The Use of an Ensemble Approach to Study the Background Error Covariances in a Global NWP Model
By Margarida Pereira, L. Berre. 2006
149 citations recorded by the source corpus.
Subfields: Atmospheric Science, Environmental Engineering, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/b32fff2153b2751b83f37f3413412d9c316d03bc
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Citations in the corpus
- Background‐error covariances for a convective‐scale data‐assimilation system: AROME–France 3D‐Var (2011, methodology): “This is also partly visible for background-error variances, as illustrated by Belo-Pereira and Berre (2006).”
- An extended specification of flow‐dependent background error variances in the Météo‐France global 4D‐Var system (2011, methodology): “A climatological estimate of the correlations is obtained from an ensemble of perturbed variational assimilations (Fisher, 2003; Belo Pereira and Berre, 2006), and is specified in Bu.”
- Linear Filtering of Sample Covariances for Ensemble-Based Data Assimilation. Part I: Optimality Criteria and Application to Variance Filtering and Covariance Localization (2015, methodology): “These operators are generally calibrated using ensemble of forecast differences (e.g., Belo Pereira and Berre 2006).”
- Flow‐dependent background‐error covariances for a convective‐scale data assimilation system (2012, methodology): “138: 310–322 (2012) 312 P. Brousseau et al. NWP system (Berre, et al., 2009), attempts to simulate the sources and evolution of errors during the assimilation cycle (Fisher, 2003; Belo-Pereira and Berre, 2006).”
- An overview of the variational assimilation in the ALADIN/France numerical weather‐prediction system (2005, result): “This is consistent with previous studies on the globe (Fisher 2003; Belo Pereira and Berre 2006).”
- A review of operational methods of variational and ensemble‐variational data assimilation (2017, methodology): “…example, use the spread of their ensemble prediction systems (initialized not from an EnKF but from ensembles of perturbed 4D-Var assimilations; Belo Pereira and Berre, 2006; Berre et al., 2007, 2009; Isaksen et al., 2010) to model the ‘variances of the day’ of Bh (Bonavita et al., 2011, 2012;…”
- The evolution of the ECMWF hybrid data assimilation system (2016, methodology): “Variational methods used in operational DA systems are moving from static, climatological representation of the backgrounderror covariance matrix B (Fisher, 2003; Belo Pereira and Berre, 2006) to ensemble-based, flow-dependent B matrix models.”