Allen Arthur

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Allen
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Arthur
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  • Article
    Search and rescue at sea aided by hidden flow structures
    (Nature Communications, 2020-05-26) Serra, Mattia ; Sathe, Pratik ; Rypina, Irina I. ; Kirincich, Anthony R. ; Ross, Shane D. ; Lermusiaux, Pierre F. J. ; Allen, Arthur ; Peacock, Thomas ; Haller, George
    Every year, hundreds of people die at sea because of vessel and airplane accidents. A key challenge in reducing the number of these fatalities is to make Search and Rescue (SAR) algorithms more efficient. Here, we address this challenge by uncovering hidden TRansient Attracting Profiles (TRAPs) in ocean-surface velocity data. Computable from a single velocity-field snapshot, TRAPs act as short-term attractors for all floating objects. In three different ocean field experiments, we show that TRAPs computed from measured as well as modeled velocities attract deployed drifters and manikins emulating people fallen in the water. TRAPs, which remain hidden to prior flow diagnostics, thus provide critical information for hazard responses, such as SAR and oil spill containment, and hence have the potential to save lives and limit environmental disasters.
  • Preprint
    Advancing coastal ocean modelling, analysis, and prediction for the US Integrated Ocean Observing System
    ( 2017-04-19) Wilkin, John L. ; Rosenfeld, Leslie K. ; Allen, Arthur ; Baltes, Rebecca ; Baptista, Antonio ; He, Ruoying ; Hogan, Patrick ; Kurapov, Alexander ; Mehra, Avichal ; Quintrell, Josie ; Schwab, David ; Signell, Richard P. ; Smith, Jane
    This paper outlines strategies that would advance coastal ocean modeling, analysis and prediction as a complement to the observing and data management activities of the coastal components of the U.S. Integrated Ocean Observing System (IOOS®) and the Global Ocean Observing System (GOOS). The views presented are the consensus of a group of U.S. based researchers with a cross-section of coastal oceanography and ocean modeling expertise and community representation drawn from Regional and U.S. Federal partners in IOOS. Priorities for research and development are suggested that would enhance the value of IOOS observations through model-based synthesis, deliver better model-based information products, and assist the design, evaluation and operation of the observing system itself. The proposed priorities are: model coupling, data assimilation, nearshore processes, cyberinfrastructure and model skill assessment, modeling for observing system design, evaluation and operation, ensemble prediction, and fast predictors. Approaches are suggested to accomplish substantial progress in a 3-8 year timeframe. In addition, the group proposes steps to promote collaboration between research and operations groups in Regional Associations, U.S. Federal Agencies, and the international ocean research community in general that would foster coordination on scientific and technical issues, and strengthen federal-academic partnerships benefiting IOOS stakeholders and end users.