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.

翻译：在这项工作中,我们提出了一个高效的光流算法,用于从红外探测器的不完全超光谱图像数据测量中提取垂直分辨率3D大气运动矢量(AMV)场。模型位于能量中心的核心,与大气动态一致,包括热力学、静水平衡和统计动荡等成分。现代优化技术用于设计一个能最小化问题的低复杂度解答器,这是非凝固、不可区分、高维度和受物理制约的。特别是,利用乘数法的替代方向,我们展示了如何将原有的高维问题分为一个循环,涉及一套标准且可移动的光流子问题。通过比较欧洲中程天气预报中心(ECMWF)操作数字模拟所提供的地面真相,我们表明,在实际红外大气探测干涉仪(ISI)观测中,拟议方法的性能优于最新光学流算法。</s>