Tests of an Ensemble Kalman Filter for Mesoscale and Regional-Scale Data Assimilation. Part I: Perfect Model Experiments
By Fuqing Zhang, Z. Meng, A. Aksoy. 2006
228 citations recorded by the source corpus.
Subfields: Atmospheric Science, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/c4d1bd063768588326f284522ac0f8072c606265
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
- Takemasa Miyoshi
- Fuqing Zhang
- Z. Meng
- Thomas M. Hamill
- R. Bannister
- Eugenia Kalnay
- J. Whitaker
- Jeffrey L. Anderson
In Global and Planetary Change
- Takemasa Miyoshi
- Fuqing Zhang
- Z. Meng
- Thomas M. Hamill
- R. Bannister
- Eugenia Kalnay
- J. Whitaker
- Jeffrey L. Anderson
Citations in the corpus
- Simultaneous estimation of covariance inflation and observation errors within an ensemble Kalman filter (2009, background): “…(e.g. Houtekamer et al., 2005), and from globalscale (e.g. Whitaker et al., 2008; Szunyogh et al., 2008) to regional-scale models (e.g. Snyder and Zhang, 2003; Zhang et al., 2006; Torn et al., 2006), due to their ease of implementation and the flow-dependent background error covariance.”
- Tests of an Ensemble Kalman Filter for Mesoscale and Regional-Scale Data Assimilation. Part III: Comparison with 3DVAR in a Real-Data Case Study (2008, background): “... sciences field (Houtekamer and Mitchell 1998), the EnKF has been widely examined with different models at different scales and to different realistic extents (Houtekamer and Mitchell 1998; Hamill and Snyder 2000; Anderson 2001; Whitaker and Hamill 2002; Mitchell et al. 2002; Snyder and Zhang 200”
- Ensemble Data Assimilation with the NCEP Global Forecast System (2008, methodology): “Another method for dealing with model error, which we have not investigated here, involves explicitly accounting for the uncertainty in the forecast model by using a multimodel ensemble (Meng and Zhang 2007; Houtekamer et al. 1996).”
- Cloud-Resolving Ensemble Simulations of Mediterranean Heavy Precipitating Events: Uncertainty on Initial Conditions and Lateral Boundary Conditions (2011, methodology): “Zhang et al. (2006) applied an ensemble Kalman filter (EnKF) approach to generate different ICs.”
- Ensemble-based simultaneous state and parameter estimation in a two-dimensional sea-breeze model (2006, unknown): “…background on the application of the EnKF in atmospheric sciences, see, for example, Mitchell and Houtekamer (2000), Keppenne (2000), Hamill and Snyder (2000), Anderson (2001, 2003), Keppenne and Rienecker (2002), Snyder and Zhang (2003), Zhang et al. (2004, 2006), and Houtekamer et al. (2005).”