A review of operational methods of variational and ensemble‐variational data assimilation
By R. Bannister. 2017
430 citations recorded by the source corpus.
Variational and ensemble methods have been developed separately by various research and development groups and each brings its own benefits to data assimilation. In the last decade or so, various ways have been developed to combine these methods, especially with the aims of improving the background‐error covariance matrices and of improving efficiency. The field has become confusing, even to many specialists, and so there is now a need to summarize the methods in order to show how they work, how they are related, what benefits they bring, why they have been developed, how they perform, and what improvements are pending. This article starts with a reminder of basic variational and ensemble techniques and shows how they can be combined to give the emerging ensemble‐variational (EnVar) and hybrid methods. A key part of the article includes details of how localization is commonly represented.
Subfields: Atmospheric Science, Environmental Engineering, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/d74ac895dcc3a35ad996b39d4d602e01c47f4527
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
- Marc Bocquet
- Laurent Bertino
- Alberto Carrassi
- Geir Evensen
- Lukas Hubert Leufen
- Clara Betancourt
- Amirpasha Mozaffari
- Michael Langguth
In Environmental Engineering
In Global and Planetary Change
- Marc Bocquet
- Laurent Bertino
- Alberto Carrassi
- Geir Evensen
- Lukas Hubert Leufen
- Clara Betancourt
- Amirpasha Mozaffari
- Michael Langguth
Citations in the corpus
- Data assimilation in the geosciences: An overview of methods, issues, and perspectives (2017, methodology): “They have been reviewed in Chapter 7 of Asch et al. (2016) on theoretical grounds and with a focus on operational implementation by Bannister (2017), although the iterative ensemble Kalman smoother (IEnKS) (see Section 4.5.4) is lacking in this latter review.”
- Can deep learning beat numerical weather prediction? (2021, background): “The obtained initial state of the Earth system after the DA step therefore constitutes an optimized estimate of the real conditions (e.g. [18]).”