activity
20152024
most citedPhysically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability

324 citations · 749 across the 13 of their papers we have counts for

collaborators

16 papers

eess.SP2024

SRViT: Vision Transformers for Estimating Radar Reflectivity from Satellite Observations at Scale

Jason Stock, Kyle Hilburn, Imme Ebert-Uphoff +1

We introduce a transformer-based neural network to generate high-resolution (3km) synthetic radar reflectivity fields at scale from geostationary satellite imagery. This work aims…

cs.CV2022★ 13 cited

Tools for Extracting Spatio-Temporal Patterns in Meteorological Image Sequences: From Feature Engineering to Attention-Based Neural Networks

Akansha Singh Bansal, Yoonjin Lee, Kyle Hilburn +1

Atmospheric processes involve both space and time. This is why human analysis of atmospheric imagery can often extract more information from animated loops of image sequences than…

cs.LG2022★ 17 cited

A Primer on Topological Data Analysis to Support Image Analysis Tasks in Environmental Science

Lander Ver Hoef, Henry Adams, Emily J. King +1

Topological data analysis (TDA) is a tool from data science and mathematics that is beginning to make waves in environmental science. In this work, we seek to provide an intuitive…

cs.LG2022★ 9 cited

Can we integrate spatial verification methods into neural-network loss functions for atmospheric science?

Ryan Lagerquist, Imme Ebert-Uphoff

In the last decade, much work in atmospheric science has focused on spatial verification (SV) methods for gridded prediction, which overcome serious disadvantages of pixelwise veri…

physics.geo-ph2022★ 87 cited

Investigating the fidelity of explainable artificial intelligence methods for applications of convolutional neural networks in geoscience

Antonios Mamalakis, Elizabeth A. Barnes, Imme Ebert-Uphoff

Convolutional neural networks (CNNs) have recently attracted great attention in geoscience due to their ability to capture non-linear system behavior and extract predictive spatiot…

cs.CY2021★ 112 cited

The Need for Ethical, Responsible, and Trustworthy Artificial Intelligence for Environmental Sciences

Amy McGovern, Imme Ebert-Uphoff, David John Gagne +1

Given the growing use of Artificial Intelligence (AI) and machine learning (ML) methods across all aspects of environmental sciences, it is imperative that we initiate a discussion…