Paban Bhuyan , Cesar B. Rocha, Leonel Romero, J. Thomas Farrar

Characterizing submesoscale ocean processes requires high-resolution observations in both space O(1 km) and time O(1 hr). One way to resolve submesoscale features is to deploy multiple mobile platforms, such as Saildrones (SDs), to achieve high-resolution synchronous measurements, but this requires velocity accuracies of O(1 cm/s) to resolve submesoscale velocity gradients.
In this study, we first assess Saildrone Acoustic Doppler Current Profiler (ADCP)
measurements against a high-quality shipboard (R/V Oceanus) ADCP data,
both collected during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE).
The results show that the standard 5-minute average Saildrone ADCP along-track
velocity difference variability (3 cm/s) is consistent with shipboard ADCP data,
confirming its suitability for submesoscale studies.
However, direct ADCP comparisons between a Saildrone and the R/V Oceanus
give small biases (~1 cm/s). The biases are unlikely due to the surface waves, whose signal is expected to be significant near the surface; they are more likely be associated with spatial inhomogeneities. We also examined the 1\,Hz Saildrone ADCP data to determine the best averaging window for high-resolution analyses and found that averaging over 3 minutes ($\sim$250\,m in space) reduces the noise to acceptable levels. We investigate the uncertainty of submesoscale current gradients derived from Saildrone ADCP measurements and find that the velocity gradient at a 2\,km scale can be obtained with
a 0.1f uncertainty using four Saildrones. The methodologies we developed to ascertain optimal averaging window are versatile and applicable to other uncrewed surface vehicles (USV) or multiple-ship arrays.