Numerical linear algebra in data assimilation

Abstract: Data assimilation is a method that combines observations (ie, real world data) of a state of a system with model output for that system in order to improve the estimate of the state of the system and thereby the model output. The model is usually represented by a discretized partial differential equation. The data assimilation problem can be formulated as a large scale Bayesian inverse problem. Based on this interpretation we will derive the most important variational and sequential data assimilation approaches, in particular three‐dimensional and four‐dimensional variational data assimilation (3D‐Var and 4D‐Var) and the Kalman filter. We will then consider more advanced methods which are extensions of the Kalman filter and variational data assimilation and pay particular attention to their advantages and disadvantages. The data assimilation problem usually results in a very large optimization problem and/or a very large linear system to solve (due to inclusion of time and space dimensions). Therefore, the second part of this article aims to review advances and challenges, in particular from the numerical linear algebra perspective, within the various data assimilation approaches.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: Numerical linear algebra in data assimilation ; volume:43 ; number:3 ; year:2020 ; extent:27 ; GAMM-Mitteilungen / Gesellschaft für Angewandte Mathematik und Mechanik ; 43, Heft 3 (2020) (gesamt 27)

Contributors, Places and Time

• Creator: Freitag, Melina A.

Further information

• DOI: 10.1002/gamm.202000014
• URN: urn:nbn:de:101:1-2022062911542777158728
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 15.08.2025, 7:25 AM CEST