Pritchard, Michael S.

Deep learning to represent subgrid processes in climate models

Rasp, S., Pritchard, M. S., & Gentine, P. (2018) · Proceedings of the National Academy of Sciences

Meteorological Phenomena and SimulationsClimate variability and models

Cited by 1,044

Could Machine Learning Break the Convection Parameterization Deadlock?

Gentine, P., Pritchard, M., Rasp, S., Reinaudi, G., & Yacalis, G. (2018) · Geophysical Research Letters

Meteorological Phenomena and SimulationsClimate variability and models

Cited by 497

Enforcing Analytic Constraints in Neural Networks Emulating Physical Systems

Beucler, T., Pritchard, M., Rasp, S., Ott, J., Baldi, P., & Gentine, P. (2021) · Physical Review Letters

Model Reduction and Neural NetworksNeural Networks and Applications

Cited by 451

Forest response to rising CO2 drives zonally asymmetric rainfall change over tropical land

Kooperman, G. J., Chen, Y., Hoffman, F. M., Koven, C. D., Lindsay, K., Pritchard, M. S., Swann, A. L. S., & Randerson, J. T. (2018) · Nature Climate Change

Climate variability and modelsPlant Water Relations and Carbon Dynamics

Cited by 138

Social Constructivism, Mental Models, and Problems of Obedience

Werhane, P. H., Hartman, L. P., Moberg, D., Englehardt, E., Pritchard, M., & Parmar, B. (2011) · Journal of Business Ethics

Ethics in Business and EducationPsychology of Social Influence

Cited by 50

The response of US summer rainfall to quadrupled CO 2 climate change in conventional and superparameterized versions of the NCAR community atmosphere model

Kooperman, G. J., Pritchard, M. S., & Somerville, R. C. J. (2014) · Journal of Advances in Modeling Earth Systems

Climate variability and modelsMeteorological Phenomena and Simulations

Cited by 38