Biblio
Filtre: Forfatter er Marc Bocquet [Slett Alle Filtre]
Assimilation of lidar signals: Application to aerosol forecasting in the western Mediterranean basin. Atmospheric Chemistry and Physics (ACP) 14, (2014).
Expanding the validity of the ensemble Kalman filter without the intrinsic need for inflation. Nonlinear processes in geophysics 22, (2015). Abstract
DADA: data assimilation for the detection and attribution of weather and climate-related events. Climatic Change 136, (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. Series A, Dynamic meteorology and oceanography 69, (2017).
Optimising assimilation of hydrographic profiles into isopycnal ocean models with ensemble data assimilation. Ocean Modelling 114, (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).
Stochastic parameterization identification using ensemble Kalman filtering combined with maximum likelihood methods. Tellus. Series 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).
Data assimilation as a learning tool to infer ordinary differential equation representations of dynamical models. Nonlinear processes in geophysics 26, (2019).
Estimating model evidence using ensemble‐based data assimilation with localization – The model selection problem. Quarterly Journal of the Royal Meteorological Society 145, (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).
On Temporal Scale Separation in Coupled Data Assimilation with the Ensemble Kalman Filter. Journal of statistical physics (2020).doi:10.1007/s10955-020-02525-z
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
