ORIGINAL RESEARCH article
Front. Mar. Sci.
Sec. Coastal Ocean Processes
Volume 12 - 2025 | doi: 10.3389/fmars.2025.1656879
This article is part of the Research TopicInnovative Approaches for Predicting the Impacts of Anthropogenic and Climate Stressors on Coastal Marine EcosystemsView all 3 articles
Data Assimilation For Advanced Cross-Scale Unstructured-Grid Ocean Modelling
Provisionally accepted
- Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici, Lecce, Italy
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Coastal zones face growing threats from climate change, including sea-level rise and intensified storm activity. Accurate numerical modelling in is essential to predict the impacts of anthropogenic and climate stressors on the coastal zone. However, it is also a very challenging environment due to complex coastlines, rapid topographic changes, and high spatial-temporal variability. Unstructured grid models offer a promising solution, yet their integration with advanced data assimilation (DA) methods remains limited. This study presents the implementation of a 3D variational data assimilation (3DVar) scheme (OceanVar) within an unstructured-grid ocean model (SHYFEM). A key innovation involves generalizing the first-order recursive filter for horizontal background error covariances to work with triangular unstructured meshes. An experiment was conducted over the period 2017–2018, assimilating ARGO in-situ profiles, and sea level anomaly (SLA) data from altimetry satellite missions. Results show substantial skill improvement against a control run without assimilation, particularly in the 100–500 m depth range, where the mean absolute error was reduced by 25–30% through data assimilation. SLA assimilation had a more modest effect, improving MAE by about 3% overall and up to 20% locally, without degrading temperature or salinity estimates. The study demonstrates the feasibility and benefits of applying a 3DVar scheme to unstructured grid ocean models, paving the way for more accurate and efficient coastal forecasting systems.
Keywords: coastal ocean, Variational data assimilation, Unstructured grid ocean modelling, 3DVAR, Unstructured grid first orderrecursive filter algorithm, Recursive filter
Received: 30 Jun 2025; Accepted: 30 Sep 2025.
Copyright: © 2025 Stefanelli, Jansen, Aydogdu, Federico and Coppini. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Marco Stefanelli, marco.stefanelli@cmcc.it
Eric Jansen, eric.jansen@cmcc.it
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