A Scalable Approach for Variational Data Assimilation

• Published in: Journal of scientific computing Vol. 61; no. 2; pp. 239 - 257
• Main Authors: D’Amore, Luisa, Arcucci, Rossella, Carracciuolo, Luisa, Murli, Almerico
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01-11-2014; Springer Nature B.V
• Subjects: Algorithms; Complex systems; Computation; Computational Mathematics and Numerical Analysis; Computer simulation; Data assimilation; Decomposition; Handles; Hilbert space; Ill posed problems; Inverse; Inverse problems; Mathematical analysis; Mathematical and Computational Engineering; Mathematical and Computational Physics; Mathematical models; Mathematics; Mathematics and Statistics; Software; Theoretical; Parallel computing; 68U20; Scalable algorithm; 68W10; 65J22; 65Y05; Data assimilation; Inverse problem
• ISSN: 0885-7474; 1573-7691
• DOI: 10.1007/s10915-014-9824-2

Data assimilation (DA) is a methodology for combining mathematical models simulating complex systems (the background knowledge) and measurements (the reality or observational data) in order to improve the estimate of the system state (the forecast). The DA is an inverse and ill posed problem usually used to handle a huge amount of data, so, it is a large and computationally expensive problem. Here we focus on scalable methods that makes DA applications feasible for a huge number of background data and observations. We present a scalable algorithm for solving variational DA which is highly parallel. We provide a mathematical formalization of this approach and we also study the performance of the resulted algorithm.