Heat stress and temperature in Hamburg-Altona simulated with PALM-4U

Heat stress and temperature in Hamburg-Altona simulated with PALM-4U Leo Loprieno, Peter Hoffmann, Sabine Fritz Climate Service Center Germany (GERICS) Helmholtz-Zentrum hereon GmbH Hamburg, Germany 1 Summary Within the frame of the BMFTR-funded project CoSynHealth, numerical simulations with the high-resolution urban climate model PALM-4U version 24.04 (Maronga et al.,2020) were performed. Simulation results are presented for 8. August 2020 at 4 pm CEST (UTC+2h), which marked a peak in the heatwave that struck central Europe in the summer 2020. Table 1shows the general model set-up. Horizontal extent 2048m x 2048m Horizontal grid spacing 4m Simulation period 24h Boundary conditions Cyclic Forcing Observations (Weather Mast Hamburg), CERRA, CERRA-Land, ERA5 at 2020-08-08 00:00 CEST Pressure solver scheme Iterative multigrid Radiation scheme Clear-sky Table 1: Simulation setup and settings implemented in PALM-4U v24.04. 2 Model domain The Free and Hanseatic City of Hamburg is a major city situated in northern Germany, whose metropolitan region counts more than 5 million inhabitants (Statistikamt Nord,2025). The urban area is characterized by various land surface types, ranging from densely built residential areas to green spaces. The modeling domain comprises parts of the city quarters Altona-Altstadt and Altona-Nord. Especially the eastern part of the domain includes various parks and other green spaces, whereas the northern and the western parts are characterized by a major train station and adjacent train tracks. 3 Model forcing 3.1 Static data The simulation was performed with the urban climate model PALM-4U. Static data are included in the static driver, through which the model retrieves the necessary information to run a simulation over urbanized structures as realistic as possible. The data include the terrain height, vegetation type, pavement type, leaf area density, and building height/type. Table 2gives an overview of the data used to generate the static driver. 1 Static datasets Parameter Dataset (version) Terrain height Digital Elevation Model Hamburg DGM 1 (2022)1 Vegetation Biotope Cadaster Hamburg (2020)2 Pavement Streetmap Hamburg (2022)3 Trees Street Tree Cadaster Hamburg (2021)4 Buildings 3D Building Model LoD2-DE Hamburg (2023)5 Table 2: Static datasets to construct the simulation domain. Private vegetation and tree data, which are not registered publicly, were estimated with the help of the NDVI and by subtracting the digital elevation model from the digital surface model, respectively. 3.2 Meteorological data For the lower model atmosphere (i.e. <300 m above surface), the runs were forced by data observed at the Hamburg Weather Mast (Lange,2014) at 2020-08-08 00:00 UTC+2. Table 3shows the variables used as forcing and the associated measurement heights. For the initialization of the upper atmosphere Weather Mast Hamburg Variable Measurement height [m above surface] Air temperature 10, 50, 110, 175, 250, 280 Relative humidity Uand V-wind components Air pressure 2 Table 3: Observed variables and respective measurement height above the surface. (i.e. >300 m) and the soil, reanalysis data was used. Atmospheric variables on the 500 m-level were taken from CERRA (Schimanke et al.,2021) and from ERA5 (Hersbach et al.,2023) from the other height levels, whereas soil parameters were retrieved from CERRA-Land (Verrelle et al.,2022). 1Digitales H¨ohenmodell Hamburg DGM 1; dl-de/by-2-0, Freie und Hansestadt Hamburg, Landesbetrieb Geoinformation und Vermessung (LGV); https://registry.gdi-de.org/id/de.hh/6D10BE89-636D-4359-8B27-4AB4DCA02F3A. 2Biotopkataster Hamburg; dl-de/by-2-0, Freie und Hansestadt Hamburg, Beh¨orde f¨ur Umwelt und Energie; https://registry.gdi-de.org/id/de.hh/895ee6fe-a10b-4ecd-8884-369600f02668. 3Feinkartierung Straße Hamburg; dl-de/by-2-0, Freie und Hansestadt Hamburg, Beh¨orde f¨ur Verkehr und Mobilit¨atswende; https://registry.gdi-de.org/id/de.hh/ad7e3cb6-9a9e-4044-81b1-4c1f8d974c2f. 4Straßenbaumkataster Hamburg; dl-de/by-2-0, Freie und Hansestadt Hamburg, Beh¨orde f¨ur Umwelt, Klima, Energie und Agrarwirtschaft; https://registry.gdi-de.org/id/de.hh/C1C61928-C602-4E37-AF31-2D23901E2540. 53D-Geb¨audemodell LoD2-DE Hamburg; dl-de/by-2-0, Freie und Hansestadt Hamburg, Landesbetrieb Geoinformation und Vermessung (LGV); https://registry.gdi-de.org/id/de.hh/948321ba-e9b2-4290-88c3-8dda2912defa. 2 Reanalysis Variable Height levels [m above surface] Air temperature 500, 1100, 2100, 3200 Relative humidity Uand V-wind components 500, 1100, 2000, 3200 Soil temperature -0.01, -0.02, -0.05, -0.1, -0.2, -0.4, -0.8, -2, -3 Soil moisture content -0.01, -0.02, -0.05, -0.1, -0.2, -0.4, -0.8, -2 Table 4: Reanalysis forcing data with respective height levels. For every variable, the gradient at each level was calculated and passed to the model. Moreover, some parameters had to be converted to make them recognizable by PALM-4U. In doing so, air temperature and relative humidity had to be converted to potential temperature and specific humidity, respectively. The interpolation of pressure levels onto height levels was performed by using the measured air pressure at the Hamburg Weather Mast as a reference. Acknowledgements The simulations were conducted within the framework of the CoSynHealth (Conflicts and synergies between carbon-neutral and healthy city scenarios) Junior Research Group, which is funded by the German Federal Ministry of Research, Technology and Space (BMFTR) (01LN2204A) and supported by the German Aerospace Center (DLR). The authors thank the German Climate computing Centre (DKRZ) for the necessary computational resources to run simulations on their supercomputer cluster Levante. 3 References Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Hor´anyi, A., Mu˜noz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., et al. (2023). Era5 hourly data on pressure levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), 10:24381. DOI: 10.24381/cds.bd0915c6 (Accessed on 29-04-2025). Lange, I. (2014). Windund Temperaturdaten vom Wettermast Hamburg des Meteorologischen Instituts der Universit¨at Hamburg f¨ur den Zeitraum 1995 bis 2023. Updated March 7, 2024. Maronga, B., Banzhaf, S., Burmeister, C., Esch, T., Forkel, R., Fr¨ohlich, D., Fuka, V., Gehrke, K. F., Geletiˇc, J., Giersch, S., et al. (2020). Overview of the palm model system 6.0. Geoscientific Model Development, 13(3):1335–1372. 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