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

Abstract 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 oversmoothing 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 AMV 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. Significance Statement This research investigates the efficacy of two methods, optical flow and feature matching, for detecting atmospheric winds, referred to as atmospheric motion vectors, from satellite images of water vapor. Employing detailed simulated datasets that replicate real-world weather patterns, we found that optical flow consistently outperforms feature matching in various aspects. Notably, the optical flow method is not only more precise but also maintains its accuracy across different scenarios. These insights are critical for the design of future satellite missions focused on advancing our understanding of the atmosphere and enhancing weather predictions. This study contributes to advancements in climate research and supports improved weather forecasting, benefiting both scientific and societal needs.