An Ensemble-Based Four-Dimensional Variational Data Assimilation Scheme. Part I: Technical Formulation and Preliminary Test

By Chengsi Liu, Q. Xiao, Bin Wang. 2008

283 citations recorded by the source corpus.

Subfields: Atmospheric Science, Oceanography

View Paper: https://www.semanticscholar.org/paper/972f75d41b401736dfa645e8efbefbddab0b112e

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Citations in the corpus

Intercomparison of Variational Data Assimilation and the Ensemble Kalman Filter for Global Deterministic NWP. Part I: Description and Single-Observation Experiments (2010, methodology): “…approach, which uses the ensemble covariances in a very similar way as in the Canadian EnKF system, has been called the Ensemble4D-Var (En-4D-Var) approach and several variations of this approach have been recently examined in idealized settings (Tian et al. 2008; Liu et al. 2008; Liu et al. 2009).”
An Ensemble-Based Four-Dimensional Variational Data Assimilation Scheme. Part II: Observing System Simulation Experiments with Advanced Research WRF (ARW) (2009, methodology): “Recently, Liu et al. (2008) presented an ensemble-based four-dimensional variational data assimilation algorithm (En4DVAR), which uses the flow-dependent B matrix constructed by ensemble forecasts and performs 4DVAR optimization.”
An iterative ensemble Kalman smoother (2014, methodology): “(44) This analysis part of the linearized variant, which could be seen as an inner loop of the IEnKS, resembles the EnVar analysis step of Liu et al. (2008).”
The evolution of the ECMWF hybrid data assimilation system (2016, methodology): “This methodology can be further extended to use sampled estimates of the background-error covariance matrix throughout the length of the window (4D-En-Var; (Liu et al., 2008).”
Direct Assimilation of Radar Reflectivity without Tangent Linear and Adjoint of the Nonlinear Observation Operator in the GSI-Based EnVar System: Methodology and Experiment with the 8 May 2003 Oklahoma City Tornadic Supercell (2017, methodology): “Independent studies by Zupanski (2005) and Liu et al. (2008) suggested ways of using an ensemble in the variational framework to avoid the use of the TLA of the observation operator.”
A Localized Particle Filter for High-Dimensional Nonlinear Systems (2016, background): “…as the earth’s atmosphere and ocean; examples include the ensemble Kalman filter (EnKF; Evensen 1994; Houtekamer and Mitchell 1998; Evensen and van Leeuwen 2000) and ensemble-variational hybrid schemes (e.g., Hamill and Snyder 2000; Lorenc 2003; Buehner 2005; Liu et al. 2008; Zhang et al. 2009).”
A non-linear least squares enhanced POD-4DVar algorithm for data assimilation (2015, background): “…namely, the ‘hybrid’ (e.g. Wang et al., 2008a, 2008b; Clayton et al., 2013), the ‘En4DVar’ (e.g. Zhang et al., 2009; Zhang and Zhang, 2012) and the ‘4DEnVar’ assimilation methods (Qiu et al., 2007; Liu et al., 2008; Tian et al., 2008; Wang et al., 2010; Tian et al., 2011; Tian and Xie, 2012).”
An economical approach to four-dimensional variational data assimilation (2010, methodology): “Liu et al. (2008, 2009) proposed an ensemble-based 4DVar approach that used ensemble forecasts to produce the flow-dependent background error covariance and performed 4DVar minimization to produce the analysis.”
A review of operational methods of variational and ensemble‐variational data assimilation (2017, methodology): “(41) Notice that M0,t(xb(i)) = xb(i)(t) (comprising Xb in (32)) may be computed in advance, which means that this method is akin to using an EnKS with a 4D state vector (section 3.3.2).”
Data assimilation in the geosciences: An overview of methods, issues, and perspectives (2017, background): “Liu, Xiao, and Wang (2008) essentially proposed to generalize this idea to the computation of the sensitivities of the observations within a data assimilation window (DAW) to the variables of the initial state.”