Intercomparison of Variational Data Assimilation and the Ensemble Kalman Filter for Global Deterministic NWP. Part I: Description and Single-Observation Experiments

By M. Buehner, P. Houtekamer, C. Charette, H. Mitchell, Bin He. 2010

260 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/ee888befd1c544bd0404a06ca6cd5da7d0d52a8f

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Citations in the corpus

Adaptive Ensemble Covariance Localization in Ensemble 4D-VAR State Estimation (2011, methodology): “To illustrate AECL methods within the context of a global numerical weather prediction model, Bishop and Hodyss (2007, 2009b) incorporated them into a large observation volume local ensemble Kalman ﬁlter (Ott et al. 2004; Hunt et al. 2007).”
An exploration of the equivalent weights particle filter (2013, unknown): “Another alternative would be construct both B(τ ) and Q in the equivalent weights step such that they project the model states onto a balanced manifold, similar to the B matrix in 4D-Var or initialisation in the EnKF (Houtekamer and Mitchell, 2005; Buehner et al., 2010).”
Data assimilation in the geosciences: An overview of methods, issues, and perspectives (2017, background): “…of the 4DEnVar are possible depending on the way the perturbations are generated, or if the adjoint model is available or not (Bocquet, 2016; Buehner et al., 2010a; Buehner, Houtekamer, Charette, Mitchell, & He, 2010b; Clayton, Lorenc, & Barker, 2013; Poterjoy & Zhang, 2015; Zhang & Zhang,…”
A Second-Order Exact Ensemble Square Root Filter for Nonlinear Data Assimilation (2015, background): “It has been shown that in combination with such rather ad hoc techniques, the filter performance in high-dimensional systems can be enhanced significantly and made comparative to established variational schemes (Kalnay et al. 2007; Buehner et al. 2010; Yang et al. 2012).”
GSI 3DVar-Based Ensemble-Variational Hybrid Data Assimilation for NCEP Global Forecast System: Single-Resolution Experiments (2013, methodology): “As in Whitaker et al. (2008), EnKF performs generally better than GSI3DVar.”
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).”
Four-dimensional ensemble-variational data assimilation for global deterministic weather prediction (2013, methodology): “D ensemble covariances in EnVar is also similar to how they are used within the EnKF itself (Hunt et al., 2004; Houtekamer and Mitchell, 2005; Buehner et al., 2010a).”
Operational implementation of a hybrid ensemble/4D‐Var global data assimilation system at the Met Office (2013, methodology): “For non-local observations such as satellite radiances, Campbell et al. (2010) show that model space localization is superior to the observation-space localization schemes typically used with the ensemble Kalman filter, such as that of Houtekamer and Mitchell (2001).”