SanDyPALM v1.0: static and dynamic drivers for the PALM model to facilitate urban microclimate simulations

Abstract. This study presents SanDyPALM, an innovative toolkit designed to streamline the generation of both static and dynamic input data for the PALM model, thereby facilitating urban microclimate simulations. SanDyPALM is capable of processing a diverse range of custom input data from raster and vector files, and it incorporates two novel methods – OSM2PALM and LCZ4PALM – that introduce the automated extraction of static input data from open data sources. To investigate the impact of static input data on simulation outcomes, we developed static drivers from four distinct data sources. Our analysis reveals not only variations in the generated static drivers but also differences in the simulation results. Importantly, all simulations correlate well with measurements from two different weather stations, underscoring the robustness of the overall modeling approach. However, we observed variations in temperature, humidity, and wind speed that are dependent on the static input data. Furthermore, our findings demonstrate that automated processing methods can yield results comparable to those achieved through expert-driven approaches, significantly simplifying workflows.