Evaluating Methods to Account for System Errors in Ensemble Data Assimilation

By J. Whitaker, Thomas M. Hamill. 2012

398 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/784db4dd7fbdcb067acbf6da03e7983a08f14073

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

1. Marc Bocquet
2. Alberto Carrassi
3. Laurent Bertino
4. Mohamad E. Gharamti
5. Geir Evensen
6. Manuel Pulido
7. Alexis Hannart
8. P. Raanes

In Global and Planetary Change

1. Marc Bocquet
2. Alberto Carrassi
3. Mohamad E. Gharamti
4. Laurent Bertino
5. Manuel Pulido
6. Geir Evensen
7. Alexis Hannart
8. P. Raanes

Citations in the corpus

• Enhanced Adaptive Inflation Algorithm for Ensemble Filters (2018, background): “The third inflation category includes the relaxation-to-prior perturbations (RTPP) (Zhang et al. 2004), and spread (RTPS) (Whitaker and Hamill 2012; Ying and Zhang 2015).”
• Expanding the validity of the ensemble Kalman filter without the intrinsic need for inflation (2015, background): “This is supported by Whitaker and Hamill (2012), who experimentally showed that multiplicative inflation is well suited to account for sampling errors, whereas additive inflation is better suited to account for model errors in a meteorological context.”
• 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).”
• Data assimilation in the geosciences: An overview of methods, issues, and perspectives (2017, background): “Note that inflation is not only used to cure sampling errors (Bocquet, 2011; Bocquet, Raanes, & Hannart, 2015; Whitaker & Hamill, 2012), but is also often used to counteract model error impact.”
• A Review of Innovation-Based Methods to Jointly Estimate Model and Observation Error Covariance Matrices in Ensemble Data Assimilation (2018, methodology): “We distinguish the method pro-358 posed in Zhang et al. (2004), where the forecast and analysis ensemble perturbations are blended,359 from the one given in Whitaker and Hamill (2012), which multiplies the analysis ensemble with-360 out blending perturbations.”