55 citations · 138 across the 9 of their papers we have counts for
9 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…
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…
Hybrid physics-AI outperforms numerical weather prediction for extreme precipitation nowcasting
Puja Das, August Posch, Nathan Barber +6
Precipitation nowcasting, critical for flood emergency and river management, has remained challenging for decades, although recent developments in deep generative modeling (DGM) su…
Spectral Synthesis for Satellite-to-Satellite Translation
Thomas Vandal, Daniel McDuff, Weile Wang +2
Earth observing satellites carrying multi-spectral sensors are widely used to monitor the physical and biological states of the atmosphere, land, and oceans. These satellites have…
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…
Temporal Interpolation of Geostationary Satellite Imagery with Task Specific Optical Flow
Thomas Vandal, Ramakrishna Nemani
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-of…
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 (…
DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution
Thomas Vandal, Evan Kodra, Sangram Ganguly +3
The impacts of climate change are felt by most critical systems, such as infrastructure, ecological systems, and power-plants. However, contemporary Earth System Models (ESM) are r…
Intercomparison of Machine Learning Methods for Statistical Downscaling: The Case of Daily and Extreme Precipitation
Thomas Vandal, Evan Kodra, Auroop R Ganguly
Statistical downscaling of global climate models (GCMs) allows researchers to study local climate change effects decades into the future. A wide range of statistical models have be…