Characteristic Ocean Flow Visualization Using Helmholtz Decomposition

Recently, with the high development of satellite based ocean observation techniques, ocean flow visualization has become a hot research topic in the joint field of computer science and oceanography. It plays a significant role in supporting the detection and recognition of characteristic ocean flows, such as the mesoscale eddies, convergent and divergent ocean flows. However, this is not an easy task. Ocean flow field is a complex velocity field mixing of multi-scale dynamics including large-scale ocean circulations (100km∼)), mesoscale eddies (10km∼) 100km), and sub-mesoscale processes (1km∼10km). These dynamics change their forms and velocities at any time, making existing algorithms difficult to identify them. To solve this problem, this paper developed a novel ocean flow decomposition method using Helmholtz decomposition. In our approach, an arbitrary ocean flow field can be decomposed to two components: curl component and divergence component. Eddies, which are rotational, only present in the curl component; convergent and divergent ocean flows, which are irrotational, only exist in the divergence component. The Helmholtz decomposition helps us recognize different characteristic ocean flows with different components. To verify our method, experiments are performed on AVISO satellite observed ocean flow field in the Black sea. Experimental result demonstrates the effectiveness of our method.