Preprints
- (Submitted) Grundner, A., T. Beucler, J. Savre, A. Lauer, M. Schlund & V. Eyring: Reduced Cloud Cover Errors in a Hybrid AI-Climate Model Through Equation Discovery And Automatic Tuning.
- (Submitted) Leclerc, A., E. Koch, M. Feldmann, D. Nerini, T. Beucler: Improving Predictions of Convective Storm Wind Gusts through Statistical Post-Processing of Neural Weather Models.
- (Submitted) Yu, S., Z. Hu, A. Subramaniam, W. Hannah, L. Peng, J. Lin, M. Bhouri, R. Gupta, B. Lütjens, J. Will, G. Behrens, J. Busecke, N. Loose, C. Stern, T. Beucler, … & M. Pritchard: ClimSim-Online: A Large Multi-scale Dataset and Framework for Hybrid ML-physics Climate Emulation.
- (Submitted) Gomez, M., & T. Beucler: Lessons Learned: Reproducibility, Replicability, and When to Stop.
2025
- (Accepted) Ricard, L., T. Beucler, C. Stephan & A. Nenes: A Causal Intercomparison framework unravels precipitation drivers in Global Storm-Resolving Models. npj climate and atmospheric science.
- (Climate Informatics 2025 Conference) Tam, F. I., F. Augsburger & T. Beucler: From Winter Storm Thermodynamics to Wind Gust Extremes: Discovering Interpretable Equations from Data.
- Beucler, T., A. Grundner, S. Shamekh, P. Ukkonen, M. Chantry & R. Lagerquist: Distilling Machine Learning’s Added Value: Pareto Fronts in Atmospheric Applications. Artificial Intelligence for the Earth Systems [pdf].
- Behrens, G., T. Beucler, F. Iglesias-Suarez, S. Yu, P. Gentine, M. Pritchard, M. Schwabe & V. Eyring: Simulating Atmospheric Processes in Earth System Models and Quantifying Uncertainties with Deep Learning Multi-Member and Stochastic Parameterizations. Journal of Advances in Modeling Earth Systems [pdf].
- Lin, J., S. Yu, L. Peng, T. Beucler, E. Wong-Toi, Z. Hu, P. Gentine, M. Geleta & M. Pritchard: Navigating the Noise: Bringing Clarity to ML Parameterization Design with O(100) Ensembles. Journal of Advances in Modeling Earth Systems [pdf].
- Aarnink, J., T. Beucler, M. Vuaridel & V. Ruiz-Villanueva: Automatic detection of instream large wood in videos using deep learning. Earth Surface Dynamics, 13, 167–189.
2024
- Cache, T., M. Gomez, T. Beucler, J. Blagojevic, J. Leitao & N. Peleg: Enhancing generalizability of data-driven urban flood models by incorporating contextual information. Hydrology and Earth System Sciences.
- Tam, F. I., T. Beucler & J. Ruppert: Identifying Three-Dimensional Radiative Patterns Associated with Early Tropical Cyclone Intensification. Journal of Advances in Modeling Earth Systems [pdf].
- Feldmann, M., T. Beucler, M. Gomez & O. Martius: Lightning-Fast Convective Outlooks: Predicting Severe Convective Environments with Global AI-based Weather Models. Geophysical Research Letters [pdf].
- Christopoulos, C., I. Lopez-Gomez, T. Beucler, Y. Cohen, C. Kawczynski, O. Dunbar & T. Schneider: Online Learning of Entrainment Closures in a Hybrid Machine Learning Parameterization. Journal of Advances in Modeling Earth Systems, 16, e2024MS004485.
- Eyring, V., W.D. Collins, P. Gentine, E.A. Barnes, M. Barreiro, T. Beucler, … & L. Zanna: Pushing the frontiers in climate modeling and analysis with machine learning. Nature Climate Change.
- (Whitepaper) Beucler, T., E. Koch, S. Kotlarski, D. Leutwyler, A. Michel & J. Koh: Next-Generation Earth System Models: Towards Reliable Hybrid Models for Weather and Climate Applications. SATW Whitepaper on “AI for Climate Change Mitigation”, 5.2.
- Rampal, N., S. Hobeichi, P. B. Gibson, J. Baño-Medina, G. Abramowitz, T. Beucler, J. González-Abad, W. Chapman, P. Harder & José Manuel Gutiérrez: Enhancing Regional Climate Downscaling Through Advances in Machine Learning. Artificial Intelligence for the Earth Systems, 3(2), 230066.
- Grundner, A., T. Beucler, P. Gentine & V. Eyring: Data-Driven Equation Discovery of a Cloud Cover Parameterization. Journal of Advances in Modeling Earth Systems, 16, e2023MS003763 [pdf].
- Iglesias-Suarez, F., P. Gentine, B. Solino-Fernandez, T. Beucler, M. Pritchard, J. Runge & V. Eyring: Causally-informed deep learning to improve climate models and projections. Journal of Geophysical Research: Atmospheres, 129, e2023JD039202 [pdf].
- Mooers, G., T. Beucler, M. Pritchard & S. Mandt: Understanding Precipitation Changes through Unsupervised Machine Learning. Environmental Data Science, 3, e3 [pdf].
- Beucler, T., P. Gentine, J. Yuval, A. Gupta, L. Peng, J. Lin, S. Yu, S. Rasp, F. Ahmed, P. O’Gorman, D. Neelin, N. Lutsko & M. Pritchard: Climate-Invariant Machine Learning. Science Advances, 10, eadj7250 [pdf].
2023
- Beucler, T., I. Ebert-Uphoff, S. Rasp, M. Pritchard & P. Gentine: Machine Learning for Clouds and Climate. Clouds and Their Climatic Impact: Radiation, Circulation, and Precipitation, edited by: Sullivan, SC and Hoose, C., Wiley–American Geophysical Union: 327-346. [pdf]
- (Workshop) Lin, J., M. A. Bhouri, T. Beucler, S. Yu & M. Pritchard: Stress-testing the coupled behavior of hybrid physics-machine learning climate simulations on an unseen, warmer climate. 2023 Conference on Neural Information Processing Systems.
- Mooers, G., M. Pritchard, T. Beucler, P. Srivastava, H. Mangipudi, L. Peng, P. Gentine & S. Mandt: Comparing Storm Resolving Models and Climates via Unsupervised Machine Learning. Scientific Reports. [pdf]
- Zanetta, F., D. Nerini, T. Beucler & M. Liniger: Physics-constrained deep learning postprocessing of temperature and humidity. Artificial Intelligence for the Earth Systems, 2, e220089. [pdf]
- (NeurIPS 2023 Conference) Yu, S., W. Hannah, L. Peng, M. Bhouri, R. Gupta, J. Lin, B. Lütjens, J. Will, G. Behrens, J. Busecke, N. Loose, C. Stern, T. Beucler, … & M. Pritchard: ClimSim: A large multi-scale dataset for hybrid physics-machine learning climate emulation. Advances in Neural Information Processing Systems. “Oustanding Datasets and Benchmarks” award. [pdf]
- Ganesh S., S., T. Beucler, F. I. Tam, M. Gomez, J. Runge & A. Gerhardus: Selecting Robust Features for Machine-Learning Applications using Multidata Causal Discovery. Environmental Data Science, 2: e27. [pdf]
2022
- Grundner, A., T. Beucler, P. Gentine, F. Iglesias-Suarez, M. Giorgetta & V. Eyring: Deep Learning Based Cloud Cover Parameterization for ICON. Journal of Advances in Modeling Earth Systems, e2021MS002959. [pdf]
- Wu, Z., T. Beucler, E. Székely, W. Ball & D. Domeisen: Modeling Stratospheric Polar Vortex Variation and Identifying Vortex Extremes Using Explainable Machine Learning. Environmental Data Science 1: e17. [pdf]
- Behrens, G., T. Beucler, P. Gentine, F. Iglesias-Suarez, M. Pritchard & V. Eyring: 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]
2021
- (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.
- Mooers, G., M. Pritchard, T. Beucler, J. Ott, G. Yacalis, P. Baldi & P. Gentine: 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., M. Pritchard, S. Rasp, J. Ott, P. Baldi & P. Gentine: Enforcing Analytic Constraints in Neural-Networks Emulating Physical Systems. Physical Review Letters, 126.9: 098302. Editors’ Suggestion. [pdf]
2020
- 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., M. Pritchard, P. Gentine & S. Rasp: Towards Physically-Consistent, Data-Driven Models of Convection. IEEE International Geoscience and Remote Sensing Symposium 2020. [pdf]
- (Climate Informatics 2020 Conference) 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]
2016-2019
- 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., S. Rasp, M. Pritchard & P. Gentine: 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.: Self-aggregation phenomenon in cyclogenesis, Masters Thesis in Fluid Mechanics.
Presentation Recordings
2024
- Beucler, T.: Data-Driven Parameterization of Cloud Processes: From Deep Learning to Equation Discovery. CleanCloud Monthly Seminar Series.
- Beucler, T.: Tropical precipitation in a changing climate. Joint CLIMACT-ECCE seminar.
- Beucler, T.: Atmospheric physics-guided machine learning for climate modeling and weather forecasting. US CLIVAR PPAI Webinar.
- Beucler, T.: Causal Feature Selection for Tropical Cyclone Intensity Forecasting. 104th AMS Annual Meeting.
2023
- Beucler, T. & M. McGraw: AI for tropical meteorology: Challenges and opportunities. ITU“AI for Good” seminar series.
- Beucler, T.: Generating Climate Model Hierarchies from Data using ML. Core Science Keynote, 103rd AMS Annual Meeting.
2022
- Beucler, T.: Atmospheric Physics-Guided Machine Learning for Climate Modeling and Weather Forecasting. ESiWACE2 2nd Virtual Workshop on Emerging Technologies for Weather and Climate Modelling
- Beucler, T.: Climate-Invariant Machine Learning. AGCI Workshop on “Exploring the Frontiers in Earth System Modeling with Machine Learning and Big Data”
- Beucler, T.: Atmospheric Physics-Guided Machine Learning. IXXI Conference on ML and sampling methods for climate and physics (short version here from AMLD)
- Beucler, T.: Physics-Guided and Causally-Informed Machine Learning for Climate Modelling. ECMWF Machine Learning Workshop
2020
- 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
2016-2019
- 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