2013
DOI: 10.5194/gmd-6-1871-2013
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MEDSLIK-II, a Lagrangian marine surface oil spill model for short-term forecasting – Part 2: Numerical simulations and validations

Abstract: 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 … Show more

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Cited by 130 publications
(83 citation statements)
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“…Equation (70) can be used to provide an estimate of the number of particles for a given spill scenario and oil tracer grid discretization, knowing the lower concentration level of interest. In Part 2 of this paper (De Dominicis et al, 2013), several sensitivity experiments will be carried out to show the impact of different choices regarding number of particles and tracer grid spatial resolution.…”
Section: Oil Tracer Grid and Number Of Particlesmentioning
confidence: 99%
“…Equation (70) can be used to provide an estimate of the number of particles for a given spill scenario and oil tracer grid discretization, knowing the lower concentration level of interest. In Part 2 of this paper (De Dominicis et al, 2013), several sensitivity experiments will be carried out to show the impact of different choices regarding number of particles and tracer grid spatial resolution.…”
Section: Oil Tracer Grid and Number Of Particlesmentioning
confidence: 99%
“…Likewise, drifter observation provides very useful information for oil spill and search and rescue operations. Indeed, trajectories from Lagrangian drifter buoy data have been widely used as proxies of oil spills or floating objects on the ocean surface (Reed et al, 1994;Al-Rabeh, 1994;Price et al, 2006;García-Ladona et al, 2005;Barron et al, 2007;Sotillo et al, 2008;DeDominicis et al, 2013;Sayol et al, 2014).…”
Section: G Sotillo Et Al: the Medess-gib Databasementioning
confidence: 99%
“…Therefore, many scholars have combined remote sensing detection with numerical models, so as to provide continuous tracking of the oil range and spill diffusion direction. For example, for the first time, the combination of satellite images of surface oil slicks with Lagrangian trajectory models has been implemented in the operational oil spill trajectory hindcast/forecast for the Deepwater Horizon oil spill [2,14,15]; Zodiatis et al (2012) used the MEDSLIKoil spill model to forecast the drift and spreading of oil slicks detected from satellite images using MyOcean (www.myocean.eu.org) forecasting data, through the implementation of MEDESS-4MS(www.medess4ms.eu) project services [16]; and Dominicis et al (2013) used the MEDSLIK-II Lagrangian marine model to simulate oil slick transport and transformation processes for realistic oceanic cases and the model initialized using the slick position and slick shape provided by satellite systems, both SAR (synthetic aperture radar) and optical images [17].…”
Section: Introductionmentioning
confidence: 99%
“…As for the combined model, the location and size of the surface oil slick are inferred from satellite images, and they should be frequently re-initialized to reduce the forecast errors, which could be accumulated from the initial locations (conditions), as learned from the rapid response to the Deepwater Horizon oil spill in the Gulf of Mexico [2,14,15]. However, other related model parameters, such as data on wind forcing, sea surface temperature and sea currents, are from empirical values, model calculations or some meteorological data centers [17], and they may not be precise. Then, the inaccurate data could affect the model's predictive accuracy.…”
Section: Introductionmentioning
confidence: 99%