An Ensemble-Based Four-Dimensional Variational Data Assimilation Scheme. Part II: Observing System Simulation Experiments with Advanced Research WRF (ARW)
By Chengsi Liu, Q. Xiao, Bin Wang. 2009
157 citations recorded by the source corpus.
Subfields: Atmospheric Science, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/0eb5bde141a1e5a0eb3bf4e75285c58237cac909
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Citations in the corpus
- Adaptive Ensemble Covariance Localization in Ensemble 4D-VAR State Estimation (2011, methodology): “There is a growing in-terestin the use of localized ensemble covariances in three-dimensional variational data assimilation (3D-VAR) and 4D-VAR schemes evident in papers by Lorenc (2003), Buehner (2005), Buehner et al. (2010a,b), Wang et al. (2007), Liu et al. (2009), and others.”
- A review of operational methods of variational and ensemble‐variational data assimilation (2017, methodology): “However, there are some less encouraging studies showing that, for example, humidity is particularly sensitive to sampling error and is not improved with 4DEnVar over 3DEnVar (Liu et al., 2009).”
- 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).”
- 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.”
- Operational implementation of a hybrid ensemble/4D‐Var global data assimilation system at the Met Office (2013, methodology): “∗∗The system is called ‘En4DVar’ by Liu et al. (2008) and ‘En-4D-Var’ by Buehner et al. (2010).”
- 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).”