Publications (6)
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…
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…
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 interan…
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…