Drifter Trajectories Research Articles

Modeling of wave-induced drift based on stepwise parameter calibration

The motion of waves in water causes the slow movement of drifting sea targets—a phenomenon usually ignored in target-drift prediction models for maritime search and rescue (SAR). This study examined the wave-induced drift’s influence on field-observation experiments involving two common, differently sized SAR targets—an offshore fishing vessel (OFV) and a person in the water (PIW)—using parameter stepwise calibration and machine-learning (ML) methods. The sample of wave-induced drift velocity was obtained by gradually separating current-induced (CI) drift’s and wind-induced (WI) drift’s influence from the target-drift velocity using the least-square method and AP98 model. A force analysis method and three ML methods, long short-term memory (LSTM), back-propagation (BP) neural network, and random forest (RF), were used to fit the wave-induced drift velocity by combining eight different parameter schemes. Finally, the drift trajectories considering the influence of waves were fitted and verified based on 2 independent samples respectively. Compared with the force analysis method, the accuracy of the ML methods in the verification test was higher. In addition, the results show that for OFVs, considering wave-induced drift’s influence in the ensemble-trajectory prediction could improve the simulation accuracy. However, for a PIW, no significant improvement was observed. This result also indicates that wave-induced drift may not be simply ignored in large SAR targets’ drift prediction.

Introducing a simple convex hull method to calibrate diffusion coefficients in Lagrangian particle models

Calibrating Lagrangian particle models (LPMs) is crucial for simulating oil spill trajectories, enabling emergency responses, and mitigating harmful effects on the environment. However, there is a lack of rigorous observation data on oil spill events, especially in water bodies with no serious spills but perceived increasing risk, hampering LPM application. This study utilized trajectory data from three drifters in Lake Erie, collected in 2016, 2018, and 2019. We then proposed a simple and efficient convex hull method to identify the smallest envelope of the simulated particle cloud, covering all drifter trajectories while simultaneously controlling over-diffusion. This approach was employed to calibrate the General NOAA Operational Modeling Environment oil spill model (an LPM) and determine optimal diffusion coefficients. Based on 362 one-day-long drifter trajectory simulation cases, the recommended diffusion coefficient for particle trajectory simulation in Lake Erie was determined to be 14 m2 s−1. The calibrated model demonstrated the ability to effectively capture the movement direction of drifters and control the discrepancy between simulated and observed trajectories. Meanwhile, it underestimated drifter travel distances due to errors in input currents, the primary force driving drifter movement. These results promote LPM application for particle trajectory simulations.

Influence of Ocean Current Features on the Performance of Machine Learning and Dynamic Tracking Methods in Predicting Marine Drifter Trajectories

Accurately and rapidly predicting marine drifter trajectories under conditions of information scarcity is critical for addressing maritime emergencies and conducting marine surveys with resource-limited unmanned vessels. Machine learning-based tracking methods, such as Long Short-Term Memory networks (LSTM), offer a promising approach for trajectory prediction in such scenarios. This study combines satellite observations and idealized simulations to compare the predictive performance of LSTM with a resource-dependent dynamic tracking method (DT). The results indicate that when driven solely by historical drifter paths, LSTM achieves better trajectory predictions when trained and tested on relative trajectory intervals rather than the absolute positions of individual trajectory points. In general, LSTM provides a more accurate geometric pattern of trajectories at the initial stages of forecasting, while DT offers superior accuracy in predicting specific trajectory positions. The velocity and curvature of ocean currents jointly influence the prediction quality of both methods. In regions characterized by active sub-mesoscale dynamics, such as the fast-flowing and meandering Kuroshio Current and Kuroshio Current Extension, DT predicts more reliable trajectory patterns but lacks precision in detailed position estimates compared to LSTM. However, in areas dominated by the fast but relatively straight North Equatorial Current, the performance of the two methods reverses. The two methods also demonstrate different tolerances for noise and sampling intervals. This study establishes a baseline for selecting machine learning methods for marine drifter prediction and highlights the limitations of AI-based predictions under data-scarce and resource-constrained conditions.

Stokes drift and particle trajectories induced by surface waves atop a shear flow

Surface waves and currents are crucial to the mass transfer in the air-sea interaction as they can drive a variety of dynamical processes. How mass can be transported by surface waves and current coupling is addressed through a study of their induced motions of fluid parcels. To this end, a weakly nonlinear wavetrain is imposed on the background flow whose direction and magnitude are permitted to vary with water depth and second-order features of this configuration are investigated. A leading-order approximation to the Stokes drift is derived, correct to the second order in wave steepness, and applicable to an arbitrarily depth-dependent background flow. The reduced forms of the approximate Stokes drift are provided in a few limiting cases such as a current with an exponential profile or propagating in an orthogonal direction to the wave propagation. Novel features related to the Stokes drift and particle trajectories have been reported for the first time as a result of the rotation induced by the wave and current coupling. A non-vanishing component of the Stokes drift velocity and net-mean displacement of fluid parcels in the span-wise direction to the wave propagation are observed in the cases where a shear current propagates obliquely to the waves direction. A non-monotonic dependence on water depth of the stream-wise component of the Stokes drift is shown, and thereby the largest mass transport induced no longer occurs on the still water surface but some depth beneath. The non-monotonic behavior occurs beyond the regime of the near-irrotational assumption of wave-induced motions. It can also lead to the change of the signs for the stream-wise Stokes drift throughout the water column, and thus an overall cancellation of the integrated mass transport by waves over the water column, indicating that the depth-integrated models can likely lead to underestimated effects of the mass transport which is non-trivial at a local depth. The results from this study have far-reaching impact. The Stokes drift profile is a direct input to the parametrization of the surface waves forcing in ocean circulations and the obliquely propagating Stokes drift can be plausibly responsible for the formation of oblique Langmuir rolls to wave propagation in the open ocean.

Trough‐Scale Slope Countercurrent Over the East China Sea Continental Slope Driven by Upwelling Divergence

AbstractObservations have revealed the existence of persistent slope countercurrents (SCCs) that flow southwestward beneath the Kuroshio Current at several locations over the East China Sea (ECS) continental slope. It was not clear whether these flows are localized circulation features or segments of a trough‐scale circulation system in the Okinawa Trough (OT). We demonstrate that there indeed exists a potentially continuous trough‐scale SCC along the ECS slope that is associated with an OT‐wide cyclonic circulation using high‐resolution model simulations and physical interpretations. The detailed features of the deep OT circulation are illustrated by the trajectories of the Lagrangian drifters and the time‐varying distributions of passive tracers. The SCC in the ECS is characterized by its weak yet persistent nature, typically located in narrow sloping regions at the isopycnal layer of 26.6–27.3 kg m−3. It exhibits a characteristic speed of approximately O‐(1) cm s−1. Analyses and experiments suggest that the divergence of upwelling in the SCC layer (26.6–27.3 σθ surface) gives rise to lateral potential vorticity transport, ultimately driving the deep cyclonic circulation. Furthermore, the SCC also displays a substantial connection with the onshore intrusion of the Kuroshio Current, particularly to the northeast of Taiwan Island. The SCC may potentially play a crucial role in the transport of heat and nutrients, as well as in regulating sediment distributions within the deep OT. This mechanism offers fresh insights into explaining the presence of undercurrents in semi‐enclosed marginal seas.

Comparison of off-target pesticide drift in paddy fields from unmanned aerial vehicle spraying using cellulose deposition sampler

Off-target pesticide drift in paddy fields following unmanned aerial vehicle (UAV) spraying was evaluated using cellulose deposition samplers (CDSs). An analytical method for quantifying ferimzone Z and E isomers deposited on CDSs was developed using LC-MS/MS. The suitability of the CDS method was confirmed by comparing deposition patterns on CDSs with residue levels in rice plant samples. To assess pesticide deposition in paddy fields, CDSs were strategically placed at varying distances from target areas, followed by UAV spraying. The fungicide agrochemicals were applied with and without adjuvants, and wind direction affected the drift trajectory for all treatment groups. Adjuvants, particularly soy lecithin as the major component, significantly enhanced pesticide deposition within the spray pathway while reducing drift rates relatively by 47.9–68.0 %. Higher wind speeds were found to exacerbate drift, but adjuvant-treated sprays showed less variability in deposition patterns under these conditions. Pesticide residues in harvested brown rice were found to be below the maximum residue limits (MRLs), ensuring safety for consumption. These findings highlight the importance of selecting appropriate adjuvants in UAV-based pesticide applications to optimize deposition efficiency and minimize environmental contamination.

Insight into the Impact of Electron Drift Trajectory on Charge Collection in Silicon Drift Detector

The internal electric field distribution is one key design consideration, which affects the charge collection efficiency in silicon drift detectors (SDDs). The internal electrostatic potential distributions along SDD front and back surfaces, which are determined by the applied voltages at cathode electrodes, define the final internal field distribution. Front-back bias coupling leads to the complexity of electrode structure design and voltage tuning. Device simulation is performed to investigate the performance of SDDs with varied bias voltages. When the cathode bias is −40 V with the first ring bias of −15 V and the outermost ring bias of −80 V, the detector is biased with a uniform electric field distribution, favorable electron drift trajectories. The simulation results provide new insight into the influence of internal electric field and electron drift trajectories on the charge collection efficiency. According to the analysis of simulation results, a 2000 × 2000 μm area concentric silicon drift detector was designed and fabricated. The electrical characteristics of the designed detectors were studied to show the validity of the proposed device design methodology. The internal electric field distribution and electron drift trajectories can be tuned to improve the charge collection efficiency.

Estimates of Baroclinic Tidal Sea Level and Currents from Lagrangian Drifters and Satellite Altimetry

Abstract Internal waves generated by the interaction of the surface tides with topography are known to propagate long distances and lead to observable effects such as sea level variability, ocean currents, and mixing. In an effort to describe and predict these waves, the present work is concerned with using geographically distributed data from satellite altimeters and drifting buoys to estimate and map the baroclinic sea level associated with the M2, S2, N2, K1, and O1 tides. A new mapping methodology is developed, based on a mixed L1/L2-norm optimization, and compared with previously developed methods for tidal estimation from altimeter data. The altimeter and drifter data are considered separately in their roles for estimating tides and for cross-validating estimates obtained with independent data. Estimates obtained from altimetry and drifter data are found to agree remarkably well in regions where the drifter trajectories are spatially dense; however, heterogeneity of the drifter trajectories is a disadvantage when they are considered alone for tidal estimation. When the different data types are combined by using geodetic mission altimetry to cross validate estimates obtained with either exact-repeat altimetry or drifter data, and subsequently averaging the latter estimates, the estimates significantly improve on the previously published HRET8.1 model, as measured by their utility for predicting sea level and surface currents in the open ocean. The methodology has been applied to estimate the annual modulations of M2, which are found to have much smaller amplitudes compared to those reported in HRET8.1, and suggest that the latter estimates of these tides were not reliable. Significance Statement The mechanical and thermodynamic forcing of the ocean occurs primarily at very large scales associated with the gravitational perturbations of the sun and moon (tides), atmospheric wind stress, and solar insolation, but the frictional forces within the ocean act on very small scales. This research addresses the question of how the large-scale tidal forcing is transformed into the smaller-scale motion capable of being influenced by friction. The results show where internal waves are generated and how they transport energy across ocean basins to eventually be dissipated by friction. The results are useful to scientists interested in mapping the flows of mechanical energy in the ocean and predicting their influences on marine life, ocean temperature, and ocean currents.

The Effect of Model Input Uncertainty on the Simulation of Typical Pollutant Transport in the Coastal Waters of China

Route planning to evade potential pollution holds critical importance for aquaculture vessels. This study establishes a fish-feed pollutant drift model based on the Lagrangian particle tracking algorithm and designs four sets of sensitivity experiments in the East China Sea. The research investigates the impact of model input uncertainties on the drift trajectory, centroid position, and sweeping area of the fish-feed pollutants. Numerical results indicate that the uncertainty in the background flow field significantly affects the uncertainty in the centroid position and sweeping area in the numerical simulations. Specifically, when a 35% random error is added to the background flow field, the centroid shift distance reaches its maximum, and the sweeping area also attains its largest value. The uncertainty in the background wind field affects the centroid position of particles but to a much lesser extent compared to the background flow field. When considering only the uncertainty of the background wind field, the sweeping area does not significantly differ from the control experiment as the uncertainty of the background wind field increases. The initial release position has little effect on the drift direction of the fish-feed pollutants but does affect the drift distance; it has minimal impact on the trajectory but significantly affects the final position of the pollutant centroid. By analyzing the model uncertainties, this study reveals the key factors influencing the drift of fish-feed pollutants. This information is crucial for aquaculture vessels in planning routes, considering environmental factors, and reducing potential pollution risks.

Station-keeping HAPS mission through optimal sprint and drift trajectories

Due to the latest technological breakthroughs, High-Altitude Pseudo Satellites (HAPS) have recently become a feasible solution with great potential in the aerospace industry for Earth observation and communications, among other applications. Minimizing the energy consumption of these solar powered platforms is critical and, in the case of lighter than air vehicles, leads to smaller and more manageable platforms. When stratospheric airships perform a station-keeping mission, a certain displacement from the Earth surface reference point is usually admissible. This flexibility makes it possible to define an optimal control law for the airship that minimizes the energy required to fly in a 24-hour cycle, leading to a sprint and drift trajectory. This study analyzes the impact on the energy balance of the mission that stems from the changes in the allowed station-keeping radius. It also considers the effects of the daylight hours, the wind intensity, and the characteristics of the onboard energy system. The associated optimal control problems are rigorously solved numerically by means of a transcription method with regularization. The results define the optimal sprint and drift trajectories adapted to every scenario, providing the time evolution of the available power that controls the flight. The analysis indicates that following the optimal trajectory leads to weight savings in the energy system of about 5.4 kilograms per kilometer of the station-keeping radius. It entails that, for example, if a 20 kilometer radius is allowed, the energy required decreases more than 6% and the payload capacity increases about a 43% when compared to the fixed-point flight.

Combining barotropic and baroclinic simplified models for drift trajectory predictions

ABSTRACT The shallow-water equations are often used as a classical simplified ocean model for barotropic ocean dynamics. The same equations can also be used to model simplified baroclinic dynamics through the reduced-gravity model. Herein, we propose to utilise a GPU-accelerated shallow-water simulation framework for representing two decoupled simplified ocean models for each of the barotropic and baroclinic dynamics, and use these models for ensemble prediction of short-term drift trajectories in coastal domains. This system can be used complementary to current operational systems as a lightweight tool for uncertainty quantification and in the future for ensemble-based data assimilation of in-situ observations. We show the relevance of our approach by demonstrating how the barotropic and baroclinic dynamical components are of varying importance at different geographical locations, and that one of the models can be used alone or in combination with the other. We benchmark our approach by showing the resulting ensemble trajectories in reference to deterministic trajectories produced by operational models for three Norwegian coastal regions.

Prediction of Drift Trajectory in the Ocean Using Double-Branch Adaptive Span Attention

The accurate prediction of drift trajectories holds paramount significance for disaster response and navigational safety. The future positions of underwater drifters in the ocean are closely related to their historical drift patterns. Additionally, leveraging the complex dependencies between drift trajectories and ocean currents can enhance the accuracy of predictions. Building upon this foundation, we propose a Transformer model based on double-branch adaptive span attention (DBASformer), aimed at capturing the multivariate time-series relationships within drift history data and predicting drift trajectories in future periods. DBASformer can predict drift trajectories more accurately. The proposed adaptive span attention mechanism exhibits enhanced flexibility in the computation of attention weights, and the double-branch attention structure can capture the cross-time and cross-dimension dependencies in the sequences. Finally, our method was evaluated using datasets containing buoy data with ocean current velocities and Autonomous Underwater Vehicle (AUV) data. The raw data underwent cleaning and alignment processes. Comparative results with five alternative methods demonstrate that DBASformer improves prediction accuracy.

Multiple Lagrangian Jet‐Core Structures in the Malvinas Current

AbstractThe Malvinas Current (MC) in the Southwestern Atlantic is characterized as a system of multiple Lagrangian jets. This is done by applying geodesic theory of Lagrangian Coherent Structures (LCSs) on altimetry‐derived velocity. The number and spatial disposition of jet‐cores organizing the MC are found to vary in time influenced by topographic waves propagating along the current. The Lagrangian analysis reveals that permanent jet‐cores tend to approach one another upstream, yet do not merge into a single jet as the Eulerian analysis of the data suggests. Independent support for the existence of multiple jets is provided by trajectories of surface drifters from a deployment of unparalleled dimensions for the region and ocean color images. The drifters develop the characteristic boomerang‐shaped patterns into which passive tracers straddling jet‐cores deform and exhibit Lagrangian metrics that are largely consistent with those derived from altimetry data. Ocean color imagery reveals similar “chevrons” and suggest upwelling along the jets, which is consistent with a new solution of arrested topographic wave dynamics. This observation and the finding that the MC jet system is influenced by propagating waves suggests that the MC variability may have major implications in the modulation of primary productivity on synoptic timescales. Drifters flowing into the open ocean, exhibit organizing patterns that are largely shaped by mesoscale circulation and essentially differ from those observed along the MC. Evidence of this observation is provided on the basis of LCS analysis and the calculation of a quasi‐objective Lagrangian metric derived directly from drifter trajectories.

An improved drift model of dropped containers under wind and current effects

In the face of the increasing risk of Maritime accidents caused by global climate change, it is necessary to improve the accuracy of drift model of the dropped containers at seas for Maritime Search and Rescue (SAR). Sensitivity test on the classic drift model illustrates that immersion ratio (Ir) of container plays a dominate role in the drift trajectory of container compared to the drag coefficients. Inspired from it, a new scheme of variable Ir is proposed to improve the model by considering the water entering process of container in the study. Based on the stability analysis of four floating attitudes of intact empty container under strong wind condition, the variable Ir is estimated according to the orifice inflow theory and the Archimedes' principle of buoyancy. The new scheme is compared with the fixed Ir scheme from the simulation of the drift trajectory of the dropped container at Xiamen Bay of China. The result shows that error arisen from the fixed Ir would be accumulated as the time integral, but the new scheme of variable Ir can obviously improve the model accuracy for the long-time prediction of SAR. In addition, the drag coefficients of wind and water in the drift model of the dropped container are suggested to be 0.8 and 1.2, respectively.

Experimental and numerical investigation of shelf flow crossing over a strait

Motivated by the phenomenon of Scotian Shelf Crossover events, the problem of a shelf flow that is interrupted by a strait is considered. Laboratory experiments in a rotating tank with barotropic and baroclinic flow over flat and sloping shelves confirm that the flow is steered by the bathymetric contours and mainly circumnavigates the gulf. In order to jump across the strait, as suggested by earlier theories, the flow must have unrealistically high Rossby numbers. However, the near bottom friction relaxes the bathymetric constraint and causes the formation of a peculiar jet crossing the strait diagonally. For the dissipation values such that a half of the transport goes around the gulf and half crosses the strait diagonally, the diagonal crossover jet becomes most evident. Numerical solutions for realistic values of the frictional parameter reproduce the results of the laboratory experiments and consideration of the actual Gulf of Maine bathymetry reproduces patterns similar to those observed by drift trajectories and in the satellite derived sea surface temperature fields.

Satellite based oil spill emergency response and recovery support

Oil spills at sea pose a complex and challenging problem. Detecting, reporting, and potentially acting on the spill are difficult tasks, especially if the information is only obtained from the perspective of a vessel. Near-real time and relevant information is crucial for effective management of an oil spill event. This paper demonstrates how satellite-based images, combined with methods for thickness extraction and oil drift trajectory, can provide end-users with actionable and timely information for emergency response and recovery support. The results show the high value of quick delivery and the importance of using multiple sensors and data sources during an emergency, which enhance situational awareness and decision making. We highlight the value of satellite imagery for oil detection and show how it can be used to document the polluter, identify relative oil thickness, model oil drift, and compare different sensors.

GAT-ABiGRU Based Prediction Model for AUV Trajectory

Autonomous underwater vehicles (AUVs) are critical components of current maritime operations. However, because of the complicated marine environment, AUVs are at significant risk of being lost, and such losses significantly impact the continuity and safety of aquatic activities. This article suggests a methodology for forecasting the trajectory of lost autonomous underwater vehicles (AUVs) based on GAT-ABiGRU. Firstly, the time-series data of the AUV are transformed into a graph structure to represent the dependencies between data points. Secondly, a graph attention network is utilized to capture the spatial features of the trajectory data, while an attention-based bidirectional gated recurrent unit network learns the temporal features of the trajectory data; finally, the predicted drift trajectory is obtained. The findings show that the GAT-ABiGRU model outperforms previous trajectory prediction models, is highly accurate and robust in drift trajectory prediction, and presents a new method for forecasting the trajectory of wrecked AUVs.

Lagrangian characterization of the southwestern Atlantic from a dense surface drifter deployment

The Southwestern Atlantic (SWA) is characterized by its large Eddy Kinetic Energy as the result of the confluence of two major western boundary currents, the northward flowing Malvinas Current (MC) and the southward flowing Brazil Current. The SWA study was addressed in the literature based on altimetry data, in situ measurements, regional models and ocean reanalysis. The present study constitutes the first effort to sample a portion of the SWA, with a dense drifter array (N = 62) deployment. The drifters, drogued at 15 m depths, were deployed across the MC and the Argentine Continental Shelf along two zonal transects located at 47°S and 47.25°S, between the 8th and the September 9, 2021. Drifters were set to deliver their position every 10 and 60 min, providing accurate Lagrangian trajectories that provide information on a large range of space and time scales of the surface currents. Three regions are clearly identified based on the analysis of the speed of the drifters, of their trajectories and of the spectral density of their velocities: the continental shelf, the slope and the open ocean. The comparison of the trajectories of the drifters with satellite altimetry images shows that, in general, drifters follow mesoscale features that are detectable in satellite altimetry maps. The analysis of the drifter trajectories also allowed us the study of submesoscale features of the flow (1–10 km) that are not observable in satellite altimetry data. Comparison with cloud-free, high-resolution color images, shows that drifter trajectories organized by the mesoscale flow might also locally follow sub-mesoscale features. In frontal regions it was found that drifter velocities double satellite altimetry geostrophic velocities, which suggests that the dynamics at those regions is largely dominated by ageostrophic components. The ageostrophic Ekman component might explain the direction of the drifters when strong winds from a given direction prevail for several days and the drifters are not in a region with large sea surface height (SSH) gradients. The joint analysis of drifters’ trajectory and SSH clearly depicts that mesoscale features on the open ocean region control the cross-shelf exchanges between the MC and open ocean regions as well as the strength and width of the MC. Finally, the spatial density distribution of the drifters during the first hours after deployment and within a small eddy also allowed us to characterize the flow in terms of its divergence, vorticity and strain, indicating that the MC is geostrophic and has a jet-like behavior while the eddy is largely ageostrophic and has a dominant vorticity component over strain. We conclude observing that the analysis of a dense array of drifters provides valuable information of the flow that cannot be attained solely based on satellite data.

Lagrangian surface drifter observations in the North Sea: an overview of high-resolution tidal dynamics and surface currents

Abstract. A dataset of 85 Lagrangian surface drifter trajectories covering the central North Sea area and the Skagerrak from 2017–2021 of 17 deployments is presented. The data have been quality-controlled, uniformly structured, and assimilated in a standard NetCDF format (https://doi.org/10.1594/PANGAEA.963166, Meyerjürgens et al., 2023a). Using appropriate methods presented in detail here, surface currents were calculated from the drifter position data. Based on a drifter deployment in the Skagerrak, it is demonstrated that the Lagrangian measurements can be converted into an Eulerian representation by calculating mean current velocities. Tidal energy spectra were analyzed separately for the southern and northern areas of the North Sea, and tidal ellipses were calculated to determine the tidal impact on surface currents. Significant differences between the shallow shelf and the deeper areas of the North Sea are evident. While the shallow nearshore areas are dominated by tidal currents, deeper areas such as the Skagerrak record a high mean residual circulation driven by high-density gradients. Measurements using Eulerian approaches and remote sensing methods are restricted in temporal and spatial coverage, in particular, to capture fine-scale dynamics. For this reason, Lagrangian measurements, to a large extent, provide new insights into the complex submesoscale dynamics of the North Sea. Exemplarily, the Skagerrak region is used to demonstrate that high-resolution drifter observations capture both mesoscale and small-scale current patterns. This unique dataset, covering the entire southeastern North Sea and the Skagerrak, offers further analysis possibilities and can be used for the investigation of various hydrodynamic and environmental issues, e.g., the analysis of submesoscale current dynamics at ocean fronts, the determination of the kinetic eddy energy, and the propagation of pollutants in the North Sea.

From snow accumulation to snow depth distributions by quantifying meteoric ice fractions in the Weddell Sea

Abstract. Year-round snow cover is a characteristic of the entire Antarctic sea ice cover, which has significant implications for the energy and mass budgets of sea ice, e.g., by preventing surface melt in summer and enhancing sea ice growth through extensive snow ice formation. However, substantial observational gaps in the seasonal cycle of Antarctic sea ice and its snow cover limit the understanding of important processes in the ice-covered Southern Ocean. They also introduce large uncertainties in satellite remote sensing applications and climate studies. Here we present results from 10 years of autonomous snow observations from Snow Buoys in the Weddell Sea. To distinguish between actual snow depth and potential snow ice thickness within the accumulated snowpack, a one-dimensional thermodynamic sea ice model is applied along the drift trajectories of the buoys. The results show that potential snow ice formation, with an average maximum thickness of 35 cm, was detected along 41 % of the total track length of the analyzed Snow Buoy tracks, which corresponds to about one-quarter of the snow accumulation. In addition, we simulate the evolution of internal snow properties along the drift trajectories with the more complex SNOWPACK model, which results in superimposed ice thicknesses between 0 and 14 cm on top of the snow ice layer. These estimates will provide an important reference dataset for both snow depth and meteoric ice rates in the Southern Ocean.