Data assimilation for advanced cross-scale unstructured grid ocean modelling

Coastal zones face growing threats from climate change, including sea-level rise and intensified storm activity. Accurate numerical modelling 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.