Contribution of Cosmo/SkyMed data into PRIMI: A pilot project on marine oil pollution. results after one year of operations

Nirchio, F.; Pandiscia, G.; Ruggieri, G.; Santoleri, Rosalia; Pinardi, Nadia; Trivero, P.; Castellani, C.; Tataranni, F.; Masini, Andrea; Adamo, Maria; Archetti, Renata; Biamino, W.; Bignami, Fabio; Böhm, E.; Borasi, M.; Nardelli, Bruno Buongiorno; Cavagnero, M.; Colao, F.; Colella, Simone; Coppini, Giovanni; Debettio, V.; Carolis, Giacomo De; Dominicis, Michela De; Forneris, V.; Fontebasso, F.; Griffa, Annalisa; Iacono, Roberto; lombardi, emanuele; Marullo, Salvatore; Manzella, G. M. R.; Mercatini, A.; Napolitano, Ernesto; Pisano, Andrea; Reseghetti, Franco; Sorgente, R.; Sprovieri, Mario; Terranova, G.; Volpe, Gianluca; Zambianchi, Enrico

doi:10.1109/igarss.2010.5654006

Cited by 3 publications

(3 citation statements)

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“…In Fig. 10 two slicks are shown: the first is observed by ASAR sensor (Trivero et al, 1998;Fiscella et al, 2000;Trivero et al, 2001;Nirchio et al, 2005Nirchio et al, , 2007Nirchio et al, , 2010 for 6 August 2008 and the other is observed by (Hu et al, 2003(Hu et al, , 2009) 25 h later. We consider that the two images represent the evolution of the same oil slick, so we have both an initialization image and a verification one for the successive 25 h. The time of observation, the slick shape and area from the ASAR image are taken as initial surface slick variables for the simulation.…”

Section: Sensitivity Of Oil Concentration To Uncertain Input Parametementioning

confidence: 99%

“…These limitations may be overcome by setting a proper tolerance threshold, n, as suggested by Liu and Weisberg (2011). Finally, the model has also been validated using remote sensing data from satellite images obtained using both synthetic aperture radar (SAR) (Trivero et al, 1998;Fiscella et al, 2000;Trivero et al, 2001;Nirchio et al, 2005Nirchio et al, , 2007Nirchio et al, , 2010 and MODIS optical sensors (Hu et al, 2003(Hu et al, , 2009). The satellite data allowed for a study of the importance of shape initialization, the sensitivity to oil slick input properties (thickness, oil type and age) and to the number of constituent particles.…”

Section: Verification Drifters and Satellite Datamentioning

confidence: 99%

“…Synthetic aperture radar (SAR) and optical images can provide, as satellite image post-processing products, the area covered by the slick and the slick contour coordinates (Trivero et al, 2001;Nirchio et al, 2007Nirchio et al, , 2010. The total oil slick area at the initial time t 0 is the sum of the thick and thin parts, A(t 0 ) = A TN (t 0 ) + A TK (t 0 ).…”

Section: Oil Slick Initialization From Satellite Imagesmentioning

confidence: 99%

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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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Section: Sensitivity Of Oil Concentration To Uncertain Input Parametementioning

confidence: 99%

Section: Verification Drifters and Satellite Datamentioning

confidence: 99%

Section: Oil Slick Initialization From Satellite Imagesmentioning

confidence: 99%

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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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Automatic oil slick detection from SAR images: Results and improvements in the framework of the PRIMI pilot project

Trivero

Adamo

Biamino

et al. 2016

Deep Sea Research Part II: Topical Studies in Oceanography

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

Dominicis¹,

Pinardi²,

Zodiatis³

et al. 2013

Preprint

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Abstract. In this paper we use MEDSLIK-II, a Lagrangian marine oil spill model described in Part 1 of this paper (De Dominicis et al., 2013), 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 and SAR (Synthetic Aperture Radar) 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 often 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.

show abstract

Contribution of Cosmo/SkyMed data into PRIMI: A pilot project on marine oil pollution. results after one year of operations

Cited by 3 publications

References 2 publications

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

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

Automatic oil slick detection from SAR images: Results and improvements in the framework of the PRIMI pilot project

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

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