Variational Assimilation of Meteorological Observations With the Adjoint Vorticity Equation. Ii: Numerical Results

By P. Courtier, O. Talagrand. 2007

435 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change, Oceanography

View Paper: https://www.semanticscholar.org/paper/ca0c49ba56aded0608a24dfcd15ee8067294d6ec

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Citations in the corpus

Global adjoint tomography: first-generation model (2016, background): “…basic theory of adjoint methods (Chavent 1974) for seismic inversions was introduced in the 80s (Bamberger et al. 1977; Lailly 1983; Tarantola 1984a,b; Gauthier et al. 1986; Tarantola 1988; Talagrand & Courtier 1987), their application has only recently become possible with the availability of 3-”
The ensemble Kalman filter for combined state and parameter estimation (2009, methodology): “T he ensemble Kalman fi lter (EnKF) [1] is a sequential Monte Carlo method that provides an alternative to the traditional Kalman fi lter (KF) [2], [3] and adjoint or four-dimensional variational (4DVAR) methods [4]–[6] to better handle large state spaces and nonlinear error evolution.”
A review of operational methods of variational and ensemble‐variational data assimilation (2017, methodology): “Var has been the workhorse of DA for many years (Le Dimet and Talagrand, 1986; Talagrand and Courtier, 1987; Thepaut and Courtier, 1991; Zupanski, 1997; Rabier et al., 2000; Rawlins et al., 2007).”
Monte Carlo full-waveform inversion of crosshole GPR data using multiple-point geostatistical a priori information (2012, methodology): “This approach has later been referred to as the adjoint method (Talagrand and Courtier, 1987).”
Model-reduced gradient-based history matching (2011, background): “In this case the minimum of objective function J has a χ2 distribution with mNo degrees of freedom (see Tarantola (1987)).”
An Ensemble-Based Four-Dimensional Variational Data Assimilation Scheme. Part I: Technical Formulation and Preliminary Test (2008, background): “Therefore, development of the tangent linear and adjoint models is usually necessary for the realization of the 4DVAR approach in data assimilation (Talagrand and Courtier 1987; Zupanski”
Lagrangian data assimilation for river hydraulics simulations (2006, positive): “Variational data assimilation [15,24] is based on optimal control theory [17] and consists in identifying the control vector k that minimizes a cost function measuring the discrepancy between the state variable of the model and data obtained from the observation of the physical system.”
Construction of non-diagonal background error covariance matrices for global chemical data assimilation (2010, methodology): “Variational methods solve the data assimilation problem in an optimal control framework (Sasaki, 1958; LeDimet and Talagrand, 1986; Courtier and Talagrand, 1987; Lions, 1971; Sandu and Zhang, 2008; Khattatov et al., 2000).”
Data assimilation in the geosciences: An overview of methods, issues, and perspectives (2017, methodology): “An attractive alternative is represented by the descent methods, which use the gradient vector of the cost function in an iterative minimization procedure (Talagrand & Courtier, 1987, see also Appendix B).”
Finite‐frequency tomography using adjoint methods—Methodology and examples using membrane surface waves (2007, methodology): “Our approach to the inverse problem utilizes adjoint methods (Tarantola 1984; Talagrand & Courtier 1987), which provide the gradient of the misfit function but not its second derivatives, that is, the Hessian.”
The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation systems Part I - System overview and formulation (2011, background): “…is the calculus of variations, and some of the first notable applications in meteorology and oceanography are those of Lewis and Derber (1985), Derber (1987), Le Dimet and Talagrand (1986), Talagrand and Courtier (1987), Courtier and Talagrand (1987), Thacker and Long (1988) and Thacker (1989).”
Full seismic waveform tomography for upper-mantle structure in the Australasian region using adjoint methods (2009, methodology): “We decided to implement the adjoint method, which has been effective in as diverse fields as seismology (e.g. Sieminski et al. 2007a,b; Liu & Tromp 2008; Stich et al. 2009), geodynamics (e.g. Bunge et al. 2003) or meteorology (e.g. Talagrand & Courtier 1987).”
Sampling the posterior: An approach to non-Gaussian data assimilation (2007, background): “4DVAR was first studied for data assimilation in [24, 6, 36].”