Cooperative Institute for Research in the Atmosphere

Publications

Selected recent journal papers

2023:

• 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
• 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
• 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. “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 (2023): e220058, Jan 2023.  https://doi.org/10.1175/AIES-D-22-0058.1

2022:

• Lagerquist, R. and Ebert-Uphoff, I., 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, Nov 2022.  https://doi.org/10.1175/AIES-D-22-0021.1
• Mamalakis, A., Barnes, E.A. and Ebert-Uphoff, I., 2022. Investigating the fidelity of explainable artificial intelligence methods for applications of convolutional neural networks in geoscience. Artificial Intelligence for the Earth Systems, 1(4), p.e220012, Sept 2022. https://doi.org/10.1175/AIES-D-22-0012.1
• McGovern, A., Bostrom, A., Davis, P., Demuth, J.L., Ebert-Uphoff, I., He, R., Hickey, J., Gagne II, D.J., Snook, N., Stewart, J.Q. and Thorncroft, C., 2022. NSF AI institute for research on trustworthy AI in weather, climate, and coastal oceanography (AI2ES). Bulletin of the American Meteorological Society, 103(7), pp.E1658-E1668, July 2022.  https://doi.org/10.1175/BAMS-D-21-0020.1
• Rader, J.K., Barnes, E.A., Ebert-Uphoff, I. and Anderson, C., 2022. Detection of forced change within combined climate fields using explainable neural networks. Journal of Advances in Modeling Earth Systems, 14(7), p.e2021MS002941, June 2022.  https://doi.org/10.1029/2021MS002941
• Mamalakis, A., Ebert-Uphoff, I. and Barnes, E.A., 2022. Neural network attribution methods for problems in geoscience: A novel synthetic benchmark dataset. Environmental Data Science, 1, p.e8., June 2022. https://doi.org/10.1017/eds.2022.7
• Haynes, J.M., Noh, Y.J., Miller, S.D., Haynes, K.D., Ebert-Uphoff, I. and Heidinger, A., Low Cloud Detection in Multilayer Scenes using Satellite Imagery with Machine Learning Methods, Journal of Atmospheric and Oceanic Technology, March 2022, https://doi.org/10.1175/JTECH-D-21-0084.1.

2021:

• Roebber, P.J., 2021. Toward an Adaptive Artificial Neural Network–Based Postprocessor. Monthly Weather Review, 149(12), pp.4045-4055, Dec 2021, https://doi.org/10.1175/MWR-D-21-0089.1.
• Lagerquist, R., Turner, D., Ebert-Uphoff, I., Stewart, J. and Hagerty, V., Using Deep Learning to Emulate and Accelerate a Radiative Transfer Model. Journal of Atmospheric and Oceanic Technology, 38(10), pp.1673-1696, Oct 2021, https://doi.org/10.1175/MWR-D-21-0096.1.
• Lagerquist, R., Stewart, J.Q., Ebert-Uphoff, I. and Kumler, C., Using Deep Learning to Nowcast the Spatial Coverage of Convection from Himawari-8 Satellite Data. Monthly Weather Review, 149(12), pp.3897-3921, Oct 2021, https://doi.org/10.1175/JTECH-D-21-0007.1.
• Lee, Y., Kummerow, C. D., Ebert-Uphoff, I.  Applying machine learning methods to detect convection using GOES-16 ABI data, Atmospheric Measurement Techniques, April 2021, https://doi.org/10.5194/amt-2020-420.
• Samarasinghe, S.M., Barnes, E.A., Connolly, C., Ebert-Uphoff, I., Sun, L.  Strengthened causal connections between the MJO and the North Atlantic with climate warming, Geophysical Research Letters, Feb 1 2021, https://doi.org/10.1029/2020GL091168.
  Featured as research highlight in Nature Climate Change: B. Langenbrunner, The Madden–Julian oscillation strengthens its reach. Nature Climate Change, 11, 183, 3 March 2021. https://doi.org/10.1038/s41558-021-01008-7
• McGovern, Amy; Bostrom, Ann; Ebert-Uphoff, Imme; He, Ruoying; Thorncroft, Chris; Tissot, Philippe; Boukabara, Sid; Demuth, Julie; Gagne II, David John; Hickey, Jason; Williams, John K. (2020) Weathering Environmental Change Through Advances in AI. EOS, Volume 101, https://doi.org/10.1029/2020EO147065, July 2020.
• Barnes, E. A., Toms, B., Hurrell, J. W., Ebert-Uphoff, I., Anderson, C., Anderson, D. Indicator patterns of forced change learned by an artificial neural network. Journal of Advances in Modeling Earth Systems (JAMES), https://doi.org/10.1029/2020MS002195, Aug 2020.  (arXiv preprint from May 2020: here).
• Ebert-Uphoff, I., Hilburn, K. A. Evaluation, Tuning and Interpretation of Neural Networks for Meteorological Applications, Bulletin of the American Meteorological Society (BAMS), https://doi.org/10.1175/BAMS-D-20-0097.1, Aug 2020.  (arXiv preprint from May 2020 here).
• Hilburn, K. A., Ebert-Uphoff, I., and Miller, S. D., Development and Interpretation of a Neural Network-Based Synthetic Radar Reflectivity Estimator Using GOES-R Satellite Observations. Journal of Applied Meteorology and Climatology, https://doi.org/10.1175/JAMC-D-20-0084.1, Jan 2021. (arXiv preprint from April 2020: here)
• Roebber, P.J., and Crockett, J.,, Using a coevolutionary postprocessor to improve skill for both forecasts of surface temperature and nowcasts of convection occurrence. Monthly Weather Review, 147(11), pp.4241-4259, https://doi.org/10.1175/MWR-D-19-0063.1, Nov 2019.