Internal waves are large-amplitude gravity waves that occur below the ocean surface and propagate along interfaces separating water layers of different densities. Understanding their generation, propagation, and evolution is essential, as these waves play a vital role in the ocean system by contributing to nutrient transport, biological productivity, and the transfer of energy across the ocean and continental shelf. Domain scientists use high-resolution numerical ocean models, to study internal-wave dynamics and associated coastal and nearshore processes on hybrid computational grids. These models generate large-scale, three-dimensional spatiotemporal datasets that capture internal wave flow behavior and interactions with multiple ocean variables. These datasets are generally analyzed using command-line tools with limited interactivity. To address these challenges, we in collaboration with domain scientists designed a task-driven visualization methodology for analyzing multiscale, multivariate flow data on hybrid grids. The framework incorporates a hybrid-grid volumetric reconstruction method, enabling continuous 3D analysis and a coordinated multi-view design that supports interactive exploration of complex flow structures. An insight-based evaluation with domain experts demonstrates that the system enables the identification of previously difficult-to-observe phenomena, including transverse wave propagation, energy transport pathways, and shoaling-driven mixing. Beyond the application domain, our contributions provide generalizable techniques and design principles for visual analysis of multiscale, multivariate flow data on irregular grids.

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