Ensemble‐derived stationary and flow‐dependent background‐error covariances: Evaluation in a quasi‐operational NWP setting

By M. Buehner. 2005

358 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/b8cda2a4576512d7f991ace49de9902924687fba

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

In Environmental Engineering

In Global and Planetary Change

Citations in the corpus

Expanding the validity of the ensemble Kalman filter without the intrinsic need for inflation (2015, methodology): “An alternative is to use the α control variables (Lorenc, 2003; Buehner, 2005).”
Operational implementation of a hybrid ensemble/4D‐Var global data assimilation system at the Met Office (2013, methodology): “Buehner (2005) used a Gaussian-like correlation function defined with respect to pressure scale height, which is probably a reasonable compromise.”
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).”
B‐preconditioned minimization algorithms for variational data assimilation with the dual formulation (2014, background): “, 2003; Weaver and Mirouze, 2013) and with hybrid ensemble-variational B formulations (Buehner, 2005; Wang et al., 2007, 2008).”
An economical approach to four-dimensional variational data assimilation (2010, methodology): “As Buehner (2005) suggested, the ensembles from the EnKF could be used to provide flow-dependent B-matrices to 4DVar at no extra computational cost.”
Adaptive Ensemble Covariance Localization in Ensemble 4D-VAR State Estimation (2011, methodology): “While C A allows for variable-dependent localization, in this paper we suppress the capability because, using the techniques discussed in Bishop et al. (2011), doing so reduces the cost of AECL by more than a factor of 4.”
A multivariate balance operator for variational ocean data assimilation (2005, background): “In meteorology, it is typical to estimate the standard deviations (and parameters of the correlation model) from a suitably constructed ensemble of forecast differences (Parrish and Derber 1992; Buehner 2005).”
The Hybrid Local Ensemble Transform Kalman Filter (2014, methodology): “For example, a hybrid system based on the developmental work of Barker (1999), Hamill and Snyder (2000), Lorenc (2003), Buehner (2005), Buehner et al. (2010a,b), andWang et al. (2007a,b, 2008a,b, 2013) has recently been implemented at the National Centers for Environmental Prediction (NCEP) for use…”
Feasibility of a 100-Year Reanalysis Using Only Surface Pressure Data (2006, background): “One can speculate that an advanced 3DVAR with reformulated, more complex background error covariances that vary spatially (e.g., Wu et al. 2002; Purser et al. 2003) or with the f low (e.g. Riishojgaard 1998; Buehner 2005) could result in a large improvement relative to the CDASSFC results.”
An Ensemble-Based Four-Dimensional Variational Data Assimilation Scheme. Part I: Technical Formulation and Preliminary Test (2008, methodology): “Buehner (2005) adopted a similar hybrid ensemble 3DVAR (En3DVAR) scheme, and implemented it to a global model data assimilation system.”
Background‐error correlation length‐scale estimates and their sampling statistics (2008, background): “This is all the more important as ongoing research is devoted to the representation of existing heterogeneities and anisotropies (e.g. Fisher, 2003; Buehner, 2005).”
Incorporating Ensemble Covariance in the Gridpoint Statistical Interpolation Variational Minimization: A Mathematical Framework (2010, methodology): “Different from Buehner (2005) and Wang et al. (2008a,b), in the widely used GSI the minimization procedure is preconditioned upon the full background error covariance (Derber and Rosati 1989).”
Four-Dimensional Variational Data Assimilation for WRF : Formulation and Preliminary Results (2009, background): “Here B is the background error covariance matrix, which is typically climatological estimates, but it may also be derived from prior or ensemble-based flow-dependent estimates or, with slightly different formulation, combination of both climatological and ensemble estimates (e.g., Buehner 2005).”
Intercomparison of Variational Data Assimilation and the Ensemble Kalman Filter for Global Deterministic NWP. Part I: Description and Single-Observation Experiments (2010, methodology): “This approach has previously been examined by Buehner (2005) in the context of 3D-Var using covariances derived from a preoperational version of the Canadian EnKF.”
On the Theoretical Equivalence of Differently Proposed Ensemble 3DVAR Hybrid Analysis Schemes (2007, background): “He also showed how a localizing Schur product, which will reduce the effects of sampling error in the ensemble covariances, could be implemented in the variational framework with preconditioning.”
Strongly Coupled Data Assimilation Using Leading Averaged Coupled Covariance (LACC). Part I: Simple Model Study* (2015, methodology): “It should be remarked that even though the covariance covhTf o(t), T a(t)i shows up in the numerator of the Kalman gain K, the corresponding correlation determines the signal-to-noise ratio when using the sample covariance from forecast ensembles (Buehner 2005; Houtekamer et al. 2005).”