Operational implementation of a hybrid ensemble/4D‐Var global data assimilation system at the Met Office

By Adam Clayton, A. Lorenc, D. Barker. 2013

380 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/4908deb0bff077d8e5956dc148c5356314628014

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Citations in the corpus

A non-linear least squares enhanced POD-4DVar algorithm for data assimilation (2015, methodology): “…the existing composite methods in the literature can be roughly divided into three groups, 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…”
Data assimilation: making sense of Earth Observation (2014, background): “Such a hybrid approach has been operational at ECMWF for some time (Buizza et al., 2008; Isaksen et al., 2010; Bonavita et al., 2012), and is now operational at the Met Office, UK, for the global model (Clayton et al., 2013) and at Environment Canada (Buehner et al., 2010).”
Data assimilation in the geosciences: An overview of methods, issues, and perspectives (2017, background): “Many variants of the 4DEnVar are possible depending on the way the perturbations are generated, or if the adjoint model is available or not (Buehner et al., 2010a,b; Zhang and Zhang , 2012; Clayton et al., 2013; Poterjoy and Zhang , 2015; Bocquet , 2016).”
The GIGG‐EnKF: ensemble Kalman filtering for highly skewed non‐negative uncertainty distributions (2016, background): “From a theoretical perspective, current operational data assimilation techniques (Rabier et al., 2000; Xu et al., 2005; Bonavita et al., 2012; Clayton et al., 2013; Kuhl et al., 2013; Wang et al., 2013) are designed to be accurate in the case where the distribution of truth given prior information…”
The evolution of the ECMWF hybrid data assimilation system (2016, methodology): “…2004; Wang et al., 2007b) or, more commonly, by augmentation of the variational control variable (‘extended control variable’ method; Barker, 1999; Lorenc, 2003; Buehner, 2005; Wang et al., 2007a, 2008; Buehner et al., 2010a, 2010b; Bishop and Hodyss, 2011; Clayton et al., 2013; Wang et al., 2013).”
GSI 3DVar-Based Ensemble-Variational Hybrid Data Assimilation for NCEP Global Forecast System: Single-Resolution Experiments (2013, background): “1 2013 American Meteorological Society 2010) and implemented for regional (e.g., Wang et al. 2008ab; Wang 2011; Zhang and Zhang 2012; Barker et al. 2012; Li et al. 2012) and global (e.g., Buehner 2005; Buehner et al. 2010a,b; Bishop and Hodyss 2011, Clayton et al. 2012) NWP.”
Improved variational analyses using a nonlinear humidity control variable (2013, background): “At the same time a hybrid system in which ensemble perturbations add ‘errors of the day’ and replace 20% of the background error covariances was introduced in the global NWP system (Clayton et al., 2012).”
A review of operational methods of variational and ensemble‐variational data assimilation (2017, methodology): “…in 4D-Var – 25 Berre et al. (2007); Raynaud et al. (2011, 2012), Berre et al. (2015) Met Office UM (global) (Davies et al., 2005) Hybrid En4DVar L03 23 Clayton et al. (2012) NRL NOGAPS (global) (Hogan et al., 1991) Hybrid En4DVar B05 80 Kuhl et al. (2013); Bishop et al. (2011) NCEP GFS Hybrid…”
A Localized Particle Filter for High-Dimensional Nonlinear Systems (2016, methodology): “Despite their limitations, techniques that rely on Gaussian assumptions have performed well for operational weather prediction and research (e.g., Buehner et al. 2010; Bishop and Hodyss 2011; Clayton et al. 2013; Kuhl et al. 2013; Wang and Lei 2014).”