Accounting for an imperfect model in 4D‐Var

By Yannick Trémolet. 2006

281 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/12f9dc9ced2fdbba1318fc45f1e9401e03b5019b

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In Atmospheric Science

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Citations in the corpus

Reduced basis approximation and a posteriori error bounds for 4D-Var data assimilation (2018, background): “In the weak-constraint case, the unknown initial condition and unknown model-error forcing term thus serve as control variables; for various weak-constraint formulations see e.g. Trémolet (2006).”
B‐preconditioned minimization algorithms for variational data assimilation with the dual formulation (2014, background): “For weak-constraint variational assimilation problems (Courtier, 1997; Bennett, 2002; Trémolet, 2006), n can be very large since the control vector includes a time-sequence of corrective terms or model states in order to account for model error.”
Deep learning for physical processes: incorporating prior scientific knowledge (2017, methodology): “éziat and Herlin, 2015], the ocean’s dynamics are modeled using shallow water equations [Vallis, 2017] and the initial conditions, along with other terms, are estimated using assimilation techniques [Trémolet, 2006]. This is a state of the art assimilation model for predicting ocean dynamics, here S”
A review of operational methods of variational and ensemble‐variational data assimilation (2017, background): “Cost function (3) is weak-constraint, meaning that x does not have to be exactly a model trajectory (Sasaki, 1970; Trémolet, 2006).”
Adaptive bias correction for satellite data in a numerical weather prediction system (2007, background): “Such explicit treatment is under way in various NWP centres (Trémolet, 2007), but is beyond the scope of this paper.”
Beyond Gaussian Statistical Modeling in Geophysical Data Assimilation (2010, methodology): “Indeed, the propagation of information within the assimilation window with the tangent linear model is shortened as compared to the strong constraint 4D-Var, thanks to model error present at each time step (Andersson et al. 2005; Fisher et al. 2005; Trémolet 2006).”
Model‐error estimation in 4D‐Var (2007, methodology): “Trémolet (2006) discusses several formulations of weak-constraint 4D-Var.”
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).”