Publications from AI2ES


By AI2ES participants and members

2022

  • 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, Randy J., Amy McGovern, Amanda Burke, David Harrison, G. Lackman (2022) A Machine Learning Tutorial for Operational Meteorology, Part I: Traditional Machine Learning. Weather and Forecasting. https://arxiv.org/abs/2204.07492
  • Flansburg, Conner, Dimitrios I. Diochnos (2022) Wind Prediction under Random Data Corruption (Student Abstract). 36th AAAI Conference on Artificial Intelligence. https://www.aaai.org/AAAI22Papers/SA-00313-FlansburgC.pdf
  • 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
  • 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
  • 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.

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