EGU General Assembly 2020
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

An adaptive optimal interpolation based on analog forecasting: application to SSH in the Gulf of Mexico

Yicun Zhen1, Pierre Tandeo1, Stephanie Leroux2, Sammy Metref3, Thierry Penduff3, and Julien Le Sommer3
Yicun Zhen et al.
  • 1IMT-Atlantique, Signal and Communications, Plouzané, France (
  • 2Ocean-Next, Grenoble, France
  • 3Universite Grenoble Alpes, CNRS, IRD, IGE, Grenoble, France

Because of the irregular sampling pattern of raw altimeter data, many oceanographic applications rely on information from sea surface height (SSH) products gridded on regular grids where gaps have been filled with interpolation. Today, the operational SSH products are created using the simple, but robust, optimal interpolation (OI) method. If well tuned, the OI becomes computationally cheap and provides accurate results at low resolution. However, OI is not adapted to produce high resolution and high frequency maps of SSH. To improve the interpolation of SSH satellite observations, a data-driven approach was recently proposed: analog data assimilation (AnDA). AnDA adaptively chooses analog situations from a catalog of SSH scenes -- originating from numerical simulations or a large database of observations -- which allow the temporal propagation of physical features at different scales, while each observation is assimilated. In this article, we review the AnDA and OI algorithms and compare their skills in numerical experiments. The experiments are observing system simulation experiments (OSSE) on the Lorenz-63 system and on an SSH reconstruction problem in the Gulf of Mexico. The results show that AnDA, with no necessary tuning, produces comparable reconstructions as does OI with tuned parameters. Moreover, AnDA manages to reconstruct the signals at higher frequencies than OI. Finally, an important additional feature for any interpolation method is to be able to assess the quality of its reconstruction. This study shows that the standard deviation estimated by AnDA is flow-dependent, hence more informative on the reconstruction quality, than the one estimated by OI.

How to cite: Zhen, Y., Tandeo, P., Leroux, S., Metref, S., Penduff, T., and Le Sommer, J.: An adaptive optimal interpolation based on analog forecasting: application to SSH in the Gulf of Mexico, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22004,, 2020.


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