The complexity of dynamical laws governing 3D atmospheric flows associated with incomplete and noisy observations makes the recovery of atmospheric dynamics from satellite images sequences very difficult. In this report, we face the challenging problem of estimating physical sound and time consistent horizontal motion fields at various atmospheric depths for a whole image sequence. Based on a vertical decomposition of the atmosphere, we propose two dynamically consistent atmospheric motion estimators relying on different multi-layer dynamical models. Both estimators use a framework derived from data assimilation and are applied on noisy and incomplete pressure difference observations derived from satellite images. In the first model, dense pressure difference maps are reconstructed according to a shallow-water model on each cloud layer. While performing this reconstruction, the variational process estimates the average horizontal wind fields of the multi-layer model. The second model relies on a simplified vorticity-divergence form of the previous multi-layer shallow-water model. In this case, average horizontal motion fields are estimated for each layer without reconstructing pressure maps. While the simplified model is not as precise as the exact shallow-water model, the latter estimator exploits finer spatio-temporal image structures and succeeds in characterizing motion at smaller spatial scales. The performance of both methods is assessed on synthetic examples and on real world meteorological satellite image sequences.