OpenDA-NEMO framework for ocean data assimilation

Nils van Velzen*, Muhammad Altaf, Martin Verlaan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Data assimilation methods provide a means to handle the modeling errors and uncertainties in sophisticated ocean models. In this study, we have created an OpenDA-NEMO framework unlocking the data assimilation tools available in OpenDA for use with NEMO models. This includes data assimilation methods, automatic parallelization, and a recently implemented automatic localization algorithm that removes spurious correlations in the model based on uncertainties in the computed Kalman gain matrix. We have set up a twin experiment where we assimilate sea surface height (SSH) satellite measurements. From the experiments, we can conclude that the OpenDA-NEMO framework performs as expected and that the automatic localization significantly improves the performance of the data assimilation algorithm by successfully removing spurious correlations. Based on these results, it looks promising to extend the framework with new kinds of observations and work on improving the computational speed of the automatic localization technique such that it becomes feasible to include large number of observations.

Original languageEnglish (US)
Pages (from-to)691-702
Number of pages12
JournalOcean Dynamics
Volume66
Issue number5
DOIs
StatePublished - May 1 2016

Keywords

  • Data assimilation
  • Double-gyre ocean model
  • Localization techniques
  • NEMO
  • OpenDA

ASJC Scopus subject areas

  • Oceanography

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