Open AccessEfficient Ensemble Covariance Localization in Variational Data Assimilation
Author(s) -
Craig H. Bishop,
Daniel Hodyss,
Peter Steinle,
H Sims,
Adam Clayton,
Andrew C. Lorenc,
Dale Barker,
Mark Buehner
Publication year - 2011
Publication title -
monthly weather review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.862
H-Index - 179
eISSN - 1520-0493
pISSN - 0027-0644
DOI - 10.1175/2010mwr3405.1
Subject(s) - data assimilation , covariance , separable space , grid , function (biology) , computer science , algorithm , mathematics , scale (ratio) , covariance function , covariance matrix , mathematical optimization , mathematical analysis , geometry , physics , meteorology , statistics , quantum mechanics , evolutionary biology , biology
Previous descriptions of how localized ensemble covariances can be incorporated into variational (VAR) data assimilation (DA) schemes provide few clues as to how this might be done in an efficient way. This article serves to remedy this hiatus in the literature by deriving a computationally efficient algorithm for using nonadaptively localized four-dimensional (4D) or three-dimensional (3D) ensemble covariances in variational DA. The algorithm provides computational advantages whenever (i) the localization function is a separable product of a function of the horizontal coordinate and a function of the vertical coordinate, (ii) and/or the localization length scale is much larger than the model grid spacing, (iii) and/or there are many variable types associated with each grid point, (iv) and/or 4D ensemble covariances are employed.