Acoustic Doppler Current Profiler Measurements from Saildrones, with Applications to Submesoscale Studies

Abstract Characterizing submesoscale ocean processes requires high-resolution observations in both space O (1) km and time O (1) h. Resolving their velocity gradients requires velocity accuracies of O (1) cm s −1 . In the present analysis, we utilize multiple mobile platforms, including Saildrones (SDs), to achieve high-resolution measurements of submesoscale features. We assess Saildrone acoustic Doppler current profiler (ADCP) measurements against shipboard ADCP data, both collected during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE). The results show that the standard 5-min average Saildrone ADCP along-track velocity difference variability (3 cm s −1 ) is consistent with shipboard ADCP, considered in the present study as a reference. However, direct ADCP comparisons between a Saildrone and the R/V Oceanus give a small mean difference (∼1 cm s −1 ). The mean difference could stem from spatial inhomogeneities rather than surface waves, whose influence is expected to be negligible at most sampled depths. Based on 1-Hz Saildrone ADCP data, we found that averaging over 3 min of ADCP-derived currents (250 m in space) provides minimal unwanted signal. 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 0.1 f uncertainty using four Saildrones. The methodologies we developed to ascertain the optimal averaging window are versatile and applicable to other uncrewed surface vehicles (USVs) or multiple-ship arrays. Significance Statement Submesoscale currents, ranging from a few hundred meters to several kilometers in extension and lasting hours to days, play a key role in redistributing water properties in the ocean, influencing air–sea interactions, and shaping marine ecosystems. Yet, observing these currents remains challenging. Saildrone, an innovative autonomous platform, collects simultaneous oceanic and atmospheric measurements, including ocean currents. Assessing and refining these data is crucial for studying submesoscale processes. In this paper, we evaluate Saildrone ocean current data, develop methods to characterize measurement noise, and use the improved dataset to estimate uncertainties in currents and their spatial gradients, ultimately assessing the reliability of the data for submesoscale current analysis.