{"id":3597,"date":"2025-05-25T12:14:52","date_gmt":"2025-05-25T10:14:52","guid":{"rendered":"https:\/\/wp.unil.ch\/dawn\/?page_id=3597"},"modified":"2026-04-14T13:37:59","modified_gmt":"2026-04-14T11:37:59","slug":"publications","status":"publish","type":"page","link":"https:\/\/wp.unil.ch\/dawn\/publications\/","title":{"rendered":"Publications"},"content":{"rendered":"\n
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Preprints<\/strong><\/p>\n\n\n\n

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  1. (Submitted) Heuer, H., T. Beucler<\/strong>, M. Schwabe, J. Savre, M. Schlund & Veronika Eyring: Beyond the Training Data: Confidence-Guided Mixing of Parameterizations in a Hybrid AI-Climate Model<\/a>.<\/li>\n\n\n\n
  2. (Submitted) Ferretti, S., J. Lin, S. Shamekh, J. W. Baldwin, M. S. Pritchard, T. Beucler<\/strong>: Data-Driven Integration Kernels for Interpretable Nonlocal Operator Learning<\/a>.<\/li>\n\n\n\n
  3. (Submitted) Simm, M., C. Hoose & T. Beucler<\/strong>: Calibrated Conformal Prediction Intervals for Microphysical Process
     Rates<\/a>.<\/li>\n\n\n\n
  4. (Submitted) Quarenghi, F.<\/strong>, R. Cotsakis, T. Beucler<\/strong>: Emulating Non-Differentiable Metrics via Knowledge-Guided Learning: Introducing the Minkowski Image Loss<\/a>. <\/li>\n\n\n\n
  5. (Submitted) Gomez, M.<\/strong>, M. McGraw, S. Ganesh S.<\/strong>, F. I.-H. Tam<\/strong>, I. Azizi, S. Darmon<\/strong>, M. Feldmann, S. Bourdin, L. Poulain- -Auz\u00e9au, S. J. Camargo, J. Lin, D. Chavas, C.-Y. Lee, R. Gupta, A. Jenney & T. Beucler<\/strong>: TCBench: A Benchmark for Tropical Cyclone Track and Intensity at the Global Scale<\/a>.<\/li>\n\n\n\n
  6. (Submitted) Fons, E., I. L. McCoy, T. Beucler<\/strong>, D. Neubauer & U. Lohmann: Dissipating the correlation smokescreen: Causal decomposition of the radiative effects of biomass burning aerosols over the South-East Atlantic<\/a>.<\/li>\n\n\n\n
  7. (Submitted) Ganesh S., S., F. I.-H. Tam, M. Gomez<\/strong>, M. McGraw, M. DeMaria, K. Musgrave, J. Runge & T. Beucler<\/strong>: Multidata Causal Discovery for Statistical Hurricane Intensity Forecasting<\/a>.<\/li>\n\n\n\n
  8. (Submitted) Fatihi, A., J. Caldeira, T. Beucler<\/strong>, S. T. Thiele, and A. Samsu. Towards robust fracture mapping: benchmarking automatic fracture mapping in 2D outcrop imagery<\/a>. EGUsphere<\/em> 2026: 1-35.<\/li>\n<\/ol>\n\n\n\n

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    2026<\/strong><\/p>\n\n\n\n

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

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      2025<\/strong><\/p>\n\n\n\n

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      1. Grundner, A., T. Beucler<\/strong>, J. Savre, A. Lauer, M. Schlund & V. Eyring: Reduced Cloud Cover Errors in a Hybrid AI-Climate Model Through Equation Discovery and Automatic Tuning<\/a>. Scientific Reports<\/em>, 15<\/strong>, 43836 [pdf<\/a>].<\/li>\n\n\n\n
      2. Leclerc, A.<\/strong>, E. Koch, M. Feldmann, D. Nerini & T. Beucler<\/strong>: Improving Predictions of Convective Storm Wind Gusts through Statistical Post-Processing of Neural Weather Models<\/a>. npj Natural Hazards<\/em>, 2<\/strong>(1), 100 [pdf<\/a>].<\/li>\n\n\n\n
      3. Hibbert, D., T. Beucler<\/strong>, K. Domingo & S. Leibel: Respiratory Emergencies in Pediatrics: Associations in Redlining, Air Quality and Traffic Regulation<\/a>. Journal of Racial and Ethnic Health Disparities<\/em>, 13<\/strong>: 1-7 [pdf<\/a>].<\/li>\n\n\n\n
      4. Yu, S., Z. Hu, A. Subramaniam, W. Hannah, L. Peng, J. Lin, M. Bhouri, R. Gupta, B. L\u00fctjens, J. Will, G. Behrens, J. Busecke, N. Loose, C. Stern, T. Beucler<\/strong>, … & M. Pritchard: ClimSim-Online: A Large Multi-scale Dataset and Framework for Hybrid ML-physics Climate Emulation<\/a>. Journal of Machine Learning Research<\/em>, 26<\/strong>, 142 [pdf<\/a>].<\/li>\n\n\n\n
      5. Wang, Z., R. Rios-Berrios, D. P. Stern, A. J. Baker, T. Beucler<\/strong>, S. J. Camargo, J.-P. Duvel, … & E. Wisinski: On the Definition and Tracking of Tropical Cyclone Seeds from a Climate Perspective<\/a>. Bulletin of the American Meteorological Society<\/em>, 106<\/strong>, E1815\u2013E1822.<\/li>\n\n\n\n
      6. Tam, F. I., F. Augsburger & T. Beucl<\/strong>er: <\/strong>From Winter Storm Thermodynamics to Wind Gust Extremes: Discovering Interpretable Equations from Data<\/a>. Environmental Data Science<\/em>, 4<\/strong>:e48 [pdf<\/a>].<\/li>\n\n\n\n
      7. Sullivan, S. C., P. Vautravers, T. Beucler<\/strong>, T. Makgoale & J. Yin: Moisture-Precipitation Couplings for Mesoscale Convective Systems in Tracking Data and Idealized Simulations<\/a>. Journal of the Atmospheric Sciences<\/em>, 82<\/strong>, 1885\u20131902.<\/li>\n\n\n\n
      8. Ricard, L., T. Beucler<\/strong>, C. Stephan & A. Nenes: A Causal Intercomparison framework unravels precipitation drivers in Global Storm-Resolving Models<\/a>. npj climate and atmospheric science<\/em>, 8<\/strong>, 245.<\/li>\n\n\n\n
      9. Beucler, T.<\/strong>, A. Grundner, S. Shamekh, P. Ukkonen, M. Chantry & R. Lagerquist: Distilling Machine Learning’s Added Value: Pareto Fronts in Atmospheric Applications<\/a>. Artificial Intelligence for the Earth Systems<\/em>, 4<\/strong>, e240078 [pdf<\/a>].<\/li>\n\n\n\n
      10. Behrens, G., T. Beucler<\/strong>, 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<\/a>. Journal of Advances in Modeling Earth Systems<\/em>, 17<\/strong>, e2024MS004272 [pdf<\/a>].<\/li>\n\n\n\n
      11. Lin, J., S. Yu, L. Peng, T. Beucler<\/strong>, E. Wong-Toi, Z. Hu, P. Gentine, M. Geleta & M. Pritchard: Navigating the Noise: Bringing Clarity to ML Parameterization Design with O(100) Ensembles<\/a>. Journal of Advances in Modeling Earth Systems<\/em>, 17<\/strong>, e2024MS004551 [pdf<\/a>].<\/li>\n\n\n\n
      12. Aarnink, J., T. Beucler<\/strong>, M. Vuaridel & V. Ruiz-Villanueva: Automatic detection of instream large wood in videos using deep learning<\/a>. Earth Surface Dynamics<\/em>, 13<\/strong>, 167\u2013189.<\/li>\n<\/ol>\n\n\n\n

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        2024<\/strong><\/p>\n\n\n\n

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        1. Cache, T., M. Gomez, T. Beucler<\/strong>, J. Blagojevic, J. Leitao & N. Peleg: Enhancing generalizability of data-driven urban flood models by incorporating contextual information<\/a>. Hydrology and Earth System Sciences<\/em>, 28<\/em>(24)<\/strong>, 5443-5458.<\/li>\n\n\n\n
        2. Tam, F. I., T. Beucler<\/strong> & J. Ruppert: Identifying Three-Dimensional Radiative Patterns Associated with Early Tropical Cyclone Intensification<\/a>. Journal of Advances in Modeling Earth Systems<\/em>, 16<\/strong>, e2024MS004401 [pdf<\/a>].<\/li>\n\n\n\n
        3. Feldmann, M., T. Beucler, M. Gomez<\/strong> & O. Martius: Lightning-Fast Convective Outlooks: Predicting Severe Convective Environments with Global AI-based Weather Models<\/a>. Geophysical Research Letters<\/em>, 51<\/strong>, e2024GL110960 [pdf<\/a>].<\/li>\n\n\n\n
        4. Christopoulos, C.<\/a>, I. Lopez-Gomez<\/a>, T. Beucler<\/strong><\/a>, Y. Cohen, C. Kawczynski, O. Dunbar & T. Schneider: Online Learning of Entrainment Closures in a Hybrid Machine Learning Parameterization<\/a>. Journal of Advances in Modeling Earth Systems<\/em>, 16<\/strong>, e2024MS004485.<\/li>\n\n\n\n
        5. Eyring, V., W.D. Collins, P. Gentine, E.A. Barnes, M. Barreiro, T. Beucler<\/strong>, M. Bocquet, \u2026 & L. Zanna: Pushing the frontiers in climate modeling and analysis with machine learning<\/a>. Nature Climate Change<\/em>, 14<\/strong>, 916\u2013928.<\/li>\n\n\n\n
        6. (Whitepaper) Beucler, T.<\/strong>, E. Koch, S. Kotlarski, D. Leutwyler, A. Michel & J. Koh: Next-Generation Earth System Models: Towards Reliable Hybrid Models for Weather and Climate Applications.<\/a>SATW Whitepaper on “AI for Climate Change Mitigation”<\/a>, <\/em>5.2<\/strong>, <\/em>32-45.<\/em><\/li>\n\n\n\n
        7. Rampal, N., S. Hobeichi, P. B. Gibson, J. Ba\u00f1o-Medina, G. Abramowitz, T. Beucler<\/strong>, J. Gonz\u00e1lez-Abad, W. Chapman, P. Harder & Jos\u00e9 Manuel Guti\u00e9rrez: Enhancing Regional Climate Downscaling Through Advances in Machine Learning<\/a>. Artificial Intelligence for the Earth Systems<\/em>, 3<\/em>(2)<\/strong>, 230066.<\/li>\n\n\n\n
        8. Grundner, A., T. Beucler<\/strong>, P. Gentine & V. Eyring: Data-Driven Equation Discovery of a Cloud Cover Parameterization<\/a>. Journal of Advances in Modeling Earth Systems<\/em>, 16<\/strong>, e2023MS003763 [pdf<\/a>].<\/li>\n\n\n\n
        9. Iglesias-Suarez, F., P. Gentine, B. Solino-Fernandez, T. Beucler<\/strong>, M. Pritchard, J. Runge & V. Eyring: Causally-informed deep learning to improve climate models and projections<\/a>. Journal of Geophysical Research: Atmospheres<\/em>, 129<\/strong>, e2023JD039202 [pdf<\/a>].<\/li>\n\n\n\n
        10. Mooers, G., T. Beucler<\/strong>, M. Pritchard & S. Mandt: Understanding Precipitation Changes through Unsupervised Machine Learning<\/a>. Environmental Data Science<\/em>, 3<\/strong>, e3 [pdf<\/a>]. <\/li>\n\n\n\n
        11. Beucler, T.<\/strong>, 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<\/a>. Science Advances<\/em>, 10<\/strong>, eadj7250 [pdf<\/a>].<\/li>\n<\/ol>\n\n\n\n

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          2023<\/strong><\/p>\n\n\n\n

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          1. Beucler, T.<\/strong>, I. Ebert-Uphoff, S. Rasp, M. Pritchard & P. Gentine: Machine Learning for Clouds and Climate<\/a>. Clouds and Their Climatic Impact: Radiation, Circulation, and Precipitation<\/em>, edited by: Sullivan, SC and Hoose, C., Wiley\u2013American Geophysical Union: 327-346. [pdf<\/a>]<\/li>\n\n\n\n
          2. (Workshop) Lin, J., M. A. Bhouri, T. Beucler<\/strong>, S. Yu & M. Pritchard: Stress-testing the coupled behavior of hybrid physics-machine learning climate simulations on an unseen, warmer climate.<\/a> 2023 Conference on Neural Information Processing Systems<\/em>.<\/li>\n\n\n\n
          3. Mooers, G., M. Pritchard, T. Beucler<\/strong>, P. Srivastava, H. Mangipudi, L. Peng, P. Gentine & S. Mandt: Comparing Storm Resolving Models and Climates via Unsupervised Machine Learning.<\/a> Scientific Reports<\/em>. [pdf<\/a>]<\/li>\n\n\n\n
          4. Zanetta, F., D. Nerini, T. Beucler<\/strong> & M. Liniger: Physics-constrained deep learning postprocessing of temperature and humidity.<\/a> Artificial Intelligence for the Earth Systems, 2, e220089.<\/em> [pdf<\/a>]<\/li>\n\n\n\n
          5. (NeurIPS 2023 Conference) Yu, S., W. Hannah, L. Peng, M. Bhouri, R. Gupta, J. Lin, B. L\u00fctjens, J. Will, G. Behrens, J. Busecke, N. Loose, C. Stern, T. Beucler<\/strong>, … & M. Pritchard: ClimSim: A large multi-scale dataset for hybrid physics-machine learning climate emulation.<\/a> Advances in Neural Information Processing Systems<\/em>. “Oustanding Datasets and Benchmarks” award<\/strong>. [pdf<\/a>]<\/li>\n\n\n\n
          6. Ganesh S., S., T. Beucler, F. I. Tam, M. Gomez<\/strong>, J. Runge & A. Gerhardus: Selecting Robust Features for Machine-Learning Applications using Multidata Causal Discovery<\/a>. Environmental Data Science, 2: e27<\/em>. [pdf<\/a>]<\/li>\n<\/ol>\n\n\n\n

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            2022<\/strong><\/p>\n\n\n\n

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            1. Grundner, A., T. Beucler<\/strong>, P. Gentine, F. Iglesias-Suarez, M. Giorgetta & V. Eyring: Deep Learning Based Cloud Cover Parameterization for ICON.<\/a> <\/em>Journal of Advances in Modeling Earth Systems<\/em>, e2021MS002959. [pdf<\/a>]<\/li>\n\n\n\n
            2. Wu, Z., T. Beucler<\/strong>, E. Sz\u00e9kely, W. Ball & D. Domeisen: Modeling Stratospheric Polar Vortex Variation and Identifying Vortex Extremes Using Explainable Machine Learning.<\/a> <\/em>Environmental Data Science 1: e17<\/em>. [pdf<\/a>]<\/li>\n\n\n\n
            3. Behrens, G., T. Beucler<\/strong>, P. Gentine, F. Iglesias-Suarez, M. Pritchard & V. Eyring: Non\u2010Linear Dimensionality Reduction with a Variational Encoder Decoder to Understand Convective Processes in Climate Models.<\/a> Journal of Advances in Modeling Earth Systems<\/em>, e2022MS003130. [pdf<\/a>]<\/li>\n<\/ol>\n\n\n\n

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              2021<\/strong><\/p>\n\n\n\n

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              1. (Workshop) Mangipudi, H., G. Mooers, M. Pritchard, T. Beucler<\/strong> & S. Mandt: Analyzing High-Resolution Clouds and Convection using Multi-Channel VAEs.<\/a> <\/em>2021 Conference on Neural Information Processing Systems<\/em>.<\/li>\n\n\n\n
              2. Gentine, P., V. Eyring & T. Beucler<\/strong>: Deep Learning for the Parametrization of Subgrid Processes in Climate Models.<\/a> <\/em>Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote Sensing, Climate Science, and Geosciences<\/em>, 307-314.<\/li>\n\n\n\n
              3. Mooers, G., M. Pritchard, T. Beucler<\/strong>, 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<\/a>. Journal of Advances in Modeling Earth Systems<\/em>, 13, e2020MS002385. [pdf<\/a>]<\/li>\n\n\n\n
              4. Beucler, T.<\/strong>, M. Pritchard, S. Rasp, J. Ott, P. Baldi & P. Gentine: Enforcing Analytic Constraints in Neural-Networks Emulating Physical Systems.<\/a> <\/em>Physical Review Letters<\/em>, 126.9: 098302. Editors\u2019 Suggestion<\/strong>. [pdf<\/a>]<\/li>\n<\/ol>\n\n\n\n

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                2020<\/strong><\/p>\n\n\n\n

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                1. Brenowitz, N., T. Beucler<\/strong>, M. Pritchard & C. Bretherton: Interpreting and Stabilizing Machine-Learning Parametrizations of Convection.<\/a> <\/em>Journal of the Atmospheric Sciences<\/em>, 77, 4357-4375.<\/li>\n\n\n\n
                2. (Workshop) Beucler, T.<\/strong>, M. Pritchard, P. Gentine & S. Rasp: Towards Physically-Consistent, Data-Driven Models of Convection.<\/a> <\/em>IEEE International Geoscience and Remote Sensing Symposium 2020<\/em>. [pdf<\/a>]<\/li>\n\n\n\n
                3. (Climate Informatics 2020 Conference) Mooers, G., J. Tuyls, S. Mandt, M. Pritchard & T. Beucler<\/strong>: Generative Modeling of Atmospheric Convection.<\/a> Proceedings of the 10th International Conference on Climate Informatics<\/em>, 98-105. [pdf<\/a>]<\/li>\n\n\n\n
                4. Beucler, T.<\/strong>, D. Leutwyler & J. Windmiller: Quantifying Convective Aggregation Using the Tropical Moist Margin\u2019s Length.<\/a> <\/em>Journal of Advances in Modeling Earth Systems<\/em>, 12, e2020MS002092. [pdf<\/a>]<\/li>\n\n\n\n
                5. Abbott, T., T. Cronin & T. Beucler<\/strong>: Convective dynamics and the response of precipitation extremes to warming in radiative-convective equilibrium.<\/a> <\/em>Journal of the Atmospheric Sciences<\/em>, 77, 1637-1660. [pdf<\/a>]<\/li>\n<\/ol>\n\n\n\n

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                  2016-2019<\/strong><\/p>\n\n\n\n

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                  1. Beucler, T.<\/strong>, T. Abbott, T. Cronin & M. Pritchard: Comparing Convective Self\u2010Aggregation in Idealized Models to Observed Moist Static Energy Variability Near the Equator.<\/a> Geophysical Research Letters<\/em>, 46, 17-18. [pdf<\/a>]<\/li>\n\n\n\n
                  2. (Workshop) Beucler, T.<\/strong>, S. Rasp, M. Pritchard & P. Gentine: Achieving Conservation of Energy in Neural Network Emulators for Climate Modeling.<\/a> 2019 International Conference on Machine Learning<\/em>.<\/li>\n\n\n\n
                  3. (Thesis) Beucler, T.<\/strong>: Interaction between Water Vapor, Radiation and Convection in the Tropics.<\/a> Ph.D. Thesis in Atmospheric Science<\/em>.<\/li>\n\n\n\n
                  4. Beucler, T. <\/strong>& T. Cronin: A Budget for the Size of Convective Self-Aggregation.<\/a> Quarterly Journal of the Royal Meteorological Society<\/em>, 145, 947-966.<\/li>\n\n\n\n
                  5. Beucler, T.<\/strong>, T. Cronin & K. Emanuel: A Linear Response Framework for Radiative-Convective Instability.<\/a> Journal of Advances in Modeling Earth Systems<\/em>, 10(8), 1924-1951.<\/li>\n\n\n\n
                  6. Beucler, T.<\/strong> & T. Cronin: Moisture-Radiative Cooling Instability.<\/a> Journal of Advances in Modeling Earth Systems<\/em>, 8, 1620\u20131640.<\/li>\n\n\n\n
                  7. Beucler, T.<\/strong>: A Correlated Stochastic Model for the Large-scale Advection, Condensation and Diffusion of Water Vapour.<\/a> Quarterly Journal of the Royal Meteorological Society<\/em>, 142, 1721\u20131731.<\/li>\n\n\n\n
                  8. (Thesis) Beucler, T.<\/strong>: Self-aggregation phenomenon in cyclogenesis<\/a>, Masters Thesis in Fluid Mechanics<\/em>.<\/li>\n<\/ol>\n<\/div>\n\n\n\n

                    Presentation Recordings<\/h3>\n\n\n\n
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                    2024<\/strong><\/p>\n\n\n\n