Workshop on the future of coastal and estuarine modeling; June 2018; North Carolina State University

准确的数值模型对于增进我们对沿海和河口尺度的基本物理过程的了解至关重要，在这些尺度上，人口和海洋之间的联系最为明显，特别是在世界各地迅速扩张的沿海特大城市附近。由于计算能力的提高和包括非结构网格/模式嵌套、数据同化和模式耦合在内的改进的数值方法，模式在过去20年中取得了重大进展。此外，遥感和现场观测技术的进步带来了更大和更准确的数据集，大大提高了评估模型性能和量化不确定性的能力。然而，尽管最近取得了一些进展，但在过去二十年里发展了许多沿海和河口模式，而且区域模式缺乏统一的办法。这次讲习班将使模型使用者和开发人员汇聚一堂，讨论如何改进沿海和河口尺度物理海洋学科学研究的建模工具。该预算将为美国职业生涯早期的专业人士提供旅行和参与支持，并将重点吸引代表不足的少数族裔和妇女参加研讨会。早期职业科学家将在一个注重讨论和合作的环境中与有经验的模型开发人员和用户互动，从而帮助促进一个更强大的社区，继续提高海岸和河口模型的准确性和效率。这次研讨会将侧重于发展对当前最先进的海岸和河口过程数值模拟的优势和局限性的理解，并就提高此类模型的准确性和可靠性所需的关键研究和开发领域形成共识。研讨会的成果将包括对不同方法的利弊形成统一的认识，如有限体积方法与有限元方法、曲线网格与非结构网格、海洋、大气、陆地(水文)模型的耦合、大涡模拟与RANS湍流建模，以及不同模型嵌套和自适应网格策略。讨论将集中在这些方法以及强制、实施边界条件、验证、测试和不确定性量化的战略上，并有可能开发一个统一的模型基准框架。研讨会的主要主题将是统一建模方法，这些方法是理解模型用户和开发人员都感兴趣的新兴科学主题所需的。讲习班的第二个重点将是评估社区如何能够更好地利用现有和未来的计算技术来改进模型性能，以便更好地解决河口和沿海海洋的多尺度动力学问题。不仅迫切需要制定高性能的计算战略，以改进单个模式的性能，而且迫切需要实施集合方法，并开发数据同化和不确定性量化的方法。

Accurate numerical models are essential for improving our understanding of fundamental physical processes at coastal and estuarine scales, where the connection between human populations and the ocean are most apparent, particularly near rapidly expanding coastal megacities around the world. Models have seen significant advances in the past two decades owing to increases in computational power and improved numerical methods including unstructured grids/model nesting, data assimilation, and model coupling. Furthermore, advances in remote-sensing and in-situ observational technologies have led to substantially larger and more accurate datasets which have significantly improved the ability to assess model performance and quantify uncertainty. However, despite recent advances, a multitude of coastal and estuarine models have been developed in the past two decades, and there has been a lack of a unified approach as seen in regional modeling. This workshop will bring together model users and developers to discuss ways to improve modeling tools for scientific research in physical oceanography at coastal and estuarine scales. The budget will provide travel and participation support to U.S.-based early-career professionals, and there will be an emphasis on attracting underrepresented minorities and women to attend the workshop. Early-career scientists will interact with experienced model developers and users in an environment that focuses on discussion and collaboration, thus helping to promote a stronger community that will continue to improve the accuracy and efficiency of coastal and estuarine models.This workshop will focus on developing an understanding of the advantages and limitations of the current state-of-the-art in numerical modeling of coastal and estuarine processes and to form a consensus on key areas of research and development needed to improve the accuracy and reliability of such models. Outcomes of the workshop will include the development of a unified understanding of the pros and cons of different approaches, such as finite-volume vs. finite-element methods, curvilinear vs. unstructured grids, couplings of ocean, atmosphere, land (hydrology) models, large-eddy simulation vs. RANS turbulence modeling, as well as of different model nesting and adaptive-grid strategies. Discussions will focus on these methods along with strategies for forcing, implementation of boundary conditions, validation, testing, and uncertainty quantification, with the potential for development of a unified framework for model benchmarking. The overarching theme of the workshop will be on the unification of modeling approaches that are needed to understand emerging science topics of interest to both model users and developers. A second focus of the workshop will be to assess how the community can better leverage existing and future computational technologies to improve model performance in better resolving multiscale dynamics in estuaries and the coastal ocean. Not only is there a pressing need for the development of high performance computational strategies for improvement of individual model performance, but also for the implementation of ensemble approaches and the development of methods for data assimilation and uncertainty quantification.