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Weather radar is a crucial tool for rainfall observation and forecasting, providing high-resolution estimates in both space and time. Despite this, radar rainfall estimates are subject to many error sources – including attenuation, ground clutter, beam blockage and drop-size distribution – with the true rainfall field unknown. A flexible stochastic model for simulating errors relating to the radar rainfall estimation process is implemented, inverting standard weather radar processing methods and imposing path-integrated attenuation effects, a stochastic drop-size-distribution field, and sampling and random errors. This can provide realistic weather radar images, of which we know the true rainfall field and the corrected "best-guess" rainfall field which would be obtained if they were observed in a real-world case. The structure of these errors is then investigated, with a focus on the frequency and behaviour of "rainfall shadows". Half of the simulated weather radar images have at least 3 % of their significant rainfall rates shadowed, and 25 % have at least 45 km2 containing rainfall shadows, resulting in underestimation of the potential impacts of flooding. A model framework for investigating the behaviour of errors relating to the radar rainfall estimation process is demonstrated, with the flexible and efficient tool performing well in generating realistic weather radar images visually for a large range of event types. [ABSTRACT FROM AUTHOR]

Flood risk in cities and built‐up areas is a major threat which is likely to grow due to increased urbanisation and climate change. It is a priority for urban planning, civil defence and insurance to accurately represent buildings and urban features in hydrodynamic models to assess flood risk to people, properties, assets and infrastructure in an uncertain future. The correct representation of urban features in models is currently blocked by the lack of detailed and accurate techniques and has become a priority for the improvement of urban flood modelling now that better data and computational resources are available. This study has reviewed the available approaches for the representation of buildings and urban features and implemented the widely used 'stubby building' approximation as well as a more realistic and innovative 'building hole' approach using the hydrodynamic model CityCAT. The city centre of Newcastle upon Tyne, UK, was used as a case study, allowing independent validation of the methods and direct, systematic comparison of performance. Shortcomings of the approximate method are described, and guidance given on limits to its reliable application and scope for improvement. [ABSTRACT FROM AUTHOR]

Weather radar is a crucial tool in rainfall estimation, providing high-resolution estimates in both space and time. Despite this, radar rainfall estimates are subject to many error sources – including attenuation, ground clutter, beam blockage and the drop-size distribution – with the true rainfall field unknown. A flexible stochastic model for simulating errors relating to the radar rainfall estimation process is implemented, inverting standard weather radar processing methods, imposing path-integrated attenuation effects, a stochastic drop-size distribution field, along with sampling and random errors. This can provide realistic weather radar images, of which we know the true rainfall field, and the corrected 'best guess' rainfall field which would be obtained if they were observed in the real-world case. The structure of these errors is then investigated, with a focus on frequency and behaviour of 'rainfall shadows'. Half of simulated weather radar images have at least 3 % of significant rainfall rates shadowed and 25 % had at least 45 km2 containing rainfall shadows, resulting in underestimation of potential impacts of flooding. A model framework for investigating the behaviour of errors relating to the radar rainfall estimation process is demonstrated, with the flexible and efficient tool performing well at generating realistic weather radar images visually, for a large range of event types. [ABSTRACT FROM AUTHOR]

Non-asymptotic (NA) probability distributions of block maxima (BM) have been proposed as an alternative to asymptotic distributions of BM derived by classic extreme value theory (EVT). Their advantage should be the inclusion of moderate quantiles as well as extremes in the inference procedures. This would increase the amount of used information and reduce the uncertainty characterizing the inference based on short samples of BM or peaks over high threshold. In this study, we show that NA distributions of BM suffer from two main drawbacks that make them of little usefulness for practical applications. Firstly, unlike classic EVT distributions, NA models of BM imply the preliminary definition of their conditional parent distributions, which explicitly appears in their expression. However, when such conditional parent distributions are known or estimated also the unconditional parent distribution is readily available, and the corresponding NA distribution of BM is no longer needed, as it is just an approximation of the upper tail of the parent. Secondly, when declustering procedures are used to remove autocorrelation characterizing hydro-climatic records, NA distributions of BM devised for independent data are strongly biased even if the original process exhibits low/moderate autocorrelation. On the other hand, NA distributions of BM accounting for autocorrelation are less biased but still of little practical usefulness. Such conclusions are supported by theoretical arguments, Monte Carlo simulations, and re-analysis of sea level data. [ABSTRACT FROM AUTHOR]