Biblio
Filters: Author is Carrassi, Alberto [Clear All Filters]
Deterministic Treatment of Model Error in Geophysical Data Assimilation. Mathematical Paradigms of Climate Science (2016).doi:10.1007/978-3-319-39092-5_9
Sources of skill in near-term climate prediction: generating initial conditions. Climate Dynamics 47, (2016).
Degenerate Kalman filter error covariances and their convergence onto the unstable subspace. SIAM/ASA Journal on Uncertainty Quantification (JUQ) 5, (2017).
Estimating model evidence using data assimilation. Quarterly Journal of the Royal Meteorological Society 143, (2017).
Four-dimensional ensemble variational data assimilation and the unstable subspace. Tellus A: Dynamic Meteorology and Oceanography 69, (2017).
Rank Deficiency of Kalman Error Covariance Matrices in Linear Time-Varying System With Deterministic Evolution. SIAM Journal of Control and Optimization 55, (2017).
Asymptotic Forecast Uncertainty and the Unstable Subspace in the Presence of Additive Model Error. SIAM/ASA Journal on Uncertainty Quantification (JUQ) 6, (2018).
Chaotic dynamics and the role of covariance inflation for reduced rank Kalman filters with model error. Nonlinear processes in geophysics 25, (2018).
Data assimilation in the geosciences - An overview of methods, issues and perspectives. WIREs Climate Change (2018).doi:doi: 10.1002/wcc.535
Scientific challenges of convective-scale numerical weather prediction. Bulletin of The American Meteorological Society - (BAMS) (2018).doi:10.1175/BAMS-D-17-0125.1 Abstract
Stochastic parameterization identification using ensemble Kalman filtering combined with maximum likelihood methods. Tellus A: Dynamic Meteorology and Oceanography 70, (2018).
Adaptive covariance inflation in the ensemble Kalman filter by Gaussian scale mixtures. Quarterly Journal of the Royal Meteorological Society 145, (2019).
Estimating model evidence using ensemble‐based data assimilation with localization – The model selection problem. Quarterly Journal of the Royal Meteorological Society 145, (2019).
Improving weather and climate predictions by training of supermodels. Earth System Dynamics (ESD) 10, (2019).
Improving weather and climate predictions by training of supermodels. Earth System Dynamics (2019).
Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization. Foundations of Data Science (FoDS) 2, (2020). 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).
On the numerical integration of the Lorenz-96 model, with scalar additive noise, for benchmark twin experiments. Geoscientific Model Development 13, (2020).
Probabilistic Forecasts of Sea Ice Trajectories in the Arctic: Impact of Uncertainties in Surface Wind and Ice Cohesion. Oceans (2020).doi:10.3390/oceans1040022
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
