Publications
For a complete list, see my Google Scholar profile.
Feel free to email me (wchapman@colorado.edu) if you would like a PDF of any of these papers.
2025
Chapman, William E., and Judith Berner. “Improving climate bias and variability via CNN-based state-dependent model-error corrections.” Geophysical Research Letters, 52(6), e2024GL114106 (2025).
Chapman, William E., Francine Schevenhoven, Judith Berner, Noel Keenlyside, Ingo Bethke, Ping-Gin Chiu, Alok Gupta, and Jesse Nusbaumer. “Implementation and validation of a supermodeling framework into Community Earth System Model version 2.1.5.” Geoscientific Model Development, 18(17), 5451-5465 (2025).
Chapman, William E., John S. Schreck, Yingkai Sha, David John Gagne II, Dhamma Kimpara, Laure Zanna, Kirsten J. Mayer, and Judith Berner. “CAMulator: Fast emulation of the community atmosphere model.” arXiv preprint arXiv:2504.06007 (2025).
Sha, Yingkai, John S. Schreck, William Chapman, and David John Gagne. “Improving AI weather prediction models using global mass and energy conservation schemes.” Journal of Advances in Modeling Earth Systems, 17(11), e2025MS005138 (2025).
Sha, Yingkai, John S. Schreck, William Chapman, and David John Gagne II. “Investigating the contribution of terrain-following coordinates and conservation schemes in AI-driven precipitation forecasts.” arXiv preprint arXiv:2503.00332 (2025).
Sha, Yingkai, John S. Schreck, William Chapman, and David John Gagne. “Investigating the Use of Terrain-Following Coordinates in AI-Driven Precipitation Forecasts.” Geophysical Research Letters, 52(20), e2025GL118478 (2025).
Schreck, John S., Yingkai Sha, William Chapman, Dhamma Kimpara, Judith Berner, Seth McGinnis, Arnold Kazadi, Negin Sobhani, Ben Kirk, Charlie Becker, and David John Gagne II. “Community Research Earth Digital Intelligence Twin: a scalable framework for AI-driven Earth System Modeling.” npj Climate and Atmospheric Science, 8(1), 239 (2025).
Zanna, Laure, William Gregory, Pavel Perezhogin, Aakash Sane, Cheng Zhang, Alistair Adcroft, Mitch Bushuk, Carlos Fernandez-Granda, Brandon Reichl, Dhruv Balwada, William Chapman, et al. “A Framework for Hybrid Physics-AI Coupled Ocean Models.” arXiv preprint arXiv:2510.22676 (2025).
Mamalakis, Antonios, William E. Chapman, and Kirsten J. Mayer. “Expanding the Limits of Explainable AI Tools in Generating Physical Insights.” Authorea Preprints (2025).
2024
Du, Danni, Aneesh C. Subramanian, Weiqing Han, William E. Chapman, Jeffrey B. Weiss, and Elizabeth Bradley. “Increase in MJO predictability under global warming.” Nature Climate Change, 14(1), 68-74 (2024).
Schreck, John S., David John Gagne, Charlie Becker, William E. Chapman, Kim Elmore, Da Fan, Gabrielle Gantos, Eliot Kim, Dhamma Kimpara, Thomas Martin, et al. “Evidential deep learning: Enhancing predictive uncertainty estimation for earth system science applications.” Artificial Intelligence for the Earth Systems, 3(4), 230093 (2024).
Schreck, John, Yingkai Sha, William Chapman, Dhamma Kimpara, Judith Berner, Seth McGinnis, Arnold Kazadi, Negin Sobhani, Ben Kirk, and David John Gagne II. “Community Research Earth Digital Intelligence Twin (CREDIT).” arXiv preprint arXiv:2411.07814 (2024).
Rampal, Neelesh, Sanaa Hobeichi, Peter B. Gibson, Jorge Baño-Medina, Gab Abramowitz, Tom Beucler, Jose González-Abad, William Chapman, Paula Harder, and José Manuel Gutiérrez. “Enhancing regional climate downscaling through advances in machine learning.” Artificial Intelligence for the Earth Systems, 3(2), 230066 (2024).
Higgins, Timothy B., Aneesh C. Subramanian, Will E. Chapman, David A. Lavers, and Andrew C. Winters. “Subseasonal potential predictability of horizontal water vapor transport and precipitation extremes in the North Pacific.” Weather and Forecasting, 39(6), 833-846 (2024).
Mayer, Kirsten J., William E. Chapman, and William A. Manriquez. “Exploring the relative importance of the MJO and ENSO to North Pacific subseasonal predictability.” Geophysical Research Letters, 51(10), e2024GL108479 (2024).
Wirz, Christopher D., Carly Sutter, Julie L. Demuth, Kirsten J. Mayer, William E. Chapman, Mariana Goodall Cains, Jacob Radford, Vanessa Przybylo, Aaron Evans, Thomas Martin, et al. “Increasing the reproducibility and replicability of supervised AI/ML in the Earth systems science by leveraging social science methods.” Earth and Space Science, 11(7), e2023EA003364 (2024).
Balwada, Dhruv, Ryan Abernathey, Shantanu Acharya, Alistair Adcroft, Judith Brener, V. Balaji, Mohamed Aziz Bhouri, Joan Bruna, Mitch Bushuk, Will Chapman, et al. “Learning Machine Learning with Lorenz-96.” Journal of Open Source Education, 7(82), 241 (2024).
Chapman, William E., and Judith Berner. “A State-Dependent Model-Error Representation for Online Climate Model Bias Correction.” Authorea Preprints (2024).
Pope, James, Md Hassanuzzaman, William Chapman, Huw Day, Mingmar Sherpa, Omar Emara, Nirmala Adhikari, and Ayush Joshi. “Skin cancer machine learning model tone bias.” arXiv preprint arXiv:2410.06385 (2024).
2023
Chapman, William E., and Judith Berner. “Deterministic and Stochastic Tendency Adjustments Derived from Data Assimilation and Nudging.” Quarterly Journal of the Royal Meteorological Society (2023).
Chapman, William E., and Judith Berner. “Benefits of Deterministic and Stochastic Tendency Adjustments in a Climate Model.” arXiv preprint arXiv:2308.15295 (2023).
Schevenhoven, Francine, Noel Keenlyside, François Counillon, Alberto Carrassi, William E. Chapman, Marion Devilliers, Alok Gupta, Shunya Koseki, Frank Selten, Mao-Lin Shen, et al. “Supermodeling: improving predictions with an ensemble of interacting models.” Bulletin of the American Meteorological Society, 104(9), E1670-E1686 (2023).
Badrinath, Anirudhan, Luca Delle Monache, Negin Hayatbini, Will Chapman, Forest Cannon, and Marty Ralph. “Improving precipitation forecasts with convolutional neural networks.” Weather and Forecasting, 38(2), 291-306 (2023).
Hu, Weiming, Mohammadvaghef Ghazvinian, William E. Chapman, Agniv Sengupta, Fred Martin Ralph, and Luca Delle Monache. “Deep learning forecast uncertainty for precipitation over the Western United States.” Monthly Weather Review, 151(6), 1367-1385 (2023).
Higgins, Timothy B., Aneesh C. Subramanian, Andre Graubner, Lukas Kapp-Schwoerer, Peter A. G. Watson, Sarah Sparrow, Karthik Kashinath, Sol Kim, Luca Delle Monache, and Will Chapman. “Using deep learning for an analysis of atmospheric rivers in a high-resolution large ensemble climate data set.” Journal of Advances in Modeling Earth Systems, 15(4), e2022MS003495 (2023).
Balwada, Dhruv, Ryan Abernathey, Shantanu Acharya, Alistair Adcroft, Judith Brener, V. Balaji, Mohamed Aziz Bhouri, Joan Bruna, Mitch Bushuk, Will Chapman, et al. “Learning Machine Learning with Lorenz-96.” Authorea Preprints (2023).
2022
Chapman, William E., Luca Delle Monache, Stefano Alessandrini, Aneesh C. Subramanian, F. Martin Ralph, Shang-Ping Xie, Sebastian Lerch, and Negin Hayatbini. “Probabilistic predictions from deterministic atmospheric river forecasts with deep learning.” Monthly Weather Review, 150(1), 215-234 (2022).
2021
Chapman, William E., Aneesh C. Subramanian, Shang-Ping Xie, Michael D. Sierks, F. Martin Ralph, and Youichi Kamae. “Monthly modulations of ENSO teleconnections: Implications for potential predictability in North America.” Journal of Climate, 34(14), 5899-5921 (2021).
Gibson, Peter B., William E. Chapman, Alphan Altinok, Luca Delle Monache, Michael J. DeFlorio, and Duane E. Waliser. “Training machine learning models on climate model output yields skillful interpretable seasonal precipitation forecasts.” Communications Earth & Environment, 2(1), 159 (2021).
Haupt, Sue Ellen, William Chapman, Samantha V. Adams, Charlie Kirkwood, J. Scott Hosking, Niall H. Robinson, Sebastian Lerch, and Aneesh C. Subramanian. “Towards implementing artificial intelligence post-processing in weather and climate: Proposed actions from the Oxford 2019 workshop.” Philosophical Transactions of the Royal Society A, 379(2194), 20200091 (2021).
Meech, Scott, Stefano Alessandrini, William Chapman, and Luca Delle Monache. “Post-processing rainfall in a high-resolution simulation of the 1994 Piedmont flood.” Bulletin of Atmospheric Science and Technology, 1-13 (2021).
2020
Schamberg, Gabriel, William Chapman, Shang-Ping Xie, and Todd P. Coleman. “Direct and indirect effects—An information theoretic perspective.” Entropy, 22(8), 854 (2020).
Wilson, Anna M., William Chapman, Ashley Payne, Alexandre M. Ramos, Christoph Boehm, Diego Campos, Jason Cordeira, Rene Garreaud, Irina V. Gorodetskaya, Jonathan J. Rutz, et al. “Training the next generation of researchers in the science and application of atmospheric rivers.” Bulletin of the American Meteorological Society, 101(6), E738-E743 (2020).
2019
Chapman, William E., Aneesh C. Subramanian, Luca Delle Monache, Shang-Ping Xie, and F. Martin Ralph. “Improving atmospheric river forecasts with machine learning.” Geophysical Research Letters, 46(17-18), 10627-10635 (2019). https://doi.org/10.1029/2019GL083662
2017
Jacobson, Mark Z., Mark A. Delucchi, Zack A. F. Bauer, Savannah C. Goodman, William E. Chapman, Mary A. Cameron, Cedric Bozonnat, Liat Chobadi, Hailey A. Clonts, Peter Enevoldsen, et al. “100% clean and renewable wind, water, and sunlight all-sector energy roadmaps for 139 countries of the world.” Joule, 1(1), 108-121 (2017). https://doi.org/10.1016/j.joule.2017.07.005