Preprints
https://doi.org/10.5194/egusphere-2025-1553
https://doi.org/10.5194/egusphere-2025-1553
10 Jun 2025
 | 10 Jun 2025
Status: this preprint is open for discussion and under review for Geoscientific Model Development (GMD).

OceanVar2.0: an open-source variational ocean data assimilation scheme. Sensitivity to altimetry sea level anomaly assimilation

Paolo Oddo, Mario Adani, Francesco Carere, Andrea Cipollone, Anna Chiara Goglio, Eric Jansen, Ali Aydogdu, Francesca Mele, Italo Epicoco, Jenny Pistoia, Emanuela Clementi, Nadia Pinardi, and Simona Masina

Abstract. This study presents recent developments of the OceanVar oceanographic three-dimensional variational data assimilation scheme to create OceanVar2.0. The code has been extensively revised to integrate past developments into a single, consistent, fully parallelized framework. In OceanVar, the background error covariance matrix is decomposed into a sequence of physically based linear operators, allowing for individual analysis of specific error matrix components. We focus on the sea level operator, which provides correlation between Sea Level Anomaly, temperature and salinity increments. OceanVar2.0 offers the flexibility to use either a dynamic height or a barotropic model for closed domains as sea level operators. A diffusive operator to model the horizontal error correlations, replacing the previously used recursive filter, has been implemented. The new code was tested in the Mediterranean Sea and the quality of the analysis assessed by comparing background estimates with observations for the period January–December 2021. The results highlight the better skill of the barotropic model operator with respect to the dynamic height one due to the assumptions required for the level-of-no-motion. Furthermore, we present a method to assimilate along track satellite altimetry considering a forecasting model with tides.

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Paolo Oddo, Mario Adani, Francesco Carere, Andrea Cipollone, Anna Chiara Goglio, Eric Jansen, Ali Aydogdu, Francesca Mele, Italo Epicoco, Jenny Pistoia, Emanuela Clementi, Nadia Pinardi, and Simona Masina

Status: open (until 13 Sep 2025)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • EC1: 'Comment on egusphere-2025-1553', Julia Hargreaves, 13 Jul 2025 reply
    • AC1: 'Reply on EC1', Paolo Oddo, 16 Jul 2025 reply
  • RC1: 'Comment on egusphere-2025-1553', Anonymous Referee #1, 29 Jul 2025 reply
  • RC2: 'Comment on egusphere-2025-1553', Anonymous Referee #2, 28 Aug 2025 reply
Paolo Oddo, Mario Adani, Francesco Carere, Andrea Cipollone, Anna Chiara Goglio, Eric Jansen, Ali Aydogdu, Francesca Mele, Italo Epicoco, Jenny Pistoia, Emanuela Clementi, Nadia Pinardi, and Simona Masina
Paolo Oddo, Mario Adani, Francesco Carere, Andrea Cipollone, Anna Chiara Goglio, Eric Jansen, Ali Aydogdu, Francesca Mele, Italo Epicoco, Jenny Pistoia, Emanuela Clementi, Nadia Pinardi, and Simona Masina

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Short summary
This study present a data assimilation scheme that combines ocean observational data with ocean model results to better understand the ocean and predict its future state. The method uses a variational approach focusing on the physical relationships between all the state vector variables errors. Testing in the Mediterranean Sea showed that a complex sea level operator based on a barotropic model works best.
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