Publications from AI2ES


By AI2ES participants and members

2024

  • McGovern, Amy, Philippe Tissot, and Ann Bostrom (2024) Developing trustworthy AI for weather and climate. Physics Today 1 January 2024; 77 (1): 26–31. https://doi.org/10.1063/PT.3.5379

2023

  • Chase, R. J., D. R. Harrison, G. M. Lackmann, and A. McGovern (2023) A Machine Learning Tutorial for Operational Meteorology, Part II: Neural Networks and Deep Learning. Weather Forecasting, https://doi.org/10.1175/WAF-D-22-0187.1, in press.
  • Haynes, K., Lagerquist, R., McGraw, M., Musgrave, K. and Ebert-Uphoff, I. (2023) Creating and evaluating uncertainty estimates with neural networks for environmental-science applications. Artificial Intelligence for the Earth Systems, pp.1-58, Jan 2023. https://doi.org/10.1175/AIES-D-22-0061.1
  • Mamalakis, Antonios, Elizabeth A. Barnes, and Imme Ebert-Uphoff. (2023) Carefully choose the baseline: Lessons learned from applying XAI attribution methods for regression tasks in geoscience. Artificial Intelligence for the Earth Systems, 2, no. 1: e220058, Jan 2023. https://doi.org/10.1175/AIES-D-22-0058.1
  • McGovern, A., Gagne II, D. J., Wirz, C. D., Ebert-Uphoff, I., Bostrom, A., Rao, Y., Schumacher, A., Flora, M., Chase, R., Mamalakis, A., McGraw, M., Lagerquist, R., Redmon, R. J., and Peterson, T. (2023) Trustworthy Artificial Intelligence for Environmental Sciences: An Innovative Approach for Summer School. Available in Early Online Release in the Bulletin of the American Meteorological Societyhttps://doi.org/10.1175/BAMS-D-22-0225.1
  • McGovern, A., Gagne, D.J., Wirz, C.D., Ebert-Uphoff, I., Bostrom, A., Rao, Y., Schumacher, A., Flora, M., Chase, R., Mamalakis, A. and McGraw, M. (2023) Trustworthy Artificial Intelligence for Environmental Sciences: An Innovative Approach for Summer School. Bulletin of the American Meteorological Society, Apr 2023. https://doi.org/10.1175/BAMS-D-22-0225.1
  • McGovern, A., R. J. Chase, M. Flora, D. J. Gagne, R. Lagerquist, C. K. Potvin, N. Snook, and E. Loken, (2023) A Review of Machine Learning for Convective Weather. Artificial Intelligence for the Earth Systems, https://doi.org/10.1175/AIES-D-22-0077.1, in press.
  • Murphy, A. M., C. R. Homeyer, and K. Q. Allen, (2023) Development and Investigation of GridRad-Severe, a Multi-Year Severe Event Radar Database, Monthly Weather Review, in press, https://doi.org/10.1175/MWR-D-23-0017.1
  • Shinoda, T., Tissot, P., & Reisinger, A. (2023). Influence of Loop Current and eddy shedding on subseasonal sea level variability along the western Gulf Coast. Frontiers in Marine Science, 9, 2726. https://doi.org/10.3389/fmars.2022.1049550
  • Sobash, R. A., D. J. Gagne, C. L. Becker, D. Ahijevych, G. Gantos, and C. S. Schwartz, (2023) Diagnosing storm mode with deep learning in convection-allowing models. Monthly Weather Review, In Press, https://doi.org/10.1175/MWR-D-22-0342.1.
  • Ver Hoef, L., Adams, H., King, E.J. and Ebert-Uphoff, I., (2023) A Primer on Topological Data Analysis to Support Image Analysis Tasks in Environmental Science. Artificial Intelligence for the Earth Systems, pp.1-38, Jan 2023. ​​https://doi.org/10.1175/AIES-D-22-0039.1

2022

  • Barnes, Elizabeth A., Randal Barnes, and Mark DeMaria. (2022) “Sinh-Arcsinh-Normal Distributions to Add Uncertainty to Neural Network Regression Tasks: Applications to Tropical Cyclone Intensity Forecasts.” EarthArXiv, July. link, in press in Environmental Data Science.
  • Barnes, Elizabeth A., Randal J. Barnes, Zane K. Martin, and Jamin K. Rader (2022) This Looks Like That There: Interpretable neural networks for image tasks when location matters, Artificial Intelligence for the Earth Systems, preprint available
  • Chase, R. J., D. R. Harrison, A. Burke, G. M. Lackmann, and A. McGovern, (2022) A Machine Learning Tutorial for Operational Meteorology. Part I: Traditional Machine Learning. Weather and Forecasting, 37, 1509–1529, https://doi.org/10.1175/WAF-D-22-0070.1.
  • Dueben, P., M. G. Schultz, M. Chantry, D. J. Gagne, D. M. Hall, and A. McGovern, (2022) Challenges and benchmark datasets for machine learning in the atmospheric sciences: Definition, status and outlook. Artificial Intelligence for the Earth Systems, 1, e210002, https://doi.org/10.1175/AIES-D-21-0002.1.
  • Flansburg, C., & Diochnos, D. I. (2022). Wind Prediction under Random Data Corruption (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12945-12946. https://doi.org/10.1609/aaai.v36i11.21609
  • Haynes, J.M., Y.J. Noh, S.D. Miller, Katherine D. Haynes, Imme Ebert-Uphoff and A. Heidinger (2022). Low Cloud Detection in Multilayer Scenes Using Satellite Imagery with Machine Learning Methods. Journal of Atmospheric and Oceanic Technology, 39(3), pp. 319-334, https://doi.org/10.1175/JTECH-D-21-0084.1
  • Kamangir, Hamid, Evan Krell, Waylon Collins, Scott A. King and Philippe Tissot (2022). Importance of 3D Convolution and Physics-based Feature Grouping in Atmospheric Predictions. Environmental Modeling & Software, 154, 105424, https://doi.org/10.1016/j.envsoft.2022.105424
  • Lagerquist, Ryan and Imme Ebert-Uphoff (2022) Can We Integrate Spatial Verification Methods into Neural Network Loss Functions for Atmospheric Science? Artificial Intelligence for the Earth Systems, 1(4), p.e220021. https://doi.org/10.1175/AIES-D-22-0021.1
  • Liu, N., Liu, C., & Tissot, P. E. (2022). Relative Importance of Large‐Scale Environmental Variables to the World‐Wide Variability of Thunderstorms. Journal of Geophysical Research: Atmospheres, 127(17), e2021JD036065. https://doi.org/10.1029/2021JD036065
  • Mamalakis, A., Barnes, E.A. and Ebert-Uphoff, I., (2022) Carefully choose the baseline: Lessons learned from applying XAI attribution methods for regression tasks in geoscience. Artificial Intelligence for the Earth Systems, pp.1-18. https://doi.org/10.1175/AIES-D-22-0058.1
  • Mamalakis, Antonios, Elizabeth A. Barnes and Imme Ebert-Uphoff (2022). Investigating the fidelity of Explainable Artificial Intelligence methods for applications of Convolutional Neural Networks in Geoscience, accepted to Artificial Intelligence for the Earth Systems (AMS), 08/2022, preprint available https://arxiv.org/abs/2202.03407
  • Mamalakis, Antonios, Imme Ebert-Uphoff and Elizabeth A. Barnes (2022). Neural Network attribution methods for problems in Geoscience: A novel synthetic benchmark dataset, Environmental Data Science, https://doi.org/10.1017/eds.2022.7
  • Mamalakis, Antonios, Imme Ebert-Uphoff and Elizabeth A. Barnes (2022) Explainable Artificial Intelligence in Meteorology and Climate Science: Model fine-tuning, calibrating trust and learning new science, in Beyond explainable Artificial Intelligence by Holzinger et al. (Editors), Springer Lecture Notes on Artificial Intelligence (LNAI), https://link.springer.com/chapter/10.1007/978-3-031-04083-2_16
  • McGovern, Amy, Imme Ebert-Uphoff, David John Gagne II and Ann Bostrom (2022) Why we need to focus on developing ethical, responsible, and trustworthy artificial intelligence approaches for environmental science, Environmental Data Science, 1, E6. doi:10.1017/eds.2022.5
  • McGovern, Amy, Ann Bostrom, Phil Davis, Julie L. Demuth, Imme Ebert-Uphoff, Ruoying He, Jason Hickey, David John Gagne II, Nathan Snook, Jebb Q. Stewart, Christopher Thorncroft, Philippe Tissot and John K. Williams (2022) NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES). Bulletin of the American Meteorological Society, https://doi.org/10.1175/BAMS-D-21-0020.1
  • Ver Hoef, L., Adams, H., King, E. J., & Ebert-Uphoff, I. (2022). A Primer on Topological Data Analysis to Support Image Analysis Tasks in Environmental Science, Artificial Intelligence for the Earth Systems (published online ahead of print 2022). https://doi.org/10.1175/AIES-D-22-0039.1
  • Vicens-Miquel, Marina, Antonio Medrano, Philippe Tissot, Hamid Kamangir, Michael Starek, Katie Colburn (2022) A Deep Learning Based Method to Delineate the Wet/Dry Shoreline and Compute Its Elevation Using High-Resolution UAS Imagery. Remote Sensing, 14(23), 5990. https://doi.org/10.3390/rs14235990 
  • Vicens-Miquel, Marina, Antonio Medrano, Philippe Tissot, Michael Starek and Katie Colburn (2022). Deep Learning Automatic Detection of the Wet/Dry Shoreline at Fish Pass, Texas. Paper 2446, Proceedings of the IGARSS 2022 conference. https://ieeexplore.ieee.org/document/9884633 

2021

  • Barnes, Elizabeth A. and Randal J. Barnes (2021) Controlled abstention neural networks for identifying skillful predictions for regression problems, Journal of Advances in Modeling Earth Systems  https://doi.org/10.1029/2021MS002575
  • Barnes, Elizabeth A. and Randal J. Barnes (2021) Controlled abstention neural networks for identifying skillful predictions for classification problems, Journal of Advances in Modeling Earth Systems, https://doi.org/10.1029/2021MS002573
  • Diochnos, Dimitrios I. and Theodore B. Trafalis (2021) Learning Reliable Rules under Class Imbalance. SIAM International Conference on Data Mining, SDM 2021: 28-36. doi: 10.1137/1.9781611976700.4
  • Flora, Monte L., Corey K. Potvin, P. S. Skinner, S. Handler, and Amy McGovern (2021) Using machine learning to generate storm-scale probabilistic guidance of severe weather hazards in the Warn-on-Forecast system. Monthly Weather Review, 149, 1535–1557, https://doi.org/10.1175/MWR-D-20-0194.1
  • Kamangir, Hamid, Waylon Collins, Philippe Tissot, Scott A. King, Hue Thi Hong Dinh, Niall Durham and James Rizzo (2021) FogNet: A Multiscale 3D CNN with Double-Branch Dense Block and Attention Mechanism for Fog Prediction, Machine Learning with Applications, 5: 100038. doi: 10.1016/j.mlwa.2021.100038
  • Lagerquist, Ryan, D. Turner, Imme Ebert-Uphoff, Jebb Q. Stewart and V. Hagerty (2021) Using Deep Learning to Emulate and Accelerate a Radiative Transfer Model. Journal of Atmospheric and Oceanic Technology, 38(10), pp.1673-1696, Sept 2021, https://doi.org/10.1175/JTECH-D-21-0007.1
  • Lagerquist, Ryan, Jebb Q. Stewart, Imme Ebert-Uphoff and Christina Kumler (2021) Using deep learning to nowcast the spatial coverage of convection from Himawari-8 satellite data. Monthly Weather Review, 149(12), pp.3897-3921, Nov 2021, https://doi.org/10.1175/MWR-D-21-0096.1
  • Lee, Yoonjin, Christian D. Kummerow, and Imme Ebert-Uphoff  (2021)  Applying machine learning methods to detect convection using Geostationary Operational Environmental Satellite-16 (GOES-16) advanced baseline imager (ABI) data, Atmospheric Measurement Techniques, Volume 14, issue 4, 2699–2716. doi: 10.5194/amt-14-2699-2021

2020

  • McGovern, Amy, Ann Bostrom, Imme Ebert-Uphoff, Ruoying He, Chris Thorncroft, Philippe Tissot, Sid Boukabara, Julie Demuth, David John Gagne II, Jason Hickey, and John K. Williams (2020) Weathering environmental change through advances in AI, Eos, 101, doi: 10.1029/2020EO147065