19 citations · 31 across the 9 of their papers we have counts for
10 papers
The Influence of the Cluster Point on Rounding Errors and Sensitivity in the Spectral Limited-Memory Preconditioner
Hisham Elzayyadi, Jemima M. Tabeart
The spectral limited-memory preconditioner (sLMP) clusters leading eigenvalues of symmetric positive definite matrices to accelerate conjugate gradient (CG) convergence. In practic…
Advanced Linear Algebra with Applications - Part I (Numerical linear algebra for PDEs, machine learning, and data assimilation)
Victorita Dolean, Jemima Tabeart
These lecture notes form the first part of a master's-level course on advanced numerical linear algebra. Their aim is not only to present the classical algorithms, but to show why…
An Introduction to Solving the Least-Squares Problem in Variational Data Assimilation
I. Daužickaitė, M. A. Freitag, S. Gürol +4
Variational data assimilation is a technique for combining measured data with dynamical models. It is a key component of Earth system state estimation and is commonly used in weath…
Block Alpha-Circulant Preconditioners for All-at-Once Diffusion-Based Covariance Operators
Jemima M. Tabeart, Selime Gürol, John W. Pearson +1
Covariance matrices are central to data assimilation and inverse methods derived from statistical estimation theory. Previous work has considered the application of an all-at-once…
Stein-based preconditioners for weak-constraint 4D-var
Davide Palitta, Jemima M. Tabeart
Algorithms for data assimilation try to predict the most likely state of a dynamical system by combining information from observations and prior models. Variational approaches, suc…
Model Reduction of Linear Dynamical Systems via Balancing for Bayesian Inference
Elizabeth Qian, Jemima M. Tabeart, Christopher Beattie +4
We consider the Bayesian approach to the linear Gaussian inference problem of inferring the initial condition of a linear dynamical system from noisy output measurements taken afte…
Saddle point preconditioners for weak-constraint 4D-Var
Jemima M. Tabeart, John W. Pearson
Data assimilation algorithms combine information from observations and prior model information to obtain the most likely state of a dynamical system. The linearised weak-constraint…
New bounds on the condition number of the Hessian of the preconditioned variational data assimilation problem
Jemima M. Tabeart, Sarah L. Dance, Amos S. Lawless +2
Data assimilation algorithms combine prior and observational information, weighted by their respective uncertainties, to obtain the most likely posterior of a dynamical system. In…
The impact of using reconditioned correlated observation error covariance matrices in the Met Office 1D-Var system
Jemima M. Tabeart, Sarah L. Dance, Amos S. Lawless +4
Recent developments in numerical weather prediction have led to the use of correlated observation error covariance (OEC) information in data assimilation and forecasting systems. H…
Improving the condition number of estimated covariance matrices
Jemima M. Tabeart, Sarah L. Dance, Amos S. Lawless +2
High dimensional error covariance matrices and their inverses are used to weight the contribution of observation and background information in data assimilation procedures. As obse…