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

英国人口增长和工业化的综合影响使得沿海土地越来越多地被具有不同和相互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发生的可能性增加，从而导致风暴潮和洪水，这将对低洼地区产生明显影响。因此，越来越需要提高我们预测洪水的能力（特别是在广泛的空间尺度上-几米到许多公里）。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极为宝贵。观测数据被同化到模型中，以产生对海洋状态的准确估计（在某种最佳意义上）。然而，有效的数据同化方法的应用（例如，变分数据同化）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）;以及高计算成本。为了解决这些问题，拟议的工作将通过以下方式改进现有模式：1）新开发的数据同化公式，以大大降低代码复杂性并增加可维护性; 2）新的高度稳定和准确的润湿和干燥方法，能够解决多尺度物理问题，并为下一代海洋模式而独特设计; 3）模型简化，其中大规模模型被减少到几百个未知数，使得所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自由面主导的流动。预测将通过在我们先进的自适应网格海洋模式中开发变分数据同化（在这里解决的时间相关问题的内容）框架来实现。该框架将能够量化模型不确定性的影响，进行敏感性分析，并捕捉突然变化的领域，如湿润和干燥的自由表面占主导地位的区域流。这将为开源、社区、下一代区域海洋模型铺平道路，该模型具有多尺度自适应有限元网格划分功能和预测能力。总体交付将是一个能够解决从海洋到河口（和较小尺度）尺度的自由表面主导流动的模型。最近开发的技术的建议组合是解决这些要求多尺度流的唯一可行的方法。拟议中的研究预计也将产生重大影响的未来发展的业务实施变分数据同化在气象学和海洋学。计算工作量和内存需求的减少将使数据同化成为一种更负担得起的研究和业务工具。整个社会将受益于这项研究，通过改进预测的多尺度海岸流，特别是风暴洪水的预测。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.