Comparison of Optical Flow Derivation Techniques for Retrieving Tropospheric Winds from Satellite Image Sequences

• Published in: Journal of atmospheric and oceanic technology Vol. 39; no. 12; pp. 2005 - 2021
• Main Authors: Apke, Jason M., Noh, Yoo-Jeong, Bedka, Kristopher
• Format: Journal Article
• Language: English
• Published: Boston American Meteorological Society 01.12.2022
• Subjects: Accuracy; Aerosols; Algorithms; Atmospheric motion; Automation; Brightness; Correspondence; Geostationary satellites; Gravity waves; Height; Infrared tracking; Lidar; Mean winds; Multilayers; Natural variability; Numerical prediction; Numerical weather forecasting; Optical flow (image analysis); Prediction models; Quality control; Regions; Retrieval; Satellite imagery; Synchronous satellites; Texture; Tracking control; Tropospheric winds; Vectors; Water vapor; Water vapour; Weather forecasting; Wind; Wind profiles; Winds
• ISSN: 0739-0572; 1520-0426
• DOI: 10.1175/JTECH-D-22-0057.1

Abstract This study introduces a validation technique for quantitative comparison of algorithms that retrieve winds from passive detection of cloud- and water vapor–drift motions, also known as atmospheric motion vectors (AMVs). The technique leverages airborne wind-profiling lidar data collected in tandem with 1-min refresh-rate geostationary satellite imagery. AMVs derived with different approaches are used with accompanying numerical weather prediction model data to estimate the full profiles of lidar-sampled winds, which enables ranking of feature tracking, quality control, and height-assignment accuracy and encourages mesoscale, multilayer, multiband wind retrieval solutions. The technique is used to compare the performance of two brightness motion, or “optical flow,” retrieval algorithms used within AMVs, 1) patch matching (PM; used within operational AMVs) and 2) an advanced variational optical flow (VOF) method enabled for most atmospheric motions by new-generation imagers. The VOF AMVs produce more accurate wind retrievals than the PM method within the benchmark in all imager bands explored. It is further shown that image regions with low texture and multilayer-cloud scenes in visible and infrared bands are tracked significantly better with the VOF approach, implying VOF produces representative AMVs where PM typically breaks down. It is also demonstrated that VOF AMVs have reduced accuracy where the brightness texture does not advect with the mean wind (e.g., gravity waves), where the image temporal noise exceeds the natural variability, and when the height assignment is poor. Finally, it is found that VOF AMVs have improved performance when using fine-temporal refresh-rate imagery, such as 1- versus 10-min data.