@Article{BarretoDTSKIWM:2021:CoCoMo,
               author = "Barreto, Fernando T{\'u}lio Camilo and Dammann, Dyre O. and 
                         Tessarolo, Luciana de Freitas and Skancke, Jorgen and Keghouche, 
                         Intissar and Innocentini, Valdir and Winther-Kaland, Nina and 
                         Marton, Lu{\'{\i}}s",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and StormGeo 
                         and {Instituto Nacional de Pesquisas Espaciais (INPE)} and SINTEF 
                         and StormGeo and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and StormGeo and Climatempo",
                title = "Comparison of the Coupled Model for Oil spill Prediction (CMOP) 
                         and the Oil Spill Contingency and Response model (OSCAR) during 
                         the DeepSpill field experiment",
              journal = "Ocean and Coastal Management",
                 year = "2021",
               volume = "204",
                pages = "e105552",
                month = "Apr.",
             keywords = "Oil spill, Computational modeling, Model comparison, CMOP, 
                         OSCAR.",
             abstract = "An oil spill model is an important tool for environmental risk 
                         assessment, strategic planning, and tactical decision making in 
                         the event of an oil spill. However, limited data exist to evaluate 
                         such models and their performance. During the DeepSpill field 
                         campaign, a unique dataset was acquired by monitoring a deliberate 
                         deep-water oil blowout. In this work, we evaluate and compare two 
                         oil spill models the Coupled Model for Oil spill Prediction (CMOP) 
                         and the Oil Spill Contingency and Response model (OSCAR) against 
                         the DeepSpill experiment. We find that the general plume 
                         trajectory is captured well with a default model setup for both 
                         models. However, to accurately model the surface slick 
                         development, it was necessary to alter modeling parameters and 
                         incorporate model changes to increase scenario flexibility. 
                         Through this work, we build further confidence in the two models 
                         and provide suggestions for improvements.",
                  doi = "10.1016/j.ocecoaman.2021.105552",
                  url = "http://dx.doi.org/10.1016/j.ocecoaman.2021.105552",
                 issn = "0964-5691 and 1873-524X",
             language = "en",
           targetfile = "barreto_comparison.pdf",
        urlaccessdate = "06 maio 2024"
}