Modeling the conversion of a train station with PALM-4U: the effects of urban greening on the microclimate of a Hamburg neighborhood

Many cities around the world undergo constant changes that ultimately affect heat stress and related health aspects. Urban climate models like PALM-4U are suitable tools to simulate meteorological variables that ultimately determine the microscale heat stress. PALM-4U is an obstacle-resolving large-eddy simulation (LES) model that is specifically designed to calculate urban climate processes.
The analysis presented here involves two PALM-4U simulations at a horizontal grid spacing of a few meters over a neighborhood within Hamburg-Altona run over one day during the heatwave that struck the city of Hamburg in August 2020. The simulation domain is mostly made up of residential area, but further includes a major train station, train tracks, and several smaller green spaces.
In a first run, the current city structure is used to evaluate the performance of PALM-4U over the domain against in-situ observations to adequately model the heat stress under present-day heatwave conditions. Observation data from the Field Experiment on Submesoscale Spatio-Temporal variability at Hanseatic city of Hamburg (FESST@HH) includes three APOLLO (Autonomous cold POol LOgger) stations, sampling air temperature data with fast-response sensors at a temporal resolution of 1 second over the months June-August 2020.
The second run focuses on the effects of the transformation of the train station and its corresponding tracks into a large park and residential buildings on the urban microclimate in Hamburg-Altona. More precisely, it is investigated how the generation of large green spaces change the climatic conditions in the neighborhood in a way that favors the health of its dwellers. In doing so, we will present how the green space influences the temperature, heat stress, and air flow in the neighborhood adjacent to the Hamburg-Altona train station.