Combining networks of drifting profiling floats and gliders for adaptive sampling of the Ocean

Monitoring ocean dynamics is extremely difficult due to its enormous physical dimensions and the wide range of spatio-temporal scales involved in its dynamical behaviour. It has been recently proposed that the most efficient way to monitor the ocean is through networks of small, intelligent and cheap robotic platforms. Drifting profiling floats and gliders were developed in this context. Floats move with the currents meanwhile they periodically sample the water column through controlled immersions. Conversely, gliders are underwater autonomous vehicles with controllable motion at sea. Both platforms are extensively employed in oceanography due to their high autonomy. A network called Argo of around 3000 profiling floats spreads out around the world's ocean. Glider networks are starting to settle down at smaller scale in different places. The advent of these networks and the still scarce resources for ocean sampling, create a demand for quantitative tools for optimizing their use. In this work, the problem of optimally merging networks of profiling floats and gliders is considered. Specifically, a genetic algorithm is employed to find optimal glider trajectories to get together an unevenly distributed network of floats the best quality of the sampled field. A measure of the quality of the oceanographic field (objective function to minimize) is defined in terms of the mean formal error obtained from an optimum interpolation scheme. Results show that the quality of the sampled field can be greatly improved by merging both networks if the resolution of glider observations is adequately selected. The spatial lag between glider observations is related to the geometry of the network of profiling floats and must be order of the grid spacing obtained from the mean data spacing of the network of floats.