55 citations · 138 across the 9 of their papers we have counts for
4 papers · 1 filter
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…
Global atmospheric data assimilation with multi-modal masked autoencoders
Thomas J. Vandal, Kate Duffy, Daniel McDuff +2
Global data assimilation enables weather forecasting at all scales and provides valuable data for studying the Earth system. However, the computational demands of physics-based alg…
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…
Quantifying Uncertainty in Discrete-Continuous and Skewed Data with Bayesian Deep Learning
Thomas Vandal, Evan Kodra, Jennifer Dy +3
Deep Learning (DL) methods have been transforming computer vision with innovative adaptations to other domains including climate change. For DL to pervade Science and Engineering (…