Validation of Sentinel-1A SAR Coastal Wind Speeds Against Scanning LiDAR

Ahsbahs, Tobias Torben; Badger, Merete; Karagali, Ioanna; Larsén, Xiaoli Guo

doi:10.3390/rs9060552

Cited by 35 publications

(16 citation statements)

References 40 publications

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“…Although this work focuses on predicting coastal winds, we are not considering the effects of the wind speed gradient present at the discontinuity between the land and the sea, which increases the uncertainty in the predictions. Studies on coastal gradients [37] have reported velocity reductions between 4% and 8% from 3 km to 1 km from the shore. Over 3 km from the coast, velocity gradients of 0.5%/km have been observed for different offshore sites [38].…”

Section: Evaluation Of Predicted Wind Speedsmentioning

confidence: 99%

On the Use of Dual-Doppler Radar Measurements for Very Short-Term Wind Power Forecasts

et al. 2018

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Very short-term forecasts of wind power provide electricity market participants with extremely valuable information, especially in power systems with high penetration of wind energy. In very short-term horizons, statistical methods based on historical data are frequently used. This paper explores the use of dual-Doppler radar observations of wind speed and direction to derive five-minute ahead deterministic and probabilistic forecasts of wind power. An advection-based technique is introduced, which estimates the predictive densities of wind speed at the target wind turbine. In a case study, the proposed methodology is used to forecast the power generated by seven turbines in the North Sea with a temporal resolution of one minute. The radar-based forecast outperforms the persistence and climatology benchmarks in terms of overall forecasting skill. Results indicate that when a large spatial coverage of the inflow of the wind turbine is available, the proposed methodology is also able to generate reliable density forecasts. Future perspectives on the application of Doppler radar observations for very short-term wind power forecasting are discussed in this paper. methods can be found in [6][7][8]. Very short-term forecasts of wind speed and power are often based on machine learning methods such as artificial neural networks [9] or Markov chain models [10,11], which are trained with historical data. Alternatively, combinations of different models (known as hybrid models) have been proposed to overcome the deficiencies of single models [12]. Parallel to the development of single point or deterministic forecasting models, the use of probabilistic forecasts, which include information about the uncertainty associated with the predicted events, has increased. Examples of very short-term probabilistic forecasts of wind power using statistical methods can be found in [13,14]. These probabilistic forecasts are known as predictive densities or probability distributions and provide important information for making risk-based decisions [15].Over the last two decades, the use of remote sensing measurements such as long-range lidars [16] has been extended in the wind industry. These systems are capable of measuring wind speed and direction (under certain assumptions) up to 30 km [17]. Unlike conventional wind measurements from met-mast or satellites, they present an adequate trade-off between temporal and spatial resolution for wind farm applications. However, the prediction horizon of a remote sensing-based forecasting model is limited by the maximum range of the remote sensing measurements and also influenced by meteorological conditions [18]. Indeed, publications on the use of long-range lidar measurements for wind energy applications have reported measurements with a maximum range of less than ten kilometers [19][20][21]. Despite those limitations, a recent contribution showed that a lidar-based forecasting technique can provide better results than conventional statistical benchmarks when forecasting near-coastal winds with le...

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Section: Evaluation Of Predicted Wind Speedsmentioning

confidence: 99%

On the Use of Dual-Doppler Radar Measurements for Very Short-Term Wind Power Forecasts

et al. 2018

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“…SAR is better suited for resolving winds in coastal zones because of its higher spatial resolution (Christiansen et al, 2006). It has been shown that SAR-derived winds can accurately depict wind speed gradients measured by ground-based lidars near the coastline (Ahsbahs et al, 2017) and that SAR wind fields show similar mean wind speed variations as those experienced by wind turbines (Ahsbahs et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

US East Coast synthetic aperture radar wind atlas for offshore wind energy

Ahsbahs¹,

Maclaurin

Draxl

et al. 2020

Wind Energ. Sci.

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Abstract. We present the first synthetic aperture radar (SAR) offshore wind atlas of the US East Coast from Georgia to the Canadian border. Images from RADARSAT-1, Envisat, and Sentinel-1A/B are processed to wind maps using the geophysical model function (GMF) CMOD5.N. Extensive comparisons with 6008 collocated buoy observations of the wind speed reveal that biases of the individual systems range from −0.8 to 0.6 m s−1. Unbiased wind retrievals are crucial for producing an accurate wind atlas, and intercalibration of the SAR observations is therefore applied. Wind retrievals from the intercalibrated SAR observations show biases in the range of to −0.2 to 0.0 m s−1, while at the same time improving the root-mean-squared error from 1.67 to 1.46 m s−1. The intercalibrated SAR observations are, for the first time, aggregated to create a wind atlas at the height 10 m a.s.l. (above sea level). The SAR wind atlas is used as a reference to study wind resources derived from the Wind Integration National Dataset Toolkit (WTK), which is based on 7 years of modelling output from the Weather Research and Forecasting (WRF) model. Comparisons focus on the spatial variation in wind resources and show that model outputs lead to lower coastal wind speed gradients than those derived from SAR. Areas designated for offshore wind development by the Bureau of Ocean Energy Management are investigated in more detail; the wind resources in terms of the mean wind speed show spatial variations within each designated area between 0.3 and 0.5 m s−1 for SAR and less than 0.2 m s−1 for the WTK. Our findings indicate that wind speed gradients and variations might be underestimated in mesoscale model outputs along the US East Coast.

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“…For these directions, coastal wind speed gradients are expected to occur, caused by the roughness change between land and sea. Coastal gradients from SAR have been shown to agree well with lidar wind speeds for the first few kilometers from shore (Ahsbahs et al, 2017).…”

Section: D) Influences Of Diurnal Variabilitymentioning

confidence: 78%

US East Coast synthetic aperture radar wind atlas for offshore wind energy

Ahsbahs¹,

Maclaurin²,

Draxl³

et al. 2019

Preprint

Self Cite

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Abstract. We present the first synthetic aperture radar (SAR)-based offshore wind atlas of the US East Coast from Georgia to the Canadian border. Images from Radarsat-1, Envisat, Sentinel-1A, and Sentinel-1B are processed to wind maps using the Geophysical Model Function (GMF) CMOD5.N. Extensive comparisons with 6,008 collocated buoy observations revealed that biases of the individual system range from −0.8 to 0.6 m/s. Unbiased wind retrievals are crucial for producing an accurate wind atlas and intercalibration for correcting these biases by adjusting the normalized radar cross section is applied. The intercalibrated SAR observations show biases in the range of to −0.2 to 0.0 m/s, while at the same time improving the root mean squared error from 1.67 to 1.46 m/s. These intercalibrated SAR observations are, for the first time, aggregated to create a wind atlas. Monthly averages are used to correct artefacts from seasonal biases. The SAR wind atlas is used as a reference to study wind resources derived from the Weather Research and Forecasting (WRF) model. Comparisons focus on the spatial variation of wind resources and show that model results estimate lower coastal wind speed gradients than those from SAR. At sites designated for offshore wind development by the Bureau of Ocean Energy Management, mean wind speeds typically vary between 0.3 and 0.5 m/s for SAR and less than 0.2 m/s for the WRF model within each site. Findings indicate that wind speed gradients and variation might be underestimated in mesoscale model outputs along US East Coast.

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Validation of Sentinel-1A SAR Coastal Wind Speeds Against Scanning LiDAR

Cited by 35 publications

References 40 publications

On the Use of Dual-Doppler Radar Measurements for Very Short-Term Wind Power Forecasts

On the Use of Dual-Doppler Radar Measurements for Very Short-Term Wind Power Forecasts

US East Coast synthetic aperture radar wind atlas for offshore wind energy

US East Coast synthetic aperture radar wind atlas for offshore wind energy

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