| Deep learning subgrid-scale parametrisations for short-term forecasting of sea-ice dynamics with a Maxwell elasto-brittle rheology TS Finn, C Durand, A Farchi, M Bocquet, Y Chen, A Carrassi, ... The Cryosphere 17 (7), 2965-2991, 2023 | 16 | 2023 |
| Towards assimilation of wind profile observations in the atmospheric boundary layer with a sub-kilometre-scale ensemble data assimilation system TS Finn, G Geppert, F Ament Tellus A: Dynamic Meteorology and Oceanography 72 (1), 1-14, 2020 | 10 | 2020 |
| Data-driven surrogate modeling of high-resolution sea-ice thickness in the Arctic C Durand, TS Finn, A Farchi, M Bocquet, G Boutin, E Ólason The Cryosphere 18 (4), 1791-1815, 2024 | 8 | 2024 |
| Self-attentive ensemble transformer: Representing ensemble interactions in neural networks for earth system models TS Finn arXiv preprint arXiv:2106.13924, 2021 | 8 | 2021 |
| Representation learning with unconditional denoising diffusion models for dynamical systems TS Finn, L Disson, A Farchi, M Bocquet, C Durand Nonlinear Processes in Geophysics 31 (3), 409-431, 2024 | 5 | 2024 |
| Towards diffusion models for large-scale sea-ice modelling TS Finn, C Durand, A Farchi, M Bocquet, J Brajard arXiv preprint arXiv:2406.18417, 2024 | 5 | 2024 |
| Emulating present and future simulations of melt rates at the base of Antarctic ice shelves with neural networks C Burgard, NC Jourdain, P Mathiot, RS Smith, R Schäfer, J Caillet, ... Journal of Advances in Modeling Earth Systems 15 (12), e2023MS003829, 2023 | 4 | 2023 |
| Multivariate state and parameter estimation with data assimilation applied to sea-ice models using a Maxwell elasto-brittle rheology Y Chen, P Smith, A Carrassi, I Pasmans, L Bertino, M Bocquet, TS Finn, ... The Cryosphere 18 (5), 2381-2406, 2024 | 3* | 2024 |
| Generative diffusion for regional surrogate models from sea‐ice simulations TS Finn, C Durand, A Farchi, M Bocquet, P Rampal, A Carrassi Journal of Advances in Modeling Earth Systems 16 (10), e2024MS004395, 2024 | 2 | 2024 |
| Accurate deep learning-based filtering for chaotic dynamics by identifying instabilities without an ensemble M Bocquet, A Farchi, TS Finn, C Durand, S Cheng, Y Chen, I Pasmans, ... Chaos: An Interdisciplinary Journal of Nonlinear Science 34 (9), 2024 | 1 | 2024 |
| Assessing the weather conditions for urban cyclists by spatially dense measurements with an agent‐based approach AU Schmitt, F Burgemeister, H Dorff, T Finn, A Hansen, B Kirsch, I Lange, ... Meteorological Applications 30 (6), e2164, 2023 | 1 | 2023 |
| Assessing the weather conditions for urban cyclists by spatially dense measurements F Ament, A Schmitt, F Burgemeister, H Dorff, T Finn, A Hansen, B Kirsch, ... EMS2022, 2022 | 1 | 2022 |
| Ensemble-based data assimilation of atmospheric boundary layer observations improves the soil moisture analysis TS Finn, G Geppert, F Ament Hydrology and Earth System Sciences Discussions 2021, 1-27, 2021 | 1 | 2021 |
| Self-attentive Transformer for Fast and Accurate Postprocessing of Temperature and Wind Speed Forecasts A Van Poecke, TS Finn, R Meng, JV Bergh, G Smet, J Demaeyer, ... arXiv preprint arXiv:2412.13957, 2024 | | 2024 |
| Deep learning-based sequential data assimilation for chaotic dynamics identifies local instabilities from single state forecasts M Bocquet, A Farchi, TS Finn, C Durand, S Cheng, Y Chen, I Pasmans, ... arXiv e-prints, arXiv: 2408.04739, 2024 | | 2024 |
| Latent Diffusion Model for Generating Ensembles of Climate Simulations J Meuer, M Witte, TS Finn, C Timmreck, T Ludwig, C Kadow arXiv preprint arXiv:2407.02070, 2024 | | 2024 |
| Multivariate state and parameter estimation using data assimilation in a Maxwell-Elasto-Brittle sea ice model Y Chen, P Smith, A Carrassi, I Pasmans, L Bertino, M Bocquet, TS Finn, ... EGU24, 2024 | | 2024 |
| A data-driven sea-ice model with generative deep learning TS Finn, C Durand, F Porro, A Farchi, M Bocquet, Y Chen, A Carrassi EGU24, 2024 | | 2024 |
| Deep learning for surrogate modelling of neXtSIM C Durand, T Finn, A Farchi, M Bocquet, E Olason EGU General Assembly Conference Abstracts, EGU-12810, 2023 | | 2023 |
| Bayesian online algorithms for learning data-driven models of chaotic dynamics M Bocquet, A Farchi, Q Malartic, T Finn, C Durand, M Bonavita, ... XXVIII General Assembly of the International Union of Geodesy and Geophysics …, 2023 | | 2023 |
