Evaluating the Performance of a Novel WUDAPT Averaging Technique to Define Urban Morphology with Mesoscale Models

The definition of accurate input datasets, appropriately representing urban morphology characteristics, has been identified as a crucial point for improving the simulation of urban boundary layer (UBL) dynamics by means of mesoscale numerical weather prediction (NWP) models. However, the scarcity of suitable data to adequately describe urban morphology is in many cases a significant obstacle to overcome. For this purpose, the World Urban Database Portal Tool (WUDAPT) framework was developed in order to obtain a standard classification of urban morphology, even in the absence of ad-hoc data for any city. In the present work, a modified WUDAPT method to define urban morphology is proposed and compared with two state-of-the-art methods, i.e. the standard WUDAPT method and a urban morphology parameters obtained from LIDAR data. In particular, here morphological features of Local Climate Zones (LCZs), resulting from a 30-m resolution WUDAPT classification, are interpolated to NWP cells, providing averaged features of the urban morphology. In this way, the method produces a unique value of the different urban morphology parameters for each model cell. This technique is tested by means of simulations with the Weather Research and Forecasting (WRF) model at 500 m resolution for the city of Bologna (Italy), located in the Po Plain. Simulation output is compared with measurements from weather stations. Results show that simulations using the modified WUDAPT method reproduce better atmospheric dynamics with respect to those implementing the standard WUDAPT method, and are comparable with simulations using the urban morphology defined with LIDAR data, considered as reference. In particular, the most relevant improvements are found in areas where LCZs are more heterogeneous and where the boundary between rural and urban areas is not clearly defined. It follows that the use of this method improves the classic WUDAPT method for small and scattered cities with respect to large and clustered urban areas, and can be particularly useful for urban areas where detailed LIDAR data are not available.