Probabilistic framework for the calibration of mean wind profiles in urban areas

Due to the increasing complexity of urban morphology, there is a growing interest for a reliable calibration of vertical wind profiles for the assessment of wind loading on structures. It is found that use of anemometric methods for the assessment of roughness parameters provides reasonably reliable results. However, there are issues related to such methods inducing uncertainty in the calibrated parameters. In this field, the paper proposes a probabilistic framework as an alternative to the classic deterministic anemometric methods for the assessment of wind profile parameters. Based on the Bayesian approach, the framework also allows quantification of uncertainty in the parameters and its propagation to the wind loading on structures. The proposed framework is first applied to a database of pseudo-experimental wind profiles to show its use and assess its robustness, as well as to highlight its advantages with respect to the deterministic approach. Then, it is applied to field measurements in a complex urban environment obtained with a LiDAR wind profiler to show its practical application. It is found that the proposed framework is able to estimate reliable roughness parameters, with a bias mainly depending on the minimum measurement height and uncertainty decreasing with increasing sample size. The use of field measurements is shown to introduce further uncertainty in calibrated parameters.