Flow‐dependent background‐error covariances for a convective‐scale data assimilation system
By P. Brousseau, L. Berre, F. Bouttier, G. Desroziers. 2012
47 citations recorded by the source corpus.
Subfields: Atmospheric Science, Environmental Engineering, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/a8fa4a885188e3bc1c0a3629439757ccc97a3a14
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
- T. Montmerle
- Lars Isaksen
- B. Menetrier
- Y. Michel
- Massimo Bonavita
- Elias V. Holm
- Michael Fisher
- L. Berre
In Environmental Engineering
- Lars Isaksen
- T. Montmerle
- Massimo Bonavita
- Elias V. Holm
- Michael Fisher
- B. Menetrier
- Y. Michel
- L. Berre
In Global and Planetary Change
- T. Montmerle
- Lars Isaksen
- B. Menetrier
- Y. Michel
- Massimo Bonavita
- Elias V. Holm
- Michael Fisher
- L. Berre
Citations in the corpus
- Linear Filtering of Sample Covariances for Ensemble-Based Data Assimilation. Part I: Optimality Criteria and Application to Variance Filtering and Covariance Localization (2015, background): “As shown in many studies [e.g., Brousseau et al. (2012) for the AROMEmodel], forecast error covariances of a limitedarea model at convective scale are indeed strongly flow dependent, and the optimal filter may change consequently depending on the considered date and season.”
- The evolution of the ECMWF hybrid data assimilation system (2016, background): “…increase, the expected increased heterogeneity and anisotropy of error correlations, the sharper error gradients and the increased flow-dependent aspects of the balance relationships (Brousseau et al., 2011, 2012; Ménétrier et al., 2014) would pose greater challenges to the current B model.”