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Progress in Operational Modeling in Support of Oil Spill Response Journal of Marine Science and Engineering Review Progress in Operational Modeling in Support of Oil Spill Response Christopher H. Barker 1,* , Vassiliki H. Kourafalou 2,* , CJ Beegle-Krause 3 , Michel Boufadel 4, Mark A. Bourassa 5 , Steve G. Buschang 6 , Yannis Androulidakis 2,7, Eric P. Chassignet 5 , Knut-Frode Dagestad 7 , Donald G. Danmeier 8 , Anusha L. Dissanayake 3,9 , Jerry A. Galt 10, Gregg Jacobs 11 , Guillaume Marcotte 12 , Tamay Özgökmen 2, Nadia Pinardi 13 , Rafael V. Schiller 14 , Scott A. Socolofsky 15, Dalina Thrift-Viveros 1, Brian Zelenke 16, Aijun Zhang 17 and Yangxing Zheng 5 1 National Oceanic and Atmospheric Administration, National Ocean Service, Office of Response and Restoration, Emergency Response Division, Silver Spring, MD 20910, USA; [email protected] 2 Department of Ocean Sciences, University of Miami/RSMAS, Miami, FL 33149, USA; [email protected] (Y.A.); [email protected] (T.Ö.) 3 SINTEF Ocean, 7465 Trondheim, Norway; [email protected] (C.B.-K.); [email protected] (A.L.D.) 4 New Jersey Institute of Technology, Civil Engineering, Newark, NJ 07102, USA; [email protected] 5 Center for Ocean-Atmospheric Prediction Studies, Florida State University, Tallahassee, FL 32306, USA; [email protected] (M.A.B.); [email protected] (E.P.C.); [email protected] (Y.Z.) 6 Texas General Land Office, Oil Spill Prevention and Response, Austin, TX 78711, USA; [email protected] 7 Norwegian Meteorological Institute, 0371 Oslo, Norway; [email protected] 8 Chevron Energy Technology Company, Environmental Technology Unit, Houston, TX 77002, USA; [email protected] 9 RPS Group, South Kingstown, RI 02879, USA 10 Genwest Systems Inc., Edmonds, WA 98020, USA; [email protected] 11 Naval Research Laboratory, 1005 Balch Boulevard, Stennis Space Center, MS 39529, USA; [email protected] 12 Canadian Centre for Meteorological and Environmental Prediction, Environmental Emergencies Response Section, Dorval, QC H9P 1J3, Canada; [email protected] 13 Department of Physics and Astronomy, University of Bologna, 40127 Bologna, Italy; [email protected] 14 FUGRO USA Marine, Houston, TX 77801, USA; [email protected] 15 Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX 77843, USA; [email protected] 16 Bureau of Ocean Energy Management, Washington, DC 20240, USA; [email protected] 17 National Oceanic and Atmospheric Administration, National Ocean Service, Center for Oceanographic and Operational Products and Services, Silver Spring, MD 20910, USA; [email protected] * Correspondence: [email protected] (C.H.B.); [email protected] (V.H.K.) Received: 11 July 2020; Accepted: 13 August 2020; Published: 31 August 2020 Abstract: Following the 2010 Deepwater Horizon accident of a massive blow-out in the Gulf of Mexico, scientists from government, industry, and academia collaborated to advance oil spill modeling and share best practices in model algorithms, parameterizations, and application protocols. This synergy was greatly enhanced by research funded under the Gulf of Mexico Research Initiative (GoMRI), a 10-year enterprise that allowed unprecedented collection of observations and data products, novel experiments, and international collaborations that focused on the Gulf of Mexico, but resulted in the generation of scientific findings and tools of broader value. Operational oil spill modeling greatly benefited from research during the GoMRI decade. This paper provides a comprehensive synthesis of the related scientific advances, remaining challenges, and future outlook. Two main modeling components are discussed: Ocean circulation and oil spill models, to provide J. Mar. Sci. Eng. 2020, 8, 668; doi:10.3390/jmse8090668 www.mdpi.com/journal/jmse J. Mar. Sci. Eng. 2020, 8, 668 2 of 55 details on all attributes that contribute to the success and limitations of the integrated oil spill forecasts. These forecasts are discussed in tandem with uncertainty factors and methods to mitigate them. The paper focuses on operational aspects of oil spill modeling and forecasting, including examples of international operational center practices, observational needs, communication protocols, and promising new methodologies. Keywords: oil spill; ocean modeling; oil spill response; operational modeling 1. Introduction The Deepwater Horizon (DwH) accident involved the Macondo well (MC 252) blow-out in April 22, 2010 and resulted in a massive oil spill (the largest accidental spill in the world), with about 4.4 millions barrels (0.7 million m3, almost 600,000 tons) released in the Gulf of Mexico (Crone and Tolstoy [1]). In the decade following this incident, many scientific advances have been made in support of operational oil spill modeling, a crucial component of response. This paper provides a synthesis of these advances, focusing on the Gulf of Mexico, but in the context of international best practices. For oceanographic research and development in the Gulf of Mexico, the post-DwH decade has been greatly influenced by the “Gulf of Mexico Research Initiative” (GoMRI1) that funded a broad range of projects under its mission to “improve society’s ability to understand, respond to, and mitigate the impacts of petroleum pollution and related stressors of the marine and coastal ecosystems.” This paper (a) reviews the state of the art of operational modeling before GoMRI; (b) establishes the advances already made and/or now achievable as a result of GoMRI research; and (c) identifies desirable future developments, the opportunities for achieving them, and remaining gaps in the knowledge and technology required. Operational oil spill modeling is based on predictive numerical models for oceanic circulation, winds, and waves, as well as oil fate and transport, suitably executed to represent the prevailing environmental conditions and oil chemistry characteristics and to obtain forecasts to guide response activities, focusing on the short time period after an oil spill (hours to days). Numerical models use computer simulations to solve complex equations that describe the phenomena of interest and have the advantage that they can be employed for past (hindcast), current (nowcast), and future (forecast) applications. Forecasting ability is essential in case of a spill, providing crucial information for response operations. Oil spill models are used in multiple ways: To support response decision-making, in planning for spill response, in environmental impact analysis of oil industry infrastructure, and in the assessment of injury to the environment (and economy) after a spill has occurred. The main goal of this paper is to present a comprehensive synthesis of modeling used to support response, focusing on the mechanisms that act on short time scales relevant to operational oil spill modeling. When applied to computer modeling, the word “operational” can mean multiple things. One definition, sometimes called big “O” Operational (for the capitalization of the word), is used for systems that have met well-defined standards for accuracy, reliability, and availability, so that users can count on them to meet their needs and be available when needed. An example of this is the U.S. National Oceanic and Atmospheric Administration (NOAA) Center for Operational Oceanographic Products and Services (CO-OPS) Operational Forecast Systems2, which operates at many U.S. ports. These systems meet well-specified criteria for accuracy, and are implemented on computer systems 1 https://gulfresearchinitiative.org/ 2 https://tidesandcurrents.noaa.gov/models.html J. Mar. Sci. Eng. 2020, 8, 668 3 of 55 with backups and contingency plans so that mariners can count on their results to make critical navigation decisions. Another way the term operational (with a lowercase “o”) is used is to describe systems that are J. Mar. Sci. Eng. 2020, 8, x FOR PEER REVIEW 3 of 54 operating continuously, providing results on a regular basis. Many such systems are operated by academicAnother institutions, way the and term may operational not meet (with the robustness a lowercase and “o”) accuracy is used is criteria to describe defined systems for Operational that are systems,operating particularly continuously, as they providing do not results have fullon a 24regu/7 supportlar basis. andMany reliability. such systems An exampleare operated of this by in theacademic U.S. is theinstitutions, Texas General and may Land not meet Offi cethe (TGLO) robustness Texas and Automatedaccuracy criteria Buoy defined System for (TABS) Operational model, operatedsystems, by particularly Texas A&M as they University. do not have Such full systems 24/7 support are often and called reliability. “real-time” An example to distinguish of this in the from Operational.U.S. is the AsTexas (thankfully) General thereLand areOffice not enough(TGLO) majorTexas oilAutomated spills tojustify Buoy fullySystem Operational (TABS) model, systems specificallyoperated by to Texas support A&M oil spillUniversity. modeling, Such mostsystems oil spillare often modeling called systems“real-time” are to designed distinguish to usefrom any informationOperational. available As (thankfully) at the time there and are placenot enough of the ma spilljor (preferablyoil spills to justify in real-time), fully Operational whether orsystems not it is strictlyspecifically Operational. to support oil spill modeling, most oil spill modeling systems are designed to use any informationWhen an oilavailable spill occurs, at the thetime
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