Rainfall spatial predictions: a two-part model and its assessment

Spatial prediction of hourly rainfall via radar calibration is addressed. The change of support problem (COSP), arising when the spatial supports of different data sources do not coincide, is faced in a non-Gaussian setting; in fact, hourly rainfall in Emilia-Romagna region, in Italy, is characterized by abundance of zero values and right-skeweness of the distribution of positive amounts. Rain gauge direct measurements on sparsely distributed locations and hourly cumulated radar grids are provided by the ARPA-SIMC Emilia-Romagna. We propose a three-stage Bayesian hierarchical model for radar calibration, exploiting rain gauges as reference measure. Rain probability and amounts are modeled via linear relationships with radar in the log scale; spatial correlated Gaussian effects capture the residual information. We employ a probit link for rainfall probability and Gamma distribution for rainfall positive amounts; the two steps are joined via a two-part semicontinuous model. Three model specifications differently addressing COSP are presented; in particular, a stochastic weighting of all radar pixels, driven by a latent Gaussian process defined on the grid, is employed. Estimation is performed via MCMC procedures implemented in C, linked to R software. Communication and evaluation of probabilistic, point and interval predictions is investigated. A non-randomized PIT histogram is proposed for correctly assessing calibration and coverage of two-part semicontinuous models. Predictions obtained with the different model specifications are evaluated via graphical tools (Reliability Plot, Sharpness Histogram, PIT Histogram, Brier Score Plot and Quantile Decomposition Plot), proper scoring rules (Brier Score, Continuous Rank Probability Score) and consistent scoring functions (Root Mean Square Error and Mean Absolute Error addressing the predictive mean and median, respectively). Calibration is reached and the inclusion of neighbouring information slightly improves predictions. All specifications outperform a benchmark model with incorrelated effects, confirming the relevance of spatial correlation for modeling rainfall probability and accumulation.

Wind loading predictions using computational fluid dynamics

Using Computational Wind Engineering, CWE, for solving wind-related problems is still a challenging task today, mainly due to the high computational cost required to obtain trustworthy simulations. In particular, the Large Eddy Simulation, LES, has been widely used for evaluating wind loads on buildings. The present thesis assesses the capability of LES as a design tool for wind loading predictions through three cases. The first case is using LES for simulating the wind field around a ground-mounted rectangular prism in Atmospheric Boundary Layer (ABL) flow. The numerical results are validated with experimental results for seven wind attack angles, giving a global understanding of the model performance. The case with the worst model behaviour is investigated, including the spatial distribution of the pressure coefficients and their discrepancies with respect to experimental results. The effects of some numerical parameters are investigated for this case to understand their effectiveness in modifying the obtained numerical results. The second case is using LES for investigating the wind effects on a real high-rise building, aiming at validating the performance of LES as a design tool in practical applications. The numerical results are validated with the experimental results in terms of the distribution of the pressure statistics and the global forces. The mesh sensitivity and the computational cost are discussed. The third case is using LES for studying the wind effects on the new large-span roof over the Bologna stadium. The dynamic responses are analyzed and design envelopes for the structure are obtained. Although it is a numerical simulation before the traditional wind tunnel tests, i.e. the validation of the numerical results are not performed, the preliminary evaluations can effectively inform later investigations and provide the final design processes with deeper confidence regarding the absence of potentially unexpected behaviours.