  In this work, we present an efficient optic flow algorithm for the extraction
of vertically resolved 3D atmospheric motion vector (AMV) fields from
incomplete hyperspectral image data measures by infrared sounders. The model at
the heart of the energy to be minimized is consistent with atmospheric
dynamics, incorporating ingredients of thermodynamics, hydrostatic equilibrium
and statistical turbulence. Modern optimization techniques are deployed to
design a low-complexity solver for the energy minimization problem, which is
non-convex, non-differentiable, high-dimensional and subject to physical
constraints. In particular, taking advantage of the alternate direction of
multipliers methods (ADMM), we show how to split the original high-dimensional
problem into a recursion involving a set of standard and tractable optic-flow
sub-problems. By comparing with the ground truth provided by the operational
numerical simulation of the European Centre for Medium-Range Weather Forecasts
(ECMWF), we show that the performance of the proposed method is superior to
state-of-the-art optical flow algorithms in the context of real infrared
atmospheric sounding interferometer (IASI) observations.
