Second-Order Information in Data Assimilation*

By François-Xavier Le Dimet, Ionel M. Navon, Dacian N. Daescu. 2002

201 citations recorded by the source corpus.

Subfields: Atmospheric Science, Computational Mechanics, Numerical Analysis

View Paper: https://www.semanticscholar.org/paper/b2a8b1872074021f84a6b3372d070335f6f9ea65

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. Dacian N. Daescu
2. Ionel M. Navon
3. Gianluigi Rozza
4. Andrea Manzoni
5. Toni Lassila
6. Alfio Maria Quarteroni
7. Ricardo Todling

In Computational Mechanics

1. Dacian N. Daescu
2. D. Papadimitriou
3. Ionel M. Navon
4. Kyriakos C. Giannakoglou
5. Stéphane Operto
6. Jean Virieux
7. Costas Papadimitriou
8. Andrea Manzoni

In Numerical Analysis

1. Dacian N. Daescu
2. Ionel M. Navon
3. D. Papadimitriou
4. Kyriakos C. Giannakoglou

Citations in the corpus

• Aerodynamic Shape Optimization Using First and Second Order Adjoint and Direct Approaches (2008, background): “A different view of the same problem can be found in [125–127], for variational data assimilation problems in meteorology with the shallowwater equations as state equations.”
• Direct, adjoint and mixed approaches for the computation of Hessian in airfoil design problems (2008, unknown): “The computation and use of the exact Hessian matrix may also be found in variational data assimilation problems in meteorology with the shallow-water equations as state equations [21].”
• On the Sensitivity Equations of Four-Dimensional Variational (4D-Var) Data Assimilation (2008, methodology): “Recently, Zhu and Gelaro (2008) developed the adjoint of the Gridpoint Statistical Interpolation analysis scheme (Wu et al. 2002) to obtain observation sensitivities that are fully consistent with the data assimilation procedure.”
• Efficiency of a POD‐based reduced second‐order adjoint model in 4D‐Var data assimilation (2007, methodology): “Significant research efforts to reduce the computational burden of the data assimilation by reducing the dimension of the system have centred on Kalman and extended Kalman filter methods (see, for example, References [10–13]).”
• Full Waveform Inversion and the Truncated Newton Method (2013, methodology): “The computation of Hessianvector products through second-order adjoint methods is a topic that has already been investigated in the field of data assimilation and weather forecasting [35].”
• Nonlinear Least Squares for Inverse Problems (2010, unknown): “, [14, 52, 53]).”