105 citations · 154 across the 5 of their papers we have counts for
5 papers
Distilling deep optical flow stereo methods to retrieve dense three-dimensional wind fields
Thomas J. Vandal, Dong L. Wu, James L. Carr +5
Geostationary atmospheric motion vectors (AMVs) provide the dense horizontal wind vectors (u,v) and heights ingested into data assimilation systems. Traditional AMVs track features…
Climate-mediated shifts in temperature fluctuations promote extinction risk
Kate Duffy, Tarik C. Gouhier, Auroop R. Ganguly
Climate-mediated changes in the spatiotemporal distribution of thermal stress can destabilize animal populations and promote extinction risk. Using quantile, spectral, and wavelet…
Explainable deep learning for insights in El Niño and river flows
Yumin Liu, Kate Duffy, Jennifer G. Dy +1
The El Niño Southern Oscillation (ENSO) is a semi-periodic fluctuation in sea surface temperature (SST) over the tropical central and eastern Pacific Ocean that influences interann…
A framework for deep learning emulation of numerical models with a case study in satellite remote sensing
Kate Duffy, Thomas Vandal, Weile Wang +2
Numerical models based on physics represent the state-of-the-art in earth system modeling and comprise our best tools for generating insights and predictions. Despite rapid growth…
Progressively Growing Generative Adversarial Networks for High Resolution Semantic Segmentation of Satellite Images
Edward Collier, Kate Duffy, Sangram Ganguly +8
Machine learning has proven to be useful in classification and segmentation of images. In this paper, we evaluate a training methodology for pixel-wise segmentation on high resolut…