English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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

Temporal Interpolation of Geostationary Satellite Imagery with Task Specific Optical Flow