Twenty-Seven Years of Scatterometer Surface Wind Analysis over Eastern Boundary Upwelling Systems

Abstract

More than twelve satellite scatterometers have operated since 1992 through the present, providing the main source of surface wind vector observations over global oceans. In this study, these scatterometer winds are used in combination with radiometers and synthetic aperture radars (SAR) for the better determination and characterization of high spatial and temporal resolution of regional surface wind parameters, including wind speed and direction, wind stress components, wind stress curl, and divergence. In this paper, a 27-year-long (1992–2018) 6-h satellite wind analysis with a spatial resolution of 0.125° in latitude and longitude is calculated using spatial structure functions derived from high-resolution SAR data. The main objective is to improve regional winds over three major upwelling regions (the Canary, Benguela, and California regions) through the use of accurate and homogenized wind observations and region-specific spatial and temporal wind variation structure functions derived from buoy and SAR data. The long time series of satellite wind analysis over the California upwelling, where a significant number of moorings is available, are used for assessing the accuracy of the analysis. The latter is close to scatterometer wind retrieval accuracy. This assessment shows that the root mean square difference between collocated 6-h satellite wind analysis and buoys is lower than 1.50 and 1.80 m s−1 for offshore and nearshore locations, respectively. The temporal correlation between buoy and satellite analysis winds exceeds 0.90. The analysis accuracy is lower for 1992–1999 when satellite winds were mostly retrieved from ERS-1 and/or ERS-2 scatterometers. To further assess the improvement brought by this new wind analysis, its data and data from three independent products (ERA5, CMEMS, and CCMP) are compared with purely scatterometer winds over the Canary and Benguela regions. Even though the four products are generally similar, the new satellite analysis shows significant improvements, particularly in the upwelling areas.

Assessment and Calibration of ERA5 Severe Winds in the Atlantic Ocean Using Satellite Data

“…A complete discussion about the accuracy of altimeter data can be found by Ribal and Young [43] and an additional assessment was performed by Yang et al [44]. Fortunately, there are more than 20 years of high-quality satellite data of surface winds [42,45,46] publicly available.…”

Impact of Chlorophyll Shading on the Peruvian Upwelling System

“…A nearshore cold bias has been reported in several EBUS regional simulations (e.g., Colas et al, 2012;Dufois et al, 2012;Veitch et al, 2009Veitch et al, , 2010; Figure S2). It has been attributed to either an observational warm bias in SST satellite observations (e.g., Dufois et al, 2012) or to overly strong nearshore winds in satellite-derived model forcing, as nearshore wind (or wind stress) values are generally interpolated from the offshore reliable scatterometer measurements into the so-called ∼25-50-km-wide nearshore "satellite blind zone" lacking accurate observations (e.g., Bentamy et al, 2021;Capet et al, 2004). The too strong alongshore winds may then drive a too intense coastal upwelling, transporting overly deep and cold source waters to the surface in the models.…”

Long-Term Assessment of Morocco’s Offshore Wind Energy Potential Using ERA5 and IFREMER Wind Data

“…In the first stage, the European Centre for Medium Range Weather Forecast (ECMWF) operational wind analysis or ERA-Interim wind data were used. Later, the wind representation in upwelling regions, such as California, Canary and Benguela, was improved with Synthetic Aperture Radar (SAR) data and ERA5 data [43].…”

“…Generally, wind observations are not fitted satisfactorily using Gaussian probability distributions. However, some research works, such as [24] or [43], include linear statistics diagnoses among their descriptors, which then become a convenient intercomparison tool. Additionally, in many wind resources assessments, such as [14], a two-parameter Weibull distribution was used to model the wind data.…”