  An algorithm to estimate motion from satellite imagery is presented. Dense
displacement fields are computed from time-separated images of of significant
convective activity using a Bayesian formulation of the motion estimation
problem. Ordinarily this motion estimation problem is ill-posed; there are far
too many degrees of freedom than necessary to represent the motion. Therefore,
some form of regularization becomes necessary and by imposing smoothness and
non-divergence as desirable properties of the estimated displacement vector
field, excellent solutions are obtained. Our approach provides a marked
improvement over other methods in conventional use. In contrast to correlation
based approaches, the displacement fields produced by our method are dense,
spatial consistency of the displacement vector field is implicit, and
higher-order and small-scale deformations can be easily handled. In contrast
with optic-flow algorithms, we can produce solutions at large separations of
mesoscale features between large time-steps or where the deformation is rapidly
evolving.
