Modelling of error covariances by 4D‐Var data assimilation

By A. Lorenc. 2003

161 citations recorded by the source corpus.

Subfields: Atmospheric Science, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/c77fd68c3144c4306122800a5560d13d802f440a

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Citations in the corpus

Improved variational analyses using a nonlinear humidity control variable (2013, methodology): “…data assimilation methods which are necessary for large atmospheric models are based on the assumption that the errors have Gaussian distributions (Lorenc, 2003), usually also assumed to be unbiased (if the errors are non-Gaussian, least-squares methods still give the best linear unbiased…”
On the Sensitivity Equations of Four-Dimensional Variational (4D-Var) Data Assimilation (2008, background): “A significant amount of research in NWP is focused on modeling the error covariance matrices (Janjic´ and Cohn 2006; Frehlich 2006; Kucuk-karaca and Fisher 2006; Etherton and Bishop 2004; Lorenc 2003; Hamill and Snyder 2002).”
Limited‐memory preconditioners, with application to incremental four‐dimensional variational data assimilation (2008, methodology): “In practice, U−1 is constructed using prior knowledge of the physical and smoothness properties of the model fields (Derber and Bouttier, 1999; Lorenc, 2003; Weaver et al., 2005).”
The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation systems Part I - System overview and formulation (2011, background): “A beneficial first-level of preconditioning is a transformation of variable v 1⁄4 D 12dxU via the prior error covariance matrix (Lorenc, 2003), where dxU are the unbalanced components of the increment (Weaver et al.”
The potential of the ensemble Kalman filter for NWP—a comparison with 4D‐Var (2003, methodology): “The main advantage is that we can have a hybrid approach, with the traditional variation control variable (Lorenc 2003) supplemented by .”
Adjoint sensitivity of the model forecast to data assimilation system error covariance parameters (2010, unknown): “…weather prediction (NWP) is dedicated to observation- and background-error covariance modelling (Gaspari and Cohn, 1999; Hamill and Snyder, 2002; Lorenc, 2003; Buehner et al., 2005; Frehlich, 2006; Janjić and Cohn, 2006; Bannister, 2008a,b) and to the development of effective techniques for…”
A review of forecast error covariance statistics in atmospheric variational data assimilation. II: Modelling the forecast error covariance statistics (2008, background): “…the closer the B-matrix is to the ‘true’ forecast error covariance matrix (e.g. Pf from the Kalman filter if the system is linear), the closer the analysis is to that produced from the optimal combination of background and observational information (e.g. Daley, 1991; Lorenc, 2003a; Fisher, 2007).”
A Bayesian approach to state and parameter estimation in a Phytoplankton-Zooplankton model (2010, methodology): “Within the last 15 years there have been significant advances in statistical techniques available for data assimilation (DA), largely driven by a combination of the availability of high performance computing and dense observational data-sets (Lorenc 2003; Evensen 2007).”
Intercomparison of Variational Data Assimilation and the Ensemble Kalman Filter for Global Deterministic NWP. Part I: Description and Single-Observation Experiments (2010, unknown): “This is referred to as the four-subensemble configuration in Mitchell and Houtekamer (2009).”
Incorporating State-Dependent Temperature–Salinity Constraints in the Background Error Covariance of Variational Ocean Data Assimilation (2005, unknown): “By ignoring unbalanced errors in salinity (i.e., by setting S U 0, where S KSTT S U), we are in effect assuming K to be exact, and treating it as a strong constraint in the analysis (Lorenc 2003).”
Four-Dimensional Variational Data Assimilation for WRF : Formulation and Preliminary Results (2009, methodology): “The WRF 4D-Var algorithm takes the incremental 4D-Var formulation that is commonly used in operational systems (Courtier et al. 1994; Veersé and Thépaut 1998; Lorenc 2003).”
A multivariate balance operator for variational ocean data assimilation (2005, background): “In other words, the balance operator is applied as a strong constraint (Lorenc 2003).”