The three-dimensional (3D) structure of the global horizontal wind field remains largely unobserved . Atmospheric Motion Vectors (AMVs) based on cloud tracking have been used since the 1960s to fill some of the gaps in global wind fields observation (Eyre et al., 2020). In this type of AMVs, features in clouds are tracked across sequential images. These cloud-tracking AMVs are derived from geostationary (GEO) satellite and Low Earth Orbiting (LEO) satellite measurements (Key et al., 2003;.However, all these cloud-tracking AMVs face the same problem: clouds in general are sparse in the 850-500 hPa layers, leaving most of the vertical structure in the middle troposphere unobserved . Furthermore, each cloud-tracking AMV derived from an image sequence is assigned to a single level of cloud top at a given horizontal coordinate thus, dynamic quantities such as wind shear, vorticity, and divergence cannot be obtained. Finally, in the case of cloud-tracking winds, height assignment forms the largest source of error (Salonen et al., 2015).An alternative is AMVs retrieved from radiances of water vapor bands (Velden et al., 1997(Velden et al., , 2005. Indeed, these AMVs are widely used (e.g., http://tropic.ssec.wisc.edu/misc/winds/info.html). However, these water vapor AMVs have poor vertical resolution, since they are derived from instruments with limited water vapor bands. Other relevant observations to our work are the wind speeds derived by a wind lidar carried by a LEO satellite (Aeolus) that calculates the wind speed in its line of sight (LOS) with high vertical resolution (e.g., 0.25 km near surface; Stoffelen et al., 2020). Assimilation of Aeolus winds in numerical weather prediction models causes statistically significant increases in forecast skill (Rennie et al., 2021). Recent work shows that the LOS wind component derived by Aeolus is around 5 m s −1 for Rayleigh scattering and 4 m s −1 for Mie scattering (these values