Publications

Preprints

1. (Submitted) Heuer, H., T. Beucler, M. Schwabe, J. Savre, M. Schlund & Veronika Eyring: Beyond the Training Data: Confidence-Guided Mixing of Parameterizations in a Hybrid AI-Climate Model.
2. (Submitted) Ferretti, S., J. Lin, S. Shamekh, J. W. Baldwin, M. S. Pritchard, T. Beucler: Data-Driven Integration Kernels for Interpretable Nonlocal Operator Learning.
3. (Submitted) Simm, M., C. Hoose & T. Beucler: Calibrated Conformal Prediction Intervals for Microphysical Process
    Rates.
4. (Submitted) Quarenghi, F., R. Cotsakis, T. Beucler: Emulating Non-Differentiable Metrics via Knowledge-Guided Learning: Introducing the Minkowski Image Loss.
5. (Submitted) Gomez, M., M. McGraw, S. Ganesh S., F. I.-H. Tam, I. Azizi, S. Darmon, M. Feldmann, S. Bourdin, L. Poulain- -Auzéau, S. J. Camargo, J. Lin, D. Chavas, C.-Y. Lee, R. Gupta, A. Jenney & T. Beucler: TCBench: A Benchmark for Tropical Cyclone Track and Intensity at the Global Scale.
6. (Submitted) Fons, E., I. L. McCoy, T. Beucler, D. Neubauer & U. Lohmann: Dissipating the correlation smokescreen: Causal decomposition of the radiative effects of biomass burning aerosols over the South-East Atlantic.
7. (Submitted) Ganesh S., S., F. I.-H. Tam, M. Gomez, M. McGraw, M. DeMaria, K. Musgrave, J. Runge & T. Beucler: Multidata Causal Discovery for Statistical Hurricane Intensity Forecasting.
8. (Submitted) Fatihi, A., J. Caldeira, T. Beucler, S. T. Thiele, and A. Samsu. Towards robust fracture mapping: benchmarking automatic fracture mapping in 2D outcrop imagery. EGUsphere 2026: 1-35.

2026

1. (Accepted) Lin, J., Z. Hu, T. Beucler, K. Frields, H. Christensen, W. Hannah, H. Heuer, … & M. Pritchard: Crowdsourcing the Frontier: Advancing Hybrid Physics-ML Climate Simulation via $50,000 Kaggle Competition. Journal of Advances in Modeling Earth Systems.
2. (Accepted) Ismaili, E., R. J. Wills & T. Beucler: Machine Learning of Vertical Fluxes by Unresolved Midlatitude Mesoscale Processes. Machine Learning: Earth.
3. (Accepted) Furtado, J. C., M. J. Molina, M. C. Arcodia, W. Anderson, T. Beucler, J. A. Callahan, L. M. Ciasto, … & B. G. Zimmerman: Setting the Standard: Recommended Practices for Data Preprocessing in Data-Driven Climate Prediction. Bulletin of the American Meteorological Society.
4. Largeau, L., E. Koch, D. Leutwyler, G. Mariethoz, V. Chavez-Demoulin & T. Beucler: Investigating the Robustness of Extreme Precipitation Super-Resolution Across Climates. Weather and Climate Extremes [pdf].
5. Gomez, M., L. Poulain–Auzeau, A. Berne & T. Beucler: Global Forecasting of Tropical Cyclone Intensity Using Neural Weather Models. Artificial Intelligence for the Earth Systems, 5, 250073 [pdf].

2025

1. 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. Scientific Reports, 15, 43836 [pdf].
2. 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. npj Natural Hazards, 2(1), 100 [pdf].
3. Hibbert, D., T. Beucler, K. Domingo & S. Leibel: Respiratory Emergencies in Pediatrics: Associations in Redlining, Air Quality and Traffic Regulation. Journal of Racial and Ethnic Health Disparities, 13: 1-7 [pdf].
4. 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. Journal of Machine Learning Research, 26, 142 [pdf].
5. Wang, Z., R. Rios-Berrios, D. P. Stern, A. J. Baker, T. Beucler, S. J. Camargo, J.-P. Duvel, … & E. Wisinski: On the Definition and Tracking of Tropical Cyclone Seeds from a Climate Perspective. Bulletin of the American Meteorological Society, 106, E1815–E1822.
6. Tam, F. I., F. Augsburger & T. Beucler: From Winter Storm Thermodynamics to Wind Gust Extremes: Discovering Interpretable Equations from Data. Environmental Data Science, 4:e48 [pdf].
7. Sullivan, S. C., P. Vautravers, T. Beucler, T. Makgoale & J. Yin: Moisture-Precipitation Couplings for Mesoscale Convective Systems in Tracking Data and Idealized Simulations. Journal of the Atmospheric Sciences, 82, 1885–1902.
8. 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, 8, 245.
9. 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, 4, e240078 [pdf].
10. 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, 17, e2024MS004272 [pdf].
11. 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, 17, e2024MS004551 [pdf].
12. 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

1. 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, 28(24), 5443-5458.
2. 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, 16, e2024MS004401 [pdf].
3. 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, 51, e2024GL110960 [pdf].
4. 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.
5. Eyring, V., W.D. Collins, P. Gentine, E.A. Barnes, M. Barreiro, T. Beucler, M. Bocquet, … & L. Zanna: Pushing the frontiers in climate modeling and analysis with machine learning. Nature Climate Change, 14, 916–928.
6. (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, 32-45.
7. 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.
8. 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].
9. 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].
10. Mooers, G., T. Beucler, M. Pritchard & S. Mandt: Understanding Precipitation Changes through Unsupervised Machine Learning. Environmental Data Science, 3, e3 [pdf].
11. 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

1. 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]
2. (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.
3. 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]
4. 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]
5. (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]
6. 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

1. 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]
2. 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]
3. 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

1. (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.
2. 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.
3. 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]
4. 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

1. Brenowitz, N., T. Beucler, M. Pritchard & C. Bretherton: Interpreting and Stabilizing Machine-Learning Parametrizations of Convection. Journal of the Atmospheric Sciences, 77, 4357-4375.
2. (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]
3. (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]
4. 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]
5. 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

1. 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]
2. (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.
3. (Thesis) Beucler, T.: Interaction between Water Vapor, Radiation and Convection in the Tropics. Ph.D. Thesis in Atmospheric Science.
4. Beucler, T. & T. Cronin: A Budget for the Size of Convective Self-Aggregation. Quarterly Journal of the Royal Meteorological Society, 145, 947-966.
5. 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.
6. Beucler, T. & T. Cronin: Moisture-Radiative Cooling Instability. Journal of Advances in Modeling Earth Systems, 8, 1620–1640.
7. 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.
8. (Thesis) Beucler, T.: Self-aggregation phenomenon in cyclogenesis, Masters Thesis in Fluid Mechanics.