Submesoscale Currents from UAV: An Experiment over Small-Scale Eddies in the Coastal Black Sea

Abstract

A commercial unmanned aerial vehicle (UAV) is used for coastal submesoscale current estimation. The measurements were conducted in the Black Sea coastal area with a DJI Mavic quadcopter operated in self-stabilized mode at different look geometry (200–500-m altitude, 0–30∘ incidence angle). The results of four flights during 2020–2021 are reported. Some scenes captured a train of or individual eddies, generated by a current flowing around a topographic obstacle (pier). The eddies were optically visible due to the mixing of clear and turbid waters in the experiment area. Wave dispersion analysis (WDA), based on dispersion shell signature recognition, is used to estimate the sea surface current in the upper 0.5-m-thick layer. The WDA-derived current maps are consistent with visible eddy manifestations. The alternative method, based on 4D-variational assimilation (4DVAR), agrees well with WDA and can complement it in calm wind conditions when waves are too short to be resolved by the UAV sensor. The error of reconstructed velocity due to the uncontrolled UAV motions is assessed from referencing to static land control points. At a 500-m altitude and 7–10 m s−1 wind speed (reported by a local weather station for 10-m height), the UAV drift velocity, or the bias of the current velocity estimate, is about 0.1 m s−1, but can be reduced to 0.05 m s−1 if the first 10 s of the UAV self-stabilization period are excluded from the analysis. The observed anticyclonic eddies (200–400 m in diameter with 0.15–0.30 m s−1 orbital velocity) have an unexpectedly high Rossby number, Ro∼15, suggesting the importance of nonlinear centrifugal force for such eddies and their significant role in coastal vertical transport.

Accumulation and Cross-Shelf Transport of Coastal Waters by Submesoscale Cyclones in the Black Sea

“…One such feature is the dominance of submesoscale cyclonic eddies (SCEs) in satellite images [8][9][10], which is also confirmed with the results of numerical models [11]. Strong submesoscale anticyclones (SAEs) are also detected [6], but much less frequently than SCEs, which can be explained by the impact of centrifugal forces pulling the waters out of SAEs, thereby making them unstable and and short-lived [4,12]. In contrast, we often observe a larger number of stronger AEs compare to CEs on mesoscales [13,14].…”

“…Submesoscale eddies (SEs) are one order smaller O (1-10 km) than the mesoscale, having approximately the same orbital velocity (~0.1 m/s) [4]. Thus, they are often characterized by very high values of vorticity [5], which in some extreme cases can exceed 10f [6], where f is Coriolis frequency. Such a strong vorticity probably intensifies the transport barrier between the eddy core and the surrounding waters, making SCEs even more effective for the accumulation and transport of fluid in oceans [7].…”

Decomposition of Submesoscale Ocean Wave and Current Derived from UAV-Based Observation

“…With the advent of high-resolution remote sensing and increasing sampling performance, high-accuracy measurements focusing on submesoscale events are being conducted [2]. The capability of flexible utilization and acquisition of over 1 km 2 coverage spatial data at the centimeter scale in near-real-time has established unmanned aerial vehicles (UAVs) as being one of the major tools in oceanographic observation [14]. In particular, high-spatiotemporal resolution image data from UAVs are the key factors in facilitating the observation and visualization of short-term submesoscale structures measuring hundreds of meters in surface area, which is challenging using other in situ instruments such as single point buoys, high-frequency radars, acoustic Doppler current profilers, and satellite sensors.…”

“…In particular, high-spatiotemporal resolution image data from UAVs are the key factors in facilitating the observation and visualization of short-term submesoscale structures measuring hundreds of meters in surface area, which is challenging using other in situ instruments such as single point buoys, high-frequency radars, acoustic Doppler current profilers, and satellite sensors. These image data allow us to obtain intuitive interpretation and information regarding spatial changes over time, such as submesoscale current estimations from UAVs using wave dispersion analysis [14], wave-averaged current quantifications derived from optical data in the surf zone [15], wavelet-based surface current estimations from shore-based and UAV videos [16], surf zone circulation derivations by filtering the wave from optical video samples [17], submesoscale sea surface temperature variability analysis from UAVs and satellite measurements [18], surface wave property retrieval [19], and submesoscale eddy dynamic analysis on the transport of suspended matter [20].…”

Obtaining surface current field from drone imaging

“…(Perkovic et al, 2009;Eltner et al, 2020;Strelnikova et al, 2020). Stresser et al (2017), presented a new approach that has the advantage of a fast response, using linear wave dispersion in which it is possible to obtain the surface current field directly from videos generated by low-cost drones, which a priori seems too good to be true and has been tested in recent studies of submesoscale currents (Yurovsky et al, 2022).…”