Wind speed and significant wave height are the most relevant metocean variables that support a wide range of engineering and economic activities. Their characterization through remote sensing estimations is required to compensate for the shortage of in situ observations. This study demonstrates the value of satellite altimetry to identify typical spatial patterns of wind speed and significant wave height in the northeastern region of the United States. Data from five altimetry satellite missions were evaluated against the available in situ observations with a 10 km sampling radius and a 30 min time window. An objective analysis of the collective altimeter dataset was performed to create aggregated composite maps of the wind speed and significant wave height. This asynchronous compositing of multi-mission altimeter data is introduced to compile a sufficient sampling of overpasses over the area of interest. The results of this approach allow for quantifying spatial patterns for the wind speed and significant wave height in the summer and winter seasons. The quality of altimeter estimations was assessed regarding the distance from the coast and the topography. It was found that while the altimeter data are highly accurate for the two variables, bias increases near the coast. The average minimum and maximum wind speed values detected in buoy stations less than 40 km from the coast were not matched by the aggregated altimeter time series. The method exposes the spatial and time gaps to be filled using data from future missions. The challenges of the objective analysis near the coast, especially in semi-enclosed areas, and the implications of the altimeter estimations due to the land contamination are explained. The results indicate that the combination of altimetry data from multiple satellite missions provides a significant complementary information resource for nearshore and coastal wind and wave regime estimations.

The SARAL-AltiKa dataset was evaluated for refined offshore wind energy resources assessment and potential metocean monitoring capability in the Southern New England region. Surface wind speed and Significant Wave Height ($H_s$) products were assessed with corresponding variables from buoy observations for 2014–2019. To increase the sample size, this study analyzed and applied an approach to collect data around the reference buoys beyond the satellite footprint at the expense of a bias increment. The study corroborated the accuracy of the SARAL-AltiKa measurements for the offshore area of interest and added details for stations closer to the coast compared with past studies. A proportional bias with underestimation of high values of $H_s$ was found in coastal sites. Wind speed estimates on the other hand appear to be less sensitive to the closeness to the coast. The empirical relationship between wind strength and $H_s$ in the buoy observations is reproduced to a large extent by the AltiKa measurements in locations where land contamination is minimal. The histograms of surface wind and $H_s$ are well described by the Weibull distribution and the shape and scale parameters closely resemble those of the histograms of the collocated in situ observations. We use these results to extrapolate the winds to a target domain with no in situ observations for wind energy resource estimation.

This study aims to evaluate wind speed and significant wave height data from the Copernicus Marine Environment Monitoring Service (CMEMS) reanalysis using buoy measurements for offshore energy application off the east coast of Brazil. Such analysis has become important, since reanalysis datasets can be fundamental tools in identifying regions with wind energy potential that are suitable for the installation of offshore farms. Two sets of reanalysis were used: wind speed (with spatial resolution of 0.25° and temporal resolution of 6 h) and significant wave height (with spatial resolution of 0.2° and temporal resolution of 3 h). For validation, seven MetOcean buoys were selected. In the statistical validation, Pearson’s correlation, coefficient of determination (R²), slope of the straight line, root mean square error (RMSE), mean square error (MSE), probability density function (PDF), mean and standard deviation were calculated. In the evaluation of offshore wind energy resources, the calculation of energy density was performed. The results showed correlations above 0.70 for wind speed and above 0.91 for significant wave height, and additionally, the RMSE values showed maximums of 2.31 m/s for wind speed and 0.28 cm for significant wave height. In the PDF comparison of buoy data and reanalysis, similarities were observed, mainly in the PDF parameters. The energy density presented values consistent with other studies (352–461 W/m²). The results show that the reanalysis data can be applicable in studies focusing on offshore wind potential.

This study assesses the impact of assimilating remote sensing observations of significant wave height (SWH) and 10 m wind speed (WSPD) into the analysis using a weakly coupled data assimilation (WCDA) framework to improve ocean surface monitoring offshore of the Contiguous US (CONUS). We assimilated SWH and WSPD data from 10 altimeters and six scatterometers into the Local Ensemble Transform Kalman Filter (LETKF) data assimilation (DA) scheme over 6 months, from 1 January to 1 July 2024, to update the SWH and WSPD variables of the 30-member Global Ensemble Forecast System wave component (GEFS-wave), which provided the background fields. We validated the SWH and WSPD analyses against assimilated remote sensing observations and found that assimilating these observations corrected the systematic background bias in WSPD values above 15 m/s to near zero, with uncertainty as low as 0.4 m/s. For SWH, the background overestimation averaged 30 cm for observed values between 6.2 and 6.4 m and was reduced to 6 cm in the analysis, with a 30 cm RMSE. These results were consistent with cross-validation experiments that assimilated data from six altimeters for the SWH analysis and three scatterometers for WSPD analysis updates, respectively, and were evaluated against independent remote sensing observations. We show that the diurnal cycle in the spatial coverage and temporal sampling of state-of-the-art altimeter and scatterometer constellations over 6 months in maritime CONUS identified regions where remote sensing data had the greatest impact on the analysis and where they were scarce and had limited impact when assimilated using a reduced 1-h window. A case study showed that assimilating altimeter data during a Pacific storm that developed on 1 February 2024 allowed the LETKF to adjust the SWH analysis spatially.