<p>Global Observing Systems (GOS) encounter some limitations due to a lack of worldwide real-time wind measurements. In this context, the European Space Agency (ESA) has developed the Aeolus satellite mission, based on the ALADIN (Atmospheric Laser Doppler Instrument) Doppler wind lidar, aimed to obtain near real-time wind retrievals at global scale. As spin-off products, the instrument retrieves aerosol optical properties such as particle backscatter and extinction coefficients. In this work, a validation of Aeolus reprocessed (baseline 10) co-polar backscatter coefficients <img 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" alt="" width="72" height="27" /> is presented through an intercomparison with analogous ground-based measurements taken at the ACTRIS/EARLINET stations of Granada (Spain), Évora (Portugal) and Barcelona (Spain) over the period from July 2019 until October 2020. Case studies are first presented, followed by a statistical analysis. The stations are located in a hot spot between Africa and the rest of Europe, which guarantees a variety of aerosol types, from mineral dust layers to continental/anthropogenic aerosol, and allow us to test Aeolus performance under different scenarios. The so called Aeolus-like profiles <img 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" alt="" width="105" height="30" /> are obtained from total particle backscatter coefficient and linear particle depolarization ratio <img src="data:image/png;base64,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" alt="" width="64" height="30" /> profiles at 355 nm and 532 nm measured from surface, through a thorough bibliographic review of dual-polarization measurements for relevant aerosol types. Finally, the study proposes a relation for the spectral conversion of <img 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F2O3VUGrszvjp/QjIA+35PipFjmtLW8Vk1qHHBFHlyN6rXq0LhFW7r2G8QH079nZ8JtVHoHD1ZG/afqxLVlTIDxOkt6+gtBBNB7aUqBsM+ctob+wUJQwoEtuV6w3qG78BXPCAftEhzNasd+cSGw1a+EWHtCn55+On8ro4DyWIy1z9+t9jC2pdqCB5VKjVojdXGLmN3iFD6UUf9p2WtAzk5GhAs7XSWSycdt68rikJgo9n5gbR/0ah7DMatptQgHaz0lYCYt9hXrypN3mEW+Xwjd3cPIuiLycK/LKHtPqEkkivcEoNg/lvpiXo/GozjoXNmwGMm7lcptx/nKqP9UQpkSoP9zEc8KB+UiHOjGTAsmfRaJxxPJtqqCkWtLuwkz4kK1F9eRIWXDWRc4ei7TrikKfhvTQKi3O/VG78lXPzIkLaWzsMmyQKE5yXoMeZfZsXIrCXk29rSJX/GsVXP6suyKiJqkXzpKmalOxa3DCxnydHMGLnPo5CiT/lMbypQAbcIcOshFBBMsYuQDJ9g2sz9PPzOW7bckc6Pl/NyO1v7NAOH49h/fxsxXW9N+5GZSpMRFm8Cs9nJr/eglp/qRRECPACl3eJJ35y5mypsdaNV7BvvvNYmJe7/uVR0PV08a95vIfzbH8dOGpXwxqi+RtevSfsRyTjuGK2XUfyqhbE2Q4iCTOgbj5emJf1AojbuPY13CgwZc5aEYOoVIma4/QaGN6P5hLGeybb7AfGsTg+v4IA/tzMxzTmGmPonlg58kwLsKAdWb0P1f3xMvbIqjDJTn1/Nx1/oE+vkREBhEUEg4Ed2GMm3zGTLUBf1NmfSfCpQtAQLK60fZtmo5sb+cICVX57RlqeT6ka2s+CGWX06kkKtzPGuxZ95KIYSCMKmzSLmQwKUb2ai09t+g5YMRnfouaUnnOHkqkaSbd8hTa9EXWTopm/7TMidA+jCpcmk0FaGjZqlqaBQryv7/Q0CqVhbbx/mwPajSrp39z3woavwhUQ4EVMARFQSUMyoIKGdUEFDOqCCgXAH/D9LDq2WGJtW4AAAAAElFTkSuQmCC" alt="" width="49" height="28" />, which is implemented in the Aeolus-like profile calculation. The statistical results show the ability of the satellite to detect and characterize significant aerosol layers under cloud free conditions, along with the surface effect on the lowermost measurements, which causes the satellite to largely overestimate co-polar backscatter coefficients. Finally, Aeolus standard correct algorithm middle bin (SCAmb) shows a better agreement with ground-based measurements than the standard correct algorithm (SCA), which tends to retrieve negative and meaningless coefficients in the clear troposphere. The implementation of Aeolus quality flags entails a vast reduction in the number of measurements available for comparison, which affects the statistical significance of the results, without improving significantly the statistical agreement between satellite and ground-based measurements.</p>