Local and global consequences of climate change (enhanced urban heat islands, worsening environmental conditions) affect most of the world's urban population, but only recently have cities been represented, albeit crudely, in weather forecast models. To manage and develop sustainable, resilient and healthy cities requires improved forecasting and observations that cross neighbourhood-influenced scales which the next generation weather forecast models need to resolve. ASSURE addresses the critical issue of which processes need to be parameterised, and which resolved, to capture urban heterogeneity in space and time. We will advance understanding to develop new approaches and parameterisations for larger-scale urban meteorological and dispersion models by combining the results of field observations, high-resolution numerical simulations and wind tunnel experiments. Field work and modelling will focus on Bristol, as its physical geography provides suitably high levels of complexity and allows whole-city approaches. With mid-sized cities being large sources of greenhouse gases, and where large numbers of people live, it is critical agencies can provide predictions of weather and climate variability across cities of this scale as they need this information to manage and provide their services. ASSURE will include idealised simulations and theoretical analyses to ensure generic applicability. The ASSURE objectives are: * To understand how sources of urban heterogeneity (physical setting, layout of buildings and neighbourhoods, human activities) combine to influence the urban atmosphere in space and time. * To quantify effects of urban heterogeneity at different scales (street to neighbourhood, to city and beyond) on flow, temperature, moisture and air quality controlling processes and to determine how these processes interact. * To develop a theoretical framework that captures key processes and feedbacks with reduced complexity to aid mesoscale and larger model parameterisations. * To inform the development priorities of current weather and climate models that have meso-scale capabilities and are used in decision-making processes (e.g. integrated urban services). The ASSURE high-fidelity simulations and carefully designed experiments will allow us to explore implications of urban heterogeneity in isolated and combined configurations; interpret and integrate field observations (e.g. 3D meteorological and city-scale tracer dispersion experiments); integrate different approaches to understand the magnitude, source, and geographical extent of uncertainties in process models at different scales; synthesize the new knowledge to conduct theoretical analyses; develop algorithms reflecting this analysis. Novel in ASSURE are simulations resolving street to city-scale features that are linked to mesoscale models; field observations capturing vertical and horizontal variations in the urban boundary- and canopy-layers, including novel multi-source gas tracer experiments; and wind tunnel simulations across atmospheric stabilities and model resolution. New insights will be gained on the role of variations in the building morphology (or form), local topography, and human activities (e.g. waste heat, and AQ emissions). ASSURE will produce detailed datasets; in-depth understanding across the scale of atmospheric processes involved; high-fidelity multiscale urban modelling tools; theoretical models taking account of multiscale effects; improved assessment of current meso-scale model skill and the data used by practitioners to explore future urban scenarios as city form and function change. We will work with local and international organisations and companies to ensure the project benefits a broad range of society. They include: Avon Longitudinal Study of Parents and Children, CERC, COWI, ECMWF, Met Office, Delft University of Technology, Stanford University, University Hannover, RWDI, Surrey Sensors and UKCRIC.

The purpose of this project is to develop techniques to reduce the amount of time personnel need to spend on the ground examining tunnel structures and hence reducing exposure to health & safety risks and reducing costs while enhancing asset knowledge of rail staff. This will lead to less disruption to rail passengers by reducing disruptive possessions required for examinations. The project will develop a mobile mapping solution using a combination of technologies. It will build on the road-rail tunnel inspection vehicle developed by Railview as a subcontractor within the IN2TRACK2 project of SHIFT2RAIL and co-financed by Network Rail. This test vehicle builds on the previous Innovate UK INFRAMONIT project. The INFRAMONIT-TUNNEL test vehicle developed incorporates the next generation of Infrastructure Inspection Radar that will scan the tunnel lining and invert to build a 3D visualisation of the asset condition and this will be dedicated to this new project. This technology will be combined with mobile mapping laser scanners and associated positioning equipment to acquire accurate and precise geo-located spatial data as a point cloud representing the surface of the tunnel. The mobile mapping sensor platform will not only capture point cloud data but also 3600 imaging data. This multi-platform approach builds in redundancy in the data and improves survey reliability. The data collected will be processed to build an accurate three-dimensional model which can be viewed and manipulated by engineers. This is a development of current state of the art tools which have been used by COWI for virtual inspections of bridges. A key part of this proposal is to develop the use of machine learning algorithms to process the survey data to automatically detect anomalies and then further, to categories the defects in accordance with the Tunnel Condition Monitoring Index (TCMI). By adding intelligence to defects and using state of the art survey equipment, data from future surveys can be compared with the baseline, quickly identifying changes since the previous survey. A combination of semi-automated defect detection and manual virtual inspections will be used to allocate the TCMI codes to defects and generate a report in the required format. The scanning and modelling process will result in much richer background data than that contained in the standard inspection report, which can then be used as part of the ongoing asset management process, including specification of remedial measures. The final stage of the project will be to undertake an Operational Environment Demonstration and evaluate the results in conjunction with Network Rail's asset managers. The key innovations of this proposal are to develop a new work flow for tunnel examination including: application of subsurface inspection radar technologies to the tunnel environment; improvements in positional accuracy; machine learning to automate the identification of certain defects; and production of a data-rich three-dimensional model of the tunnel to facilitate ongoing asset management and maintenance activities.