Advanced new methods for multi-scale free surface regional ocean modelling with adjoint data assimilation

在英国，人口增长和工业化的综合影响是这样的，沿海土地地区越来越多地被具有不同和竞争需求的多个用户群体所占据（例如，环境，旅游，工业）。气候变化的一个重要方面是风暴的可能性增加，因此风暴潮和洪水，这将对低洼地区产生明显的影响。因此，我们越来越需要提高预测洪水的能力（特别是在大范围的空间尺度上——从几米到几公里）。改进的建模能力将为参与海洋、气候变化和减少风险战略的决策者、救援服务机构和科学家提供信息。数据同化技术在弥补海洋信息的缺乏方面非常有价值。观测到的数据被吸收到模型中，以产生对海洋状况的准确估计（在某种最佳意义上）。然而，有效的数据同化方法（例如，变分数据同化）的应用受到两个主要困难的阻碍：通常需要复杂的代码（实现和可维护性）；以及高昂的计算成本。为了解决这些问题，建议的工作将通过以下方式改进现有模型：1)新开发的数据同化公式，以显着降低代码复杂性并提高可维护性；2)一种新的高度稳定和精确的干湿方法，能够解决多尺度物理问题，并专门为下一代海洋模式设计；3)模型缩减，将大尺度模型缩减到几百个未知数，从而得到的模型比原始模型快几个数量级。我们的总体目标是准确预测沿海地区以自由地表为主的水流。预测将通过在我们先进的自适应网格海洋模型中开发变分数据同化（在这里解决的时间相关问题的内容中）框架来实现。该框架将能够量化模式不确定性的影响，进行敏感性分析，并捕捉突然变化的场，如在自由地表主导的区域流动中的湿锋和干锋。这将为具有多尺度自适应有限元网格特征和预测能力的开源、社区、下一代区域海洋模型铺平道路。总体成果将是一个能够解决从海洋到河口（和更小范围）的自由地表主导流的模型。最近开发的技术组合是解决这些苛刻的多尺度流动的唯一可行方法。拟议的研究预计也将对气象学和海洋学变分资料同化的业务实施的未来发展产生重大影响。计算工作量和内存需求的减少将使数据同化成为一种更实惠的研究和操作工具。整个社会将受益于这项研究，通过改进对多尺度沿海流动的预测，特别是对风暴洪水的预测。特别是，政府、监管机构和利益攸关方、公司/行业、气象学、海洋学界和研究所将受益于可用于自然灾害、污染和快速应急的预测和影响评估的新技术。

The combined effect of population growth and industrialisation in the UK is such that coastal land areas are increasingly occupied by multiple user groups with diverse and competing needs (e.g. environmental, tourism, industrial). An important aspect of climate change is the increased likelihood of storms, and hence storm-surges and flooding, and this will have obvious impact upon low lying areas. There is thus an increased need to improve our capacity to predicti (especially over a wide range of spatial scales - a few meters to many kilometres) flooding. Improved modelling ability will inform policy makers, rescue services and scientists involved with ocean, climate change and risk reduction strategies. Data assimilation techniques are extremely valuable in compensating for lack of information about our oceans. Observed data is assimilated into models to produce an accurate estimate (in some optimal sense) of the state of the ocean. However, applications of efficient data assimilation approaches (e.g., variational data assimilation) are hampered by two major difficulties: the often complex code (implementation and maintainability) required; and the high computational costs. To address these issues, the proposed work will improve the existing models by using: 1) a newly developed data assimilation formulation to dramatically reduce the code complexity and increase maintainability; 2) a new highly stable and accurate wetting and drying method capable of resolving multi-scale physics and uniquely designed for use with a next generation ocean model; 3) model reduction in which large-scale models are reduced down to a few hundred unknowns so that the resulting models are orders of magnitude faster than the original model. Our overall aim is the accurate prediction of free surface dominated flows in coastal regions. Prediction will be achieved by developing a variational data assimilation (in the content of the time dependent problems solved here) framework within our advanced adaptive mesh ocean model. This framework will be capable of quantifying the effect of model uncertainties, performing sensitivity analysis, and capturing abruptly changing fields such as wetting and drying fronts in free surface dominated regional flows. This will pave the way towards an open source, community, next generation regional ocean model with multi-scale adaptive finite element meshing features and predictive capability. The overall deliverable will be a model capable of resolving free surface dominated flows from ocean to estuary (and smaller scale) scale. The proposed combination of recently developed techniques is the only feasible way of resolving these demanding multi-scale flows. The proposed research is also expected to have a substantial impact on the future development of operational implementation of variational data assimilation in both meteorology and oceanography. The reduction in computational effort and memory requirements will render data assimilation a more affordable research and operational tool. Society as a whole will benefit from this research through improved prediction of multi-scale coastal flows, especially the prediction of storm flooding. In particular, government, regulatory bodies and stakeholder, companies/industries, meteorology, oceanography communities and institutes would benefit from the new technologies that could be used for prediction and impact assessment of natural disasters, pollution and rapid emergency response.