OpenDA-NEMO framework for ocean data assimilation

OpenDA-NEMO framework for ocean data assimilation

Van Velzen, C.
Altaf, M.U.
Verlaan, M.

Electrical Engineering, Mathematics and Computer Science

Delft Institute of Applied Mathematics

2016-03-22

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.

NEMO
OpenDA
data assimilation
localization techniques
double-gyre ocean model

http://resolver.tudelft.nl/uuid:cda830f3-5865-4f7a-a735-d4658532ebcc

Springer

1616-7341

https://doi.org/10.1007/s10236-016-0945-z

Ocean Dynamics, 2016; Topical Collection on the 47th International Liège Colloquium on Ocean Dynamics, Liège, Belgium, 4-8 May 2015

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journal article

© 2016 The Author(s)
This article is published with open access at Springerlink.com