(Submitted) Mooers, G., M. Pritchard, T. Beucler et al.: Comparing Storm Resolving Models and Climates via Unsupervised Machine Learning.
(Submitted) Grundner, A., T. Beucler et al.: Deep Learning Based Cloud Cover Parameterization for ICON.
(Submitted) Beucler, T. et al.: Climate-Invariant Machine Learning.
(In press) Beucler, T. et al.: Machine Learning for Clouds and Climate (Invited Chapter for the AGU Geophysical Monograph Series: Clouds and Climate).
Behrens, G., T. Beucler et al.: Non‐Linear Dimensionality Reduction with a Variational Encoder Decoder to Understand Convective Processes in Climate Models. Journal of Advances in Modeling Earth Systems, e2022MS003130. [pdf]
(Workshop) Mangipudi, H., G. Mooers, M. Pritchard, T. Beucler & S. Mandt: Analyzing High-Resolution Clouds and Convection using Multi-Channel VAEs. 2021 Conference on Neural Information Processing Systems.
Gentine, P., V. Eyring & T. Beucler: Deep Learning for the Parametrization of Subgrid Processes in Climate Models. Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote Sensing, Climate Science, and Geosciences, 307-314.
Griffin, M., M. Pritchard, T. Beucler et al.: Assessing the Potential of Deep Learning for Emulating Cloud Superparameterization in Climate Models with Real-Geography Boundary Conditions. Journal of Advances in Modeling Earth Systems, 13, e2020MS002385. [pdf]
Beucler, T. et al.: Enforcing Analytic Constraints in Neural-Networks Emulating Physical Systems. Physical Review Letters, 126.9: 098302. Editors’ Suggestion. [pdf]
Brenowitz, N., T. Beucler, M. Pritchard & C. Bretherton: Interpreting and Stabilizing Machine-Learning Parametrizations of Convection. Journal of the Atmospheric Sciences, 77, 4357-4375.
(Workshop) Beucler, T. et al.: Towards Physically-Consistent, Data-Driven Models of Convection. IEEE International Geoscience and Remote Sensing Symposium 2020. [pdf]
(Workshop) Mooers, G., J. Tuyls, S. Mandt, M. Pritchard & T. Beucler: Generative Modeling of Atmospheric Convection.Proceedings of the 10th International Conference on Climate Informatics, 98-105. [pdf]
Beucler, T., D. Leutwyler & J. Windmiller: Quantifying Convective Aggregation Using the Tropical Moist Margin’s Length. Journal of Advances in Modeling Earth Systems, 12, e2020MS002092. [pdf]
Abbott, T., T. Cronin & T. Beucler: Convective dynamics and the response of precipitation extremes to warming in radiative-convective equilibrium. Journal of the Atmospheric Sciences, 77, 1637-1660. [pdf]
Beucler, T., T. Abbott, T. Cronin & M. Pritchard: Comparing Convective Self‐Aggregation in Idealized Models to Observed Moist Static Energy Variability Near the Equator. Geophysical Research Letters, 46, 17-18. [pdf]
(Workshop) Beucler, T. et al.: Achieving Conservation of Energy in Neural Network Emulators for Climate Modeling. 2019 International Conference on Machine Learning.
(Thesis) Beucler, T.: Interaction between Water Vapor, Radiation and Convection in the Tropics, Ph.D. Thesis in Atmospheric Science.
Beucler, T. & T. Cronin: A Budget for the Size of Convective Self-Aggregation. Quarterly Journal of the Royal Meteorological Society, 145, 947-966.
Beucler, T., T. Cronin & K. Emanuel: A Linear Response Framework for Radiative-Convective Instability. Journal of Advances in Modeling Earth Systems, 10(8), 1924-1951.
Beucler, T. & T. Cronin: Moisture-Radiative Cooling Instability. Journal of Advances in Modeling Earth Systems, 8, 1620–1640.
Beucler, T.: A Correlated Stochastic Model for the Large-scale Advection, Condensation and Diffusion of Water Vapour. Quarterly Journal of the Royal Meteorological Society, 142, 1721–1731.
(Thesis) Beucler, T. & K. Emanuel: Self-aggregation phenomenon in cyclogenesis, Masters Thesis in Fluid Mechanics.
Selected Conference Presentations
Beucler, T.: Physics-Guided and Causally-Informed Machine Learning for Climate Modelling. ECMWF Machine Learning Workshop
Beucler, T.: Climate-Invariant Nets: Physical Rescalings Help NNs Generalize to Out-of-sample Climates. SIAM Mathematics of Planet Earth 2020
Beucler, T.: Towards Physically-Consistent, Data-Driven and Interpretable Models of Convection. NOAA STAR Artificial Intelligence Seminar
Beucler, T.: Building a Hierarchy of Hybrid, Neural Network Models of Convection. 100th American Meteorological Society Annual Meeting
Beucler, T.: Comparing Convective Self-Aggregation in Models to Obs. MSE Variability. 100th American Meteorological Society Annual Meeting
Beucler, T.: A Spectral Budget for the Size of Convective Self-Aggregation. 33rd Conference on Hurricanes and Tropical Meteorology
Beucler, T.: A Moist Static Energy Perspective on Atmospheric Rivers. 17th Conference on Mesoscale Processes
Beucler, T.: The Vertical Structure of Radiative-Convective Instability. 21st Conference on Atmospheric and Oceanic Fluid Dynamics
Beucler, T.: Instabilities of Radiative Convective Equilibrium with an Interactive Surface. 32nd Conference on Hurricanes and Tropical Meteorology