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

3D wind field profiles from hyperspectral sounders: revisiting optic-flow from a meteorological perspective