Tapio Schneider
Theodore Y. Wu Professor of Environmental Science and Engineering
Research Options
Profile
Our climate dynamics group studies atmospheric dynamics, both here on Earth and on other planets, on scales from clouds to the globe.
We aim to elucidate fundamental questions about climate such as, What controls the surface temperatures and winds? What shapes rainfall patterns? Where and when do clouds form in the atmosphere?
To answer such questions, we analyze observational data and perform systematic studies with numerical models, with which we simulate flows ranging from the meter-scale motions in clouds to global circulations. Thanks to the availability of unprecedented observations from space and ever increasing computational power, ours is the age in which the physical laws that govern climate as an aggregate system will likely be discovered. Our goal is to contribute to that discovery.
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2019 Rosenstiel Award, University of Miami (in recognition of "outstanding contributions to atmospheric science and climate dynamics")
2012 World Economic Forum Young Scientist
2010 Houghton Lecturer, Massachusetts Institute of Technology
2008 Discover Magazine "20 Best Brains under 40"
2005-2010 David and Lucile Packard Fellow
2004-2006 Alfred P. Sloan Research Fellow
2004 James R. Holton Junior Scientist Award, American Geophysical Union
Publications
- Patel, Ronak N.;Bonan, David B. et al. (2024) Changes in the Frequency of Observed Temperature Extremes Largely Driven by a Distribution ShiftGeophysical Research Letters
- Christopoulos, Costa;Lopez‐Gomez, Ignacio et al. (2024) Online Learning of Entrainment Closures in a Hybrid Machine Learning ParameterizationJournal of Advances in Modeling Earth System (JAMES)
- Wu, Jin-Long;Levine, Matthew E. et al. (2024) Learning about structural errors in models of complex dynamical systemsJournal of Computational Physics
- Chammas, Sheide;Wang, Qing et al. (2023) Accelerating Large-Eddy Simulations of Clouds With Tensor Processing UnitsJournal of Advances in Modeling Earth System (JAMES)
- Schneider, Tapio;Altintas, Ilkay et al. (2023) Accelerating Scientific Discovery With AI-Aided AutomationComputing in Science & Engineering
- Schneider, Tapio;Behera, Swadhin et al. (2023) Harnessing AI and computing to advance climate modelling and predictionNature Climate Change
- Braghiere, R. K.;Wang, Y. et al. (2023) The Importance of Hyperspectral Soil Albedo Information for Improving Earth System Model ProjectionsAGU Advances
- Griffies, Stephen M.;Fan, Jiwen et al. (2023) Thank You to Our 2022 Peer ReviewersJournal of Advances in Modeling Earth Systems
- Souza, A. N.;He, J. et al. (2023) The Flux‐Differencing Discontinuous Galerkin Method Applied to an Idealized Fully Compressible Nonhydrostatic Dry AtmosphereJournal of Advances in Modeling Earth Systems
- Griffies, Stephen M.;Fan, Jiwen et al. (2023) Aims and Scope of JAMESJournal of Advances in Modeling Earth Systems
- Souza, A. N.;He, J. et al. (2023) The Flux-Differencing Discontinuous Galerkin Method Applied to an Idealized Fully Compressible Nonhydrostatic Dry AtmosphereJournal of Advances in Modeling Earth Systems
- Levin, Jon;Pacala, Steve et al. (2023) Extreme Weather Risk in a Changing Climate: Enhancing prediction and protecting communities
- Griffies, Stephen M.;Fan, Jiwen et al. (2023) Aims and Scope of JAMESJournal of Advances in Modeling Earth Systems
- Schneider, Tapio;Stuart, Andrew M. et al. (2022) Ensemble Kalman inversion for sparse learning of dynamical systems from time-averaged dataJournal of Computational Physics
- Dunbar, Oliver R. A.;Howland, Michael F. et al. (2022) Ensemble-Based Experimental Design for Targeting Data Acquisition to Inform Climate ModelsJournal of Advances in Modeling Earth Systems
- Huang, Daniel Zhengyu;Schneider, Tapio et al. (2022) Iterated Kalman methodology for inverse problemsJournal of Computational Physics
- Lopez-Gomez, Ignacio;Christopoulos, Costa et al. (2022) Training physics‐based machine‐learning parameterizations with gradient‐free ensemble Kalman methodsJournal of Advances in Modeling Earth Systems
- Bieli, Melanie;Dunbar, Oliver R. A. et al. (2022) An Efficient Bayesian Approach to Learning Droplet Collision Kernels: Proof of Concept Using "Cloudy," a New n-Moment Bulk Microphysics SchemeJournal of Advances in Modeling Earth Systems
- Schneider, Tapio;Dunbar, Oliver R. A. et al. (2022) Epidemic management and control through risk-dependent individual contact interventionsPLoS Computational Biology
- De Jong, Emily K.;Bischoff, Tobias et al. (2022) Spanning the Gap from Bulk to Bin: A Novel Spectral Microphysics Method
- Griffies, Stephen M.;Blyth, Eleanor Mary et al. (2022) Thank You to Our 2021 ReviewersJournal of Advances in Modeling Earth Systems
- Lopez-Gomez, Ignacio;Christopoulos, Costa et al. (2022) Training physics-based machine-learning parameterizations with gradient-free ensemble Kalman methods
- Howland, Michael F.;Dunbar, Oliver R. A. et al. (2022) Parameter Uncertainty Quantification in an Idealized GCM With a Seasonal CycleJournal of Advances in Modelling Earth Systems
- Shen, Zhaoyi;Sridhar, Akshay et al. (2022) A Library of Large-Eddy Simulations Forced by Global Climate ModelsJournal of Advances in Modelling Earth Systems
- Bieli, Melanie;Dunbar, Oliver R. A. et al. (2022) An efficient Bayesian approach to learning droplet collision kernels: Proof of concept using "Cloudy", a new n-moment bulk microphysics scheme
Instructor: Schneider
Instructor: Schneider
Instructor: Schneider
Instructor: Schneider
Instructor: Schneider
Instructor: Schneider
Instructor: Schneider
Course Website: http://climate-dynamics.org/courses/ese-101-earths-atmosphere/