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General circulation models (GCMs) are the foundation of weather and climate prediction.
A Multi-Level Spectral Model. I. Formulation and Hemispheric Integrations
Bourke, W · 1974
Earlier work this paper cites.
Normal mode initialization
Daley, R · 1981
Earlier work this paper cites.
An introduction to dynamic meteorology Fifth edn (Elsevier Academic Press, Waltham, MA, USA, 2004)
Holton, J. R · 2004
Earlier work this paper cites.
Marine boundary layer clouds at the heart of tropical cloud feedback uncertainties in climate models
Bony, S. & Dufresne, J.-L · 2005
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Strictly Proper Scoring Rules, Prediction, and Estimation
Gneiting, T. & Raftery, A. E · 2007
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Toward elimination of the warm bias in historic radiosonde temperature records—some new results from a comprehensive intercomparison of upper-air data
Haimberger, L., Tavolato, C. & Sperka, S · 2008
Earlier work this paper cites.
Intercomparison of spatial forecast verification methods
Gilleland, E., Ahijevych, D., Brown, B. G., Casati, B. & Ebert, E. E · 2009
Earlier work this paper cites.
Stochastic parametrization and model uncertainty (2009)
Palmer, T. N. et al · 2009
Earlier work this paper cites.
Numerical methods for fluid dynamics: With applications to geophysics Second edn, Vol. 32 (Springer, New York, 2010)
Durran, D. R · 2010
Earlier work this paper cites.
The pros and cons of diffusion, filters and fixers in atmospheric general circulation models
Jablonowski, C. & Williamson, D. L · 2011
Earlier work this paper cites.
in The pros and cons of diffusion, filters and fixers in atmospheric general circulation models
Jablonowski, C. & Williamson, D. L · 2011
Earlier work this paper cites.
Managing the risks of extreme events and disasters to advance climate change adaptation: special report of the intergovernmental panel on climate change (Cambridge University Press, Cambridge, UK, 2012)
Field, C. B · 2012
Earlier work this paper cites.
Origins of differences in climate sensitivity, forcing and feedback in climate models
Webb, M. J., Lambert, F. H. & Gregory, J. M · 2013
Earlier work this paper cites.
Robust spatially aggregated projections of climate extremes
Fischer, E. M., Beyerle, U. & Knutti, R · 2013
Earlier work this paper cites.
Implicit–explicit runge–kutta methods for fast–slow wave problems
Whitaker, J. S. & Kar, S. K · 2013
Earlier work this paper cites.
Estimating model parameters with ensemble-based data assimilation: A review
Ruiz, J. J., Pulido, M. & Miyoshi, T · 2013
Earlier work this paper cites.
Spread in model climate sensitivity traced to atmospheric convective mixing
Sherwood, S. C., Bony, S. & Dufresne, J.-L · 2014
Earlier work this paper cites.
Why should ensemble spread match the RMSE of the ensemble mean?
Fortin, V., Abaza, M., Anctil, F. & Turcotte, R · 2014
Earlier work this paper cites.
Sequence to sequence learning with neural networks
Sutskever, I., Vinyals, O. & Le, Q. V · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. & Ba, J · 2014
Earlier work this paper cites.
The quiet revolution of numerical weather prediction
Bauer, P., Thorpe, A. & Brunet, G · 2015
Earlier work this paper cites.
Response of the large-scale structure of the atmosphere to global warming
Vallis, G. K., Zurita-Gotor, P., Cairns, C. & Kidston, J · 2015
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Overview of the coupled model intercomparison project phase 6 (cmip6) experimental design and organization
Eyring, V. et al · 2016
Cited alongside, same era.
The art and science of climate model tuning
Hourdin, F. et al · 2017
Cited alongside, same era.
Earth system modeling 2.0: A blueprint for models that learn from observations and targeted high-resolution simulations
Schneider, T., Lan, S., Stuart, A. & Teixeira, J · 2017
Cited alongside, same era.
Deep learning to represent subgrid processes in climate models
Rasp, S., Pritchard, M. S. & Gentine, P · 2018
Cited alongside, same era.
JAX: composable transformations of Python+NumPy programs (2018)
GSPMD: General and scalable parallelization for ML computation graphs (2021)
Xu, Y. et al · 2021
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Are general circulation models obsolete?
Balaji, V. et al · 2022
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Forecasting global weather with graph neural networks
Keisler, R · 2022
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Non-local parameterization of atmospheric subgrid processes with neural networks
Wang, P., Yuval, J. & O’Gorman, P. A · 2022
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Impact of warmer sea surface temperature on the global pattern of intense convection: insights from a global storm resolving model
Cheng, K.-Y. et al · 2022
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Bradbury, J. et al · 2018
Cited alongside, same era.
Neural networks for postprocessing ensemble weather forecasts
Rasp, S. & Lerch, S · 2018
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Relational inductive biases, deep learning, and graph networks
Battaglia, P. W. et al · 2018
Cited alongside, same era.
The scientific challenge of understanding and estimating climate change
Palmer, T. & Stevens, B · 2019
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Toward convective-scale prediction within the next generation global prediction system
Zhou, L. et al · 2019
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Deep learning and process understanding for data-driven earth system science
Reichstein, M. et al · 2019
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Spatially extended tests of a neural network parametrization trained by coarse-graining
Brenowitz, N. D. & Bretherton, C. S · 2019
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Metpy: A meteorological python library for data analysis and visualization
May, R. M. et al · 2022
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WeatherBench 2: A benchmark for the next generation of data-driven global weather models (2023)
Rasp, S. et al · 2023
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Accurate medium-range global weather forecasting with 3d neural networks
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Learning skillful medium-range global weather forecasting
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On the limitations of data-driven weather forecasting models
Bonavita, M · 2023
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ACE: A fast, skillful learned global atmospheric model for climate prediction
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Old dog, new trick: Reservoir computing advances machine learning for climate modeling
Bretherton, C. S · 2023
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Machine-learned climate model corrections from a global storm-resolving model: Performance across the annual cycle
Kwa, A. et al · 2023
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A hybrid atmospheric model incorporating machine learning can capture dynamical processes not captured by its physics-based component
Arcomano, T., Szunyogh, I., Wikner, A., Hunt, B. R. & Ott, E · 2023
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An ensemble of neural networks for moist physics processes, its generalizability and stable integration
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Differentiable programming for earth system modeling
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shmap (shard_map) for simple per-device code (2023)
Douglas, S., Vikram, S., Bradbury, J., Zhang, Q. & Johnson, M · 2023
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Opinion: Optimizing climate models with process-knowledge, resolution, and AI
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Climate-invariant machine learning
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