Comparison Of Multiple Surface Ocean Wind Products With Buoy Data Over Blue Amazon (Brazilian Continental Margin)

Remote sensing data for space-time characterization of wind fields in extensive oceanic areas have been shown to be increasingly useful. Orbital sensors, such as radar scatterometers, provide data on ocean surface wind speed and direction with spatial and temporal resolutions suitable for multiple applications and air-sea studies. Even considering the relevant role of orbital scatterometers to estimate ocean surface wind vectors on a regional and global scale, the products must be validated regionally. Six different ocean surface wind datasets, including advanced scatterometer (ASCAT-A and ASCAT-B products) estimates, numerical modelling simulations (BRAMS), reanalysis (ERA5), and a blended product (CCMP), were compared statistically with in situ measurements obtained by anemometers installed in fifteen moored buoys in the Brazilian margin (8 buoys in oceanic and 7 in shelf waters) to analyze which dataset best represents the wind field in this region. The operational ASCAT wind products presented the lowest differences in wind speed and direction from the in situ data (0.77 ms(-1) < RMSEspd < 1.59 ms(-1), 0.75 < R-spd < 0.96, -0.68 ms(-1) < bias(spd) < 0.38 ms(-1), and 12.7 degrees < RMSEdir < 46.8 degrees). CCMP and ERA5 products also performed well in the statistical comparison with the in situ data (0.81 ms(-1) < RMSEspd < 1.87 ms(-1), 0.76 < R-spd < 0.91, -1.21 ms(-1) < bias(spd) < 0.19 ms(-1), and 13.7 degrees < RMSEdir < 46.3 degrees). The BRAMS model was the one with the worst performance (RMSEspd > 1.04 m center dot s(-1), R-spd < 0.87). For regions with a higher wind variability, as in the southern Brazilian continental margin, wind direction estimation by the wind products is more susceptible to errors (RMSEdir > 42.4 degrees). The results here presented can be used for climatological studies and for the estimation of the potential wind power generation in the Brazilian margin, especially considering the lack of availability or representativeness of regional data for this type of application.