Exploring the Relative Importance of the MJO and ENSO to North Pacific Subseasonal Predictability von Mayer, Kirsten J., Chapman, William E., Manriquez, William A.

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Monthly Modulations of ENSO Teleconnections: Implications for Potential Predictability in North America von Chapman, William E., Subramanian, Aneesh C., Xie, Shang-Ping, Sierks, Michael D., Ralph, F. Martin, Kamae, Youichi

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Training machine learning models on climate model output yields skillful interpretable seasonal precipitation forecasts von Gibson, Peter B., Chapman, William E., Altinok, Alphan, Delle Monache, Luca, DeFlorio, Michael J., Waliser, Duane E.

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Deterministic and stochastic tendency adjustments derived from data assimilation and nudging von Chapman, William E., Berner, Judith

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Increasing the Reproducibility and Replicability of Supervised AI/ML in the Earth Systems Science by Leveraging Social Science Methods von Wirz, Christopher D., Sutter, Carly, Demuth, Julie L., Mayer, Kirsten J., Chapman, William E., Cains, Mariana Goodall, Radford, Jacob, Przybylo, Vanessa, Evans, Aaron, Martin, Thomas, Gaudet, Lauriana C., Sulia, Kara, Bostrom, Ann, Gagne, David John, Bassill, Nick, Schumacher, Andrea, Thorncroft, Christopher

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Increase in MJO predictability under global warming von Du, Danni, Subramanian, Aneesh C., Han, Weiqing, Chapman, William E., Weiss, Jeffrey B., Bradley, Elizabeth

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100% Clean and Renewable Wind, Water, and Sunlight All-Sector Energy Roadmaps for 139 Countries of the World von Jacobson, Mark Z., Delucchi, Mark A., Bauer, Zack A.F., Goodman, Savannah C., Chapman, William E., Cameron, Mary A., Bozonnat, Cedric, Chobadi, Liat, Clonts, Hailey A., Enevoldsen, Peter, Erwin, Jenny R., Fobi, Simone N., Goldstrom, Owen K., Hennessy, Eleanor M., Liu, Jingyi, Lo, Jonathan, Meyer, Clayton B., Morris, Sean B., Moy, Kevin R., O'Neill, Patrick L., Petkov, Ivalin, Redfern, Stephanie, Schucker, Robin, Sontag, Michael A., Wang, Jingfan, Weiner, Eric, Yachanin, Alexander S.

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Probabilistic Predictions from Deterministic Atmospheric River Forecasts with Deep Learning von Chapman, William E., Delle Monache, Luca, Alessandrini, Stefano, Subramanian, Aneesh C., Ralph, F. Martin, Xie, Shang-Ping, Lerch, Sebastian, Hayatbini, Negin

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Deep Learning Forecast Uncertainty for Precipitation over the Western United States von Hu, Weiming, Ghazvinian, Mohammadvaghef, Chapman, William E., Sengupta, Agniv, Ralph, Fred Martin, Delle Monache, Luca

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Supermodeling: Improving Predictions with an Ensemble of Interacting Models von Schevenhoven, Francine, Keenlyside, Noel, Counillon, François, Carrassi, Alberto, Chapman, William E., Devilliers, Marion, Gupta, Alok, Koseki, Shunya, Selten, Frank, Shen, Mao-Lin, Wang, Shuo, Weiss, Jeffrey B., Wiegerinck, Wim, Duane, Gregory S.

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Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications von Schreck, John S., Gagne, David John, Becker, Charlie, Chapman, William E., Elmore, Kim, Fan, Da, Gantos, Gabrielle, Kim, Eliot, Kimpara, Dhamma, Martin, Thomas, Molina, Maria J., Przybylo, Vanessa M., Radford, Jacob, Saavedra, Belen, Willson, Justin, Wirz, Christopher

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Differentiating between Squamous Cell Carcinoma and Pigmented Squamous Cell Carcinoma von Chang, Christopher Y., Chapman, William E., Furdyna, Julia A.

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Benefits of Deterministic and Stochastic Tendency Adjustments in a Climate Model von Chapman, William E, Berner, Judith

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Jejunal Diverticular Hemorrhage Localized by Red Blood Cell Scintigraphy von SCHUSTER, DAVID M, CHAPMAN, WILLIAM E, AHL, ERNEST T, AHEARNE, PAUL

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Benefits of Deterministic and Stochastic Tendency Adjustments in a Climate Model von Chapman, William E, Berner, Judith

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Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications von Schreck, John S, Gagne, David John, Becker, Charlie, Chapman, William E, Elmore, Kim, Fan, Da, Gantos, Gabrielle, Kim, Eliot, Kimpara, Dhamma, Martin, Thomas, Molina, Maria J, Pryzbylo, Vanessa M, Radford, Jacob, Saavedra, Belen, Willson, Justin, Wirz, Christopher

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IVT postprocessing model V 1.0. In Integrated Vapor Transport Forecast Models, Reanalysis, and Postprocessing Machine Learning Models for the North American West Coast [2008-2017] von Chapman, William E., Kawzenuk, Brian

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Babesia rodhaini: Requirement of Complement for Penetration of Human Erythrocytes von Chapman, William E., Ward, Peter A.

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The Complement Profile in Babesiosis von Chapman, William E, Ward, Peter A

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Data V 1.0. In Data from: Towards Implementing AI Post-processing in Weather and Climate: Proposed Actions from the Oxford 2019 Workshop von Chapman, William E., Lerch, Sebastian, Kirkwood, Charlie, Subramanian, Aneesh C., Matsueda, Mio, Haupt, Sue E.

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