  Applications of satellite data in areas such as weather tracking and
modeling, ecosystem monitoring, wildfire detection, and land-cover change are
heavily dependent on the trade-offs to spatial, spectral and temporal
resolutions of observations. In weather tracking, high-frequency temporal
observations are critical and used to improve forecasts, study severe events,
and extract atmospheric motion, among others. However, while the current
generation of geostationary satellites have hemispheric coverage at 10-15
minute intervals, higher temporal frequency observations are ideal for studying
mesoscale severe weather events. In this work, we apply a task specific optical
flow approach to temporal up-sampling using deep convolutional neural networks.
We apply this technique to 16-bands of GOES-R/Advanced Baseline Imager
mesoscale dataset to temporally enhance full disk hemispheric snapshots of
different spatial resolutions from 15 minutes to 1 minute. Experiments show the
effectiveness of task specific optical flow and multi-scale blocks for
interpolating high-frequency severe weather events relative to bilinear and
global optical flow baselines. Lastly, we demonstrate strong performance in
capturing variability during a convective precipitation events.
