Characterization of surface flow using Lagrangian drifter for particle tracking model applications.

Accurate ocean data (i.e., tide, current velocity and direction, wave) are essential for numerous environmental studies: 1) development of marine renewable energy (e.g., maximize the efficiency of energy conversion); 2) aquaculture (e.g., offshore development); 3) anthropic pollution (e.g., plastic/pollutant dispersal); and 4) ecology (e.g., spread of invasive species).

The exponential increase of computational power has made numerical models, such as Eularian hydrodynamic models and Lagrangian particle tracking models (PTM) useful tools to characterize physical oceanographic parameters. However, methods to validate PTMs appear less developed due the complexity of biophysical process interactions; for example, uncertainty remains on the impact of wind on surface currents and how the effects of wind are propagated through the water column

Here, we use a novel set of data representing the travel of drifters in the Irish Sea during two consecutive years (summer 2021 and 2022). The experiment aim is to reduce the near surface flow uncertainty influencing particle dispersal. Data were collecting using a range of drifters released in coastal and offshore locations of a tidally dominate shelf-sea (Irish Sea): 1) variation of drogue depth between 1m and 5m; 2) variation of period from tidal cycles to spring-neap cycles; and 3) some with reduced “windage” designs (no drogue and minimal exposure above surface).

Preliminary results show the importance of wind driven current between the surface and 5 m depth, which should be take into account when considering the development of PTM. Furthermore, we find some scales of oceanographic processes that affect transport, such as turbulent eddies and waves, were not resolved - and yet our predictions broadly matched observations.