GSI 3DVar-Based Ensemble-Variational Hybrid Data Assimilation for NCEP Global Forecast System: Single-Resolution Experiments
By Xuguang Wang, David D. Parrish, Daryl Timothy Kleist, J. Whitaker. 2013
281 citations recorded by the source corpus.
Subfields: Atmospheric Science, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/fa66397a3a6ef368f28ba5e2c461f004c31dcca8
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
- R. Bannister
- Michael Fisher
- Xiaobing H. Feng
- Lars Isaksen
- Xiangjun Tian
- C. Bishop
- Massimo Bonavita
- Elias V. Holm
In Global and Planetary Change
- R. Bannister
- Michael Fisher
- Xiaobing H. Feng
- Lars Isaksen
- Xiangjun Tian
- C. Bishop
- Massimo Bonavita
- Elias V. Holm
Citations in the corpus
- 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).”
- A review of operational methods of variational and ensemble‐variational data assimilation (2017, methodology): “However EnVar permits further constraints to the analysis, such as a tangent linear normal mode constraint (Kleist et al., 2009; Wang et al., 2013) or a variationally-based initialization term applied with an extra term–normally called JC –added to the cost function”
- A non-linear least squares enhanced POD-4DVar algorithm for data assimilation (2015, methodology): “…efficient composite method, which seems a natural idea to compensate the shortcomings of both methods by combining their strength (e.g. Lorenc, 2003; Harlim and Hunt, 2007; Tian et al., 2008, 2011; Wang et al., 2010; Zhang et al., 2009; Cheng et al., 2010; Zhang and Zhang, 2012; Wang et al., 2013).”
- The GIGG‐EnKF: ensemble Kalman filtering for highly skewed non‐negative uncertainty distributions (2016, background): “…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 is a multivariate Gaussian…”