%0 Journal Article
%4 sid.inpe.br/mtc-m21c/2021/04.02.15.28
%2 sid.inpe.br/mtc-m21c/2021/04.02.15.28.30
%@doi 10.1016/j.ocecoaman.2021.105552
%@issn 0964-5691
%@issn 1873-524X
%T Comparison of the Coupled Model for Oil spill Prediction (CMOP) and the Oil Spill Contingency and Response model (OSCAR) during the DeepSpill field experiment
%D 2021
%8 Apr.
%9 journal article
%A Barreto, Fernando Túlio Camilo,
%A Dammann, Dyre O.,
%A Tessarolo, Luciana de Freitas,
%A Skancke, Jorgen,
%A Keghouche, Intissar,
%A Innocentini, Valdir,
%A Winther-Kaland, Nina,
%A Marton, Luís,
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation StormGeo
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation SINTEF
%@affiliation StormGeo
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation StormGeo
%@affiliation Climatempo
%@electronicmailaddress fernandotcbarreto@gmail.com
%@electronicmailaddress
%@electronicmailaddress luciana.tessarolo@inpe.br
%@electronicmailaddress
%@electronicmailaddress
%@electronicmailaddress valdir.innocentini@inpe.br
%B Ocean and Coastal Management
%V 204
%P e105552
%K Oil spill, Computational modeling, Model comparison, CMOP, OSCAR.
%X 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.
%@language en
%3 barreto_comparison.pdf