Research Article Comparison of Multiple Surface Ocean Wind Products with Buoy Data over Blue Amazon (Brazilian Continental Margin) Vitor Paiva , 1 Milton Kampel , 1 and Rosio Camayo 2 1 Earth Science Coordination, Earth Observation and Geoinformatics, National Institute for Space Research, Sao Jose dos Campos 12227-010, Brazil 2 Earth Science Coordination, Numerical Modelling of the Earth System, National Institute for Space Research, Cachoeira Paulista 12630-000, Brazil Correspondence should be addressed to Milton Kampel; milton.kampel@inpe.br Received 10 December 2020; Revised 24 May 2021; Accepted 31 August 2021; Published 14 September 2021 Academic Editor: Maria ´ AngelesGarc´ ıa Copyright © 2021 Vitor Paiva et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Remotesensingdataforspace-timecharacterizationofwindfieldsinextensiveoceanicareashavebeenshowntobeincreasingly 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 scat- terometers 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, nu- mericalmodellingsimulations(BRAMS),reanalysis(ERA5),andablendedproduct(CCMP),werecomparedstatisticallywithin situmeasurementsobtainedbyanemometersinstalledinfifteenmooredbuoysintheBrazilianmargin(8buoysinoceanicand7 in shelf waters) to analyze which dataset best represents the wind field in this region. e operational ASCAT wind products presented the lowest differences in wind speed and direction from the in situ data (0.77ms −1 < RMSE spd < 1.59ms −1 , 0.75 < R spd < 0.96, −0.68ms −1 < bias spd < 0.38ms −1 ,and12.7 ° < RMSE dir < 46.8 ° ). CCMP and ERA5 products also performed well in the statistical comparison with the in situ data (0.81ms −1 < RMSE spd < 1.87ms −1 , 0.76 < R spd < 0.91, −1.21ms −1 < bias spd < 0.19ms −1 , and 13.7 ° < RMSE dir < 46.3 ° ). e BRAMS model was the one with the worst performance (RMSE spd > 1.04m·s −1 , R spd < 0.87). For regions with a higher wind variability, as in the southern Brazilian continental margin, winddirectionestimationbythewindproductsismoresusceptibletoerrors(RMSE dir > 42.4 ° ).eresultsherepresentedcanbe 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. 1. Introduction Ocean surface wind is one of the main drivers of several oceanic, atmospheric, and climate processes, thus being an important indicator of climate change [1]. Ocean surface wind vector variability is intrinsically linked to that of oceanic processes, such as coastal upwelling [2, 3], primary productivity [4, 5], deep water formation [6, 7], advective volume transport, air-sea momentum, and heat fluxes [8–11]. Having the ability to measure wind speed and di- rection with high accuracy becomes essential to these processes’ studies, also considering the impacts on weather forecasting, navigation, marine engineering, and offshore wind energy [1, 12, 13]. e vast extension of the global ocean imposes technical and financial limitations on in situ ocean surface winds sampling, which is necessary and valuable in remote sensing calibration and validation process. Direct measurements are acquiredbyanemometersonmooredbuoys,researchcruises, and vessels of opportunity, or by light detection and ranging (Lidar) sensors, providing precise and accurate data. How- ever,thesetechniquesarelimitedtopointmeasurementsthat are not able to provide satisfactory spatial and temporal coverage to resolve variability at different scales [14]. Hindawi Advances in Meteorology Volume 2021, Article ID 6680626, 19 pages https://doi.org/10.1155/2021/6680626