STAR Science Seminars
Note: This seminar will be presented online only.Presenter(s):
Cooperative Institute for Research in the Atmosphere (CIRA)
Contributions From: Steven Miller (CIRA), Dan Lindsey (NOAA/STAR), Kristopher Bedka (NASA/Langley Research Center), and Eric Olson (CIRA)Sponsor(s):
STAR Science Seminar SeriesAbstract:
The science of computing brightness motion in imagery pairs and sequences at every image pixel, or so-called “Dense Optical Flow” (DOF), has advanced considerably in the last four decades to support applications like objective robotic vision, autonomous driving, augmented reality, and motion picture special effects. While seldom explored, DOF derivation is now enabled in visible and infrared satellite imagery by the spatial and temporal resolution of new-generation instruments like the Advanced Baseline Imager on the Geostationary Operational Environmental Satellite (GOES)-R series platform. DOF derivation from satellite imagery would have a variety of unique applications that are beneficial for research,forecasting, and decision-making products currently in development. These applications include atmospheric motion vector retrieval, temporal brightness interpolation, feature tracking, feature nowcasting, image stereoscopy, and semi-Lagrangian cloud-top cooling derivation. This presentation will go into detail on how some of these new DOF techniques are derived and highlight studies at the Cooperative Institute for Research in the Atmosphere to explore and validate novel applications. Demonstrations will also be shown on how improving feature tracking with DOF can complement machine-learning and artificial intelligence efforts for image classification and prognosis tasks. Examples of several DOF satellite imagery applications will be presented along with validation comparisons to state-of-the-art Derived Motion Wind products. Finally, this presentation will highlight current efforts to bring novel DOF applications into relevant operational environments.Bio(s):
Jason Apke is a Research Scientist I at the Cooperative Institute for Research in the Atmosphere. He received his Bachelor of Sciences degree in Meteorology from the University of Northern Colorado in Greeley, CO in 2011, a Master's degree in Earth and Atmospheric Sciences from the University of Nebraska-Lincoln in 2013, and a Ph.D. in Atmospheric Sciences from the University of Alabama-Huntsville in 2018. His dissertation focused on using atmospheric motion vectors to depict flow fields over deep convection observed from super-rapid scan geostationary satellite imagery, and how they could be used to identify signals relevant severe weather forecasting. He currently works on developing and implementing dense-optical flow derivation algorithms for a variety of satellite meteorology-related applications.
Stacy Bunin, firstname.lastname@example.org