This paper presents a method for estimating offshore extractable wind power at hub height using Sentinel-1 synthetic aperture radar (SAR) data and machine learning. The method was tested in two areas off the Dutch coast, where measurements from Doppler wind lidars installed at the sea surface were available and could be used as a reference. A first machine learning algorithm improved the accuracy of SAR sea surface wind speeds by using geometrical characteristics of the sensor and metadata. This algorithm was trained with wind data measured by a large network of weather buoys at 4 m above sea level. After correction, the bias in SAR wind speed at 4 m versus buoys was 0.02 m s⁻¹, with a standard deviation of error of 0.74 m s⁻¹. Corrected surface wind speeds were then extrapolated to hub height with a second machine learning algorithm, which used meteorological parameters extracted from a high-resolution numerical model. This algorithm was trained with lidar vertical wind profiles and was able to extrapolate sea surface wind speeds at various altitudes up to 200 m. Once wind speeds at hub height were obtained, the Weibull parameters of their distribution were estimated, taking into account the satellites' irregular temporal sampling. Finally, we assumed the presence of a 10 MW turbine and obtained extractable wind power with a 1 km spatial resolution by multiplying the Weibull distribution point by point by its power curve. Accuracy for extractable wind power versus lidars was ± 3 %. Wind power maps at hub height were presented and compared with the outputs of the numerical model. The maps based on SAR data had a much higher level of detail, especially regarding coastal wind gradient. We concluded that SAR data combined with machine learning can improve the estimation of extractable wind power at hub height and provide useful insights to optimize siting and risk management. The algorithms presented in this study are independent and can also be used in a more general context to correct SAR surface winds, extrapolate surface winds to higher altitudes, and produce instantaneous SAR wind fields at hub height.

Through the Synthetic Aperture Radar (SAR) embarked on the satellites Sentinel-1A and Sentinel-1B of the Copernicus program, a large quantity of observations is routinely acquired over the oceans. A wide range of features from both oceanic (e.g., biological slicks, icebergs, etc.) and meteorologic origin (e.g., rain cells, wind streaks, etc.) are distinguishable on these acquisitions. This paper studies the semantic segmentation of ten metoceanic processes either in the context of a large quantity of image-level groundtruths (i.e., weakly-supervised framework) or of scarce pixel-level groundtruths (i.e., fully-supervised framework). Our main result is that a fully-supervised model outperforms any tested weakly-supervised algorithm. Adding more segmentation examples in the training set would further increase the precision of the predictions. Trained on 20 × 20 km imagettes acquired from the WV acquisition mode of the Sentinel-1 mission, the model is shown to generalize, under some assumptions, to wide-swath SAR data, which further extents its application domain to coastal areas.

Rain Signatures on C-band Synthetic Aperture Radar (SAR) images acquired over ocean are common and can dominate the backscattered signal from the ocean surface. In many cases, the inability to decipher between ocean and rain signatures can disturb the analysis of SAR scenes for maritime applications. This study relies on Sentinel-1 SAR acquisitions in the Interferometric Wide swath mode and high-resolution measurements from ground-based weather radar to document the rain impact on the radar backscattered signal in both co- and cross-polarization channels. The dark and bright rain signatures are found in connection with the timeliness of the rain cells. In particular, the bright patches are demonstrated by the hydrometeors (graupels, hails) in the melting layer. In general, the radar backscatter under rain increases with rain rate for a given sea state and decreases when the sea state strengthens. The rain also has a stronger impact on the radar signal in both polarizations when the incidence angle increases. The complementary sensitivity of the SAR signal of rain in both channels is then used to derive a filter to locate the areas in SAR scenes where the signal is not dominated by rain. The filter optimized to match the rain observed by the ground-based weather radar is more efficient when both polarization channels are considered. Case studies are presented to discuss the advantages and limitations of such a filtering approach.

Synthetic Aperture Radar (SAR) in wave mode is a powerful tool for monitoring sea states in terms of long-period ocean swells of a specific wave directional partition. Since 2016, SARs aboard Sentinel-1A/B operating in wave mode have provided ocean swell spectra dataset as Level-2 Ocean products on a continuous and global basis over open oceans. Furthermore, Level-3 swell products are processed by Copernicus Marine Environment Monitoring Services (CMEMS) taking the benefit of the unique “fireworks” analysis. In this paper, swell wave heights from Sentinel-1A/B wave mode during the period from June 2016 to June 2020 are evaluated. The reference data include the collocated in situ measurements from directional wave buoys and WaveWatch III (WW3) hindcasts. Assessment results show systematic overestimation of approximately 0.2 m in terms of the partitioned swell heights for Sentinel-1A/B Level-2 products compared to the directional buoy observations in eastern Pacific and the western Atlantic. Based on the reliable SAR-WW3 collocations after quality-controls, empirical corrections have been proposed for Sentinel-1 Level-2 swell heights. Independent comparisons against WW3 hindcasts and buoy observations demonstrate the validity of our postprocessing correction for both Level-2 and Level-3 swell heights by eliminating the biases and reducing the root mean square errors. The consistency between CMEMS Level-3 swells and buoy in situ is also examined and discussed by case studies.

Winds generate waves over the oceans with a wide range of properties. The largest wave heights and periods are important parameters in the design of marine structures. Extreme waves also play an outsize role in air–sea fluxes and coastal dynamics, and leave imprints on seismic and sediment records. Rare events have so far escaped measurements, with few wave heights from satellite altimeters exceeding 16 m, and no associated measurement of wave periods. Here, we use swells radiated from storms to reveal long wave periods within the storms, and their generation mechanism. Swells are resolved in the Surface Water and Ocean Topography (SWOT) satellite sea level measurements. Patterns of increasing swell wavelength and decreasing swell height away from storms are consistent with a nonlinear transfer of energy from short to long period waves. We propose an updated parametric shape for wave spectra in storms that aligns with SWOT swell measurements. It reduces energy levels by a factor of 20 at 1.2 to 1.4 times the peak period compared to commonly used spectral shapes and allows estimation of storm wave periods from swell heights. Consistent with less extreme conditions, the peak period generally increases with wave height. This was particularly verified for the largest storm peak period of 20.2 ± 0.6 s, obtained for the event with the largest significant wave height 19.7 ± 0.3 m measured by altimeters. These observations of long period swells should have a wide range of applications from coastal dynamics to seismology.

Synthetic Aperture Radar (SAR) in wave mode is a powerful sensor for monitoring the swells propagating across ocean basins. Here, we investigate crossing swells in the Indian Ocean using 10-years Envisat SAR wave mode archive spanning from December 2003 to April 2012. Taking the benefit of the unique “fireworks” analysis on SAR observations, we reconstruct the origins and propagating routes that are associated with crossing swell pools in the Indian Ocean. Besides, three different crossing swell mechanisms are discriminated from space by the comparative analysis between results from “fireworks” and original SAR data: (1) in the mid-ocean basin of the Indian Ocean, two remote southern swells form the crossing swell; (2) wave-current interaction; and, (3) co-existence of remote Southern swell and shamal swell contribute to the crossing swells in the Agulhas Current region and the Arabian Sea.