Robust Data Assimilation Using L1 and Huber Norms

Vishwas Rao, Adrian Sandu, Michael Ng, Elias D. Nino-Ruiz

Research output: Contribution to journalJournal articlepeer-review

12 Citations (Scopus)
49 Downloads (Pure)


Data assimilation is a process used to fuse information from priors, observations of nature, and numerical models, in order to obtain best estimates of the parameters or state of a physical system of interest. Presence of large errors in some observational data, e.g., data collected from a faulty instrument, negatively affect the quality of the overall assimilation results. This work develops a systematic framework for robust data assimilation. The new algorithms continue to produce good estimates of parameters or state in the presence of observation outliers. The approach is based on replacing the traditional L2 norm formulation of data assimilation problems with formulations based on L1 and Huber norms. Numerical experiments using the Lorenz-96 and the shallow water on the sphere models illustrate how the new algorithms outperform traditional data assimilation approaches in the presence of data outliers.

Original languageEnglish
Pages (from-to)B548-B570
Number of pages23
JournalSIAM Journal on Scientific Computing
Issue number3
Publication statusPublished - 22 Jun 2017

Scopus Subject Areas

  • Computational Mathematics
  • Applied Mathematics

User-Defined Keywords

  • 4D-var
  • ADMM
  • Data assimilation
  • Huber norm


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