A comparison of combined data assimilation and machine learning methods for offline and online model error correction
Recent studies have shown that it is possible to combine machine learning methods with
data assimilation to reconstruct a dynamical system using only sparse and noisy …
data assimilation to reconstruct a dynamical system using only sparse and noisy …
Revising the stochastic iterative ensemble smoother
PN Raanes, AS Stordal… - Nonlinear Processes in …, 2019 - npg.copernicus.org
Ensemble randomized maximum likelihood (EnRML) is an iterative (stochastic) ensemble
smoother, used for large and nonlinear inverse problems, such as history matching and data …
smoother, used for large and nonlinear inverse problems, such as history matching and data …
[HTML][HTML] A square-root, dual-resolution 3DEnVar for the AROME Model: Formulation and evaluation on a summertime convective period
Y Michel, P Brousseau - Monthly Weather Review, 2021 - journals.ametsoc.org
A three-dimensional ensemble-variational (3DEnVar) data assimilation algorithm has been
developed for the high-resolution AROME NWP system. Building on previous work on …
developed for the high-resolution AROME NWP system. Building on previous work on …
A fast, single-iteration ensemble Kalman smoother for sequential data assimilation
C Grudzien, M Bocquet - Geoscientific Model Development, 2022 - gmd.copernicus.org
Ensemble variational methods form the basis of the state of the art for nonlinear, scalable
data assimilation, yet current designs may not be cost-effective for real-time, short-range …
data assimilation, yet current designs may not be cost-effective for real-time, short-range …
An iterative ensemble Kalman smoother in presence of additive model error
Ensemble variational methods are being increasingly used in the field of geophysical data
assimilation. Their efficiency comes from the combined use of ensembles, which provide …
assimilation. Their efficiency comes from the combined use of ensembles, which provide …
A kernel extension of the Ensemble Transform Kalman Filter
Data assimilation methods are mainly based on the Bayesian formulation of the estimation
problem. For cost and feasibility reasons, this formulation is usually approximated by …
problem. For cost and feasibility reasons, this formulation is usually approximated by …
[HTML][HTML] Ensemble transport smoothing. Part II: Nonlinear updates
Smoothing is a specialized form of Bayesian inference for state-space models that
characterizes the posterior distribution of a collection of states given an associated …
characterizes the posterior distribution of a collection of states given an associated …
Batch seismic inversion using the iterative ensemble Kalman smoother
M Gineste, J Eidsvik - Computational Geosciences, 2021 - Springer
An ensemble-based method for seismic inversion to estimate elastic attributes is considered,
namely the iterative ensemble Kalman smoother. The main focus of this work is the …
namely the iterative ensemble Kalman smoother. The main focus of this work is the …
Novel optimisation methods for data assimilation
MH Kaouri - 2021 - centaur.reading.ac.uk
Data assimilation (DA) is a technique used to estimate the state of a dynamical system. In
DA, a prior estimate (background state) is combined with observations to estimate the initial …
DA, a prior estimate (background state) is combined with observations to estimate the initial …