Model Error Representation in an Operational Ensemble Kalman Filter

By P. Houtekamer, H. Mitchell, Xingxiu Deng. 2009

205 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/1af93b05ca5084e3eb6215111380860a1a643daa

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In Atmospheric Science

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Citations in the corpus

Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier-Stokes simulations: A data-driven, physics-informed Bayesian approach (2015, result): “This finding is consistent with earlier studies when EnKF was used in data assimilations in applications such as weather forecasting [43].”
The Gaussian Approach to Adaptive Covariance Inflation and Its Implementation with the Local Ensemble Transform Kalman Filter (2011, methodology): “…to account for the inhomogeneity of covariance underestimation, previous studies adopted additive inflation (e.g., Whitaker et al. 2008; Houtekamer et al. 2009), where random perturbations are added to the analysis ensemble members, or the relaxation-to-prior method (Zhang et al.…”
Performance of the New NCEP Global Ensemble Forecast System in a Parallel Experiment (2017, methodology): “Two inﬂation methods, ensemble covariance inﬂation (e.g., Whitaker and Hamill 2002, 2012) and additive noise inﬂation (e.g., Whitaker et al. 2008; Houtekamer et al. 2005, 2009), are applied to the posterior ensemble perturbations to account for errors from other sources (e.g., the GSM).”
Beyond Gaussian Statistical Modeling in Geophysical Data Assimilation (2010, background): “…generated with different versions of the underlying forecast model (e.g., with different physical parameterizations; Meng and Zhang Unauthenticated | Downloaded 03/27/22 05:02 AM UTC 2007; Fujita et al. 2007; Houtekamer et al. 2009; or with perturbations of model parameters; Wu et al. 2008).”
An Iterative EnKF for Strongly Nonlinear Systems (2012, background): “Houtekamer and Mitchell for a number of years propose the use of multiensemble EnKF configurations for cross validation, and have recently reported a near-operational implementation with no need for inflation (Houtekamer and Mitchell 2009).”
Four-dimensional ensemble-variational data assimilation for global deterministic weather prediction (2013, methodology): “…focuses on a comparison between 4D-Var and another variational approach, called ensemble-variational data assimilation (EnVar), which relies heavily on the ensembles produced by an ensemble data assimilation approach, the EnKF (Houtekamer and Mitchell, 2005; Houtekamer et al., 2009) in our case.”
A Review of Innovation-Based Methods to Jointly Estimate Model and Observation Error Covariance Matrices in Ensemble Data Assimilation (2018, background): “In the additive inflation case, the diagonal terms of the352 forecast and analysis empirical covariance matrices is increased (Mitchell and Houtekamer 2000;353 Corazza et al. 2003; Whitaker et al. 2008; Houtekamer et al. 2009).”