Article

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Schevenhoven, F. and Carrassi, A. ORCID: https://orcid.org/0000-0003-0722-5600 (2022) Training a supermodel with noisy and sparse observations: a case study with CPT and the synch rule on SPEEDO – v.1. Geoscientific Model Development, 15 (9). pp. 3831-3844. ISSN 1991-9603 doi: https://doi.org/10.5194/gmd-15-3831-2022

Chen, Y. ORCID: https://orcid.org/0000-0002-2319-6937, Carrassi, A. ORCID: https://orcid.org/0000-0003-0722-5600 and Lucarini, V. ORCID: https://orcid.org/0000-0001-9392-1471 (2021) Inferring the instability of a dynamical system from the skill of data assimilation exercises. Nonlinear Processes in Geophysics, 28 (4). pp. 633-649. ISSN 1023-5809 doi: https://doi.org/10.5194/npg-28-633-2021

Evensen, G., Amezcua, J., Bocquet, M., Carrassi, A. ORCID: https://orcid.org/0000-0003-0722-5600, Farchi, A., Fowler, A. ORCID: https://orcid.org/0000-0003-3650-3948, Houtekamer, P. L., Jones, C. K., de Moraes, R. J., Pulido, M., Sampson, C. and Vossepoel, F. C. (2021) An international initiative of predicting the Sars-Cov-2 pandemic using ensemble data assimilation. Foundations of Data Science, 3 (3). pp. 413-477. ISSN 2639-8001 doi: https://doi.org/10.3934/fods.2021001

Brajard, J. ORCID: https://orcid.org/0000-0003-0634-1482, Carrassi, A. ORCID: https://orcid.org/0000-0003-0722-5600, Bocquet, M. and Bertino, L. (2021) Combining data assimilation and machine learning to infer unresolved scale parametrization. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 379 (2194). 20200086. ISSN 1364-503X doi: https://doi.org/10.1098/rsta.2020.0086

Bonavita, M., Arcucci, R., Carrassi, A. ORCID: https://orcid.org/0000-0003-0722-5600, Dueben, P., Geer, A. J., Le Saux, B., Longépé, N., Mathieu, P.-P. and Raynaud, L. (2021) Machine learning for earth system observation and prediction. Bulletin of the American Meteorological Society, 102 (4). E710-E716. ISSN 1520-0477 doi: https://doi.org/10.1175/BAMS-D-20-0307.1

Sampson, C. ORCID: https://orcid.org/0000-0002-4275-4877, Carrassi, A. ORCID: https://orcid.org/0000-0003-0722-5600, Aydogdu, A. ORCID: https://orcid.org/0000-0002-1557-7502 and Jones, C. K.R.T. (2021) Ensemble Kalman Filter for non‐conservative moving mesh solvers with a joint physics and mesh location update. Quarterly Journal of the Royal Meteorological Society, 147 (736). pp. 1539-1567. ISSN 0035-9009 doi: https://doi.org/10.1002/qj.3980

Cheng, S. ORCID: https://orcid.org/0000-0002-7304-2442, Aydoğdu, A. ORCID: https://orcid.org/0000-0002-1557-7502, Rampal, P., Carrassi, A. ORCID: https://orcid.org/0000-0003-0722-5600 and Bertino, L. (2020) Probabilistic forecasts of sea ice trajectories in the arctic: impact of uncertainties in surface wind and ice cohesion. Oceans, 1 (4). pp. 326-342. ISSN 2673-1924 doi: https://doi.org/10.3390/oceans1040022

Tandeo, P., Ailliot, P., Bocquet, M., Carrassi, A. ORCID: https://orcid.org/0000-0003-0722-5600, Miyoshi, T., Pulido, M. and Zhen, Y. (2020) A review of innovation-based methods to jointly estimate model and observation error covariance matrices in ensemble data assimilation. Monthly Weather Review, 148 (10). ISSN 0027-0644 doi: https://doi.org/10.1175/MWR-D-19-0240.1

Brajard, J., Carrassi, A. ORCID: https://orcid.org/0000-0003-0722-5600, Bocquet, M. and Bertino, L. (2020) Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: a case study with the Lorenz 96 model. Journal of Computational Science, 44. 101171. ISSN 2153-4136 doi: https://doi.org/10.1016/j.jocs.2020.101171

Grudzien, C., Bocquet, M. and Carrassi, A. ORCID: https://orcid.org/0000-0003-0722-5600 (2020) On the numerical integration of the Lorenz-96 model, with scalar additive noise, for benchmark twin experiments. Geoscientific Model Development, 13 (4). pp. 1903-1924. ISSN 1991-9603 doi: https://doi.org/10.5194/gmd-13-1903-2020

Bocquet, M., Brajard, J., Carrassi, A. ORCID: https://orcid.org/0000-0003-0722-5600 and Bertino, L. (2020) Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization. Foundations of Data Science, 2 (1). pp. 55-80. ISSN 2639-8001 doi: https://doi.org/10.3934/fods.2020004

Tondeur, M., Carrassi, A. ORCID: https://orcid.org/0000-0003-0722-5600, Vannitsem, S. and Bocquet, M. (2020) On temporal scale separation in coupled data assimilation with the ensemble Kalman filter. Journal of Statistical Physics, 179 (5-6). pp. 1161-1185. ISSN 0022-4715 doi: https://doi.org/10.1007/s10955-020-02525-z

Schevenhoven, F., Selten, F., Carrassi, A. ORCID: https://orcid.org/0000-0003-0722-5600 and Keenlyside, N. (2019) Improving weather and climate predictions by training of supermodels. Earth System Dynamics, 10 (4). pp. 789-807. ISSN 2190-4987 doi: https://doi.org/10.5194/esd-10-789-2019

Aydoğdu, A., Carrassi, A. ORCID: https://orcid.org/0000-0003-0722-5600, Guider, C. T., Jones, C. K. R. T. and Rampal, P. (2019) Data assimilation using adaptive, non-conservative, moving mesh models. Nonlinear Processes in Geophysics, 26 (3). pp. 175-193. ISSN 1607-7946 doi: https://doi.org/10.5194/npg-26-175-2019

Bocquet, M., Brajard, J., Carrassi, A. ORCID: https://orcid.org/0000-0003-0722-5600 and Bertino, L. (2019) Data assimilation as a learning tool to infer ordinary differential equation representations of dynamical models. Nonlinear Processes in Geophysics, 26 (3). pp. 143-162. ISSN 1607-7946 doi: https://doi.org/10.5194/npg-26-143-2019

Metref, S., Hannart, A., Ruiz, J., Bocquet, M., Carrassi, A. ORCID: https://orcid.org/0000-0003-0722-5600 and Ghil, M. (2019) Estimating model evidence using ensemble‐based data assimilation with localization – the model selection problem. Quarterly Journal of the Royal Meteorological Society, 145 (721). pp. 1571-1588. ISSN 0035-9009 doi: https://doi.org/10.1002/qj.3513

Raanes, P. N., Bocquet, M. and Carrassi, A. ORCID: https://orcid.org/0000-0003-0722-5600 (2019) Adaptive covariance inflation in the ensemble Kalman filter by Gaussian scale mixtures. Quarterly Journal of the Royal Meteorological Society, 145 (718). pp. 53-75. ISSN 0035-9009 doi: https://doi.org/10.1002/qj.3386

Grudzien, C., Carrassi, A. ORCID: https://orcid.org/0000-0003-0722-5600 and Bocquet, M. (2018) Asymptotic forecast uncertainty and the unstable subspace in the presence of additive model error. SIAM/ASA Journal on Uncertainty Quantification, 6 (4). pp. 1335-1363. ISSN 2166-2525 doi: https://doi.org/10.1137/17M114073X

Grudzien, C., Carrassi, A. ORCID: https://orcid.org/0000-0003-0722-5600 and Bocquet, M. (2018) Chaotic dynamics and the role of covariance inflation for reduced rank Kalman filters with model error. Nonlinear Processes in Geophysics, 25 (3). pp. 633-648. ISSN 1023-5809 doi: https://doi.org/10.5194/npg-25-633-2018

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Rabatel, M., Rampal, P., Carrassi, A. ORCID: https://orcid.org/0000-0003-0722-5600, Bertino, L. and Jones, C. K. R. T. (2018) Impact of rheology on probabilistic forecasts of sea ice trajectories: application for search and rescue operations in the Arctic. The Cryosphere, 12 (3). pp. 935-953. ISSN 1994-0424 doi: https://doi.org/10.5194/tc-12-935-2018

Carrassi, A. ORCID: https://orcid.org/0000-0003-0722-5600, Bocquet, M., Hannart, A. and Ghil, M. (2017) Estimating model evidence using data assimilation. Quarterly Journal of the Royal Meteorological Society, 143 (703). pp. 866-880. ISSN 1477-870X doi: https://doi.org/10.1002/qj.2972

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Bocquet, M. and Carrassi, A. ORCID: https://orcid.org/0000-0003-0722-5600 (2017) Four-dimensional ensemble variational data assimilation and the unstable subspace. Tellus A: Dynamic Meteorology and Oceanography, 69 (1). 1304504. ISSN 1600-0870 doi: https://doi.org/10.1080/16000870.2017.1304504

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Carrassi, A. ORCID: https://orcid.org/0000-0003-0722-5600 and Vannitsem, S. (2011) Treatment of the error due to unresolved scales in sequential data assimilation. International Journal of Bifurcation and Chaos, 21 (12). pp. 3619-3626. ISSN 1793-6551 doi: https://doi.org/10.1142/S0218127411030775

Carrassi, A. ORCID: https://orcid.org/0000-0003-0722-5600 and Vannitsem, S. (2011) State and parameter estimation with the extended Kalman filter: an alternative formulation of the model error dynamics. Quarterly Journal of the Royal Meteorological Society, 137 (655). pp. 435-451. ISSN 1477-870X doi: https://doi.org/10.1002/qj.762

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Carrassi, A. ORCID: https://orcid.org/0000-0003-0722-5600, Vannitsem, S. and Nicolis, C. (2008) Model error and sequential data assimilation: a deterministic formulation. Quarterly Journal of the Royal Meteorological Society, 134 (634). pp. 1297-1313. ISSN 1477-870X doi: https://doi.org/10.1002/qj.284

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Carrassi, A. ORCID: https://orcid.org/0000-0003-0722-5600, Ghil, M., Trevisan, A. and Uboldi, F. (2008) Data assimilation as a nonlinear dynamical systems problem: stability and convergence of the prediction-assimilation system. Chaos: An Interdisciplinary Journal of Nonlinear Science, 18 (2). 023112. ISSN 1089-7682 doi: https://doi.org/10.1063/1.2909862

Carrassi, A. ORCID: https://orcid.org/0000-0003-0722-5600, Vannitsem, S., Zupanski, D. and Zupanski, M. (2008) The maximum likelihood ensemble filter performances in chaotic systems. Tellus A: Dynamic Meteorology and Oceanography, 61 (5). pp. 587-600. ISSN 1600-0870 doi: https://doi.org/10.1111/j.1600-0870.2009.00408.x

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Carrassi, A. ORCID: https://orcid.org/0000-0003-0722-5600, Trevisan, A. and Uboldi, F. (2007) Adaptive observations and assimilation in the unstable subspace by breeding on the data-assimilation system. Tellus A: Dynamic Meteorology and Oceanography, 59 (1). pp. 101-113. ISSN 1600-0870 doi: https://doi.org/10.1111/j.1600-0870.2006.00210.x

Uboldi, F., Trevisan, A. and Carrassi, A. ORCID: https://orcid.org/0000-0003-0722-5600 (2005) Developing a dynamically based assimilation method for targeted and standard observations. Nonlinear Processes in Geophysics, 12 (1). pp. 149-156. ISSN 1023-5809 doi: https://doi.org/10.5194/npg-12-149-2005

Book or Report Section

Carrassi, A. ORCID: https://orcid.org/0000-0003-0722-5600, Bocquet, M., Demaeyer, J., Grudzien, C., Raanes, P. and Vannitsem, S. (2022) Data Assimilation for Chaotic Dynamics. In: Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. IV). Springer International Publishing, pp. 1-42. ISBN 9783030777227 doi: https://doi.org/10.1007/978-3-030-77722-7_1

Carrassi, A. ORCID: https://orcid.org/0000-0003-0722-5600 and Vannitsem, S. (2016) Deterministic treatment of model error in geophysical data assimilation. In: Ancona, F., Cannarsa, P., Jones, C. and Portaluri, A. (eds.) Mathematical Paradigms of Climate Science. Springer INdAM Series, 15. Springer, pp. 175-213. doi: https://doi.org/10.1007/978-3-319-39092-5_9

Carrassi, A. ORCID: https://orcid.org/0000-0003-0722-5600 and Vannitsem, S. (2012) Accounting for model error in data assimilation. In: Mathematical and Algorithmic Aspects of Atmosphere-Ocean Data Assimilation from the European Mathematical Society. Oberwolfach Report (58). European Mathematical Society, pp. 3437-3438. doi: https://doi.org/10.4171/OWR/2012/58