OceanVar is the variational ocean data assimilation system developed at CMCC to provide initial conditions for ocean reanalyses and operational forecasts. It is a three-dimensional variational (3DVAR) system, formulated in its classical incremental variant. It supports the assimilation of all ocean in-situ measurements of temperature and salinity and the assimilation of remotely sensed data (altimetry, sea surface temperature and salinity), from a number of different datasets depending on the specific application. The background-error covariance matrix is composed by two linear terms accounting, respectively, for vertical covariances and horizontal correlations. The former are modeled through the use of multivariate EOFs, while the latter through the application of recursive filters. Multivariate balances can be specified as purely statistical or through the application of steady state balances or simplified models. Pre-processing of the observations includes climatology and background quality checks and thinning of dense observations. OceanVar is hybrid MPI-OpenMP parallel and runs on a variety of different architectures.


Andrea Storto

  • Dobricic, S., and N. Pinardi, 2008: An oceanographic three-dimensional variational data assimilation scheme, Ocean Modelling, 22, 89-105.
  • Storto, A., S. Dobricic, S. Masina, and P. Di Pietro, 2011: Assimilating along-track altimetric observations through local hydrostatic adjustments in a global ocean reanalysis system. Mon. Wea. Rev. 139, 738-754.
  • Storto, A., Masina, S. and Dobricic, S. (2014), Estimation and Impact of Non-Uniform Horizontal Correlation Length-Scales for Global Ocean Physical Analyses. Journal of Atmospheric ad Ocean Technology, 31, 2330–2349.