Abstract. Rapid growth in the offshore wind energy sector means more offshore wind farms are placed closer to each other and in the lee of large land masses. Synthetic aperture radar (SAR) offers maps of the wind speed offshore with high resolution over large areas. These can be used to detect horizontal wind speed gradients close to shore and wind farm wake effects. SAR observations have become much more available with the free and open-access data from European satellite missions through Copernicus. Examples of applications and tools for using large archives of SAR wind maps to aid offshore site assessment are few. The Anholt wind farm operated by the utility company Ørsted is located in coastal waters and experiences strong spatial variations in the mean wind speed. Wind speeds derived from the Supervisory Control And Data Acquisition (SCADA) system are available at the turbine locations for comparison with winds retrieved from SAR. The correlation is good, both for free-stream and waked conditions. Spatial wind speed variations along the rows of wind turbines derived from SAR wind maps prior to the wind farm construction agree well with information gathered by the SCADA system and a numerical weather prediction model. Wind farm wakes are detected by comparisons between images before and after the wind farm construction. SAR wind maps clearly show wakes for long and constant fetches but the wake effect is less pronounced for short and varying fetches. Our results suggest that SAR wind maps can support offshore wind energy site assessment by introducing observations in the early phases of wind farm projects.
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.
Abstract. Europe's offshore wind resource mapping is part of the New European Wind Atlas (NEWA) international consortium effort. This study presents the results of analysis of synthetic aperture radar (SAR) ocean wind maps based on Envisat and Sentinel-1 with a brief description of the wind retrieval process and Advanced Scatterometer (ASCAT) ocean wind maps. The wind statistics at 10 and 100 m above mean sea level (a.m.s.l.) height using an extrapolation procedure involving simulated long-term stability over oceans are presented for both SAR and ASCAT. Furthermore, the Weather Research and Forecasting (WRF) offshore wind atlas of NEWA is presented. This has 3 km grid spacing with data every 30 min for 30 years from 1989 to 2018, while ASCAT has 12.5 km and SAR has 2 km grid spacing. Offshore mean wind speed maps at 100 m a.m.s.l. height from ASCAT, SAR, WRF and ERA5 at a European scale are compared. A case study on offshore winds near Crete compares SAR and WRF for flow from the north, west and all directions. The paper highlights the ability of the WRF model to simulate the overall European wind climatology and the near-coastal winds constrained by the resolution of the coastal topography in the WRF model simulations.
High-accuracy wind data for coastal regions is needed today, e.g., for the assessment of wind resources. Synthetic Aperture Radar (SAR) is the only satellite borne sensor that has enough resolution to resolve wind speeds closer than 10 km to shore but the Geophysical Model Functions (GMF) used for SAR wind retrieval are not fully validated here. Ground based scanning light detection and ranging (LiDAR) offer high horizontal resolution wind velocity measurements with high accuracy, also in the coastal zone. This study, for the first time, examines accuracies of SAR wind retrievals at 10 m height with respect to the distance to shore by validation against scanning LiDARs. Comparison of 15 Sentinel-1A wind retrievals using the GMF called C-band model 5.N (CMOD5.N) versus LiDARs show good agreement. It is found, when nondimenionalising with a reference point, that wind speed reductions are between 4% and 8% from 3 km to 1 km from shore. Findings indicate that SAR wind retrievals give reliable wind speed measurements as close as 1 km to the shore. Comparisons of SAR winds versus two different LiDAR configurations yield root mean square error (RMSE) of 1.31 ms −1 and 1.42 ms −1 for spatially averaged wind speeds.
Highlights 9• Offshore wind resource assessment requires long-term wind data records. 10• Wind speed retrievals from different European SAR sensors are offset. 11• Biases vary over time and according to scan modes and incidence angles. 12• Inter-calibration can remove biases and improve the accuracy on wind resources. Abstract 15Wind observations in the marine environment are both costly and sparse. This makes wind 16 retrievals from satellite Synthetic Aperture Radar (SAR) an attractive option in connection 17 with planning of offshore wind farms. Because the wind power density is proportional to the 18 wind speed cubed, it is important to achieve the highest possible absolute accuracy on SAR 19 wind speed retrievals for wind energy applications. A method is presented for inter-20 calibration of SAR observations from Envisat and Sentinel-1A/B. Sensor-specific effects on 21 the SAR-retrieved wind speeds are first quantified through comparisons against collocated 22 ocean buoy observations. Based on global circulation model simulations of wind speed and 23 direction, we retrieve the Normalized Radar Cross Section (NRCS) for different radar 24 incidence angles. Residuals between the retrieved and the observed NRCS are used to inter-25 calibrate the observed NRCS before reprocessing to SAR wind fields. The inter-calibration 26 2 leads to an improved agreement between SAR and buoy wind speeds with biases below 0.2 m 27 s -1 for all investigated SAR sensors. Estimates of the wind resource improve with respect to 28 the buoy observations for ten of the twelve sites investigated. The average deviation between 29 wind power densities is reduced from 20% to 8% as the SAR inter-calibration leads to more 30 conservative estimates of the wind resource. 31 32 Keywords 33 Inter-calibration, offshore wind energy, resource, Synthetic Aperture Radar, Sentinel-1, 34 Envisat 35 36 3 1. Introduction 37The Sentinel-1 mission by the European Space Agency (ESA) has secured the availability of 38 Synthetic Apertur Radar (SAR) observations for ocean wind mapping for the years to come. 39 Sentinel-1A (2014-present) and Sentinel-1B (2016-present) are designed for continuation of 40 the previous ESA mission Envisat, which delivered SAR data during 2002-12. SAR 41 instruments are active sensors, which transmit and receive pulses in the microwave range. 42 Properties of the ocean surface waves determine the measured return signal. A C-band SAR 43 sensor is sensitive to waves of the cm-scale, which are typically generated by the 44 instantaneous wind stress at the sea surface.45 46 Based on scatterometer observations, empirical relationships have been established between 47 radar backscatter from the sea surface and wind speed at the height 10 m. A similar principle 48 can be applied to retrieve wind speeds from SAR observations at a higher spatial resolution 49 and with full coverage over coastal seas (Karagali et al., 2013). Geophysical Model Functions 50 (GMF) for wind speed retrieval at C-band include CMOD4 (Stoffelen and Anderson, 1997), ...
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.