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：本文提出了一种高效的光流算法，用于从红外探测仪获取的不完整高光谱图像数据中提取垂直分辨的三维大气运动矢量场。该能量最小化问题的核心模型与大气动力学保持一致，融合了热力学、静力平衡和统计湍流等要素。我们采用现代优化技术设计了一个低复杂度的求解器，用于解决这一非凸、不可微、高维且受物理约束的能量最小化问题。特别地，利用乘子交替方向法，我们展示了如何将原始高维问题分解为一系列标准且易处理的光流子问题的递归求解。通过与欧洲中期天气预报中心业务化数值模拟提供的真值进行对比，我们证明，在实际红外大气探测干涉仪观测背景下，所提方法的性能优于现有最先进的光流算法。