The Gaussian Approach to Adaptive Covariance Inflation and Its Implementation with the Local Ensemble Transform Kalman Filter

By Takemasa Miyoshi. 2011

289 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/cfdb2ba9c61d44e5a5a479e147c9f7eb70446329

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Citations in the corpus

An iterative ensemble Kalman smoother (2014, background): “Like other adaptive inflation schemes (Anderson, 2007; Miyoshi, 2011; Liang et al., 2012), the inflation of the prior is implicitly tuned so as to optimize a criterion, related to the likelihood of the innovations given their prior statistics.”
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.”
Adaptive covariance relaxation methods for ensemble data assimilation: experiments in the real atmosphere (2017, methodology): “For the multiplicative inflation methods, we need to find the spatially dependent inflation factors (e.g. Miyoshi, 2011).”
A Second-Order Exact Ensemble Square Root Filter for Nonlinear Data Assimilation (2015, methodology): “While this simple but expensive procedure is sufficient for toy model tests, for real applications, more sophisticated methods such as adaptive inflation (e.g., Anderson 2007; Miyoshi 2011; Luo and Hoteit 2013) may be more suitable.”
Data assimilation in the geosciences: An overview of methods, issues, and perspectives (2017, unknown): “Hence, adaptive schemes have been developed to make the task more automatic (Anderson, 2007; Bocquet, 2011; Bocquet, Raanes, & Hannart, 2015; Bocquet & Sakov, 2012; Brankart, Cosme, Testut, Brasseur, & Verron, 2010; Li, Kalnay, & Miyoshi, 2009; Liang et al., 2012; Miyoshi, 2011; Wang & Bishop, 2003;”
Estimating Model Parameters with Ensemble-Based Data Assimilation: A Review (2013, methodology): “…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 (Krishnamurti et al. 1999, Meng and Zhang 2007), stochastic physical…”
A Review of Innovation-Based Methods to Jointly Estimate Model and Observation Error Covariance Matrices in Ensemble Data Assimilation (2018, background): “…have been proposed by Wang and Bishop341 (2003), Anderson (2007), Anderson (2009) conditionally to the spatial location, Li et al. (2009),342 Miyoshi (2011), Bocquet (2011), Bocquet and Sakov (2012), Miyoshi et al. (2013), Bocquet et al.343 19 Accepted for publication in Monthly Weather…”
Enhanced Adaptive Inflation Algorithm for Ensemble Filters (2018, methodology): “This is a very well-known result that has been used in many atmospheric and oceanographic DA studies (e.g., Wang and Bishop 2003; Wang et al. 2007; Li et al. 2009; Miyoshi 2011; Karspeck et al. 2013).”