Ensemble Data Assimilation with the NCEP Global Forecast System

By J. Whitaker, Thomas M. Hamill, Xue Wei, Yucheng Song, Z. Toth. 2008

453 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/a7b4a1d813a12f947dc4c5ae1dc58b7e219b410a

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In Atmospheric Science

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Citations in the corpus

A comparative study of 4D-VAR and a 4D Ensemble Kalman Filter: perfect model simulations with Lorenz-96 (2007, methodology): “Their methods have been successfully applied to operational models by Houtekamer and Mitchell (2006), Szunyogh and Kostelich (2006, personal communication) and Whitaker et al. (2006).”
A Review of Innovation-Based Methods to Jointly Estimate Model and Observation Error Covariance Matrices in Ensemble Data Assimilation (2018, unknown): “In the additive inflation case, the diagonal terms of the forecast and analysis empirical covariance matrices is increased (Mitchell and Houtekamer 2000; Corazza et al. 2003; Whitaker et al. 2008; Houtekamer et al. 2009).”
The GIGG‐EnKF: ensemble Kalman filtering for highly skewed non‐negative uncertainty distributions (2016, methodology): “In meteorology, they are widely used in research (e.g. Anderson et al., 2009) and also in operations (e.g. Houtekamer et al., 2005; Whitaker et al., 2008).”
Model Error Representation in an Operational Ensemble Kalman Filter (2009, methodology): “As shown by Whitaker et al. (2008), if a single method is to be used to account for model error, such an addition of random perturbations performs slightly better than multiplicative covariance inflation or relaxation to the prior perturbation.”
Incorporating Ensemble Covariance in the Gridpoint Statistical Interpolation Variational Minimization: A Mathematical Framework (2010, methodology): “A mathematical derivation is therefore provided to demonstrate how to incorporate the flow-dependent ensemble covariance in the GSI variational minimization.”
The Twentieth Century Reanalysis Project (2009, background): “Although Whitaker et al. (2008) found that so-called ‘additive inflation’ (adding random perturbations with a specified covariance structure to individual ensemble members) was superior to a simple multiplicative covariance inflation (Whitaker et al., 2004) for a modern complete observing network,…”
Intercomparison of Variational Data Assimilation and the Ensemble Kalman Filter for Global Deterministic NWP. Part I: Description and Single-Observation Experiments (2010, background): “In a similar experimental context, Szunyogh et al. (2008) compared the local ensemble transform Kalman ﬁlter (LETKF) to 3D-Var and found results similar to Whitaker et al. (2008).”
Using the ensemble Kalman filter to estimate multiplicative model parameters (2008, methodology): “Thus, any of the specialized techniques for solving the EnKF, for example, the advanced sequential parallel algorithm (Anderson and Collins, 2007; Whitaker et al., 2008), can be applied to solve the augmented EnKF.”
A Comparison of Variational and Ensemble-Based Data Assimilation Systems for Reanalysis of Sparse Observations (2009, background): “…satisfied and the observation and background error covariances are optimal then at the observation location the variance of the difference between observation and first guess should equal the sum of the observation-error variance and background-error variance [e.g., Whitaker et al. 2008, their Eq.”
Accounting for Model Errors in Ensemble Data Assimilation (2009, methodology): “As found in previous studies (e.g., Hamill and Whitaker 2005; Whitaker et al. 2008), additive inﬂation outperforms multiplicative inﬂation.”
Simultaneous estimation of covariance inflation and observation errors within an ensemble Kalman filter (2009, methodology): “We also note that in this study we have focused on multiplicative covariance inflation, but that our simultaneous approach is equally applicable to adaptively estimating the scale of the additive noise in additive inflation schemes (Corazza et al., 2002; Whitaker et al., 2008 ).”
Ensemble-Based Sensitivity Analysis (2008, unknown): “Whitaker et al. (2008) propose an ensemble-based observation thinning algorithm based on analysis-error variance reduction, which we extend here to forecast metrics and to statistically significant changes in the metric mean value.”
The Gaussian Approach to Adaptive Covariance Inflation and Its Implementation with the Local Ensemble Transform Kalman Filter (2011, methodology): “Recent applications to operational numerical weather prediction (NWP) systems have obtained promising results (e.g., Houtekamer and Mitchell 2005; Houtekamer et al. 2005; Bowler et al. 2008; Whitaker et al. 2008; Bonavita et al. 2008, 2010; Miyoshi et al. 2010).”