Feel free to email me (wchapman at ucar.edu) if you would like a pdf of any of these papers.
See also my Google Scholar page.
Chapman, William E., and Judith Berner. “Benefits of Deterministic and Stochastic Tendency Adjustments in a Climate Model.” arXiv preprint arXiv:2308.15295 (2023).
Christopher D. Wirz, Carly Sutter, Julie L. Demuth, Kirsten J. Mayer, William E. Chapman, et al. “Increasing the reproducibility, replicability, and evaluability of supervised AI/ML in the earth systems science by leveraging social science methods.” In Review (2023).
Schevenhoven, Francine, Noel Keenlyside, François Counillon, Alberto Carrassi, William E. Chapman, Marion Devilliers, Alok Gupta et al. “Supermodeling: improving predictions with an ensemble of interacting models.” Bulletin of the American Meteorological Society (2023).
Badrinath A, Delle Monache L, Hayatbini N, Chapman W, Cannon F, Ralph M. Improving precipitation forecasts with convolutional neural networks. Weather and Forecasting. 2023 Feb;38(2):291-306.
Hu, W., Ghazvinian, M., Chapman, W. E., Sengupta, A., Ralph, F. M., & Delle Monache, L. (2023). Deep Learning Forecast Uncertainty for Precipitation over the Western United States. Monthly Weather Review, 151(6), 1367-1385.
Chapman, W., Delle Monache, L., Alessandrini, S., Subramanian, A. C., Ralph, F. M., Xie, S. P., … & Hayatbini, N. (2021). Probabilistic Predictions from Deterministic Atmospheric River Forecasts with Deep Learning. Monthly Weather Review.https://doi.org/10.1175/JCLI-D-20-0391.1
Gibson, P. B., Chapman, W., Altinok, A., Delle Monache, L., DeFlorio, M. J., & Waliser, D. E. (2021). Training machine learning models on climate model output yields skillful interpretable seasonal precipitation forecasts. Communications Earth & Environment, 2(1), 1-13.https://doi.org/10.1038/s43247-021-00225-4
S. E. Haupt, W. Chapman, C. Kirkwood, S. Adams, S. Lerch, S. Hoskins, N. Robinson, and A. C. Subramanian, “Towards implementing ai post-processing in weather and climate: Proposed actions from the oxford 2019 workshop”, Philosophical Transactions of the Royal Society A, accepted, 2020.
W. Chapman, S. E. Haupt, C. Kirkwood, S. Lerch, M. Matsueda, and A. C. Subramanian, “Data from: Towards implementing ai post-processing in weather and climate: Proposed actions from the oxford 2019 workshop”, 2020. https://doi.org/10.6075/J08S4NDM
S. Meech, S. Alessendrini, W. Chapman, and L. Delle Monache, “Post-processing of rainfall high-resolution simulation of the 1994 piedmont flood”, Bulletin of Atmospheric Science and Technology, (2021). https://doi.org/10.1007/s42865-020-00028-z.
Schamberg, G., Chapman, W., Xie, S. P., & Coleman, T. P. (2020). Direct and Indirect Effects—An Information Theoretic Perspective. Entropy, 22(8), 854. https://doi.org/10.3390/e22080854
Kashinath, K., Mudigonda, M., Kim, S., Kapp-Schwoerer, L., Graubner, A., Karaismailoglu, E., … & Lewis, C. (2020). ClimateNet: an expert-labelled open dataset and Deep Learning architecture for enabling high-precision analyses of extreme weather. Geoscientific Model Development Discussions, 1-28.https://doi.org/10.5194/gmd-2020-72
Wilson, A. M., Chapman, W., Payne, A., Ramos, A. M., Boehm, C., Campos, D., … & Viceto, C. (2020). Training the Next Generation of Researchers in the Science and Application of Atmospheric Rivers. Bulletin of the American Meteorological Society, 101(6), E738-E743. https://doi.org/10.1175/BAMS-D-19-0311.1
Chapman, W. E., Subramanian, A. C., Delle Monache, L., Xie, S. P., & Ralph, F. M. (2019). Improving atmospheric river forecasts with machine learning. Geophysical Research Letters, 46(17-18), 10627-10635. https://doi.org/10.1029/2019GL083662
Jacobson, M. Z., Delucchi, M. A., Bauer, Z. A., Goodman, S. C., Chapman, W. E., Cameron, M. A., … & Erwin, J. R. (2017). 100% clean and renewable wind, water, and sunlight all-sector energy roadmaps for 139 countries of the world. Joule, 1(1), 108-121. https://doi.org/10.1016/j.joule.2017.07.005
Yu, Y., Moy, K. R., Chapman, W. E., O’Neill, P. L., & Rajagopal, R. (2016, July). Assessing climate change vulnerability of microgrid systems. In 2016 IEEE Power and Energy Society General Meeting (PESGM) (pp. 1-5). IEEE. https://ieeexplore.ieee.org/document/77420611 peer reviewed conference paper
A. Jakubisin, W. Chapman , and M. Sierks, “Sustainability and the Student Affairs Professional”, National Association of Student Personnel Administrators Annual Conference, March 2015 peer reviewed conference paper
Under Review
Wirz, C.D., Sutter, C., Demuth, J. L., Mayer, K. J., Chapman, W. E., Cains, M. G., Radford, J., Przybylo, V., Evans, A., Martin, T., Gaudet, L. C., Sulia, K., Bostrom, A., Gagne, D. J., Bassil, N., Schumacher, A., and Thorncroft, C. (Under review). Increasing the reproducibility, replicability, and evaluability of supervised AI/ML in the earth systems science by leveraging social science methods.