Hazards SEES: Advanced Lagrangian Methods for Prediction, Mitigation and Response to Environmental Flow Hazards

当有害物质通过空气和水的自然过程释放并扩散到环境中时，就会发生环境流灾害。最近的灾难性例子包括：深水地平线灾难期间石油的扩散，艾雅菲亚德拉火山的火山灰云穿过商业空域，以及福岛反应堆灾难的放射性废物的痕迹。这些类型的危害是常见的，许多对社会有深远的影响。当有害物质释放时，准确预测物质可能流向的地方可以大大改善应急反应，并大大减少负面后果。防备和有效应对可以挽救许多生命，避免造成无法估量的环境破坏，并造成巨大的经济损失。然而，预测材料在复杂环境流动中的去向仍然是一项艰巨的科学挑战。该项目旨在通过利用和推进四维（3D+时间）拉格朗日方法的最新基础突破，改变科学的环境流量预测能力。本研究将整合理论、计算和观测方法，开发和利用尖端的拉格朗日方法和数据驱动建模，以揭示、量化和预测海洋和大气中区域流动灾害期间的关键运输过程和结构。本项目将(i)开发和推进四维（3D+时间）拉格朗日相干结构（LCS）的数学方法，以阐明非定常拉格朗日流输运；（ii）在历史数据集上测试和开发LCS方法；（iii）制作高效、准确、分布式和基于网络的软件，以支持LCS分析和可视化；（iv）将LCS方法纳入数值模型和非高斯数据同化；进行实地测试和概念验证、海洋-大气耦合实地实验；（vi）应对项目期间的机会风险。

Environmental flow disasters occur when hazardous material is released and dispersed into the environment by the natural processes of air and water. Recent catastrophic examples include: the spread of oil during the Deep Water Horizon disaster, the passage of the ash cloud from the Eyjafjallajokull volcano through commercial air space, and the trail of radioactive waste from the Fukushima reactor disaster. These types of hazards are common and many have profound impacts on society. When hazardous material is released, accurate predictions of where the material is likely to go can greatly improve emergency response and significantly reduce negative consequences. Preparedness and effective response can save many lives, untold environmental damage and enormous financial cost. However, predicting where materials go in complex environmental flows remains a formidable scientific challenge.This project intends to transform science's environmental flow predictive capabilities by exploiting and advancing recent fundamental breakthroughs in four-dimensional (3D+time) Lagrangian methods. This research will integrate theoretical, computational, and observational approaches to develop and utilize cutting-edge Lagrangian methods with data driven modeling for the purpose of uncovering, quantifying and predicting key transport processes and structures during regional flow-based hazards in the ocean and atmosphere. This project will (i) exploit and advance mathematical methods for four-dimensional (3D+time) Lagrangian Coherent Structures (LCS) in order to elucidate unsteady Lagrangian flow transport; (ii) test and develop LCS methods on historical data sets; (iii) produce efficient, accurate, distributed and web-based software to support LCS analysis and visualization; (iv) integrate LCS methodology into numerical models and non-Gaussian data assimilation; (v) perform field testing and a proof-of-concept, coupled ocean-atmosphere field experiment; and (vi) respond to a hazard of opportunity during the tenure of the project.