Evaluating the Leeway Coefficient of Ocean Drifters Using Operational Marine Environmental Prediction Systems

Sutherland, Graig; Soontiens, Nancy; Davidson, Fraser; Smith, G. C. Moore; Bernier, Natacha B.; Blanken, Hauke; Schillinger, Douglas J.; Marcotte, Guillaume; Röhrs, Johannes; Dagestad, Knut‐Frode; Christensen, Kai Håkon; Breivik, Øyvind

doi:10.1175/jtech-d-20-0013.1

Cited by 25 publications

(14 citation statements)

References 40 publications

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“…Slightly larger values for α i using the TOPAZ data compared to the CAPS data. For α w , this range of values is consistent with 3% required for the prediction of surface drifters (Sutherland et al, 2020) and oil spill modelling (Nordam et al, 2019). The α i values are curiously consistent with canonical values used for icebergs of about 2% of the wind and 30 • to the right (Leppäranta, 2011).…”

Section: Discussionsupporting

confidence: 80%

“…These leeway values can represent direct wind forcing on the object (Kirwan Jr et al, 1975), and/or wind-dependent physics which are not included in the operational prediction system. As most operational marine prediction systems don't include surface waves it is common to include the Stokes drift (Breivik and Christensen, 2020) which can be approximated by a leeway coefficient (Breivik et al, 2011;Sutherland et al, 2020). In addition, material at the surface, such as oil but also ice, will strongly attenuate surface waves creating an additional force on the material (Weber, 2001).…”

mentioning

confidence: 99%

“…While typically leeway coefficients are determined for particular objects using detailed field measurements (Breivik et al, 2011), recently it was shown by Sutherland et al (2020) that leeway coefficients can also be predicted using operational prediction systems. The mean leeway coefficients using the operational prediction data were found to be consistent with detailed observations and independent of the choice of operational prediction system.…”

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confidence: 99%

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Determining an optimal transport velocity in the marginal ice zone using operational ice-ocean prediction systems

Sutherland

Aguiar

Hole

et al. 2021

Preprint

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Abstract. Knowledge of transport in the marginal ice zone (MIZ) is critical for operations in the Arctic and associated emergency response applications, for example, the transport of pollutants, such as oil, as well as predicting drift associated with search and rescue operations. This paper proposes a general transport equation for the MIZ that can be used for operational purposes in the MIZ. This equation is designed to use a mean velocity of the ice and water velocity, which is weighted by the ice concentration. A key component is the introduction of a leeway coefficient for both the ocean and ice components. These leeway coefficients are determined by minimizing the velocity error between the transport model and observed drifter velocity in the MIZ. These leeway values are found to be 3 % of the wind for the water leeway and 2 % and 30° to the right of the wind for the ice leeway, which are consistent with "rule of thumb" values for surface drifters and sea ice respectively. This general transport model is compared with other transport models and the error is reduced by a factor of 2 compared with traditional transport models for 48 hour lead times. The inclusion of a leeway coefficient in the ice is the key component to reduce trajectory errors in the MIZ.

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Section: Discussionsupporting

confidence: 80%

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confidence: 99%

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confidence: 99%

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Determining an optimal transport velocity in the marginal ice zone using operational ice-ocean prediction systems

Sutherland

Aguiar

Hole

et al. 2021

Preprint

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“…We did not aim to assess all specific differences between individual drifters and discuss in detail the wind forcing and the North Sea circulation during the different periods of their deployment, as this was done by [27]. Neither did we aim to tune the model to find the optimal percentage of the contribution of either the Stokes drift or windage that can be taken into account in OpenDrift simulations (as was recently done by [64]. Additional errors of the drifter simulations can be due to the errors in the atmospheric forcing, but also the boundary or tidal forcing.…”

Section: Discussionmentioning

confidence: 99%

Effects of Wave-Induced Processes in a Coupled Wave–Ocean Model on Particle Transport Simulations

Staneva

Ricker

Carrasco

et al. 2021

Water

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This study investigates the effects of wind–wave processes in a coupled wave–ocean circulation model on Lagrangian transport simulations. Drifters deployed in the southern North Sea from May to June 2015 are used. The Eulerian currents are obtained by simulation from the coupled circulation model (NEMO) and the wave model (WAM), as well as a stand-alone NEMO circulation model. The wave–current interaction processes are the momentum and energy sea state dependent fluxes, wave-induced mixing and Stokes–Coriolis forcing. The Lagrangian transport model sensitivity to these wave-induced processes in NEMO is quantified using a particle drift model. Wind waves act as a reservoir for energy and momentum. In the coupled wave–ocean circulation model, the momentum that is transferred into the ocean model is considered as a fraction of the total flux that goes directly to the currents plus the momentum lost from wave dissipation. Additional sensitivity studies are performed to assess the potential contribution of windage on the Lagrangian model performance. Wave-induced drift is found to significantly affect the particle transport in the upper ocean. The skill of particle transport simulations depends on wave–ocean circulation interaction processes. The model simulations were assessed using drifter and high-frequency (HF) radar observations. The analysis of the model reveals that Eulerian currents produced by introducing wave-induced parameterization into the ocean model are essential for improving particle transport simulations. The results show that coupled wave–circulation models may improve transport simulations of marine litter, oil spills, larval drift or transport of biological materials.

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“…However, it is not clear yet how to properly include this process when simulating drifter trajectories (e.g., Breivik & Allen, 2008;Callies et al, 2017;De Dominicis BRUCIAFERRI ET AL. et al, 2016;Röhrs et al, 2012;Staneva et al, 2021;Sutherland et al, 2020). In addition, studies on modeling the trajectory of SVP drifters are scarce (e.g., Abascal et al, 2012;Amemou et al, 2020;Kjellsson & Doos, 2012), especially during storm conditions.…”

Section: Assessment Of the Lagrangian Modeling Approachmentioning

confidence: 99%

The impact of ocean-wave coupling on the upper ocean circulation during storm events

Bruciaferri

Tonani

Lewis

et al. 2021

Preprint

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or monitoring activities of floating marine debris fate and accumulation (e.g., Liubartseva et al., 2018). However, the precision of such simulations drastically depends on the accuracy of the wind and ocean surface currents data used to force the Lagrangian transport model. For example, De Dominicis et al. (2016) showed that, after 24 h, the distance between observed and predicted drifter locations can range from 2-5 km up to 15-25 km, depending on the model data used to force the particle tracking model. Similarly, Dagestad and Röhrs (2019) found that, after 48 h, drifter trajectories simulated using surface currents detected from satellite or computed by a number of ocean models with different resolution may present a separation distance from the observed tracks of about 20-25 km.

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Evaluating the Leeway Coefficient of Ocean Drifters Using Operational Marine Environmental Prediction Systems

Cited by 25 publications

References 40 publications

Determining an optimal transport velocity in the marginal ice zone using operational ice-ocean prediction systems

Determining an optimal transport velocity in the marginal ice zone using operational ice-ocean prediction systems

Effects of Wave-Induced Processes in a Coupled Wave–Ocean Model on Particle Transport Simulations

The impact of ocean-wave coupling on the upper ocean circulation during storm events

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