Quantifying Uncertainty in AtmosphericWinds Retrieved from Optical Flow: Dependence on Weather Regime

This study explores the performance of a dense optical flow method in comparison to pattern matching techniques for retrieving Atmospheric Motion Vectors (AMVs) from water vapor images. Using high-resolution simulated datasets that represent various weather phenomena, we assess the performance of these methods across different weather regimes, time intervals, and pressure levels, and quantify the uncertainties associated with retrieved winds. The optical flow algorithm consistently outperforms the feature matching approach. Notably, it produces wind speeds and AMVs that closely resemble the wind fields from the simulations, and unlike the feature matching algorithm, the optical flow algorithm exhibits consistent performance across different time intervals. In contrast, the feature matching approach yields vector fields that exhibit over-smoothing in certain areas and erratic behavior in others, while also producing less detailed, regionally constant speed maps. Furthermore, unlike feature matching, the optical flow method effectively calculates AMVs even in areas with low wind speeds and near regions with missing data, generating a dense AMV field for every pixel in a pair of images. This superior performance and flexibility significantly influence the planning for future satellite missions aimed at retrieving atmospheric winds. As such, our work plays a critical role in determining the mission architecture and projected instrument performance for future atmospheric wind retrieval satellite missions. The study underscores the potential of the optical flow algorithm as a robust and efficient approach for atmospheric motion retrieval, thus contributing to advances in climate research and weather prediction.