### Abstract

Two oil particle trajectory forecasting systems were developed and applied to the 2010 Deepwater Horizon Oil Spill in the Gulf of Mexico. Both systems use ocean current fields from high-resolution numerical ocean circulation model simulations, Lagrangian stochastic models to represent unresolved sub-grid scale variability to advect oil particles, and Monte Carlo-based schemes for representing uncertain biochemical and physical processes. The first system assumes two-dimensional particle motion at the ocean surface, the oil is in one state, and the particle removal is modeled as a Monte Carlo process parameterized by a one number removal rate. Oil particles are seeded using both initial conditions based on observations and particles released at the location of the Maconda well. The initial conditions (ICs) of oil particle location for the two-dimensional surface oil trajectory forecasts are based on a fusing of all available information including satellite-based analyses. The resulting oil map is digitized into a shape file within which a polygon filling software generates longitude and latitude with variable particle density depending on the amount of oil present in the observations for the IC. The more complex system assumes three (light, medium, heavy) states for the oil, each state has a different removal rate in the Monte Carlo process, three-dimensional particle motion, and a particle size-dependent oil mixing model.Simulations from the two-dimensional forecast system produced results that qualitatively agreed with the uncertain " truth" fields. These simulations validated the use of our Monte Carlo scheme for representing oil removal by evaporation and other weathering processes. Eulerian velocity fields for predicting particle motion from data-assimilative models produced better particle trajectory distributions than a free running model with no data assimilation. Monte Carlo simulations of the three-dimensional oil particle trajectory, whose ensembles were generated by perturbing the size of the oil particles and the fraction in a given size range that are released at depth, the two largest unknowns in this problem. 36 realizations of the model were run with only subsurface oil releases. An average of these results yields that after three months, about 25% of the oil remains in the water column and that most of the oil is below 800 m.

Original language | English (US) |
---|---|

Pages (from-to) | 322-340 |

Number of pages | 19 |

Journal | Dynamics of Atmospheres and Oceans |

Volume | 52 |

Issue number | 1-2 |

DOIs | |

State | Published - Sep 2011 |

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### Keywords

- Lagrangian trajectory prediction
- Numerical model
- Oil spill

### ASJC Scopus subject areas

- Atmospheric Science
- Geology
- Oceanography
- Computers in Earth Sciences

### Cite this

*Dynamics of Atmospheres and Oceans*,

*52*(1-2), 322-340. https://doi.org/10.1016/j.dynatmoce.2011.06.001

**On the modeling of the 2010 Gulf of Mexico Oil Spill.** / Mariano, Arthur J; Kourafalou, Vassiliki H; Srinivasan, A.; Kang, H.; Halliwell, G. R.; Ryan, E. H.; Roffer, M.

Research output: Contribution to journal › Article

*Dynamics of Atmospheres and Oceans*, vol. 52, no. 1-2, pp. 322-340. https://doi.org/10.1016/j.dynatmoce.2011.06.001

}

TY - JOUR

T1 - On the modeling of the 2010 Gulf of Mexico Oil Spill

AU - Mariano, Arthur J

AU - Kourafalou, Vassiliki H

AU - Srinivasan, A.

AU - Kang, H.

AU - Halliwell, G. R.

AU - Ryan, E. H.

AU - Roffer, M.

PY - 2011/9

Y1 - 2011/9

N2 - Two oil particle trajectory forecasting systems were developed and applied to the 2010 Deepwater Horizon Oil Spill in the Gulf of Mexico. Both systems use ocean current fields from high-resolution numerical ocean circulation model simulations, Lagrangian stochastic models to represent unresolved sub-grid scale variability to advect oil particles, and Monte Carlo-based schemes for representing uncertain biochemical and physical processes. The first system assumes two-dimensional particle motion at the ocean surface, the oil is in one state, and the particle removal is modeled as a Monte Carlo process parameterized by a one number removal rate. Oil particles are seeded using both initial conditions based on observations and particles released at the location of the Maconda well. The initial conditions (ICs) of oil particle location for the two-dimensional surface oil trajectory forecasts are based on a fusing of all available information including satellite-based analyses. The resulting oil map is digitized into a shape file within which a polygon filling software generates longitude and latitude with variable particle density depending on the amount of oil present in the observations for the IC. The more complex system assumes three (light, medium, heavy) states for the oil, each state has a different removal rate in the Monte Carlo process, three-dimensional particle motion, and a particle size-dependent oil mixing model.Simulations from the two-dimensional forecast system produced results that qualitatively agreed with the uncertain " truth" fields. These simulations validated the use of our Monte Carlo scheme for representing oil removal by evaporation and other weathering processes. Eulerian velocity fields for predicting particle motion from data-assimilative models produced better particle trajectory distributions than a free running model with no data assimilation. Monte Carlo simulations of the three-dimensional oil particle trajectory, whose ensembles were generated by perturbing the size of the oil particles and the fraction in a given size range that are released at depth, the two largest unknowns in this problem. 36 realizations of the model were run with only subsurface oil releases. An average of these results yields that after three months, about 25% of the oil remains in the water column and that most of the oil is below 800 m.

AB - Two oil particle trajectory forecasting systems were developed and applied to the 2010 Deepwater Horizon Oil Spill in the Gulf of Mexico. Both systems use ocean current fields from high-resolution numerical ocean circulation model simulations, Lagrangian stochastic models to represent unresolved sub-grid scale variability to advect oil particles, and Monte Carlo-based schemes for representing uncertain biochemical and physical processes. The first system assumes two-dimensional particle motion at the ocean surface, the oil is in one state, and the particle removal is modeled as a Monte Carlo process parameterized by a one number removal rate. Oil particles are seeded using both initial conditions based on observations and particles released at the location of the Maconda well. The initial conditions (ICs) of oil particle location for the two-dimensional surface oil trajectory forecasts are based on a fusing of all available information including satellite-based analyses. The resulting oil map is digitized into a shape file within which a polygon filling software generates longitude and latitude with variable particle density depending on the amount of oil present in the observations for the IC. The more complex system assumes three (light, medium, heavy) states for the oil, each state has a different removal rate in the Monte Carlo process, three-dimensional particle motion, and a particle size-dependent oil mixing model.Simulations from the two-dimensional forecast system produced results that qualitatively agreed with the uncertain " truth" fields. These simulations validated the use of our Monte Carlo scheme for representing oil removal by evaporation and other weathering processes. Eulerian velocity fields for predicting particle motion from data-assimilative models produced better particle trajectory distributions than a free running model with no data assimilation. Monte Carlo simulations of the three-dimensional oil particle trajectory, whose ensembles were generated by perturbing the size of the oil particles and the fraction in a given size range that are released at depth, the two largest unknowns in this problem. 36 realizations of the model were run with only subsurface oil releases. An average of these results yields that after three months, about 25% of the oil remains in the water column and that most of the oil is below 800 m.

KW - Lagrangian trajectory prediction

KW - Numerical model

KW - Oil spill

UR - http://www.scopus.com/inward/record.url?scp=80053132092&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=80053132092&partnerID=8YFLogxK

U2 - 10.1016/j.dynatmoce.2011.06.001

DO - 10.1016/j.dynatmoce.2011.06.001

M3 - Article

AN - SCOPUS:80053132092

VL - 52

SP - 322

EP - 340

JO - Dynamics of Atmospheres and Oceans

JF - Dynamics of Atmospheres and Oceans

SN - 0377-0265

IS - 1-2

ER -