In recent years, there has been growing interest in renewable energy technologies, due to the combined need of providing energy security for the future and reaching current greenhouse gas (GHG) emission reduction targets. According to the European Environment Agency (EEA), the potential offshore wind energy of Europe was seven times the European energy demand in 2009 [1
], which means there was, and still is, much unrealised potential for further offshore renewable energy development. Following investment in renewable energy, 90% of the global offshore wind farms operating in 2014 were located in Northern Europe [2
] and, by 2016, the European coasts were host to more than 3500 offshore wind turbines distributed across 81 wind farms, delivering a cumulative total of 12.6 GW of power [3
]. It has been predicted that further development will lead to a European production of offshore wind power between 20–50 GW by 2050 [4
Offshore wind energy has multiple advantages over land-based wind farms. For example, the offshore winds are normally stronger, steadier, and more abundant [2
], which generally leads to lower wind variations and therefore reduces the periods of no electricity generation [2
]. Additionally, the offshore annual capacity factor of wind energy can be between 20% and 40% higher than land-based sites [7
]. In Europe, the average offshore wind turbine has the capacity to power 3300 average households, in comparison with 1500 for its land-based counterpart [5
]. Moreover, inland renewable energy projects can have reduced site availability due to multiple stakeholder interactions, environmental constraints, as well as aesthetic factors, while offshore sites may be more easily situated near coastal population centres [2
]. Consequently, the offshore wind energy market grew by 34% in the European Union in 2013, in comparison with 12% for its land-based counterpart [9
]. Furthermore, the offshore wind industry has undergone important development during the past ten years to increase energy capacity, as well as to develop structures that can be sited further offshore. Constraints such as water depth and distance to shore are becoming less binding due to the development of new technologies derived from the growing interest in Offshore Renewable Energy (ORE) markets [7
]. Additionally, offshore wind power costs are expected to be reduced by about 45% by 2050 according to the Global Wind Energy Council [10
Despite its advantages over land-based wind farms, certain technical, environmental, and economic constraints of ORE are still of relatively higher magnitude by comparison [6
]. For example, the harsher conditions from the marine environment such as waves, salt water, storms, and ocean currents create challenges that must be considered in wind turbine technology and design, support structures, electrical infrastructure, and logistics of installation and maintenance. In addition, environmental impacts such as noise and vibration, toxic effects from lubricant and oil spills, electromagnetic fields from cables, and impacts on avian species are yet to be fully understood [7
]. As a result of these issues, and because ORE is currently considerably more expensive to build than its land-based counterpart, ORE site development must rely heavily on accurate estimates and reliable assessments of the energy resource available. This is also critical for planning the location and Expected Annual energy Production (EAP) of ORE farms. Accurate knowledge of diurnal, seasonal, and intra-annual wind variability is extremely important to improve wind farm efficiency and calculate productivity scenarios to evaluate revenue risks [2
Located on the periphery of Europe, and having the calm and shallow Irish Sea to the east and the strong and powerful Atlantic Ocean to the west, Ireland has enormous potential to generate wind power [11
]. Ireland contributed 25 MW of Europe’s offshore wind power in 2016, which is predicted to increase to 4.5 GW by 2030, according to the 2014 Irish Offshore Renewable Energy Development Plan (OREDP) [12
]. In other words, Ireland’s offshore wind energy would have the potential of powering 4.5 million homes per annum by 2030 [13
]. However, the majority of Ireland’s ORE potential still remains unexploited, as reflected in the large investment in support for ORE research recently, and emerging market opportunities in the ORE sector [14
]. Due to Ireland’s extensive marine territory, reliable ORE site selection approaches are required for integration into national and international Marine Spatial Planning strategies; however, to the best of the authors’ knowledge, only one publically available ORE site selection study has been conducted in Ireland thus far. This study, conducted by the European Union FP7 “Off-shore Renewable Energy Conversion platforms—Coordination Action” project (ORECCA), used geospatial multicriteria analysis to investigate the best location for hybrid offshore wind and wave energy system development in Irish and other European waters. ORECCA identified multiple exclusion zones dictated by other users such as aquaculture, navigation routes, marine archaeology sites, underwater cables, areas with environmental restrictions, seascape degradation or other pre-existing ORE sites, and showed that the west coast of Ireland hosts the best potential location for combined floating wind and wave energy projects compared to other Western European countries [15
Studies like the ORECCA project usually calculate Wind Power Density (WPD) as a first step in determining suitable locations for wind farm development plans [2
]. Traditionally, offshore in situ meteorological measurements derived from wind masts or offshore buoys are used to build energy density maps. Unfortunately, due to their elevated costs, these data are limited in number and sparsely located which limit their efficiency to provide detailed wind climate characteristics. Alternatively, offshore wind fields may be obtained from numerical prediction models, and also from satellite remote sensing [2
]. With the advantage of being able to retrieve ocean surface winds under all weather conditions during both day- and night-time, passive microwave radiometers are typically used to provide wind speeds, while active microwave scatterometers have been providing both wind speed and wind direction data for the last three decades. Even though scatterometers tend to overestimate low wind speeds (<5 m s−1
) due to low signal-to-noise ratio issues [16
] and underestimate high wind speeds (>13 m s−1
], most satellite derived products perform well at moderate wind speeds (5–13 m s−1
). The wind speed at which offshore wind turbines reach maximum energy yield (rated power) is usually around 10–12 m s−1
], which means that satellite remote sensing has great potential for accurately estimating rated power needed in site selection studies.
Scatterometers show varying levels of accuracy and, apart from wind speed, rain and land contamination may also affect the reliability of wind speed retrievals. However, the MetOp Advanced Scatterometer (ASCAT) instruments have been shown to provide robust estimates of wind speed even in rainy conditions due to their use of the C-band, which is less sensitive to the effect of rain on the backscatter signal than Ku-band instruments [16
]. In addition, the ASCAT instruments have been shown to provide reliable wind data relatively closer to the coast than other instruments affected by land contamination, when utilising the Land Contribution Ratio formula (LCR) approach, making it possible to acquire wind vectors a few kilometres away from the shore [16
]. This agrees with findings by Verhoef et al. [20
], who showed that the ASCAT 12.5 km wind product was valid within 12–20 km from the coastline. With a global coverage of approximately twice daily at Ireland mean latitude, a spatial resolution of 12.5–25 km, an accuracy of ±2 m s−1
], and wind retrievals which have shown very good agreement to in situ wind measurements (e.g., [6
]) across the MetOp platforms [23
] at global and regional scales, the Metop ASCAT-A and -B instruments seem to be ideal candidates to showcase the potential of satellite remote sensing in ORE site selection studies. Despite the interest, an optimal ORE site selection study incorporating satellite-derived wind information has not been conducted for Irish waters yet.
Recognising Ireland’s much unrealised potential for ORE development, this study aims to exploit ASCAT-derived wind information to assess the potential of remote sensing in ORE optimal site selection and showcase this potential by retrieving wind power density, operational frequency, and maximum yield maps for Ireland, solely based on hyper-temporal (i.e., hundreds of images) satellite scatterometry. Based on this aim, the objectives of this study are to:
Validate wind speed time series derived from MetOp ASCAT-A and -B using in situ weather buoys to assess the ability of the ASCAT wind product to represent offshore wind speeds in Irish waters;
Use ASCAT wind speed time series to investigate inter- and intra-annual offshore wind speed variation, as well as diurnal variation, in order to assess the ability of the ASCAT wind product to depict temporal variability, essential to better prediction of power production scenarios from offshore winds in Ireland;
Investigate the seasonality of the spatial variation of ASCAT-derived wind speeds in Irish waters, and, thus, examine spatial patterns in offshore wind speed;
To the best of the authors’ knowledge, generate the first satellite-based wind power density, operational frequency and maximum yield frequency maps for Ireland to evaluate offshore wind power potential and available wind resource, and demonstrate the applicability of remote sensing data for use in wind ORE optimal site selection studies. This analysis will also serve to validate the multi-criteria site assessment methodologies being utilised in the Interreg project ARCWIND project to assess the offshore wind energy potential of the entire Atlantic Area Region. Whilst this validation will be specific to the Irish national waters, it will be possible to extrapolate to other regions.
Ireland currently has a limited offshore wind industry with only one operational offshore wind farm, the 25 MW Arklow Wind Park located off the east coast. However, Ireland hosts a number of very suitable sites with a significant resource in often less-challenging ground conditions than many recently developed European sites. There is a growing interest for future expansion within Irish territorial waters once the right supports are in place, one of which being the provision of reliable resource data. Satellite remote sensing is an opportunity to meet this industry need by combining hyper-temporal datasets at a higher temporal resolution than previously considered. Aiming to assess the potential use of remote sensing in the site selection of offshore wind farms in Ireland, the twice-daily KNMI Global Wind Level-3 ASCAT 12.5 km coastal wind product was used in this study.
The satellite-derived wind speed data were comprehensively validated against in situ weather buoys for inter- and intra-annual variation and diurnal temporal variability, showing a very strong positive linear relationship with the in situ measurements. The ASCAT wind product was able to depict local spatial and temporal variability, which are paramount for optimising the site selection process and understanding wind resource availability scenarios for offshore wind farm development.
The wind speed maps showed that the highest spatial variations were associated with near-shore areas, which was in agreement with other studies, however these reduced beyond 15 km from the coastline. Due to typical planning restrictions regarding the visibility of turbines in near-shore areas, it is unlikely that this will impede the viability of remotely sensed approaches for offshore wind farm site selection.
The statistical analysis showed bias in winter and summer variations due to the stability of the atmospheric conditions with air–sea temperature fluctuations. However, this bias was most evident in annual analysis, and was significantly lower when using a multi-annual dataset. Therefore, in the practical or commercial application of this study, it is important to increase the number of observations to reduce the statistical uncertainty linked to the atmospheric stability changes, and ensure that winter resource is not overestimated and summer resource underestimated when determining energy yield.
The energy density map for Ireland illustrated the significant resource offered by Irish waters, but was unable to adequately reflect the spatiotemporal variability of wind speeds, and hence a new classification scheme should be devised reflecting the stronger wind speeds in this part of the Atlantic Ocean. Nevertheless, the majority of the Irish offshore wind resource is estimated to be producing on average over 400 W m−2 annually at 10 m a.s.l., and for most coastal locations and the southern area of Ireland’s continental shelf to be producing an annual wind power average of 300–400 W m−2. By comparison, the resource maps for wind power density, operational frequency, and maximum yield that were produced solely based on hyper-temporal satellite scatterometry were descriptive of the potential power available in Irish waters and its spatial distribution. These resource maps could be used in a geo-spatial multi-criteria analysis in conjunction with other critical infrastructure and site factors such as bathymetry, protected areas, shipping lanes, port infrastructure, and electrical grid, in order to select the optimal location of offshore wind farm development in Ireland.
Further research using more complex extrapolation formulae, such as the Liu–Katsaros–Businger (LKB) algorithm, would be beneficial for the validation process of the ASCAT 12.5 km coastal wind product, especially in the occurrence of changing atmospheric conditions and variations of surface roughness [17
]. In addition, the Irish offshore wind resource assessment would benefit from the synergistic use of additional satellite platforms, such as OSCAT [6
], WindSat [37
], and RapidScat [38
], to increase the temporal resolution, which may enable more detailed observation of variations within a 24 h period, as well as reducing statistical uncertainties. Such analysis would be particularly beneficial as it would allow investigation of the wind direction and the diurnal changes in the nearshore area (sea breeze and land breeze). This could highly influence the site selection process of offshore wind farms in the near shore area as variation in wind direction can be detrimental to wind turbines in terms of fatigue loading and energy capture [39
To the best of the authors’ knowledge, these are the first satellite-based wind power density, operational frequency, and maximum yield frequency maps generated for Irish waters, which illustrate the available offshore wind resource potential, and validate applicability of a remote sensing approach for use in wind ORE optimal site selection studies such as those being undertaken in the ARWIND project. Although this research highlighted the ability of the KNMI Global Wind Level-3 ASCAT 12.5 km coastal wind product to represent offshore wind speeds and provide useful information for the development of wind farms in Irish waters, remote sensing data can be useful for preliminary site selection only. Due to its relatively low temporal resolution and lack of atmospheric stability classification, RS cannot replace essential in situ hourly measurements needed for detailed site variations and precise resource estimates. Decision-makers can therefore use RS data as a filtering technique, separating potentially suitable sites from unsuitable sites, as well as identifying areas where additional in situ buoys are required for more detailed analysis. Finally, the methodology applied in this study could also be used for other parameters retrieved by satellites, such as wave height and wind direction, which are also important for a better assessment of the ORE in Ireland.