An Optical Flow Approach to Tracking Ship Track Behavior Using GOES-R Satellite Imagery
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47 OTHER INSTRUMENTATION↗
Engineering topics
Gray, Skyler
Publications and source records attributed to Gray, Skyler.
CFTrack v.1.0
SAND2021-13952 O Cloud Feature Tracker (CFTrack) is an application for following low-cloud motion in Geostationary Operational Environmental Satellite (GOES) R-series imagery. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
Larson, Kelsie↗
Local limits of detection for anthropogenic aerosol-cloud interactions
Ship tracks are quasi-linear cloud patterns produced from the interaction of ship emissions with low boundary layer clouds. They are visible throughout the diurnal cycle in satellite images from space-borne assets like the Advanced Baseline Imagers (ABI) aboard the National Oceanic and Atmospheric Administration Geostationary Operational Environmental Satellites (GOES-R). However, complex atmospheric dynamics often make it difficult to identify and characterize the formation and evolution of tracks. Ship tracks have the potential to increase a cloud's albedo and reduce the impact of global warming. Thus, it is important to study these patterns to better understand the complex atmospheric interactions between aerosols and clouds to improve our climate models, and examine the efficacy of climate interventions, such as marine cloud brightening. Over the course of this 3-year project, we have developed novel data-driven techniques that advance our ability to assess the effects of ship emissions on marine environments and the risks of future marine cloud brightening efforts. The three main innovative technical contributions we will document here are a method to track aerosol injections using optical flow, a stochastic simulation model for track formations and an automated detection algorithm for efficient identification of ship tracks in large datasets.
54 ENVIRONMENTAL SCIENCES↗