Article

Cheng, S., Chen, Y. ORCID: https://orcid.org/0000-0002-2319-6937, Aydoğdu, A., Bertino, L., Carrassi, A. ORCID: https://orcid.org/0000-0003-0722-5600, Rampal, P. and Jones, C. K. R. T. (2023) Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020. The Cryosphere, 17 (4). pp. 1735-1754. ISSN 1994-0424 doi: https://doi.org/10.5194/tc-17-1735-2023

Ayers, D. ORCID: https://orcid.org/0000-0002-5667-8174, Lau, J., Amezcua, J. ORCID: https://orcid.org/0000-0002-4952-8354, Carrassi, A. ORCID: https://orcid.org/0000-0003-0722-5600 and Ojha, V. ORCID: https://orcid.org/0000-0002-9256-1192 (2023) Supervised machine learning to estimate instabilities in chaotic systems: estimation of local Lyapunov exponents. Quarterly Journal of the Royal Meteorological Society. ISSN 1477-870X doi: https://doi.org/10.1002/qj.4450

Baguis, P., Carrassi, A. ORCID: https://orcid.org/0000-0003-0722-5600, Roulin, E., Vannitsem, S., Modanesi, S., Lievens, H., Bechtold, M. ORCID: https://orcid.org/0000-0002-8042-9792 and De Lannoy, G. ORCID: https://orcid.org/0000-0002-6743-7122 (2022) Assimilation of backscatter observations into a hydrological model: a case study in Belgium using ASCAT data. Remote Sensing, 14 (22). 5740. ISSN 2072-4292 doi: https://doi.org/10.3390/rs14225740

De Lannoy, G. J. M., Bechtold, M., Albergel, C., Brocca, L., Calvet, J.-C., Carrassi, A. ORCID: https://orcid.org/0000-0003-0722-5600, Crow, W. T., de Rosnay, P., Durand, M., Forman, B., Geppert, G., Girotto, M., Hendricks Franssen, H.-J., Jonas, T., Kumar, S., Lievens, H., Lu, Y., Massari, C., Pauwels, V. R. N., Reichle, R. H. and Steele-Dunne, S. (2022) Perspective on satellite-based land data assimilation to estimate water cycle components in an era of advanced data availability and model sophistication. Frontiers in Water, 4. ISSN 2624-9375 doi: https://doi.org/10.3389/frwa.2022.981745

Scheffler, G. ORCID: https://orcid.org/0000-0002-6474-1372, Carrassi, A. ORCID: https://orcid.org/0000-0003-0722-5600, Ruiz, J. and Pulido, M. (2022) Dynamical effects of inflation in ensemble‐based data assimilation under the presence of model error. Quarterly Journal of the Royal Meteorological Society, 148 (746). pp. 2368-2383. ISSN 1477-870X doi: https://doi.org/10.1002/qj.4307

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., 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., 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., 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. (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. 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., 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. 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., 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. 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. 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. (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. 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. 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

Carrassi, A., Bocquet, M., Bertino, L. and Evensen, G. (2018) Data assimilation in the geosciences: an overview of methods, issues, and perspectives. Wiley Interdisciplinary Reviews: Climate Change, 9 (5). e535. ISSN 1757-7799 doi: https://doi.org/10.1002/wcc.535

Yano, J.-I., Ziemiański, M. Z., Cullen, M., Termonia, P., Onvlee, J., Bengtsson, L., Carrassi, A., Davy, R., Deluca, A., Gray, S., Homar, V., Köhler, M., Krichak, S., Michaelides, S., Phillips, V. T. J., Soares, P. M. M. and Wyszogrodzki, A. A. (2018) Scientific challenges of convective-scale numerical weather prediction. Bulletin of the American Meteorological Society, 99 (4). pp. 699-710. ISSN 1520-0477 doi: https://doi.org/10.1175/bams-d-17-0125.1

Pulido, M., Tandeo, P., Bocquet, M., Carrassi, A. and Lucini, M. (2018) Stochastic parameterization identification using ensemble Kalman filtering combined with maximum likelihood methods. Tellus A: Dynamic Meteorology and Oceanography, 70 (1). pp. 1-17. ISSN 1600-0870 doi: https://doi.org/10.1080/16000870.2018.1442099

Rabatel, M., Rampal, P., Carrassi, A., 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., 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

Bocquet, M., Gurumoorthy, K. S., Apte, A., Carrassi, A., Grudzien, C. and Jones, C. K. R. T. (2017) Degenerate Kalman filter error covariances and their convergence onto the unstable subspace. SIAM/ASA Journal on Uncertainty Quantification, 5 (1). pp. 304-333. ISSN 2166-2525 doi: https://doi.org/10.1137/16M1068712

Bocquet, M. and Carrassi, A. (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

Gurumoorthy, K. S., Grudzien, C., Apte, A., Carrassi, A. and Jones, C. K. R. T. (2017) Rank deficiency of Kalman error covariance matrices in linear time-varying system with deterministic evolution. SIAM Journal on Control and Optimization, 55 (2). pp. 741-759. ISSN 0363-0129 doi: https://doi.org/10.1137/15M1025839

Pazó, D., Carrassi, A. and López, J. M. (2016) Data assimilation by delay-coordinate nudging. Quarterly Journal of the Royal Meteorological Society, 142 (696). pp. 1290-1299. ISSN 0035-9009 doi: https://doi.org/10.1002/qj.2732

Hannart, A., Carrassi, A., Bocquet, M., Ghil, M., Naveau, P., Pulido, M., Ruiz, J. and Tandeo, P. (2016) DADA: data assimilation for the detection and attribution of weather and climate-related events. Climatic Change, 136 (2). pp. 155-174. ISSN 0165-0009 doi: https://doi.org/10.1007/s10584-016-1595-3

Carrassi, A., Guemas, V., Doblas-Reyes, F. J., Volpi, D. and Asif, M. (2016) Sources of skill in near-term climate prediction: generating initial conditions. Climate Dynamics, 47 (12). pp. 3693-3712. ISSN 0930-7575 doi: https://doi.org/10.1007/s00382-016-3036-4

Weber, R. J. T., Carrassi, A. and Doblas-Reyes, F. J. (2015) Linking the anomaly initialization approach to the mapping paradigm: a proof-of-concept study. Monthly Weather Review, 143 (11). pp. 4695-4713. ISSN 0027-0644 doi: https://doi.org/10.1175/MWR-D-14-00398.1

Mitchell, L. and Carrassi, A. (2015) Accounting for model error due to unresolved scales within ensemble Kalman filtering. Quarterly Journal of the Royal Meteorological Society, 141 (689). pp. 1417-1428. ISSN 1477-870X doi: https://doi.org/10.1002/qj.2451

Raanes, P. N., Carrassi, A. and Bertino, L. (2015) Extending the square root method to account for additive forecast noise in ensemble methods. Monthly Weather Review, 143 (10). pp. 3857-3873. ISSN 0027-0644 doi: https://doi.org/10.1175/MWR-D-14-00375.1

Carrassi, A., Weber, R. J. T., Gumeas, V., Doblas-Reyes, F. J., Asif, M. and Volpi, D. (2014) Full-field and anomaly initialization using a low-order climate model: a comparison and proposals for advanced formulations. Nonlinear Processes in Geophysics, 21 (2). pp. 521-537. ISSN 1023-5809 doi: https://doi.org/10.5194/npg-21-521-2014

Palatella, L., Carrassi, A. and Trevisan, A. (2013) Lyapunov vectors and assimilation in the unstable subspace: theory and applications. Journal of Physics A: Mathematical and Theoretical, 46 (25). 254020. ISSN 1751-8113 doi: https://doi.org/10.1088/1751-8113/46/25/254020

Carrassi, A., Hamdi, R., Termonia, P. and Vannitsem, S. (2012) Short time augmented extended Kalman filter for soil analysis: a feasibility study. Atmospheric Science Letters, 13 (4). pp. 268-274. ISSN 1530-261X doi: https://doi.org/10.1002/asl.394

Carrassi, A. 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. 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

Carrassi, A. and Vannitsem, S. (2010) Accounting for model error in variational data assimilation: a deterministic formulation. Monthly Weather Review, 138 (9). pp. 3369-3386. ISSN 0027-0644 doi: https://doi.org/10.1175/2010MWR3192.1

Porcu, F. and Carrassi, A. (2009) Toward an estimation of the relationship between cyclonic structures and damages at the ground in Europe. Natural Hazards and Earth System Sciences, 9 (3). pp. 823-829. ISSN 1561-8633 doi: https://doi.org/10.5194/nhess-9-823-2009

Yang, S.-C., Corazza, M., Carrassi, A., Kalnay, E. and Miyoshi, T. (2009) Comparison of local ensemble transform Kalman filter, 3DVAR, and 4DVAR in a quasigeostrophic model. Monthly Weather Review, 137 (2). pp. 693-709. ISSN 0027-0644 doi: https://doi.org/10.1175/2008MWR2396.1

Carrassi, A., 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

Carrassi, A., Trevisan, A., Descamps, L., Talagrand, O. and Uboldi, F. (2008) Controlling instabilities along a 3DVar analysis cycle by assimilating in the unstable subspace: a comparison with the EnKF. Nonlinear Processes in Geophysics, 15 (4). pp. 503-521. ISSN 1023-5809 doi: https://doi.org/10.5194/npg-15-503-2008

Carrassi, A., 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., 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

Porcù, F., Carrassi, A., Medaglia, C. M., Prodi, F. and Mugnai, A. (2007) A study on cut-off low vertical structure and precipitation in the Mediterranean region. Meteorology and Atmospheric Physics, 96. pp. 121-140. ISSN 1436-5065 doi: https://doi.org/10.1007/s00703-006-0224-5

Carrassi, A., 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. (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. 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. 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