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

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.

This project delivers a step-change improvement in the ability of models to predict flooding and pollution dispersal in coastal environments. This work will impact upon the following: (1) Consulting companies: with an interest in using the new technologies to support decision making and prediction and impact assessment of natural disasters, pollution and development of rapid emergency response strategies. The new technologies could help inform the design of mitigation strategies for flooding events associated with storm surges and tsunamis (e.g. identifying the origin of the tsunami generation by assimilating tide gauge data into models). (2) Engineer: would benefit from availability of tools that could be used to optimise design in coastal engineering (e.g. flooding defence, coastal structures). (3) Observational data centres/companies: would benefit from the ability to assimilate data into models so as to optimise placement of monitoring/collecting stations (significantly reducing the cost and time) e.g. the maxima of the reduced order model basis functions indicates where data needs to be collected from. (4) Designers in companies/industries: would benefit from the optimisation control approach used in design optimisation, in for example, shapes of industrial devices, cars and aeroplanes. (5) Power companies: involved in tidal barrage and nuclear projects. (6) Environmental agency/industries: would benefit from improved predictability of modelling dispersal of pollutants and industrial effluent in shallow-water regimes, furthermore this work could be also used to assess strategies used to reduce pollutant emissions and toxic releases in urban environments. (7) Government security bodies: the reduced order approached proposed here could provide a tool in the design and implementation of effective emergency response actions in the case of toxic pollutant release (for example,in city centres and the London underground). (8) Government, regulatory bodies and stakeholder: (Met Office, DEFRA/EA) could use this technology to inform policy makers, rescue services and scientists involved with ocean, climate change, risk reduction strategies for the UK. The investigators of this proposal have considerable experience in knowledge exchange and commercial exploitation of their work. They have many past and on-going collaborations with industrial companies. This will help us exploit the project results, with an expected high national and international impact. Proudman Oceanographic Laboratory (POL) and HR Wallingford will provide the corresponding observed data and test/apply the Imperial College Ocean Model (in Mersey Estuary - POL, Severn and Teignmouth Estuaries - HR Wallingford). In addition, HR Wallingford will support a PhD student for 3 years to work as part of this programme; Istituto Ambiente Marinoe Costiero (IAMC, Italy) will consider the re-application to the Venice lagoon; SeaZone will provide high-resolution bathymetric data. Planned activities to ensure good engagement and communication with beneficiaries include: i) trainging events on the science of this proposal and the resulting open source model: organising workshops,seminars, Summer school and postgraduate courses; ii) conference presentations, publications, reports etc; iii) collaboration with universities and industries; and iv) Improved web based presence.