Biblio
Filters: Author is Carrassi, Alberto [Clear All Filters]
Data assimilation using adaptive, non-conservative, moving mesh models. Nonlinear processes in geophysics 26, (2019).
Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization. Foundations of Data Science (FoDS) 2, (2020). Abstract
Four-dimensional ensemble variational data assimilation and the unstable subspace. Tellus. Series A, Dynamic meteorology and oceanography 69, (2017).
Degenerate Kalman filter error covariances and their convergence onto the unstable subspace. SIAM/ASA Journal on Uncertainty Quantification (JUQ) 5, (2017).
Data assimilation as a learning tool to infer ordinary differential equation representations of dynamical models. Nonlinear processes in geophysics 26, (2019).
Combining data assimilation and machine learning to infer unresolved scale parametrization. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, (2021). Abstract
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, (2020).
Accounting for Model Error in Data Assimilation. Oberwolfach Reports. Mathematical and Algorithmic Aspects of Atmosphere-Ocean Data Assimilation 9, 3417 - 3471 (2012).
Accounting for Model Error in Variational Data Assimilation: A Deterministic Formulation. Monthly Weather Review 138, 3369 - 3386 (2010).
Full-field and anomaly initialization using a low-order climate model: a comparison and proposals for advanced formulations. Nonlinear Processes in Geophysics 21, 521 - 537 (2014).
Data assimilation as a nonlinear dynamical systems problem: Stability and convergence of the prediction-assimilation system. Chaos: An Interdisciplinary Journal of Nonlinear Science 18, 023112 (2008).
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, 435 - 451 (2011).
Sources of skill in near-term climate prediction: generating initial conditions. Climate Dynamics 47, (2016).
Data assimilation in the geosciences - An overview of methods, issues and perspectives. WIREs Climate Change (2018).doi:doi: 10.1002/wcc.535
Model error and sequential data assimilation: A deterministic formulation. Quarterly Journal of the Royal Meteorological Society 134, 1297 - 1313 (2008).
TREATMENT OF THE ERROR DUE TO UNRESOLVED SCALES IN SEQUENTIAL DATA ASSIMILATION. International Journal of Bifurcation and Chaos 21, 3619 - 3626 (2011).
Controlling instabilities along a 3DVar analysis cycle by assimilating in the unstable subspace: a comparison with the EnKF. Nonlinear Processes in Geophysics 15, 503 - 521 (2008).
Short time augmented extended Kalman filter for soil analysis: a feasibility study. Atmospheric Science Letters 13, 268 - 274 (2012).
Adaptive observations and assimilation in the unstable subspace by breeding on the data-assimilation system. Tellus A 591583107575131948751213, 101 - 113 (2007).
Estimating model evidence using data assimilation. Quarterly Journal of the Royal Meteorological Society 143, (2017).
The maximum likelihood ensemble filter performances in chaotic systems. Tellus A 61, 587 - 600 (2009).
Deterministic Treatment of Model Error in Geophysical Data Assimilation. Mathematical Paradigms of Climate Science (2016).doi:10.1007/978-3-319-39092-5_9
Probabilistic Forecasts of Sea Ice Trajectories in the Arctic: Impact of Uncertainties in Surface Wind and Ice Cohesion. Oceans (2020).doi:10.3390/oceans1040022
An international initiative of predicting the SARS-CoV-2 pandemic using ensemble data assimilation. Foundations of Data Science (FoDS) (2020).doi:10.3934/fods.2021001 Abstract
