Elizabeth A. Barnes

According to our database, Elizabeth A. Barnes authored at least 18 papers between 2017 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2025

AI-Informed Model Analogs for Subseasonal-to-Seasonal Prediction.

Jacob B. Landsberg, Elizabeth A. Barnes, Matthew Newman

CoRR, June, 2025

Turning Up the Heat: Assessing 2-m Temperature Forecast Errors in AI Weather Prediction Models During Heat Waves.

Kelsey E. Ennis, Elizabeth A. Barnes, Marybeth C. Arcodia, Martin A. Fernandez, Eric D. Maloney

CoRR, April, 2025

Predicting Tropical Cyclone Track Forecast Errors using a Probabilistic Neural Network.

M. A. Fernandez, Elizabeth A. Barnes, Randal J. Barnes, Mark DeMaria, Marie McGraw, Galina Chirokova, Lixin Lu

CoRR, March, 2025

Multi-Year-to-Decadal Temperature Prediction using a Machine Learning Model-Analog Framework.

M. A. Fernandez, Elizabeth A. Barnes

CoRR, February, 2025

2024

Recommendations for Comprehensive and Independent Evaluation of Machine Learning-Based Earth System Models.

Paul A. Ullrich, Elizabeth A. Barnes, William D. Collins, Katherine Dagon, Shiheng Duan, Joshua Elms, Jiwoo Lee, L. Ruby Leung, Dan Lu, Maria J. Molina, Travis A. O'Brien

CoRR, 2024

AI2ES: The NSF AI Institute for Research on Trustworthy AI for Weather, Climate, and Coastal Oceanography.

Amy McGovern, Imme Ebert-Uphoff, Elizabeth A. Barnes, Ann Bostrom, Mariana G. Cains, Phillip Davis, Julie L. Demuth, Dimitrios I. Diochnos, Andrew H. Fagg, Philippe Tissot, John K. Williams, Christopher D. Wirz

AI Mag., 2024

2023

ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators.

Sungduk Yu, Walter M. Hannah, Liran Peng, Mohamed Aziz Bhouri, Ritwik Gupta, Jerry Lin, Björn Lütjens, Justus C. Will, Tom Beucler, Bryce E. Harrop, Benjamin R. Hillman, Andrea M. Jenney, Savannah L. Ferretti, Nana Liu, Anima Anandkumar, Noah D. Brenowitz, Veronika Eyring, Pierre Gentine, Stephan Mandt, Jaideep Pathak, Carl Vondrick, Rose Yu, Laure Zanna, Ryan P. Abernathey, Fiaz Ahmed, David C. Bader, Pierre Baldi, Elizabeth A. Barnes, Gunnar Behrens, Christopher S. Bretherton, Julius J. M. Busecke, Peter M. Caldwell, Wayne Chuang, Yilun Han, Yu Huang, Fernando Iglesias-Suarez, Sanket R. Jantre, Karthik Kashinath, Marat Khairoutdinov, Thorsten Kurth, Nicholas J. Lutsko, Po-Lun Ma, Griffin Mooers, J. David Neelin, David A. Randall, Sara Shamekh, Akshay Subramaniam, Mark A. Taylor, et al.

CoRR, 2023

ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation.

Sungduk Yu, Walter M. Hannah, Liran Peng, Jerry Lin, Mohamed Aziz Bhouri, Ritwik Gupta, Björn Lütjens, Justus C. Will, Gunnar Behrens, Julius Busecke, Nora Loose, Charles Stern, Tom Beucler, Bryce E. Harrop, Benjamin R. Hillman, Andrea M. Jenney, Savannah L. Ferretti, Nana Liu, Animashree Anandkumar, Noah D. Brenowitz, Veronika Eyring, Nicholas Geneva, Pierre Gentine, Stephan Mandt, Jaideep Pathak, Akshay Subramaniam, Carl Vondrick, Rose Yu, Laure Zanna, Tian Zheng, Ryan Abernathey, Fiaz Ahmed, David C. Bader, Pierre Baldi, Elizabeth A. Barnes, Christopher S. Bretherton, Peter M. Caldwell, Wayne Chuang, Yilun Han, Yu Huang, Fernando Iglesias-Suarez, Sanket R. Jantre, Karthik Kashinath, Marat Khairoutdinov, Thorsten Kurth, Nicholas J. Lutsko, Po-Lun Ma, Griffin Mooers, J. David Neelin, David A. Randall, Sara Shamekh, Mark Taylor, Nathan M. Urban, Janni Yuval, Guang Zhang, Mike Pritchard

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Carefully choose the baseline: Lessons learned from applying XAI attribution methods for regression tasks in geoscience.

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

CoRR, 2022

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

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

CoRR, 2022

2021

Towards neural Earth system modelling by integrating artificial intelligence in Earth system science.

Christopher Irrgang, Niklas Boers, Maike Sonnewald, Elizabeth A. Barnes, Christopher Kadow, Joanna Staneva, Jan Saynisch-Wagner

Nat. Mach. Intell., 2021

Controlled abstention neural networks for identifying skillful predictions for classification problems.

Elizabeth A. Barnes, Randal J. Barnes

CoRR, 2021

Controlled abstention neural networks for identifying skillful predictions for regression problems.

Elizabeth A. Barnes, Randal J. Barnes

CoRR, 2021

Neural Network Attribution Methods for Problems in Geoscience: A Novel Synthetic Benchmark Dataset.

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

CoRR, 2021

Will Artificial Intelligence supersede Earth System and Climate Models?

Christopher Irrgang, Niklas Boers, Maike Sonnewald, Elizabeth A. Barnes, Christopher Kadow, Joanna Staneva, Jan Saynisch-Wagner

CoRR, 2021

2020

Explainable Artificial Intelligence in Meteorology and Climate Science: Model Fine-Tuning, Calibrating Trust and Learning New Science.

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

Proceedings of the xxAI - Beyond Explainable AI, 2020

2019

Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability.

Benjamin A. Toms, Elizabeth A. Barnes, Imme Ebert-Uphoff

CoRR, 2019

2017

Working with Daily Climate Model Output Data in R and the futureheatwaves Package.

G. Brooke Anderson, Colin Eason, Elizabeth A. Barnes

R J., 2017