013
Nielsen et al. (2004),
Hasager et al. (2006),
Brower (2011)
Nielsen et al. (2004),
Brower (2011)
2003
References:
Brower, M., Chapter 14. Offshore resource assessment. Wind Resource Assessment: A practical guide to developing a wind project, Wiley,
2011.
Hasager, C.B., P. Astrup, M.B. Christiansen, M. Nielsen, R. Barthelmie, Wind resources and wind farm wake effects offshore observed from
satellite, Proceedings European Wind Energy Association (EWEA), 2006.
Logan, T., B. Holt, and L. Drew, The newest oldest data from Seasat’s synthetic aperture radar, EOS, 95, 93–94, 2014.
Nielsen, M., P. Astrup, C.B. Hasager, R. Barthelmie, S. Pryor, Satellite wind information for wind energy applications, RISO-R-1479, Riso
National Laboratory, Roskilde Denmark, Nov. 2004.
Srokosz, M.A., P.G. Challenor, T.H. Guymer, Satellite Remote Sensing of Metocean Parameters: Present Status and Future Prospects, Offshore
Technology Report, Health and Safety Executive, OTH 93 421, 1995.
Table 7: Satellite sea surface temperature data cited in reports of operational oceanography/offshore wind industry
Instrument Type Satellite/
Website
Time Interval
Publications referencing
Sensor
satellite
Infrared
NOAA/
http://noaasis.noaa.gov/NOAASIS/ml/ 1978-present
Srokosz et al. (1995),
radiometer
AVHRR
avhrr.html ,
Johannesen et al. (2000)
http://www.class.ncdc.noaa.gov/saa/pr
oducts/search?datatype_family=AVH
RR
ERS/ATSR
https://earth.esa.int/web/guest/mission
1991-1997, 1995Srokosz et al. (1995)
s/esa-operational-eo2003
missions/ers/instruments/atsr
ENVISAT/
http://www.neodc.rl.ac.uk/?option=dis 2002-present
AATSR
playpage&Itemid=91&op=page&Sub
33
Menu=91
Microwave
radiometer
SeaSat?
1978
Nimbus-7/
SMMR
TRMM/
TMI
Aqua/
AMSR-E
Coriolis/
Windsat
Terra/
MODIS
Aqua/
MODIS
1978-1987
http://podaac.jpl.nasa.gov/dataset/EU
R-L2P-TMI
11/1997-?
Logan et al. 2014
Gentemann et al. (2003)
05/2002-?
02/2003-?
01/2000-?
05/-2002-?
References:
Gentemann, C.L., C.J. Donlon, A. Stuart-Menteth, and F.J. Wentz, Diurnal signals in satellite sea surface temperature measurements,
Geophysical Research Letters, 30, 1140, doi: 10.1029/2002GL016291, 2003.
Logan, T., B. Holt, and L. Drew, The newest oldest data from Seasat’s synthetic aperture radar, EOS, 95, 93–94, 2014.
Table 8: Visible sensor/imager on satellites
Sensor Name
Sensor characteristics &
(Satellite)
application
ASTER/Terra
VNIR (15m res; 60km swath),
SWIR (30m res; 60km swath),
TIR (90m res; 60km swath);
internal wave, surface flick,
longwave ocean swell, ship
wake; sediment resuspension
AVHRR
Medium resolution optical
image
IKONOS
IKONOS
IRS-1C LISSIII
JERS OPS
(optical
sensor)
MERIS
(ENVISAT)
MERIS
(ENVISAT)
MODIS Terra
& Aqua
MSG-1
Meteosat
Second
Generation
Oceansat-2
(OCM-2
instrument)
Polder3/Parasol
Date
Orbit Characteristics
Reference
18/12/1999present
Alt: 705km sun-synch
Eqcross: 10:30AM
Inclination: 98.3deg
Period: 98.88min
Repeat cycles: 16d
Series of
satellite from
1978
Alt: 833 km
λ1=580-680nm
λ2=725-1110nm
Pixel res: 1.1km
Matthews (2005); Kaab
and Leprince (2014);
http://www.satimagingcorp
.com/satellitesensors/other-satellitesensors/aster/
Nielsen et al. (2004);
http://edc2.usgs/1KM/avhr
r_sensor.php
High resolution optical image
Application to infer bathymetry
from lake shoreline extant,
resolution 0.46-1m
High resolution optical image
?
Since 2000
Nielsen et al. (2004)
Patrick and Delparte
(2014)
?
Nielsen et al. (2004)
Resolution: 18.5m (cross
track)×24.2m (along track); 8
VIS and NIR bands: 0.520.60um, 0.63-0.69um, 0.760.86um, 1.60-1.71um, 2.012.12um, 2.13-2.25um, 2.272.40um
Medium resolution optical
image
ENVISAT medium resolution
sensor; application to resolve
cell structure in cloud field over
North Sea during Britta storm
Nov. 1, 2006
Medium resolution optical
image
Visible image of cloud fields at
15min intervals to resolve wind
vectors.
1992-1998
Resolution: 360×236m
8 bands of 12bit data: 404424nm, 431-451nm, 476-496nm,
500-520nm, 546-566nm, 610630nm, 725-755nm, 845-885nm
Sept.23,
2009-present
Polarized visible images to
investigate radiative properties
& microphysics of clouds &
Dec.18, 2004
–Dec.13,
2013
Altitude: 568km
Inclination: 97.7°
Period: 96min
Repeat cycle: 44days
https://directory.eoportal.or
g/web/eoportal/satellitemissions/j/jers-1
?-April, 2012
Nielsen et al. (2004)
?-April, 2012
Brusch et al. (2008)
?
Nielsen et al. (2004)
Brusch et al. (2008)
Altitude: 720–738 km
Inclination: 98.33
Period: 99.382min
Local time of pass:
12:00
Repeat cycle: 2 days
(sun synchronous)
ISRO (2007, 2010)
Antoine et al. (2008a,b)
34
Quickbird
Quickbird
Quickbird
SeaWiFS
SPOT
WorldView
aerosols; 9 wavelengths 443–
1020nm with 6000m resolution
High resolution optical image
Application to retrieve
bathymetry at 0-20m depth off
western Australia
Application to infer bathymetry
from lake shoreline extant;
resolution 0.46-1m
Medium resolution optical
image; ocean color satellite 8
visible channels with pixel
resolution of 1100m
High resolution optical image
Application to infer bathymetry
from lake shoreline extant,
resolution 0.46-1m
VIIRS/NPOES
S (NPP)
Sentinel-1A
?
Nielsen et al. (2004)
Lehner et al. (2013)
Since 2000
Patrick and Delparte
(2014)
1997– Dec.
11, 2010
?
Nielsen et al. (2004);
Antoine et al. (2008a,b)
Nielsen et al. (2004), Kaab
and Leprince (2014)
Patrick and Delparte
(2014)
Since 2000
Launch
28/10/2011
5m ground resolution, 12day
repeat cycle; continuity with
35day repeat cycle data
generated by ENVISAT (failed
2012)
Sun-synchronous,
altitude 705km; period
99min
Alt: 825.7-827.9km
Inclin: 98.7deg
Period: 101.4min
Since 2014
Antoine et al. (2008)
Showstack (2014)
References:
Antoine, D., F. d’Ortenzio, S.B. Hooker, G. Becu, B. Gentili, D. Tailliez, A.J. Scott, Assessment of uncertainty in the ocean reflectance
determined by three satellite ocean color sensors (MERIS, SeaWiFS, and MODIS-A) at the offshore site in the Mediterranean Sea
(Boussole project), JGR, 113, C07013, 2008.
Antoine, D., P. Guevel, J.-F. Deste, G. Becu, F. Louis, A.J. Scott, P. Bardey, The ‘Boussole’ buoy – a new transparent-to-swell taut mooring
dedicated tomarine optics: design, tests, and performance at sea, Journal of Atmospheric and Oceanic Technology, 25, 968-989, 2008b.
ISRO (Indian Space Research Organization), Oceansat-2, Publications and Public Relations, ISRO Headquarters, Bangalore, India, Sept., 2007.
ISRO (Indian Space Research Organization), Oceansat-2 Mission, 47th Session of STSC-UNCOPUOS, Vienna, Feb.8-19, 2010.
Kaab, A. and S. Leprince, Motion detection using near-simultaneous satellite acquisitions, Remote Sensing of Environment, 154, 164-179, 2014.
Matthews, J., Stereo observation of lakes and coastal zones using ASTER imagery, Remote Sensing of Environment, 99, 16-30, 2005.
Nielsen, M., P. Astrup, C.B. Hasager, R. Barthelmie, S. Pryor, Satellite wind information for wind energy applications, RISO-R-1479, Riso
National Laboratory, Roskilde Denmark, Nov. 2004.
Patrick, M.R. and D. Delparte, Tracking dramatic changes at Hawaii’s only alpine lake. EOS 95, 117–118, 2014.
Showstack, R., Sentinel satellites initiate new era in Earth Observation, EOS, 95, 239–240, July 1, 2014.
Table 9: Analysis & Reanalysis products
Name
Description
ECMWF ERA-15
Data assimilated model from Dec. 1978-Feb.
1994 using ECMWF archive observations and
COADS ship/buoy observations (with others)
along with Hadley Centre & NCEP SST fields
ECMWF ERA-40
Sep. 1957 – Aug. 2002; resolution T159L60
(~125km)
Location
http://www.ecmwf.int/res
earch/era/ERA-15/
Reference
Gibson et al.
(1999)
https://www.bodc.ac.uk/d
ata/online_request/waves/
(BADC & ECMWF links
from BODC)
Uppala et al.
(2005)
ERA-Interim
Dee et al. (2011)
NCEP-NCAR
Reanalysis (NRA)
Japanese Reanalysis
(JRA-25)
MERRA
Data-assimilated meteorological model at
~210km resolution
Jan. 1979 – Dec. 2004; resolution T106L40,
model top 0.4hPa; 3D-Var data assimilation
1979-present
EURO4
1989-2012; reference to a reanalysis model
product for Europe, used to assess long term
air temperature changes
http://jra.kishou.go.jp/JR
A-25/index_en.html
http://gmao.gsfc.nasa.gov
/merra/
?
Kalnay et al.
(1996)
Onogi et al.
(2005)
Reinecker et al.
(2011)
Wilson and
Candlish (2014)
References:
Dee, D.P. et al., The ERA-Interim reanalysis: configuration and performance of the data assimilation system, QJR Meteorol Soc., 137, 553-597,
2011.
Gibson, J.K., P. Kallberg, S. Uppala, A. Hernandez, A. Nomura, E. Serrano, ERA ECMWF Reanalysis Report Series, 1. ERA-15 Description,
Version 2 – January 1999.
Kalnay, E., M. Kanamitsu, R. Kistler et al., The NCEP/NCAR 40-year Reanalysis Project, Bulletin of the American Meteorological Society, 77,
437-471, 1996.
35
Rienecker, M.M. et al., MERRA: NASA’s modern-era retrospective analysis for research and applications, J. Climate, 24, 3624-3648, 2011.
Uppala, S.M., P. W. Kallberg, A.J. Simmons et al., The ERA-40 re-analysis, Q.J.R.Meteorol. Soc., 131, 2961-3012, 2005.
Wilson, C. and G. Candlish, Wind trends and variability from reanalysis and their validation using conventional and elevated wind observations,
EWEA, Barcelona, 2014.
Table 10: Derived atlases and gridded fields
Name
Description
GLOBAL: Cross-calibrated, multiSatellite-derived atlas of surface winds at 6h, 0.25degree
platform ocean surface wind velocity
resolutionfrom 1987-2011 incorporating many radiometer
product (CCMP)
(SSM/I, TMI, AMSR-E) & scatterometer (Seawinds on
ADEOS-2 & QuikSCAT) satellite platforms & ship/buoy
measurements from NCAR & PMEL TAO
GLOBAL: ocean and sea ice synthesis
Data assimilated model with information for temperature,
(ECCO2)
salinity, currents & mixed layer depth
GLOBAL: Met Office Hadley Centre
Gridded fields for: sea surface temperature, ocean
observations datasets
temperature/salinity profiles; sea level pressure; radiosondes;
atmosphere surface temperature & humidity
GLOBAL: HadISST Hadley Centre
Monthly global sea ice and SST at 1degree resolution from
Ice and Sea Surface Temperature data
1871 based on the UK Marine Data Bank and COADS and
set
also more recent AVHHR satellite data.
GLOBAL: European Space Agency
Daily fields of SST at 0.05 degree resolution from 08/1991 to
Sea Surface Temperature Climate
12/2010 based on ATSR passive microwave and AVHRR
Change Initiative (ESA SST CCI)
radiometer
GLOBAL: Daily high-resolution
blended analyses for sea surface
temperature
GLOBAL: GHRSST Level 4 OSTIA
Global Foundation Sea Surface
Temperature Analysis
GLOBAL: Global wave statistics
online
NORTH ATLANTIC: AVHRR SST at
5km and 12h resolution
MEDITERRANEAN: MEDATLAS
MEDITERRANEAN: Italian Calabria
region
NETHERLANDS: Windklimaat van
Nederland
NETHERLANDS: North Sea Climate
NORWAY: Wind Atlas for the North
Sea and the Norwegian Sea
NORWAY: Norsewind
NORWAY: Vindkarte for Norge
(NVE & Kjeller Vindteknikk)
NORWAY: Windsim Norwegian
Wind Atlas
Daily 0.25degree resolution based on interpolated fields from
AVHRR and AMSR from 1985
Blended SST Level 4 SST from UKMO at 0.054degree
resolution using AQUA/AMSR-E, ENVISAT/AATSR,
MAG/SEVERI, AVHRR-3, and TRMM/TMI; used in
UKMO NWP models
Instrument, satellite & UKMO main marine data bank to find
scatter tables, extreme wave height & storm persistence
High resolution AVHRR SST fields produced at 12h time
steps and downloadable from an ftp as grb or hdf files
Mediterranean wind and wave fields bases on ECMWF
analysis data & calibrated against ERS1, ERS2, & TopexPoseidon altimeter data
Gridded fields based on 3269 ENVISAT ASAR (c-band)
images from 2002-2012; direction information from
NOGAPS model
Reference
http://podaac.jpl.nasa.gov/dataset/CC
MP_MEASURES_ATLAS_L4_OW
_L3_5A_MONTHLY_WIND_VEC
TORS_FLK , Atlas et al. (2009);
Atlas et al. (2011)
http://ecco2.jpl.nasa.gov/,
Menemilis et al. (2008)
http://www.metoffice.gov.uk/hadobs/
http://www.metoffice.gov.uk/hadobs/
hadisst/ , Rayner et al. (2003)
Archived at the Centre for
Environmental Data Archival
(CEDA; http://catalogue.ceda.ac.uk);
Merchant al. (2014); dataset
recommended by Emma Fiedler
([email protected])
http://iridl.ldeo.columbia.edu/SOUR
CES/.NOAA/.NCDC/.OISST/.versio
n2/.AVHRR/ (Reynolds et al., 2007)
http://podaac.jpl.nasa.gov/dataset/U
KMO-L4HRfnd-GLOB-OSTIA
(Stark et al., 2007)
http://www.globalwavestatisticsonlin
e.com/
http://osisaf.met.no/ , Eastwood
(2011)
MEDATLAS Group; Cavaleri
(2005); Bertotti and Cavaleri (2008);
http://users.ntua.gr/mathan/pdf/Pages
_from%20_WIND_WAVE_ATLAS
_MEDITERRANEAN_SEA_2004.p
df
Calaudi et al. (2013)
Wieringa and Rijkoort (1983)
Gridded summary statistics of met-ocean data for the North
Sea
Wind speed and statistics from 39000 digitized weather maps:
Weibull diagram & loglog statistics, cumulative distribution,
return period, wind rose, 11 types of weather patterns for
met.no, coastal jets from the Norwegian Wind Energy
Project; diurnal variation land & sea stations, storm & calm
duration (scale invariance); polar low tracks, wave heightfetch-duration, radiosonde locations, comparison geostrophic
& measured wind, power law surface reduction, 3s gust
factor; spectral analysis with peak at 1km, 100y return period
wind on Gumbel diagram, Weibull distribution
Korevaar (1990)
Gridded wind fields for Norway & Norwegian Sea at 50, 80,
120m from WRF model forced by NCEP fields & compared
the 50m coastal masts
http://www.nve.no/Global/Publikasjo
ner/Publikasjoner%202009/Oppdrags
rapport%20A%202009/oppdragsrapp
ortA9-09.pdf (Byrkjedal and
Akervik, 2009)
http://www.windsim.com/library/nor
wegian-wind-atlas.aspx
Gridded wind fields for Norway & Norwegian Sea from
met.no coastal network, geostrophic wind estimate, & highresolution modelling
Børresen (1987)
36
IRELAND: Wind atlas
IRELAND: Wind atlas
GREAT LAKES/NORTH AMERICA:
‘A wind atlas for the Great Lakes from
satellite and in situ observations’
Gridded wind speed fields for Ireland and 25km offshore at
50, 75, 100m above ground level based and 100m resolution.
The data are based on forecasts from the UKMO Unified
Model run at 4km resolution from 2001-2010 (initialized with
ERA-Interim).
(reference from Standen and Clive, 2012)
Gridded fields of wind speed and power density at 10m based
on satellite radar sensors (Quikscat and SAR), and in situ
measurements from coastal and buoy stations over 2002–
2012.
http://maps.seai.ie/wind/, Standen
and Wilson (2012)
Brower et al. (2003)
Doubrawa et al. (2014) (poster
presentation made by R.J.
Barthelmie)
References:
Atlas, R., J.V. Ardizzone, R. Hoffman, J.C. Jusem, S.M. Leidner, Cross-calibrated, multi-platform ocean surface wind velocity product
(MEaSURES Project), Guide Document, Version 1.0, Physical Oceanography Distributed Active Archive Center (PO.DAAC), May 18,
2009.
Atlas, R., R.N. Hoffman, J. Ardizzone, S.M. Leidner, J.C. Jusem, D.K. Smith, D. Gombos, A cross-calibrated multiplatform ocean surface wind
velocity product for meteorological and oceanographic applications, Bulletin of the American Meteorological Society, 92, 157-174, 2011.
Bertotti, L. and L. Cavaleri, The predictability of the “Voyager” accident, Nat. Hazards Earth Syst. Sci., 8, 533–537, 2008.
Børresen, J.A., Wind Atlas for the North Sea and the Norwegian Sea, Norwegian University Press, The Norwegian Meteorological Institute,
Oslo, 1987.
Byrkjedal, O. and E. Akervik, Vindkarte for Norge, Rapport nr 9/2009, Oppdragsrapport A, Norges vassdrags- og energidirektorat, 2009.
Calaudi, R., F. Arena, M. Badger, A.M. Sempreviva, Offshore wind mapping Mediterranean area using SAR, Energy Procedia, 40, 38–47, 2013.
Cavaleri, L., The wind and wave atlas of the Mediterranean Sea, Advances in Geosciences, 2, 255-257, 2005.
Doubrawa, P., A wind atlas for the Great Lakes from satellite and in situ observations, poster presentation at EWEA, Barcelona, 2014.
Eastwood, S., Atlantic high latitude L3 sea surface temperature product user manual, OSI-203, Version 2.1, OSI SAF, 2011
Korevaar, C.G., North Sea Climate based on observations from ships and lightvessels, Kluwer Academic Publishers, Dordrecht, 1990.
MEDATLAS Group, Wind and Wave Atlas of the Mediterranean Sea, Western European Union, 2004.
Menemenlis, D., J.-M. Campin, P. Heimbach, et al., ECCO2: High resolution global ocean sea ice data synthesis, Mercator Ocean Quarterly
Newsletter, 31, 13-21, Oct. 2008.
Merchant, C.J., O. Embury, J. Roberts-Jones, E. Fiedler, C.E. Bulgin, C.G. Corlett, S. Good, A. McLaren, N. Rayner, S. Morak-Bozzo, C.
Donlon, Sea surface temperature datasets for climate applications from Phase 1 of the European Space Agency Climate Change Initiative
(SST CCI). Geoscience Data Journal, doi: 10.1002/gdj3.20, 2014.
Rayner, N.A., D.E. Parker, E.B. Horton, et al., Global analysis of sea surface temperature, sea ice, and night marine air temperature since the late
nineteenth century, Journal of Geophysical Research, doi:10.1029/2002JD002670, 108, 2003.
Reynolds, R.W., T.M. Smith, C. Liu, et al., Daily high-resolution blended analyses for sea surface temperature, Journal of Climate, 20, 5473–
5496.
Standen, J. and C. Wilson, A new wind atlas for Ireland, poster presentation at the RMetS event ‘Renewables and future of energy technology’, at
Imperial College, London, UK, Oct. 17, 2012.
Stark, J.D., C.J. Donlon, M.J. Martin, M.E. McCulloch, OSTIA: An operational, high resolution, real time, global sea surface temperature
analysis system, Oceans 07 IEEE Aberdeenm conference proceedings, Marine Challenges: coastline to deep sea, Aberdeen, Scotland,
IEEE., 2007.
Wieringa, J. ad P.J. Rijkoort, Windklimaat van Nederland, Staatsuitgeverij, Den Haag, 1983.
Table 11: Land use/roughness
Resource
AIS data
CORINE
Global Land Cover
Characteristics Data Base
Landsat
Description
Land use or roughness
Roughness map at 250m resolution from CORINE
land cover data
AVHRR-derived land cover characteristics at 1km
resolution; used to find roughness length for
WASP model
20m Landsat image used to assess surface
roughness for wind site in Denmark
Resource to identify location of small islands, oil
rigs and lighthouses, used by Touradre (2014) in
an altimeter study of ship traffic
Land cover product derived from PALSAR L-band
SAR at 25m resolution
Location/Source/Citation
Described by Nielsen et al. (2004) and cited as NERI
(2001)
EO-WINDFARM (Furevik et al., 2004c); Standen and
Wilson (2012); Nielsen et al. (2004)
http://edc2.usgs.gov/glcc/globdoc1_2.php
Nielsen et al. (2004)
National Center for the
http://www.nceas.ucsb.edi/globalmarine/impacts ,
Ecological Analysis and
Tournadre (2014)
Synthesis
Forest/Nonforest land cover
http://www.eorc.jaxa.jp/ALOS/en/dataset_index.htm
classification for world
References:
NERI (2001), National Environmental Research Institute, AIS – The Danish Areal Information System, 2001.
Nielsen, M., P. Astrup, C.B. Hasager, R. Barthelmie, S. Pryor, Satellite wind information for wind energy applications, RISO-R-1479, Riso
National Laboratory, Roskilde Denmark, Nov. 2004.
Standen, J. and C. Wilson, A new wind atlas for Ireland, poster presentation at the RMetS event ‘Renewables and future of energy technology’, at
Imperial College, London, UK, Oct. 17, 2012.
Tournadre, J., Anthropogenic pressure on the open ocean: the growth of ship traffic revealed by altimeter data analysis, Geophysical Research
Letter, 10.1002, 2014
Table 12: Topography/bathymetry/coastline
Resource
Description
Location/Source
37
ASTER
Bundesamt fuer Seechifffahrt
und Hydrographie (BSH)
BMY-ARGOSS
GEBCO bathymetry
ETOPO-1
Global Land Cover
Characteristics Data Base
GMTED2010 (Global Multiresolution Terrain Elevation
Data 2010)
GTOPO-30
JISAO, University of
Washington
Kartverket (Norway)
National Elevation Dataset
NOAA Shoreline Website
SRTM
WorldDEM
Land surface topography between 80N and 80S
with 1’ resolution and ~10m accuracy
50m resolution bathymetry of the southern North
Sea
Satellite-based bathymetry from visible light
attenuation
Ocean bathymetry at 1’ or 30” resolution based
mostly on ship sounding reports
Global DEM at 1’ resolution
AVHRR-derived land cover characteristics at 1km
resolution; used to find roughness length for
WASP model
Elevation data at 30”, 15”, 7.5” resolution;
supersedes GTOPO-30
http://jspacesystems.or.jp/ersdac/GDEM/E/index.htm
Global DEM at 30” (1km) resolution with coarser
subsamples
Global elevation and bathymetry netCDF data files
in low resolution from 5 minutes to 5 degrees.
Elevation, vegetation & land use data down to
1:2000 to 1:5000 resolution
USA DEM at 1/9-1/3” resolution
http://www.temis.nl/data/gtopo30.html,
http://eros.usgs.gov/elevation-products
http://research.jisao.washington.edu/data_sets/elevation
/
http://www.statkart.no/, email from V. Odegaard
High resolution shoreline information that can be
downloaded for different areas of the world
Space shuttle topography at 90m resolution
http://shoreline.noaa.gov/data/datasets/wvs.htm
Commercial product of DEM with 12m horizontal
accuracy and 2m vertical accuracy
Table 13: Lightning
Source
DWD pamphlet ‘Radar- und Blitzbilder’
http://nowcast.de
http://edc2.usgs.gov/glcc/globdoc1_2.php
http://eros.usgs.gov/elevation-products
http://ned.usgs.gov/
http://srtm.usgs.gov/index.php,
http://eros.usgs.gov/elevation-products (reviewed by
Nielsen et al., 2004)
http://www.geo-airbusds.com/worlddem/
Description
Lightning services at DWD; started 1995; data supplier nowcast GmbH in Munich; based on
VLF/LF 3-30/30-300kHz detection; DWD lightning operations 35-65N, 10-25E; resolution
lightning strikes 2km Europe & 500m Germany.
Description of commercial lightning data provider (mostly to power companies); outline of
measure station and baseline requirement of the 5 detectors within 250km.
Table 14: Tide gauge stations
Source
Description
Pegel Online
https://pegelonline.wsv.de/gast/start/
PSMSL (Permanent
Service for Mean Sea
Level)
Email contact with Lars Obermoeller
([email protected])
http://www.bmtargoss.com/media/4519665/satellite_ba
sed_shallow_water_bathymetry_assessment.pdf
http://www.gebco.net/data_and_products/gridded_bath
ymetry_data/
Note
Online water level records in real time for stations
in rivers, North Sea and Baltic Sea
http://www.psmsl.org/
Table 15: Extreme Phenomena: Waterspouts
Phenomena
Source
Waterspout
Dotzek et al. (2010)
Information
-photographs of waterspouts around FINO1 platform Aug. 25, 2005
-German database of waterspout sightings in the North Sea, Baltic Sea & Lake of
Constance
References:
Dotzek, N., S. Emeis, C. Lefebrvre, J. Gerpott, Waterspouts over the North and Baltic Seas: Observations and climatology, prediction and
reporting, Meteorologische Zeitschrift, 19, 115-129, 2010.
Table 16: Extreme Phenomena: Documented rogue wave phenomena
Phenomena
Source
Information
Rogue Wave
Liu (2007)
-List and description of freak wave encounters mainly with ships
Rogue Wave
Dysthe et al. (2008)
-Catalog of rogue wave events with some explanation of current understanding of the
theory behind the events.
Rogue Wave
Nikolkina and Didenkulova
-Catalogue of world rogue wave events reported in the media.
(2012)
Rogue Wave
Pleskachevsky et al. (2012)
-High temporal resolution record of Nov. 1, 2006 rogue wave at FINO1 platform
-Mesoscale model to investigated resonant coupling between atmospheric downdrafts and
wave phase speed across the North Sea.
References:
38
Dysthe, K., H.E. Krogstad, and P. Muller, Oceanic rogue waves, Annu. Rev. Fluid Mech., 40, 287–310, 2008.
Liu, P.C., A chronology of freaque wave encounters, Geofizika, 24, 57–70, 2007.
Nikolkina, I. and I. Didenkulova, Catalogue of rogue waves reported in media in 2006–2010, Natural Hazards, 61, 989–1006, 2012.
Pleskachevsky, A.L., S. Lehner, and W. Rosenthal, Storm observations by remote sensing and influences of gustiness on ocean waves and on
generation of rogue waves, Ocean Dynamics, 62, 1335-1351, 2012.
Table 17: Infrastructure disasters & extreme events
Event
Caithnesswindfarms wind turbine ACCIDENT
compilation
Shipping ACCIDENT compilation from poor weather &
suspected rogue wave encounters (information from
Lloyd’s Information Service Database)
ACCIDENT investigation report of 1836: The Causes of
Shipwrecks
AGULHAS current and large waves
Storm ANATOL destroys 13 older turbines of 3500
turbines in Denmark
Antarctic cruise ship BREMEN disabled by large wave
Antarctic cruise ship CALEDONIAN STAR hit by large
wave
DERBYSHIRE sinks in west Pacific typhoon
DRAUPNER wave
EKOFISK waves
Unmanned lightship ELBE capsizes
Date
All
1995-1999
Source
http://www.caithnesswindfarms.co.uk/fullaccidents.pdf ;
catalog of global wind turbine accidents
Toffoli et al. (2003, 2005)
1833-1836
Faulkner (2002)
July 11, 2000
Dec. 3, 1999
Bitner-Gregersen (2004)
Munich Re (2010)
Feb, 2001
Mar., 2001
Faulkner (2002)
Faulkner (2002)
Jan. 1, 1995
Dec.12, 1990 &
Dec. 27, 1998
dd/12/1999
Faulkner (2002)
Bitner-Gregersen (2004)
Bitner-Gregersen (2004)
http://www.bsh.de/en/Marine_data/Observations/MARNET_
monitoring_network/MARNET_Monitoring.pdf
Faulkner (2002)
Faulkner (2002)
Bitner-Gregersen (2004)
Tanker ERICA sinks off France
Ro-ro ship ESTONIA sinking
Problems with FLOATING offshore installations; west of
Shetland and North Sea
Hurricane FRAN off east coast of USA
GLYSVURSNES meteorological tower collapse
GOODWIN SANDS lightship sinks
GUJARAT cyclone
HORNS REV initial problems
1999
Sept, 1994
Feb.16, 1995
HORNS REV meteorological mast damaged by the Storm
Anatol on Dec.3, 1999 in first year of operation
HUMBER GATEWAY tower collapse
Dec.3, 1999
HURRICANE IVAN: ENSCO platform drifts 40 miles
from anchoring point (photo)
HURRICANE KATRINA: Shell Mars tension leg
platform wrecked (photo)
MAUNSELL tower collapse, Tongue Sands
Sept., 2004
-Hull Daily Mail, Dec. 13, 2012
-Cowing (2012)
Veldman and Huijsmans (2008)
Aug., 2005
Veldman and Huijsmans (2008)
Dec. 5, 1947;
Feb. 1996
Jan., 2000
1945-2005
Southernmost U-series forts in Thames estuary; tilted
15degrees 1947 & abandoned; collapsed in 1996
Neckelmann and Petersen (2000)
Mclean (2008)
Deployment
interval 19761993
Jan, 1988
Dolazalek (1992)
MIDDELGRUNDEN met mast destroyed in ship collision
Catalog of weather-related NAVAL accidents, including
ships/lost damaged in typhoons/hurricanes in the
Pacific/Indian/Atlantic ocean, probable rogue wave
events, and superstructure icing
Forschungsplattfthorm NORDSEE developed cracks in
base during its base during its period of operation in the
North Sea
US Navy Electronics Laboratory research tower NOSC
(1959-1988) collapses after rogue wave strike during an
El Nino winter storm
PIATTAFORMA OCEANOGRAFICA lower machine
deck destroyed by rogue wave in Adriatic Sea (16m water
depth). The wave rose 11m above average sea level
QUEEN MARY struck by large wave while transporting
15000 troops
SCHIEHALLION FPSO: wave damage 20m above sea
level at mooring point north of Scotland
SELKIRK SETTLER photo of large wave in North
Atlantic
Feb.9, 2000
Bitner-Gregersen (2004)
Petersen et al. (1998)
June 6, 1998
2002-2004
Winther-Jensen and Jorgensen (1999), Munich Re (2010)
Seawater corrosion problems; engineering faults; lightning
damage of blades
(http://www.modernpowersystems.com/features ...)
Neckelmann and Petersen (2000), Sommer (2002)
Nov. 2?, 2012
Period 19701992; precise
date not given
1942
Nov. 9, 1998
Feb., 1987
http://www.divebums.com/dive-sites/Wreck-Alley.html ;
http://www.cadivingnews.com/divespots/169/NOSC-Tower Wreck
H. Dolazalek, Oceanographic research towers in European
waters, ONR Europe Reports, 92-7.R, Dec. 1, 1992.
Faulkner (2002)
Gorf et al. (2000), Gunson et al. (2001), Toffoli et al. (2005),
Veldman and Huijsmans (2008)
Faulkner (2002)
39
STENFJELL bulk carrier wheelhouse damage off Esbjerg
Oct 26, 1998
Gunson et al. (2001), Bitner-Gregersen (2004)
Denmark
1953 North Sea storm SURGE; reference to 4.9m wave
Jan.31, 1953
Baxter (1953)
field on top of storm surge and wave advance like a tidal
bore
TAY BRIDGE collapse
Dec. 28, 1879
Martin and MacLeod (2004)
FPSO VARG wave impact higher than deck level
Jan.29-30, 2000
Bitner-Gregersen (2004)
VOYAGER passenger cruise ship bridge windows
Feb.14, 2005
Bertotti and Cavaleri (2008)
smashed by wave between Balearic Islands and Sardinia
WAVE: K2 & K3 buoys record highest Atlantic waves
Winter 2007/8
http://www.metoffice.gov.uk/weather/marine/observations/gat
(UKMO)
hering_data/MAWS.html
WAVE: M4 Atlantic buoy records highest individual
Dec. 13, 2011
http://www.met.ie/marine/marine_map.asp
wave height since Feb. 2011: 20.4 m
References
Baxter, P.J., The east coast Big Flood, 31 January – 1 February 1953: a summary of the human disaster, Phil. Trans. Royal Soc. A, 363, 1293 –
1312.
Bertotti, L. and L. Cavaleri, The predictability of the Voyager accident, Nat Hazards Earth Sys Sci, 8, 533–537, 2008.
Bitner-Gregersen, E., Maxwave. Rogue waves – forecast and impact on marine structures, Hovik, Mar.16, 2004. (Maxwave_Fargis_dnv.ppt).
Cowing, P.J., Notice to Mariners, No. H. 123/2012, Humber Approaches, Meteorlogical Mast - Safety Zone, Nov. 2, 2012
Dolazalek, H., Oceanographic research towers in European waters, ONR Europe Reports, 92-7.R, Dec. 1, 1992.
Faulkner, D., Shipping safety, a matter of concern. Ingenia, Royal Academy of Engineering, London (August/September), 13, 13-20, 2002.
Gorf, P., N. Barltrop, B. Okan, T. Hodgson, R. Rainey, FPSO Bow Damage in steep waves, Rogue waves 2000 workshop, Brest, 29-30 Nov.
2000
Gunson, J., S. Lehner, E. Bitner-Gergersen, Extreme wave conditions from wave model hindcasts and from synthetic aperture radar images,
Design and operation for abnormal conditions. II, Proc. Royal Institution of Naval Architects, London, UK, 2001.
Martin, T. and I.A. MacLeaod, The Tay Rail Bridge disaster revisited, Proceedings of the Institution of Civil Engineers, Bridge Engineering 157,
187-192, 2004.
Mclean, M., Naval accidents since 1945, Maritime Books, Liskeard, Cornwall, 2008.
Munich Re, Offshore wind farms: Risk and initial loss experience, online pdf (WF-loss-articles.pdf; downloaded July 6, 2015), Sept. 3, 2010.
Neckelmann, A. and J. Petersen, Evaluation of the stand-alone wind and wave measurement systems for the Horns Rev 150MW offshore wind
farm in Denmark, Paper for OWEMES 2000, Apr. 13-14, 2000, Siracusa, Sicily, Italy; downloaded from Winddata_risoe)
Petersen, E.L., N.G. Mortensen, L. Landberg, J. Hojstrup, H.P. Frank, Wind power meteorology. Part I: Climate and Turbulence, Wind Energy,
1, 2–22, 1998.
Sommer, A., Off shore measurements of wind and waves at Horns Rev and Laesoe, Denmark, European Seminar OWEMES 2003, 10-12 April,
2003, Naples, Campania (Italy).
Toffoli, A., J.M. Lefevre, J. Montbaliu, H. Savina, E. Bitner-Gregersen, Freak waves: Clues for prediction in ship accidents? Proceedings of the
Thirteenth (2003) International Offshore and Polar Engineering Conference, Honolulu, Hawaii, USA, May 25-30, 2003, The International
Society of Offshore and Polar Engineers.
Toffoli, A., J.M. Lefevre, E. Bitner-Gergersen, J. Monbaliu, Towards the identification of warning criteria: Analysis of a ship accident database,
Applied Ocean Research, 27, 281-291, 2005.
Veldman, A.E.P. and R.H.M. Huijsmans, Extreme wave impact on offshore platforms and coastal structures, Technische Wetenschappen report,
2008
Winther-Jensen, M. and E.R. Jorgensen, When real life wind speed exceeds design wind assumptions, 1999 European Wind Energy Conference,
Mar. 1-5, 1999, Nice, France, pp. 220-223, 1999
Table 18: Boundary layer and wind energy textbooks
Text
Chapters
Arya, S.P., Introduction to
1.Introduction
Micrometeorology,
2.Energy budget near the surface
Academic Press, San Diego,
3.Radiation balance near the surface
1988
4.Soil temperatures and heat transfer
5.Air temperature and humidity in the PBL
6.Wind distribution in the PBL
7.An introduction to viscous flows
8.Fundamentals of turbulence
9.Semiempirical theories of turbulence
10.Neutral boundary layers
11.Momentum and heat exchanges with homogeneous surfaces
12.Evaporation from homogeneous surfaces
13.Marine atmospheric boundary layer
14.Nonhomogeneous boundary layers
15.Agricultural and forest micrometeorology
Bohren & Albrecht,
1.Introduction: Conservation of Energy;
Atmospheric
2.Ideal gas law: pressure and absolute temperature;
thermodynamics, New York, 3.Specific heats and enthalpy: adiabatic processes;
Oxford University Press,
4.Entropy;
1998.
5.Water and its transformations;
6.Moist air and clouds;
7.Energy, momentum, and mass transfer
40
Bortkovskii, R.S., Air-sea
exchange of heat and
moisture, D.Riedel,
Dordrecht, 1983.
Emanuel, KA, Atmospheric
Convection, Oxford
University Press, New York,
1994
Emeis, S., Wind Energy
Meteorology, SpringerVerlag, Berlin, 2013
Fleagle, RG and JA
Businger, An Introduction to
Atmospheric Physics,
Academic Press, New York,
1980.
Garratt, JR, The atmospheric
boundary layer, Cambridge
University Press,
Cambridge, 1992.
Holton, J.R., An
Introduction to Dynamic
Meteorology, Elsevier,
Amsterdam, 2004
1.The oceanic and atmospheric boundary layers under windy conditions
2.The sea state
3.The transfer of energy and mass in the spray-filled lower marine atmospheric boundary layer
4.The role played by storms in macroscale and mesoscale processes
1.General principles
2.Convection from local sources
3.Global convection: the Rayleigh-Benard problem and dry convective boundary layers
4.Moist thermodynamic processes
5.Graphical techniques
6.Stability
7.Observed characteristics of nonprecipitating cumuli
8.Theory of mixing in cumulus clouds
9.Observed characteristics of precipitating convection
10.Numerical modeling of convective clouds
11.Dynamics of precipitating convection
12.Slantwise convection
13.Stratocumulus and trade-cumulus boundary layers
14.Deep convective regimes
15.Interaction off convection with large scale flows
16.Cumulus representation in numerical models
Appendix 1. Water and thermodynamic variables
Appendix 2. Thermodynamic constants and parameters
1.Introduction;
2. Wind regimes;
3. Vertical profiles over flat terrain;
4. Winds in complex terrain;
5. Offshore winds;
6. Physics of wind parks;
7. Outlook;
Appendix A. Statistical tools;
Appendix B. Remote sensing of boundary layer structure and height
1.Gravitational effects
2.Properties of atmospheric gases
3.Properties and behavior of of cloud particles
3.1.Growth
3.2.Electrical charge generation and its effects
4.Atmospheric motions
5.Solar and terrestrial the radiation
5.1.Principles of radiative transfer
5.2.Radiation outside the atmosphere
5.3.Effects of absorption and emission
5.4.Photochemical processes
6.Transfer process
6.1.Conduction and turbulence
6.2.The boundary layer
6.3.Applications
7.Atmospheric signal phenomena
7.1.General properties of waves
7.2.Scattering of radiation
7.3.Natural signal phenomena
7.4.Remote sensing
Appendix I: Mathematical topics: partial differentiation, elementary vector operation, Taylor series, the total
differential, the exact differential, Gauss theorem, Stokes theorem, the potential function, solid angle
Appendix II: Units and dimensions, significant figures, electromagnetic conversion table, Table of physical
constants, Standard atmospheres
1.The atmospheric boundary layer;
2.Basic equations for mean and fluctuating quantities;
3.Scaling laws for mean and turbulent quantities;
4.Surface roughness and local advection;
5.Energy fluxes at the land surface;
6.The thermally stratified atmospheric boundary layer;
7.The cloud-topped boundary layer;
8.Atmospheric boundary-layer modelling and parameterization schemes;
9.The atmospheric boundary layer, climate and climate modelling;
Appendices
1.Introduction
2.Basic conservation laws
3.Elementary applications of the basic equations
4.Circulation and vorticity
5.The planetary boundary layer
41
Houghton, H.G., Physical
meteorology, The MIT
Press, Cambridge,
Massachusetts, 1985
Hsu, S.A., Coastal
Meteorology, Academic
Press, San Diego, 1988.
Pasquill, F., Atmospheric
diffusion. The dispersion of
windborne material from
industrial and other sources
2nd ed., Halstead Press (John
Wiley and Sons), New
York, 1974
Plate, E.J., Engineering
meteorology. Fundamentals
of meteorology and their
application to problems in
environmental and civil
engineering, Elsevier,
Amsterdam, 1982.
Roll, H.U., Physics of the
Marine Atmosphere,
Academic Press, New York,
1965.
Stull, R.B., An Introduction
to Boundary Layer
6.Synoptic-scale motions I: Quasi-geostrophic analysis
7.Atmospheric oscillations: linear perturbation theory
8.Synoptic-scale motions II: Baroclinic instability
9.Mesoscale circulation
10.The general circulation
11.Tropical dynamics
12.Middle atmosphere dynamics
13.Numerical modeling and prediction
Appendix A. Useful constants and parameters
Appendix B. List of symbols
Appendix C. Vector analysis
Appendix D. Moisture variables
Appendix E. Standard atmosphere data
Appendix F. Symmetric baroclinic oscillations
1.The atmospheric aerosol
2.Scattering in the atmosphere
3.Solar radiation and its disposition in the atmosphere
4.Priniples of atmospheric thermal radiation
5.Radiative transfer and the radiation budget of the atmosphere
6.The nucleation of water and ice in the atmosphere
7.Growth processes of water drops and ice particles
8.Precipitation processes
9.Common optical phenomena in the atmosphere
10.Atmospheric electricity
Appendix: Some useful constants and numerical parameters
1.Introduction,
2.Radiation,
3.Atmospheric thermodynamics,
4.Atmospheric dynamics,
5.Synoptic meteorology,
6.Atmospheric boundary layers and air-sea interaction,
7.Air-sea-land interaction,
8.Engineering meteorology,
Appendix A.Units, constants, and conversions,
Appendix B. The Beaufort wind scale,
Appendix C. The Saffir/Simpson damage-potential scale,
Appendix D. Decomposition of the vector winds into u and v components,
Appendix E. List of symbols for surface analysis.
1.Introduction and brief outline,
2. The properties of atmospheric turbulence,
3. Theoretical treatments of diffusion material,
4.Experimental studies of the basic features of atmospheric diffusion,
5. The distribution of windborne material from real sources,
6.The estimation of diffusion and air pollution from meteorological data.
1.The atmosphere (HA Panofsky)
2.Radiation and the radiation budget of the atmosphere (K Bullrich)
3.Cloud and precipitation physics and the water budget of the atmosphere (HR Pruppacher)
4.Global climatology (A Baumgartner et al)
5.Atmospheric turbulence (NO Jensen and NE Busch)
6.Atmospheric boundary layers over homogeneous terrain (SP Arya)
7.Atmospheric boundary layers over nonhomogenous terrain (JCR Hunt and JE Simpson)
8.Exchange processes at the earth-atmosphere interface (W Brutsaert)
9.Precipitation evaluation in hydrology (JF Miller)
10.Turbulent diffusion: chimneys and cooling towers (SR Hanna)
11.Turbulent diffusion near buildings (RM Meroney)
12. The interaction of wind and structures (AG Davenport)
13. Wind tunnel modelling of wind effects in engineering (EJ Plate)
14. Ice problems in engineering (J. Schwarz)
15. Wind wave problems in engineering (H. Mitsuyasi)
1.Introduction and basic principles,
2.Meteorological observations and measurements at sea,
3.Composition and properties of the marine atmosphere,
4.Flow characteristics of the marine atmosphere,
5.Thermodynamic processes in the marine atmosphere,
6.Concluding remarks,
References,
Textbooks for Mariners Published Since 1950.
1.Mean boundary layer characteristics
2.Some mathematical and conceptual tools: Part1. Statistics
42
Meteorology, Kluwer
Academic Publishers,
Dordrecht, 1988
Stull, R.B., Meteorology for
Scientists and Engineers 2nd
edition, Brooks Cole,
Thompson Learning, Pacific
Grove, California, 2000.
Wallace, J.M. and P.V.
Hobbs, Atmospheric
Science, An introductory
survey, Academic Press, San
Diego, 1977
3.Application of the governing equations to turbulent flow
4.Prognostic equations for turbulent fluxes and variances
5.Turbulence kinetic energy, stability, and scaling
6.Turbulence closure techniques
7.Bourndary conditions and external forcings
8.Some mathematical and conceptual tools: Part 2:. Times Series
9.Similarity theory
10.Measurement and simulation techniques
11.Convective mixed layer
12.Stable boundary layer
13.Boundary layer clouds
14.Geographic effects
Appendix A. Scaling variables and dimensionless groups
Appendix B. Notation
Appendix C. Useful constants, parameters, and conversion factors
Appendix D. Derivation of virtual potential temperature.
1.The atmosphere,
2.Radiation,
3.Heat,
4.Boundary layers,
5.Moisture,
6.Stability,
7.Cloud formation,
8.Precipitation,
9.Dynamics,
10.Local winds,
11.Global circulation,
12.Air masses and fronts,
13.Cyclones,
14.Numerical weather prediction,
15.Thunderstorms,
16.Hurricanes,
17.Air pollution dispersion,
18. Climate change,
19.Optics,
Appendix A.Fundamentals of Science,
Appendix B.Constants and conversion factors,
Appendix C. Notation,
Appendix D. Additional reading material,
Appendix E.Answers to selected exercises,
Appendix F.Syllabus,
Appendix G.Correspondences,
Appendix H.Turbulence closure
1.A brief survey of the atmosphere
2.Atmospheric thermodynamics
3.Extratropical synoptic-scale disturbances
4.Atmospheric aerosol and cloud microphysical processes
5.Clouds and storms
6.Radiative transfer
7.The global energy balance
8.Atmospheric dynamics
9.The general circulation
Table 19: Contacts for the met-ocean information
Name
Contact info
Aanderaa
[email protected]
Aarnes, Ole
[email protected]
Johan
Adcock, Tom
[email protected]
Alsbirk, Thomas
[email protected]
Operational Meteorologist
Plant Siting and Forecasting
Technology and Service Solutions
Vestas Wind Systems A/S
Andriessen, Mrs.
[email protected]
Engel
KNMI, Klimaatdesk
Ball, Duncan
[email protected]
Bancroft, George
[email protected]
Barjenbruch,
[email protected]
Date
2014/08/27
2013/07/23,
2013/11/29
2015/01/13
2014/02/03
2014/04/23
2013/07/24
2014/07/14
2014/08/07
43
Ulrich
Barstad, Idar
Barthelmie,
Rebecca
Behrens, Arno
Beswick, Mark
Betamy,
Abderrahim
Bidlot, JeanRaymond
Blouch, Pierre
Boyer, Tim
Brand, Arno
Brendling,
William J.
Brüning, Thorger
BSH bibliothek
German Federal Institute of
Hydrology
P.O. Box 200253
56002 Koblenz
Tel.: +49 261 1306 5406
Fax: +49 261 1306 5619
[email protected]
[email protected]
Dept. of Geological Sciences
Multidisciplinary Science Building II,
#302
702 North Walnut Grove
Indiana University
Bloomington, IN 47405
[email protected]
Helmholtz-Zentrum Geesthacht
Center for Materials and Coastal
Research
Max-Planck-Strasse 1
D-21502 Geesthacht
Germany
Tel: 49-4152/87-1556
Fax: 49-4152/87-41556
Archive Information Officer
Met Office National Meteorological
Archive
Great Moor House
Bittern Road, Sowton
Exeter EX2 7NL
[email protected],
[email protected]
[email protected]
2014/08/08
2014/11/20
[email protected]
European Centre for Medium range
Weather Forecasts (ECMWF)
Shinfield Park
RG2 9AX Reading
United Kingdom
Tel: +44-118-9499-708
[email protected]
Meteo-France
E-Surfmar Operational Service
Manager (EUMETNET)
Centre de Meteorologie Marine
13, rue du Chatellier – CS 12804
29228 Brest Cedex 2
France
Tel: +33 (0) 2 98 22 18
[email protected]
National Oceanographic Data Center
[email protected]
ECN Wind Energy
P.O. Box 1, NL 1755 LE Petten
[email protected]
BMT Fluid Mechanics Ltd.
Tel: +44(0)20 8614 4400
Direct: +44(0)20 8614 4418
Fax: +44(0) 8943 3224
[email protected]
Bundesamt fuer Seeschiffahrt und
Hydrographie (BSH)
Bernhard-Nocht-Str 78
20359 Hamburg, Germany
Tel: +49 (0) 40 3190-3133
Fax: +49 (0) 40 3190-3000
2013/07/18
2013/07/19
[email protected]
2013/06/19
2014/01/20,
2014/02/10
2014/12/10
2014/06/04,
2014/06/05
2014/07/18
2014/09/29
2014/10/24
2013/12/13
2014/02/05
2014/10/27
2014/01/20,
2014/01/21,
2014/01/29
2014/08/05
2014/08/08
2014/08/25
2014/09/11
2014/09/13
2014/09/22
2014/10/02
2014/10/21
2014/11/14
2014/11/20
44
Burgers, Gerrit
Buschbaum,
Christian
Buske, J.
Camp, Tim
Cohuet, JeanBaptiste
Creamer,
Columba
Dijk, Martijn van
Downer, Richard
Eastwood,
Steinar
Ehrhardt, Konrad
EMODnet help
desk
Erhardi, Kirsten
Eronn, Anna
Feldman, Gene
Carl
Fiedler, Emma
Fisher, Jens
Georg
Furevik, Birgitte
German
Oceanographic
data Centre
(DOD)
Gies, Tobias
[email protected]
[email protected]
2014/06/20
2014/04/08
[email protected]
[email protected]
MRCC Bremen
Tel: +49 421-53 68 70
Fax: +49 421-53 68 714
[email protected]
[email protected]
Tel: +33 561079140
[email protected]
Port Meteorological Officer
Met Eireann, Glasnevin Hill, Dublin
9, Ireland
Tel: 353-01-8064228
[email protected]
Rijkswaterstaat
Dienst Centrale Informatie
Voorziening
Derde Werelddreef 1
2622 HA Delft
Kamer 2C.34
Postbus 5023
2600 GA Delft
Netherlands
BODC Web master
[email protected]
[email protected]
2015/06/25
[email protected]
Head of Maritime Emergency
Reporting and Assessment Centre
Central Command for Maritime
Emergencies (CCME)
Am Alten Hafen 2
27472 Cuxhaven
Germany
Tel: +49 4721 567 399
[email protected]
2015/06/24
2015/07/03
[email protected]
2013/11/29
2013/12/03
2014/06/13
2014/07/25
[email protected]
[email protected]
[email protected]
SST and Sea Ice Scientist
Met Office
Fitzroy Road, Exeter Devon EX1 3PB
UK
Tel: 44 (0) 1392 886043
Fax: 44 (0) 1392 885681
[email protected]
Bundesamt fuer Seeschiffahrt und
Hydrographie (BSH)
Bernhard-Nocht-Str. 78
20359 Hamburg
Tel: +49 (0) 3190-3286
Fax: +49 (0) 3190-5000
[email protected]
2014/01/24
2014/10/01
2014/10/13
2013/11/26
2013/11/29
2014/10/22
2014/11/24
2014/06/06
2014/10/24
2013/08/07
2014/10/27
2014/08/11
[email protected]
2012/12/14
2013/08/05
2014/03/28
2014/07/10
2013/07/29
[email protected]
2013/09/25
45
Gooberman,
David
Griffin, Judi
Hadziabdic, Polly
Hackett, Bruce
Hammarklint,
Thomas
Hansen, Kurt
Schaldemose
Hardie, John
Herklotz, Kai
Hessner, Katrin
Hicks, Michael
Hill, Reinhold
Hill, Tracey
Hinz, Miriam
Iden, K.A.
Department of Economic
Development
Isle of Man Ship Registry
1st floor
St. George’s Court
Upper Church Street
Douglas
Isle of Man, British Isles
IM1 1EX
Tel: 01624 688500
[email protected]
[email protected]
Code 5596.3
Research Reports Library
Naval Research Laboratory
Tel: 202 767-3425
[email protected]
[email protected]
[email protected]
Oceanographer
SMHI/Swedish Meteorological and
Hydrological Institute
Oceanography
SE-42671 Vastra Frolunda
[email protected]
DTU Wind Energy
tel. +45 45254318
[email protected]
[email protected]
[email protected]
OceanWaveS GmbH
Hansekontor
Vor dem Bardoicker Tore 6b
D-21339 Luneburg, Germany
Tel: +49 4131 699 58-22
Fax: +49 4131 699 58-29
[email protected]
USGS International Ice Patrol
1 Chelsea St.
New London, CT 06320
USA
Tel: (860) 271-2757
Fax: (860) 271-2773
[email protected]
Avitec Research GbR
Katrin und Reinhold Hill
Sachsenring 11
27711 Osterhold-Scharmbeck
Germany
Department for Transport
Information Rights & Records Unit
Zone D/04
Ashdown House
Sedlescombe Road North
St. Leonards on Sea
East Sussex TN37 7GA
[email protected]
Bundesstelle fur
Seeunfalluntersuchung (BSU)
Federal Bureau of Maritime Casualty
Investigation
Bernhard-Nocht-Str. 78
20359 Hamburg
Germany
Tel: +49 (0) 40 3190-8321
Fax: +49 (0) 40 3190-8340
[email protected]
2014/07/22
2014/07/22
2014/04/23
2013/12/18
2014/06/02
2014/06/20
2013/12/09
2013/12/19
2014/01/20
2014/03/26
2013/09/23
2014/01/14,
2014/01/24
2014/12/17
2014/04/10
2014/05/23
2014/07/14
2014/07/16
2014/07/21
2014/07/31
2014/03/11,
2014/04/22,
46
IMR (Institute of
Marine Research)
library
IMS Engineering
Jenkins, Alastair
Jensen, Kari
Johnston, Brian
Kalve, Lillian
Kinder,
Fiederieke
Kindler, Detlef
(Detlef Stein)
Kleta, Henry
Knudsen, Soeren
Bjerre
Koek, Frits
Kremers, Ton
Kvamme, Dag
Kystdirektorat
De Leeuw, Henk
Leth, Ole Krarup
Lifsey, Margaret
Lowry, Roy
Magnusson,
Anne Karin
[email protected], [email protected]
2014/08/15,
2014/08/19,
2014/08/28
2015/01/12
[email protected]
[email protected]
Uni Research Computing
2etg, Datablokken
Hoyteknologisenteret
Thormohlensgate 55
Postboks 7810, N-5020 Bergen
Norway
[email protected]
Head librarian
Royal Norwegian Naval Academy
Pb. 83 Haakonsvern, N-5886 Bergen,
Norway
Tel: +47 55518616
[email protected], Rutter Inc.,
Newfoundland, Canada
[email protected]
Meteorologisk institutt
Vervarslinga pa Vestlandet
Allegaten 70
5007 Bergen
Tel: 55236648
[email protected]
2014/09/04
2014/05/05
2014/12/12
2015/01/12
[email protected]
Deputy Head of the Department
Offshore
GL Garrad Hassan
Brooktorkai 18
20457 Hamburg
Tel: +49 40 36149 2748
Fax: +49 40 36149 5920
[email protected]
Deutscher Wetterdienst (DWD)
TI 33, Maritim Messnetz
Frahmredder 95
D-22393 Hamburg
Germany
Tel: +49 (69) 8062-6635
Fax: +49 (69) 8062-6699
[email protected]
Senior Kysttekniker
Kyst og Klima
Miljoministeriet
Kystdirektoratet
Hojbovej 1
DK – 7620 Lemvig
Tel: (+45) 99 63 63 63
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
DMI
[email protected]
Technical Information Specialist
Research Library
US Naval Laboratory
[email protected]
[email protected]
MET Norway
R&D/OMM-Bergen
2013/09/13
2014/08/25
2015/01/12
2014/01/09
2014/04/10
2011-2013
2014/10/01
2014/11/10
2014/06/24
2013/12/02
2014/04/02
2014/10/23
2013/10/23
2013/11/25
2014/10/15
2014/02/07
2014/03/10
2014/03/13
2014/08/18
47
Manzi, Paul
Marine Institute
Ireland
Martin, Keith
Matthews, Geoff
McIlroy, Nicola
Meyer, Christian
Paarup
Michelet, Dagrun
Moerstedt, Bernd
Mouritzen,
Anders Sorrig
Nielsen, Jette
Sandager
Nelsen, Jacob
Woge
NHH library,
Bergen
North, Sarah
Novollino,
Antonio
Obermoller, Lars
Odegaard, Viel
Outzen, Olaf
Pankiewicz,
Sarah
Tel: 47-55236643
Fax: +47-97565811
[email protected]
[email protected]
2014/12/21
[email protected]
National Institute of Standards and
Technology
100 Bureau Drive, Stop 2500
Gaithersburg, MD 20899-2500
Tel: 301-975-2789
[email protected]
Staff Officer Maritime Operations
HM Coastguard
Maritime and Coastguard Agency
Bay 2/07 Spring Place
105 Commercial Road
Southampton Hampshire
SO15 1EG
UK
The Information Assurance Team
Bay 3/09
The Maritime and Coastguard Agency
Spring Place
105 Commercial Road
Southampton SO15 1EG
[email protected],
DONG Energy, Wind Power – Loads,
Aerodynamics and Control
Tel: +45 99 55 56 31
2014/05/20
[email protected]
[email protected]
[email protected]
Bundesanstalt fuer ITDienstleistungen
Am Ehrenberg 8, 98693 Ilmenau
Tel: +49 3677 669 2221
Fax: +49 3677 669 3333
[email protected]
2014/08/08
2013/07/29
2014/08/13
2014/07/23
2014/01/21,
2014/01/23,
2014/02/03,
2014/03/28,
2014/04/04
2013/08/07
2014/08/21
2014/03/31
[email protected],
Vestas Wind Systems A/S
[email protected]
2014/02/05
bib@[email protected]
2015/01/09
[email protected]
Ship observations manager
Met Office
Fitzroy Road, Exeter, Devon
EX1 3PB, UK
[email protected]
2013/12/11,
2014/04/28
[email protected]
Bundesamt fuer Seeschifffahrt und
Hydrographic (BSH)
Lars Obermoller (N1381)
Bernhardt-Nocht-Str. 78
20359 Hamburg
Tel: +49 (0) 40 3190-4181
Fax: +49 (0) 40 3190-5000
[email protected]
[email protected]
Library & Archive Manager
Met Office
Fitzroy Road, Exeter
EX1 3PB
2014/11/05
2014/10/23
2013/08/08
2013/06/13
2011 & 2013
2014/03/26,
2014/06/03
48
Pearce, David
Pegelonline
Philbrow, Sue
Pleskachevsky,
Andrey
Raaijmakers, Tim
Reichert,
Konstanze
Rees, Jon
RF
Forschungsschiff
ahrt
Richards, Debbie
Rickard, Shawn
Rozenboom,
Rene
Rychtar, Paula
Saetran, Lars
Senet, Christian
Sauer, Henning
[email protected]
[email protected]
Marine Observations Systems
Manager, Cefas
Pakefield Road
Lowestoft, Suffolk NR33 0HT UK
Tel: +44(0) 1502 524504
[email protected]
Bundesanstalt fuer ITDienstleistungen im
Geschaeftsbereich des BMVBS
Am Ehrenberg 8
D-98693 Ilmenau
Tel: +49 (0)3677 669-0
Fax: +49 (0) 3677 669-3333
[email protected]
Office Manager/Operations
P/F Thor
Bryggian 5
FO-420 Hosvik
Faroe Islands
[email protected]
2014/10/22
2014/08/07
2014/08/08
2015/01/07
[email protected]
Senior offshore advisor and researcher
Deltares
Dept: Harbour, Coastal & Offshore
Engineering
Unit: Hydraulic Engineering
Postbus 177
2600 MH Delft
Rotterdamseweg 185
2629 HD Delft
Netherlands
[email protected]
OceanWaveS & Rutter
Business Development and Innovation
23 Corrondela Grove
Lower Hutt, 5010
New Zealand
[email protected]
[email protected]
2013/11/05
EUMETSAT User Service Helpdesk
Tel: +49 6151 807 377
Fax: +49 6151807379
[email protected]
[email protected]
Port Meteorological Officer
Marine Networks Specialist
Supervisor AMOS-South PMO Office
100 East Port Blvd.
Hamilton, Ontario LSH 7S4
Tel: 1-905-312-0900 ext. 202
Cell: 1-905-512-5862
Fax: 1-905-312-0730
Rene.rozeboom
PMO Office
KNMI
Tel: 31 30 2206678
[email protected]
[email protected]
[email protected]
[email protected]
Wasser- und Schiffahrtsamt Bremen
Franziuseck 5
28199 Bremen
2014/06/11,
2014/06/12,
2014/06/24
2014/03/11
2015/01/06
2013/07/23
2014/05/08
2014/11/05
2014/09/08
2014/09/26
2014/01/29
2013/07/19
2014/04/07
49
Smith, Shawn R.
Smith, Stuart D.
Sofrona, Katerina
Solberg, Stein
Stoker, E.
Suryadarma,
Yudi
Svalmark, Johan
Tambke, Jens
Tamm, Susanne
Taylor, Paul H.
Tinz, Birger
Tufto, Jon
UK
Meteorological
Office
Valheim, Jan
Peter
Van Vliet, Gerda
Verlaan, Martin
Tel: +49 (0) 421 5378 331
Fax: +49(0) 4941 602 378
[email protected]
[email protected]
Marine Traffic Support
[email protected]
Chief Operations
JRCC Southern Norway, Stavanger
(JRCC Stavanger)
Tel: 47 51646002/00
Tel: 47 98252270
Service Department
Datawell BV
Voltastraat 3
1704 RP Heerhugowaard
Netherlands
[email protected]
[email protected]
Maritime Meteorological Station
Tanjung Priok
Jakarta, Indonesia
[email protected]
SMHI/Swedish Meteorological and
Hydrographic Institute
SE-601 76 Norrkoping)
Tel: 46 (0) 11 495 3000
Fax: 46 (0) 11 495 8483
[email protected]
ForWind, Center for Wind Energy
Research
Carl von Ossietzky University
Oldenburg
[email protected]
[email protected]
Department of Engineering Science
Parks Rd, Oxford OX1 3PJ, UK
Tel: 01865-273198
Fax: 01865-273010
[email protected]
Division Maritime – Climatological
Monitoring Centre
German Meteorological Service
Bernhard-Nocht-Str 76
D-20359 Hamburg
Tel: 0049 69 8062-6250
Fax: 0049 69 8062-6209
Mediehuset Bergens Tidende
Postboks 7240, 5020 Bergen
[email protected]
[email protected]
2014/05/08
2014/02/14
2014/07/10
2014/11/05
2014/12/08
2014/06/19
2014/06/24
2014/08/07
2014/08/12
2014/11/12
2014/11/21
2014/12/16
2014/12/17
2014/12/23
2015/01/06
2014/10/23
2014/10/13
2013/11/08
2014/08/25
2014/11/17
2015/01/12
2014/03/20
2014/07/07
2013/07/23,
2014/04/03
[email protected]
2014/07/14
[email protected]
Koninklijke Nederlandse Redding
Maatschappij
Postbus 434
1970 Postbus AK IJMuiden
Haringkade 2
Tel: 088 999 60 12
Fax: 0255 52 25 72
[email protected]
2014/08/19
2013/08/12;
2013/11/22
50
Von Bargen,
Horst
Waring, Philip
Watson, Simon J.
Weinel, Beate
Wiechmann,
Wilfried
Wijk, A.J.M. van
Wilhelmi, Jens
Wray, John
FIGURE LIST.
[email protected]
Meteorologischer Hafendienst
Deuscher Wetterdienst
Seewetteramt Hamburg
Bernhard-Nocht-Str.76
D-20359 Hamburg
Germany
Tel: +49 69 8062 5312
Fax: +49 69 8062 6319
General Manager
BP Maritime Services (Isle of Man)
Ltd.
Samuel Harris House
5-11 St. Georges Street
Douglas, Isle of Man IM1 1AJ
[email protected]
[email protected]
Generaldirektion
Wasserstrasse und Schiffahrt
Aussenstellen Mitte, West, Suedwest,
Sued, und Ost
Dienstsitz Mainz
Brucknerstrasse 2
55127 Mainz
Tel: 06131 979-291
Fax: 06131 979-155
[email protected]
Federal Institute of Hydrology
Am Mainzer Tor 1
Postfach 20 02 53
56002 Koblenz
Germany
Tel: 0261 1306 5340
Fax: 0261 1306 5363
[email protected]
Faculty of Applied Sciences
TU Delft
Tel: (31) 0-015 278 6320
[email protected]
Federal Institute of Hydrology
Department of Hydrometry and
Hydrological Survey
Tel: +49 (0) 261 1306-5859
Fax: +49 (0) 261 1306-5363
[email protected]
[email protected],
Vector Instruments Technical Support
Windspeed Ltd.
2014/09/04
2014/09/04
2014/07/23
2014/03/14;
No response
2014/04/07
2014/09/15
2014/09/29
2014/10/08
2014/02/18
2014/11/26
2013/08/13
51
Figure 1. Location of offshore wind farms in the North Sea from Wiki Internet report
on 2015.
Fig1
52
Figure 2. Wundermap showing locations of amateur weather
stations in northern Germany and the German Bight area.
53
Figure 3. Global distribution of ICOADS data from volunteer
observing ships and buoys (Kent and Ingleby, 2009)
54
Figure 4. NCDC interactive map of met-ocean data for the North Sea
Fig4.
55
Figure 5. Quickscat image of the wind field over the North Sea in the early evening of Oct. 31,
2006. The storm, identified as ‘Britta’, caused remarkable storm surges along the North Sea and
Baltic Sea coasts of the Netherlands, Germany, and Denmark. There was ship and oil rig
damage in the North Sea.
Fig5.
56
Figure 6. High resolution TerraSar-X synthetic aperture radar image of the
Alpha Ventus wind farm in the German Bight (array of 12 turbines shown by
bright dots on the left) with wind wakes propagating to the upper right with
an eastward wind.
Fig6.
57
Figure 7. Synergistic used of a visible sensor (left) and SAR (right) on the same satellite platform
to get information of the boundary layer structure during a severe wind storm on Nov. 1, 2006. The
combined use of sensors was used to assess propagating cloud cell structures and gust fronts
(Pleskachevsky, 2012).
Fig7.
58
Figure 8. Eyewitness reports of waterspouts along the North Sea and Baltic Sea
coasts of Germany, and the Lake of Constance.
Fig 8.
59
Figure 9. Vessel icing cases in the Arctic region (from Overland
et al (1986) based on an original figure from Panov (1978))
Fig.9