Eddy diffusivity derived from drifter data for dispersion model applications

Dominicis, Michela De; Leuzzi, Giovanni; Monti, Paolo; Pinardi, Nadia; Poulain, Pierre‐Marie

doi:10.1007/s10236-012-0564-2

Cited by 48 publications

(28 citation statements)

References 42 publications

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“…In several other studies (e.g. Gästgifvars et al, 2006;Kjellsson and Döös, 2012;De Dominicis et al, 2012), simulated marine surface currents were found to be too small, possibly also due to insufficient resolution of the marine surface layer. As a side effect, predictions may be particularly good when marine currents and winds are nearly parallel (Gästgif-vars et al, 2006).…”

Section: Discussionmentioning

confidence: 99%

Surface drifters in the German Bight: model validation considering windage and Stokes drift

et al. 2017

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Abstract. Six surface drifters (drogued at about 1 m depth) deployed in the inner German Bight (North Sea) were tracked for between 9 and 54 days. Corresponding simulations were conducted offline based on surface currents from two independent models (BSHcmod and TRIM). Inclusion of a direct wind drag (0.6 % of 10 m wind) was needed for successful simulations based on BSHcmod currents archived for a 5 m depth surface layer. Adding 50 % of surface Stokes drift simulated with a third-generation wave model (WAM) was tested as an alternative approach. Results resembled each other during most of the time. Successful simulations based on TRIM surface currents (1 m depth) suggest that both approaches were mainly needed to compensate insufficient vertical resolution of hydrodynamic currents.The study suggests that the main sources of simulation errors were inaccurate Eulerian currents and lacking representation of sub-grid-scale processes. Substantial model errors often occurred under low wind conditions. A lower limit of predictability (about 3-5 km day −1 ) was estimated from two drifters that were initially spaced 20 km apart but converged quickly and diverged again after having stayed at a distance of 2 km or less for about 10 days. In most cases, errors in simulated 25 h drifter displacements were of similar order of magnitude.

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Section: Discussionmentioning

confidence: 99%

Surface drifters in the German Bight: model validation considering windage and Stokes drift

et al. 2017

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“…However, as sea ice, especially compact pack ice, does not behave as a turbulent fluid, the stochastic term to be used for sea ice should not be taken to represent the same underlying physical processes as in the ocean, and it therefore may have a different form or be scaled with a different coefficient. In this section, we follow an approach that is frequently used for oceanic drifters (e.g., Dominicis et al, 2012). This approach consists of applying the diffusion analysis to observed and simulated trajectories to determine how the mean and fluctuating motion are represented and how unresolved physics could be taken into account.…”

Section: Diffusion Analysis On Observed and Simulated Sea-ice Trajectmentioning

confidence: 99%

“…The diffusion term (or in the discrete models, the Wiener process term) is either neglected or defined such that it accounts for the unresolved part of the fluctuating motion. The unresolved part of the motion could be analyzed with the methodology proposed by Dominicis et al (2012) for ocean surface tracer modeling, which consists of comparing the characteristics of the fluctuating part of observed trajectories to the ones of trajectories given by a tracer model forced by model output.…”

Section: Introductionmentioning

confidence: 99%

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Arctic sea-ice diffusion from observed and simulated Lagrangian trajectories

et al. 2016

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Abstract. We characterize sea-ice drift by applying a Lagrangian diffusion analysis to buoy trajectories from the International Arctic Buoy Programme (IABP) dataset and from two different models: the standalone Lagrangian sea-ice model neXtSIM and the Eulerian coupled ice-ocean model used for the TOPAZ reanalysis. By applying the diffusion analysis to the IABP buoy trajectories over the period 1979-2011, we confirm that sea-ice diffusion follows two distinct regimes (ballistic and Brownian) and we provide accurate values for the diffusivity and integral timescale that could be used in Eulerian or Lagrangian passive tracers models to simulate the transport and diffusion of particles moving with the ice. We discuss how these values are linked to the evolution of the fluctuating displacements variance and how this information could be used to define the size of the search area around the position predicted by the mean drift. By comparing observed and simulated sea-ice trajectories for three consecutive winter seasons (2007)(2008)(2009)(2010)(2011), we show how the characteristics of the simulated motion may differ from or agree well with observations. This comparison illustrates the usefulness of first applying a diffusion analysis to evaluate the output of modeling systems that include a sea-ice model before using these in, e.g., oil spill trajectory models or, more generally, to simulate the transport of passive tracers in sea ice.

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“…4.1, the horizontal diffusivity coefficient K h is set to zero, while simulating an oil slick from satellite, see Sect. 4.2, K h has been set to 2 m 2 s −1 in the range 1-100 m 2 s −1 indicated by ASCE (1996) andDe Dominicis et al (2012).…”

Section: Oil Spill Model Parametersmentioning

confidence: 99%

MEDSLIK-II, a Lagrangian marine surface oil spill model for short-term forecasting – Part 2: Numerical simulations and validations

et al. 2013

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Abstract. In this paper we use MEDSLIK-II, a Lagrangian marine surface oil spill model described in Part 1 , to simulate oil slick transport and transformation processes for realistic oceanic cases, where satellite or drifting buoys data are available for verification. The model is coupled with operational oceanographic currents, atmospheric analyses winds and remote sensing data for initialization. The sensitivity of the oil spill simulations to several model parameterizations is analyzed and the results are validated using surface drifters, SAR (synthetic aperture radar) and optical satellite images in different regions of the Mediterranean Sea. It is found that the forecast skill of Lagrangian trajectories largely depends on the accuracy of the Eulerian ocean currents: the operational models give useful estimates of currents, but high-frequency (hourly) and high-spatial resolution is required, and the Stokes drift velocity has to be added, especially in coastal areas. From a numerical point of view, it is found that a realistic oil concentration reconstruction is obtained using an oil tracer grid resolution of about 100 m, with at least 100 000 Lagrangian particles. Moreover, sensitivity experiments to uncertain model parameters show that the knowledge of oil type and slick thickness are, among all the others, key model parameters affecting the simulation results. Considering acceptable for the simulated trajectories a maximum spatial error of the order of three times the horizontal resolution of the Eulerian ocean currents, the predictability skill for particle trajectories is from 1 to 2.5 days depending on the specific current regime. This suggests that re-initialization of the simulations is required every day.

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Eddy diffusivity derived from drifter data for dispersion model applications

Cited by 48 publications

References 42 publications

Surface drifters in the German Bight: model validation considering windage and Stokes drift

Surface drifters in the German Bight: model validation considering windage and Stokes drift

Arctic sea-ice diffusion from observed and simulated Lagrangian trajectories

MEDSLIK-II, a Lagrangian marine surface oil spill model for short-term forecasting – Part 2: Numerical simulations and validations

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