Remote sensing-supported generation of surface descriptors for a highly detailed urban climate model

Abstract

Urban climate models become increasingly important for academic research and sustainable urban planning. The turbulence-resolving micro climate model PALM-4U is currently being developed to better meet the high demands of urban planning related to the spatial detail of modeling scenarios. The study presented here, shows which data sources and approaches can be used to supply models like PALM-4U with the required highly detailed urban surface layers. It is thereby jointly made use of remote sensing, municipal and open data to generate surface layers at 1m spatial resolution. The surface layers describe buildings, vegetation, terrain, water, pavement, streets and bridges. The heterogeneous data sources and their differing quality and standards require the utilization of various remote rensing and GIS techniques. In this study it is demonstrated that the surface layers can successfully be generated for urban climate simulations. To ensure easy transferability, it should be considered to use thematically and/or coarser but more standardized data, after a sensitivity analysis of PALM-4U.