Efficient tools for marine operational forecast and oil spill tracking

Area of Science:

• Oceanography
• Environmental Science
• Computational Modeling

Background:

• Ocean forecasting and oil spill management are complex, often requiring specialized institutions.
• Existing models can be resource-intensive and difficult to implement.

Purpose of the Study:

• To present a lighter, more accessible solution for ocean forecasting and oil spill modeling.
• To demonstrate the implementation and effectiveness of the Operational Ocean Forecast Python Engine (OOFε) and General NOAA Operational Modelling Environment (GNOME).

Main Methods:

• Implementation of OOFε and GNOME in three distinct oceanic regions: Galicia, southeastern Brazil, and the Texas-Louisiana shelf.
• Utilized the Regional Ocean Modelling System (ROMS) for ocean modeling.
• Simulated historical oil spills (Prestige, Krimsk) and hypothetical scenarios (Campos Basin).

Main Results:

• Successful simulation of the Prestige oil spill in Galicia.
• GNOME accurately compared with observations for the Krimsk accident in Texas.
• Hypothetical spill scenarios in Brazil highlighted sensitivity to the dynamical system.

Conclusions:

• OOFε and GNOME provide valuable, efficient, and low-cost tools for ocean forecasting and oil spill modeling.
• These tools serve as a practical intermediate step towards more complex operational systems.
• The integrated approach demonstrates robustness across different oceanic systems.