Model‐error estimation in 4D‐Var

By Yannick Trémolet. 2007

170 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/65e9ccc7680785eb6a15ca792a79a637c02a49dc

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Citations in the corpus

Estimating Model Parameters with Ensemble-Based Data Assimilation: A Review (2013, methodology): “Carrasi and Vannitsem 2011 presented an efficient methodology to incorporate parameter estimation in a sequential EKF that does not require an extension of the adjoint model to perform the parameter estimation.”
Impact of Stochastic Physics in a Convection-Permitting Ensemble (2012, methodology): “In data assimilation, the impact of model error has been studied using ensemble-based assimilation algorithms (e.g. Houtekamer 2009, Raynaud 2011, Whitaker 2002), four-dimensional variational data assimilation (e.g. Tremolet, 2007), and particle filters (van Leeuwen, 2009).”
Data assimilation in the geosciences: An overview of methods, issues, and perspectives (2017, background): “The development of specific solutions have thus been necessary to make the weakconstraint 4DVar feasible in the geosciences (see Griffith & Nichols, 2000; Vidard et al., 2004; Trémolet, 2007), and its use in operational settings has only been very recent (Fisher, Tremolet, Auvinen, Tan, & Poli, 2011”
The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation systems Part I - System overview and formulation (2011, background): “Additional recent discussions of the issues surrounding model error and weak constraint 4D-Var can be found in Trémolet (2006, 2007).”
On the Sensitivity Equations of Four-Dimensional Variational (4D-Var) Data Assimilation (2008, methodology): “A weak-constraint 4D-Var data assimilation may be formulated to account for the model error as part of the 4D-Var control variable (Trémolet 2007).”
A review of operational methods of variational and ensemble‐variational data assimilation (2017, methodology): “Variational methods are restricted in their ability to quantify adequately the flow-dependent background and model error statistics, which are prescribed in Var by a parametrized scheme (Trémolet, 2007; Bannister, 2008).”