English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

An original graph clustering approach to efficient localization of errorcovariances is proposed within an ensemble-variational data assimilationframework. Here the localization term is very generic and refers to the idea ofbreaking up a global assimilation into subproblems. This unsupervisedlocalization technique based on a linearizedstate-observation measure isgeneral and does not rely on any prior information such as relevant spatialscales, empirical cut-off radius or homogeneity assumptions. It automaticallysegregates the state and observation variables in an optimal number of clusters(otherwise named as subspaces or communities), more amenable to scalable dataassimilation.The application of this method does not require underlyingblock-diagonal structures of prior covariance matrices. In order to deal withinter-cluster connectivity, two alternative data adaptations are proposed. Oncethe localization is completed, an adaptive covariance diagnosis and tuning isperformed within each cluster. Numerical tests show that this approach is lesscostly and more flexible than a global covariance tuning, and most oftenresults in more accurate background and observations error covariances.

A graph clustering approach to localization for adaptive covariance tuning in data assimilation based on state-observation mapping