19 citations · 38 across the 7 of their papers we have counts for
9 papers
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…
The Conditioning of Hybrid Variational Data Assimilation
Shaerdan Shataer, Amos S. Lawless, Nancy K. Nichols
In variational assimilation, the most probable state of a dynamical system under Gaussian assumptions for the prior and likelihood can be found by solving a least-squares minimizat…
Convergent least-squares optimisation methods for variational data assimilation
Coralia Cartis, Maha H. Kaouri, Amos S. Lawless +1
Data assimilation combines prior (or background) information with observations to estimate the initial state of a dynamical system over a given time-window. A common application is…
On time-parallel preconditioning for the state formulation of incremental weak constraint 4D-Var
Ieva Daužickaitė, Amos S. Lawless, Jennifer A. Scott +1
Using a high degree of parallelism is essential to perform data assimilation efficiently. The state formulation of the incremental weak constraint four-dimensional variational data…
Randomised preconditioning for the forcing formulation of weak constraint 4D-Var
Ieva Daužickaitė, Amos S. Lawless, Jennifer A. Scott +1
There is growing awareness that errors in the model equations cannot be ignored in data assimilation methods such as four-dimensional variational assimilation (4D-Var). If allowed…
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…
Spectral estimates for saddle point matrices arising in weak constraint four-dimensional variational data assimilation
Ieva Daužickaitė, Amos S. Lawless, Jennifer A. Scott +1
We consider the large-sparse symmetric linear systems of equations that arise in the solution of weak constraint four-dimensional variational data assimilation, a method of high in…
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…