Due to the sparsity of data in the ocean interior, oceans require specialized data assimilation schemes. The OceanVar2, developed at CMCC, is a state-of-the-art variational ocean data assimilation framework. It is used to study the ocean across a range of scales, from global to local and short-term to multi-decadal. Additionally, OceanVar2 provides initial conditions for short-range to seasonal forecasts. 

OceanVar was first introduced by Dobricic and Pinardi (2008) and is based on a three-dimensional variational method, formulated in its classical incremental variant. OceanVar2 features a modular design that allows for flexibility in incorporating diverse data sources and error covariance representations. It supports the assimilation of in-situ ocean measurements of temperature and salinity, as well as remotely sensed data, including altimetry, and sea surface temperature and salinity, from a variety of datasets depending on the specific application.

OceanVar2 decomposes the background error covariance matrix into physically based linear operators, allowing for individual analysis of specific error components. A key feature of OceanVar2 is its ability to represent error correlations between temperature, salinity, and sea level anomalies (the sea level operator). OceanVar2 offers the flexibility of using either a dynamic height or a barotropic model for closed domains, or EOF-based correlations. Horizontal error correlations are modeled with a diffusive operator, replacing the previous recursive filter. Furthermore, the OceanVar2 code has been extensively revised into a unified, consistent, and fully parallelized framework, integrating past developments.

The OceanVar2 code, along with a manual and user guide, is distributed through a Git repository.

Link to download OceanVar2

To help users become familiar with the code, a test-case is also provided at the following link

References using OceanVar:

• 2006: Dobricic, S., Pinardi, N., Adani, M., Bonazzi, A., Fratianni, C. and Tonani, M.Mediterranean Forecasting System: An improved assimilation scheme for sea-level anomaly and its validation. Q. J. R. Meteorol. Soc. 131: 3627–3642, doi: 10.1256/qj.05.100.
• 2007: Dobricic, S., Pinardi, N., Adani, M., Tonani, M., Fratianni, C., Bonazzi, A., Fernandez, V. Daily oceanographic analyses by the Mediterranean basin scale assimilation system. Ocean Sciences 3, 149–157.
• 2008: Dobricic, S. and Pinardi, N.: An oceanographic three-dimensional variational data assimilation scheme, Ocean Model., 22, 89–105, 2008.
• 2011: Adani, M., S. Dobricic, and N. Pinardi: Quality Assessment of a 1985–2007 Mediterranean Sea Reanalysis. J. Atmos. Oceanic Technol., 28, 569–589, https://doi.org/10.1175/2010JTECHO798.1.
• 2011: Storto, A., S. Dobricic, S. Masina, and P. Di Pietro: Assimilating Along-Track Altimetric Observations through Local Hydrostatic Adjustment in a Global Ocean Variational Assimilation System. Mon. Wea. Rev., 139, 738–754, https://doi.org/10.1175/2010MWR3350.1.
• 2012: Dobricic, S., Dufau, C., Oddo, P., Pinardi, N., Pujol, I., and Rio, M.-H.: Assimilation of SLA along track observations in the Mediterranean with an oceanographic model forced by atmospheric pressure, Ocean Sci., 8, 787–795, https://doi.org/10.5194/os-8-787-2012.
• 2012: Nilsson, J. A. U., Dobricic, S., Pinardi, N., Poulain, P.-M., and Pettenuzzo, D., 2012. Variational assimilation of Lagrangian trajectories in the Mediterranean ocean Forecasting System. Ocean Sci., 8, 249-259, doi:10.5194/os-8-249-2012
• 2014: Teruzzi, A., S. Dobricic, Solidoro, C., Cossarini, G. A 3-D variational assimilation scheme in coupled transport-biogeochemical models: forecast of Mediterranean biogeochemical properties. J. Geophys. Res.: Oceans, 119 (1) (2014), pp. 200-217 URL https://doi.org/10.1002/2013JC009277. 
• 2015: Dobricic, S., Wikle, C. K., Milliff, R. F., Pinardi, N., Berliner, L. M. Assimilation of oceanographic observations with estimates of vertical background-error covariances by a Bayesian hierarchical model Quarterly Journal of the Royal Meteorological Society, 141, 182-194, doi: http://dx.doi.org/10.1002/qj.2348
• 2016: Oddo, P. Storto, A. Dobricic, S. Russo, A. Lewis, C. Onken, R. Coelho, E., A hybrid variational-ensemble data assimilation scheme with systematic error correction for limited-area ocean models, Ocean Science,12,2016,5,1137–1153,10.5194/os-12-1137-2016
• 2016: Aydoğdu, A., Pinardi, N., Pistoia, J., Martinelli, M., Belardinelli, A., Sparnocchia, S., Assimilation experiments for the Fishery Observing System in the Adriatic Sea, Journal of Marine Systems, Volume 162, October 2016, Pages 126-136, ISSN 0924-7963, doi:10.1016/j.jmarsys.2016.03.002
• 2016: Storto, A., Masina, S. and Navarra, A., Evaluation of the CMCC eddy-permitting global ocean physical reanalysis system (C-GLORS, 1982–2012) and its assimilation components. Q.J.R. Meteorol. Soc., 142: 738-758. https://doi.org/10.1002/qj.2673
• 2016: Storto, A., Variational quality control of hydrographic profile data with non-Gaussian errors for global ocean variational data assimilation systems, Ocean Modelling, Volume 104, 2016, Pages 226-241, ISSN 1463-5003, https://doi.org/10.1016/j.ocemod.2016.06.011. 
• 2018: Storto, A.,  Oddo, P., Cipollone, A., Mirouze, I., Lemieux-Dudon, B. Extending an oceanographic variational scheme to allow for affordable hybrid and four-dimensional data assimilation. Ocean Modelling, 128, pp. 67-86, 10.1016/j.ocemod.2018.06.005
• 2018: Teruzzi, A., Bolzon, G., Salon, S., Lazzari, P., Solidoro, C., Cossarini, G. Assimilation of coastal and open sea biogeochemical data to improve phytoplankton simulation in the mediterranean sea. Ocean Model., 132 (2018), pp. 46-60
• 2019: Storto A, Oddo P. Optimal Assimilation of Daytime SST Retrievals from SEVIRI in a Regional Ocean Prediction System. Remote Sensing. 2019; 11(23):2776. https://doi.org/10.3390/rs11232776
• 2020: Cipollone A., A. Storto & S. Masina, “Implementing a parallel version of a variational scheme in a global assimilation system at eddy-resolving resolution” ,JTECH,37(10),1865-1876, https://doi.org/10.1175/JTECH-D-19-0099.1
• 2020: Storto, A., Falchetti, S., Oddo, P., Jiang, Y.-M., & Tesei, A. Assessing the impact of different ocean analysis schemes on oceanic and underwater acoustic predictions. Journal of Geophysical Research: Oceans, 125, e2019JC015636. https://doi.org/10.1029/2019JC015636
• 2021: Storto, A., G. De Magistris, S. Falchetti, and P. Oddo: A Neural Network–Based Observation Operator for Coupled Ocean–Acoustic Variational Data Assimilation. Mon. Wea. Rev., 149, 1967–1985, https://doi.org/10.1175/MWR-D-20-0320.1.
• 2021: Escudier, R., Clementi, E., Cipollone, A., Pistoia, J., Drudi, M., Grandi, A., Lyubartsev, V., Lecci, R., Aydogdu, A., Delrosso, D., et al.: A high-resolution reanalysis for the Mediterranean Sea, Frontiers in Earth Science, 9, 702285, 2021.
• 2021: Lima L., Ciliberti S.A., Aydogdu A., Masina S., Escudier R., Cipollone A., Azevedo D., Causio S., Peneva E., Lecci R., Clementi E., Jansen E., licak M., Creti S., Stefanizzi L., Palermo F. & Coppini G.“Climate signals in the black sea from a multidecadal eddy- resolving Reanalysis”,Front. Marine Sci. 8, 1214 , https://doi.org/10.3389/fmars.2021.710973
• 2022: Ciliberti, S.A.; Jansen, E.; Coppini, G.; Peneva, E.; Azevedo, D.; Causio, S.; Stefanizzi, L.; Creti, S.; Lecci, R.; Lima, L.; et al. The Black Sea Physics Analysis and Forecasting System within the Framework of the Copernicus Marine Service. J. Mar. Sci. Eng. 2022, 10, 48. https://doi.org/10.3390/jmse10010048
• 2022: Oddo P, Falchetti S, Viola S, Pennucci G, Storto A, Borrione I, Giorli G, Cozzani E, Russo A, Tollefsen C. Evaluation of different Maritime rapid environmental assessment procedures with a focus on acoustic performance. J Acoust Soc Am. 2022 Nov;152(5):2962. doi: 10.1121/10.0014805. PMID: 36456253.
• 2022: Cipollone A., Banerjee, D. S., Iovino, D., Aydogdu, A., and Masina, S. (2023): “Bivariate sea-ice assimilation for global-ocean analysis-reanalysis”, Ocean Sci.,19,1375-1392, https://doi.org/10.5194/egusphere-2022-1337
• 2023: Clementi, E., Drudi, M., Aydogdu, A., Moulin, A., Grandi, A., Mariani, A., Goglio, A. C., Pistoia, J., Miraglio, P., Lecci, R., Palermo, F., Coppini, G., Masina, S., & Pinardi, N. (2023). Mediterranean Sea Physical Analysis and Forecast (CMS MED-Physics, EAS8 system) (Version 1) [Data set]. Copernicus Marine Service (CMS). https://doi.org/10.25423/CMCC/MEDSEA_ANALYSISFORECAST_PHY_006_013_EAS8
• 2023: Coppini, G., Clementi, E., Cossarini, G., Salon, S., Korres, G., Ravdas, M., Lecci, R., Pistoia, J., Goglio, A. C., Drudi, M., et al.: The Mediterranean forecasting system. Part I: evolution and performance, EGUsphere, pp. 1–50, 2023.