Simultaneous estimation of covariance inflation and observation errors within an ensemble Kalman filter

By Hong Li, Eugenia Kalnay, Takemasa Miyoshi. 2009

317 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/399e873aac0ba87f391abbac7ca903c637d9e6e7

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Citations in the corpus

“Variable localization” in an ensemble Kalman filter: Application to the carbon cycle data assimilation (2011, methodology): “More details about the adaptive inflation methods can be found in the work of Li et al. [2009] and Zhang et al. [2004].”
Data assimilation in the geosciences: An overview of methods, issues, and perspectives (2017, methodology): “Hence, adaptive schemes have been developed to make the task more automatic (Wang and Bishop, 2003; Anderson, 2007; Li et al., 2009; Zheng , 2009; Brankart et al., 2010; Bocquet , 2011; Miyoshi , 2011; Bocquet and Sakov , 2012; Liang et al., 2012; Ying and Zhang , 2015; Bocquet et al., 2015b).”
Adjoint sensitivity of the model forecast to data assimilation system error covariance parameters (2010, background): “…for diagnosis, estimation, and tuning of unknown error covariance parameters in both variational and Kalman filter-based assimilation systems (Dee, 1995; Dee and Da Silva, 1999; Desroziers and Ivanov, 2001; Desroziers et al., 2005; Chapnik et al., 2006; Desroziers et al., 2009; Li et al., 2009).”
Expanding the validity of the ensemble Kalman filter without the intrinsic need for inflation (2015, methodology): “Other efficient adaptive inflation methods have been proposed by, e.g., Wang and Bishop (2003), Anderson (2007), Li et al. (2009), Zheng (2009), Brankart et al. (2010), Miyoshi (2011), Liang et al. (2012), and Ying and Zhang (2015) for broader uses including extrinsic model error.”
Estimating Model Parameters with Ensemble-Based Data Assimilation: A Review (2013, methodology): “Further research is needed to assess how parameter estimation methods can be optimally combined with methods designed to represent other sources of model errors within a data assimilation cycle, such as adaptive inflation (Miyoshi 2011), additive inflation (Li et al. 2009), multi-model ensembles (Kr”
Maximum likelihood estimation of inflation factors on error covariance matrices for ensemble Kalman filter assimilation (2012, methodology): “Li et al. (2009) also proposed a method to simultaneously compute the inflation factor and observation error variance at each analysis step.”
Ensemble Kalman filtering without the intrinsic need for inflation (2011, methodology): “Another adaptive approach ( Li et al., 2009) use the diagnostics of Desroziers et al.”
The Twentieth Century Reanalysis Project (2009, background): “Allowing the observation error to vary adaptively (Li et al., 2009) may account for the apparent time variations in that quantity, particularly as shown for marine observations (Kent and Berry, 2005; Chang 2005, 2007).”
On the representation error in data assimilation (2018, unknown): “Further work investigating the diagnostic in simple model experiments includes both variational (Stewart, 2010) and ensemble (Li et al. , 2009; Miyoshi et al. , 2013) data assimilation systems and its use to estimate time varying observation errors (Waller et al. , 2014a).”