71 citations · 75 across the 3 of their papers we have counts for
6 papers
Indicator patterns of forced change learned by an artificial neural network
Elizabeth A. Barnes, Benjamin Toms, James W. Hurrell +3
Many problems in climate science require the identification of signals obscured by both the "noise" of internal climate variability and differences across models. Following previou…
Evaluation, Tuning and Interpretation of Neural Networks for Meteorological Applications
Imme Ebert-Uphoff, Kyle A. Hilburn
Neural networks have opened up many new opportunities to utilize remotely sensed images in meteorology. Common applications include image classification, e.g., to determine whether…
Development and Interpretation of a Neural Network-Based Synthetic Radar Reflectivity Estimator Using GOES-R Satellite Observations
Kyle A. Hilburn, Imme Ebert-Uphoff, Steven D. Miller
The objective of this research is to develop techniques for assimilating GOES-R Series observations in precipitating scenes for the purpose of improving short-term convective-scale…
New Exploratory Tools for Extremal Dependence: Chi Networks and Annual Extremal Networks
Whitney K. Huang, Daniel S. Cooley, Imme Ebert-Uphoff +2
Understanding dependence structure among extreme values plays an important role in risk assessment in environmental studies. In this work we propose the network and the annual…
Machine Learning for the Geosciences: Challenges and Opportunities
Anuj Karpatne, Imme Ebert-Uphoff, Sai Ravela +2
Geosciences is a field of great societal relevance that requires solutions to several urgent problems facing our humanity and the planet. As geosciences enters the era of big data,…
High-Dimensional Dependency Structure Learning for Physical Processes
Jamal Golmohammadi, Imme Ebert-Uphoff, Sijie He +2
In this paper, we consider the use of structure learning methods for probabilistic graphical models to identify statistical dependencies in high-dimensional physical processes. Suc…