Modeling daily urban temperature fields using land use regression approaches

Detailed knowledge about the intra-urban air temperature variability within a city is crucial for the implementation of adaptation strategies to counteract the negative effects of urban heat stress. Various methods to model urban-rural temperature differences exist, but they often only cover certain periods (heatwave, hot day) or meteorological conditions (sunny and calm) due to computational limitations or limited data availability. Land use regressions, which are usually based on fine scaled measurements and high resolution spatiotemporal data, are one promising method to overcome those limitations and to conduct daily urban temperature fields.

In the city of Bern, Switzerland, a very dense urban temperature network (about 1 station per 1.5 km²) is operated since summer 2018. With that detailed information on temperature and publicly available land use and meteorological data, different land use regression types with a differing degree of complexity were tested in the recent past. One main outcome of the application of the method in Bern is an urban temperature dataset that covers the temperature distribution of all nights of the metropolitan area of the summers 2007 to 2022 with a resolution of 50 meters. In this talk, we would like to present the different models applied in Bern, analyze the potential of land use regression approaches in urban climate studies, and discuss possible applications of the dataset regarding urban planning and heat stress studies.