Limited‐memory preconditioners, with application to incremental four‐dimensional variational data assimilation

By Jean Tshimanga, Serge Gratton, A. T. Weaver, Annick Sartenaer. 2008

72 citations recorded by the source corpus.

Subfields: Atmospheric Science, Computational Mechanics, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/6133f33e2fe05d7c602ea9893350ba79aa41ac7f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. Andrew M. Moore
2. Hernan G. Arango
3. Andrea Piacentini
4. J. Zavala‐Garay
5. Brian S. Powell
6. G. Broquet
7. Serge Gratton
8. Selime Gürol

In Computational Mechanics

1. Selime Gürol
2. Andrew M. Moore
3. Serge Gratton
4. Hernan G. Arango
5. A. T. Weaver
6. Andrea Piacentini

In Computational Theory and Mathematics

1. Selime Gürol
2. Andrew M. Moore
3. Serge Gratton
4. Hernan G. Arango
5. A. T. Weaver
6. Andrea Piacentini
7. Luís Nunes Vicente

Citations in the corpus

• The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation systems Part I - System overview and formulation (2011, methodology): “Two second-level preconditioners are available in ROMS: the first is based on estimates of the eigenvectors of A and follows Fisher and Courtier (1995), and the second is based on the Ritz vectors of A and parallels Tshimanga et al. (2008).”
• B‐preconditioned minimization algorithms for variational data assimilation with the dual formulation (2014, methodology): “The Lanczos vectors, which are directly computed by RBLanczos, are important for preconditioning (Tshimanga et al., 2008; Fisher et al., 2009; Desroziers and Berre, 2012) and for quantifying the performance of the data assimilation system (Cardinali et al.”