On the Sensitivity Equations of Four-Dimensional Variational (4D-Var) Data Assimilation

78 citations recorded by the source corpus.

How researchers see this paper

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

Construction of non-diagonal background error covariance matrices for global chemical data assimilation (2010, background): “In variational data assimilation bothxb andB enter directly into the formulation of the cost function; errors in their specification directly impact the analysis result (Daescu, 2008).”
The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation systems Part I - System overview and formulation (2011, background): “This has recently attracted considerable attention in meteorology for routine monitoring of observing systems (Langland and Baker, 2004; Cardinali et al., 2004; Zhu and Gelaro, 2008; Daescu, 2008).”
Adjoint sensitivity of the model forecast to data assimilation system error covariance parameters (2010, methodology): “Appendix In Daescu (2008), the forecast sensitivity to the specification of the error covariances in the DAS is expressed in column vector format using the vectorization operator vec and the Kronecker product (Magnus and Neudecker, 1999): ∇vec(R)e(xa) ={R−1 [ h(xa) − y]} ⊗ ∇ye(xa) ,…”