Validation of Sentinel-1 offshore winds and  average wind power estimation around Ireland

Montera, Louis de; Remmers, Tiny; Desmond, Cian; O’Connell, Ross

doi:10.5194/wes-5-1023-2020

Cited by 14 publications

(9 citation statements)

References 12 publications

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“…wherem 0 denotes the sampled variance and dof denotes the degrees of freedom. Following Donelan and Pierson (1983) we used the spectrum to calculate the degrees of freedom for the 8.1 m event (dof = 200), which was used throughout. This value was probably too small for low wave conditions but still matched the real scatter of the wave measurements overall (Fig.…”

Section: Methods 2: Adding Variability To Model Datamentioning

confidence: 99%

“…A lot, then, hinges on whether instruments properly capture physical phenomena despite being limited by unavoidable sampling variability (Longuet-Higgins, 1952;Bitner-Gregersen and Magnusson, 2014). This uncertainty can be around 10 % in significant wave heights measured by wave buoys (Donelan and Pierson, 1983), and Forristall et al (1996) determined it to lead to a 3 %-7 % bias when estimating 100-year wave heights. Also, wave modellers have noted that their simulated extremes represent a mean over a few hours -not a single 20-30 min measurement (Bidlot et al, 2002;Aarnes et al, 2012;Breivik et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

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Wave height return periods from combined measurement–model data: a Baltic Sea case study

Björkqvist

Rikka

Alari

et al. 2020

Nat. Hazards Earth Syst. Sci.

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Abstract. This paper presents how to account for the lack of sampling variability in model data when they are combined with wave measurements. We addressed the dissimilarities between the types of data by either (i) low-pass filtering the observations or (ii) adding synthetic sampling variability to the model. Measurement–model times series combined with these methods served as the basis for return period estimates of a high wave event in January 2019. During this storm northerly wind speeds in the Baltic Sea rose to 32.5 m s−1 and an unprecedented significant wave height of 8.1 m was recorded in the Bothnian Sea sub-basin. Both methods successfully consolidated the combined time series but produced slightly different results: using low-pass-filtered observations gave lower estimates for the return period than using model data with added sampling variability. Extremes in both types of data followed the same type of theoretical distributions, and our best estimate for the return period was 104 years (95 % confidence 39–323 years). A similar wave event can potentially be more likely in the future climate, and this aspect was discussed qualitatively.

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Section: Methods 2: Adding Variability To Model Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Wave height return periods from combined measurement–model data: a Baltic Sea case study

Björkqvist

Rikka

Alari

et al. 2020

Nat. Hazards Earth Syst. Sci.

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“…215 Moreover, GMFs may not fully capture the complex relation between the sea state and the wind, in particular because they assume a neutral atmosphere. As a result, SAR surface winds are typically biased when compared with in-situ buoys (see, e.g., de Montera et al, 2020). Therefore, it is necessary to improve the accuracy of the SAR winds obtained with a GMF.…”

Section: Intra-diurnal Variabilitymentioning

confidence: 99%

“…The potential of SAR data has been assessed by numerous studies (Hasager et al, 2002;Hasager et al, 2005;Hasager et al, 2006;Christiansen 50 et al, 2006;Hasager et al, 2011;Chang et al, 2014;Chang et al, 2015;Hasager et al, 2020). However, validating SAR measurements with in-situ data has been limited (Ahsbahs et al, 2017;Badger et al, 2019;de Montera et al, 2020;Ahsbahs et al, 2020) and these studies concluded that important biases remained. Concerning the extrapolation, interesting methods have been proposed in the literature based on power laws or the statistical theory of turbulence (Grachev and Fairall, 1996;Hsu et al, 1994;Badger et al, 2016); however, the problem has not been satisfactorily resolved and becomes 55 increasingly critical as the typical height of windmills increases.…”

Section: Introductionmentioning

confidence: 99%

High-resolution offshore wind resource assessment at turbine hub height with Sentinel-1 SAR data and machine learning

Montera

Berger

Husson

et al. 2021

Preprint

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Abstract. This paper presents a method to calculate offshore wind power at turbine hub height from Sentinel-1 Synthetic Aperture Radar (SAR) data using machine learning. The method is tested in two 70 km × 70 km areas off the Dutch coast where Lidar measurements are available. Firstly, SAR winds at surface level are improved with a machine learning algorithm using geometrical characteristics of the sensor and parameters related to the atmospheric stability extracted from a high-resolution numerical model. The wind speed bias at 10 m above sea level is reduced from −0.42 m s−1 to 0.02 m s−1 and its standard deviation from 1.41 m s−1 to 0.98 m s−1. After improvement, SAR surface winds are extrapolated at higher altitudes with a separate machine learning algorithm trained with the wind profiles measured by the Lidars. We show that, if profiling Lidars are available in the area of study, these two steps can be combined into a single one, in which the machine learning algorithm is trained directly at turbine hub height. Once the wind speed at turbine hub height is obtained, the extractible wind power is calculated using the method of the moments and a Weibull distribution. The results are given assuming an 8 MW turbine typical power curve. The accuracy of the wind power derived from SAR data is in the range ±3–4 % when compared with Lidars. Then, wind power maps at 200 m are presented and compared with the raw outputs of the numerical model at the same altitude. The maps based on SAR data have a much better level of detail, in particular regarding the coastal gradient. The new revealed patterns show differences with the numerical of as much as 10 % in some locations. We conclude that SAR data combined with a high-resolution numerical model and machine learning techniques can improve the wind power estimation at turbine hub height, and thus provide useful insights for optimizing wind farm siting and risk management.

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“…The exploitation of Sentinel-1 SAR data, in particular, has been attracting increasing interest in an attempt to obtain an accurate characterization of offshore wind regimes. Relevant examples include the recent work of [18] focused on the offshore area around Cyprus, as well as the case studies of [19] and [20] assessing wind resource potential using Sentinel-1 SAR estimates northwest and northeast of Sardinia and offshore Ireland, respectively. The main drawback of utilizing Sentinel-1 SAR data for wind resource potential assessment, as concluded in all the studies mentioned above, is the relatively short data time series (2014-present) and the limited number of estimates derived from the sensors, as the satellite orbit leads to gaps between consecutive passes [21].…”

Section: Introductionmentioning

confidence: 99%

Gap-Filling Sentinel-1 Offshore Wind Speed Image Time Series Using Multiple-Point Geostatistical Simulation and Reanalysis Data

2023

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Offshore wind is expected to play a key role in future energy systems. Wind energy resource studies often call for long-term and spatially consistent datasets to assess the wind potential. Despite the vast amount of available data sources, no current means can provide relevant sub-daily information at a fine spatial scale (~1 km). Synthetic aperture radar (SAR) delivers wind field estimates over the ocean at fine spatial resolution but suffers from partial coverage and irregular revisit times. Physical model outputs, which are the basis of reanalysis products, can be queried at any time step but lack fine-scale spatial variability. To combine the advantages of both, we use the framework of multiple-point geostatistics to realistically reconstruct wind speed patterns at time instances for which satellite information is absent. Synthetic fine-resolution wind speed images are generated conditioned to coregistered regional reanalysis information at a coarser scale. Available simultaneous data sources are used as training data to generate the synthetic image time series. The latter are then evaluated via cross validation and statistical comparison against reference satellite data. Multiple realizations are also generated to assess the uncertainty associated with the simulation outputs. Results show that the proposed methodology can realistically reproduce fine-scale spatiotemporal variability while honoring the wind speed patterns at the coarse scale and thus filling the satellite information gaps in space and time.

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Validation of Sentinel-1 offshore winds and average wind power estimation around Ireland

Cited by 14 publications

References 12 publications

Wave height return periods from combined measurement–model data: a Baltic Sea case study

Wave height return periods from combined measurement–model data: a Baltic Sea case study

High-resolution offshore wind resource assessment at turbine hub height with Sentinel-1 SAR data and machine learning

Gap-Filling Sentinel-1 Offshore Wind Speed Image Time Series Using Multiple-Point Geostatistical Simulation and Reanalysis Data

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