the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 28 Feb 2025
SanDyPALM v1.0: Static and Dynamic Drivers for the PALM-4U Model to Facilitate Realistic 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-4U 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.
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Status: open (until 25 Apr 2025)
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RC1: 'Comment on egusphere-2025-144', Anonymous Referee #1, 18 Mar 2025
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The manuscript by Vogel et al. presented SanDyPALM, a new preprocessing tool for the Large Eddy Simulation (LES) model PALM. SanDyPALM was designed to streamline the creation of static and dynamic input for PALM. The existing tools like PALM CSD, GEO4PALM, WRF4PALM, etc., only cover either the static input or the dynamic input. SanDyPALM adopted several good aspects of the previous tools, combines the preprocessing of static and dynamic input, and adds several novel features. These features include interfaces for OpenStreetMap and local climate zone (LCZ) datasets. The WRF-PALM dynamic driver incorporates a roughness-corrected Monin-Obukhov surface layer representation as in Vogel et al. (2022). The authors generated static drivers using four sets of data and compared the simulation results to observations. The authors also provided detailed tutorials in their code. The manuscript is well-written, and the PALM community always welcome such a tool that simplifies and improves the preprocessing procedure.
At the end of the manuscript, the authors tried to answer this question, “which dataset most accurately represents the selected district, and how can we further improve data quality?”, but the answer given in the manuscript is that “Different data sources for the same test case can yield varying results, yet the outputs do not diverge excessively.” While the authors showed comparison in time series and averaged fields, the advantage of using an LES model emphasises more on the turbulence-resolving parts. I understand that this is a tool development paper, so more detailed validation or presentation of model results is not required. Can the authors comment more on how the simulated turbulent flow and vertical structure of the boundary layer (e.g. boundary layer height) might be impacted by different static inputs?
Specific comments:
- Figure 1, I cannot locate the TUB tower and the DWD station in the figure. Please mark them with more visible markers.
- L197: “One caveat of OSM data is that it can be quite rich in data outside of cities”. Why rich data can be a caveat? Do you mean there is an imbalance between rural and urban areas?
- LCZ4PALM, the authors developed this novel feature, but the motivation for this is not clear. The authors mentioned that PALM-4U has gap-filling issues in the paragraph in L646-L655, but the LCZ static driver looks the least realistic among the four datasets. (Figures 11-16)
- L298-299: “There are separate functions that generate the PALM-4U grid and transfer it into a geographic projection”. Please specify.
- From the user-friendly perspective, please describe the file/folder structure of SanDyPALM. This was not in the manuscript and/or the documentation.
- L324: Please add a link or proper citation for the PALM input data standard (PIDS)
- Dynamic driver generation: does this require a static driver generated by SanDyPALM? Or it is compatible with any static driver provided by the user? Please clarify.
- Section 3.1, the authors only mentioned figures in the first paragraph and did not refer to the figures later in the text. For example, somewhere in L517-L520 should refer to Figure 10. Same as in Section 3.2
- L658: “There is a clear increase in temperature from MOSAIK to Custom to OSM, ...” Why?
Citation: https://doi.org/10.5194/egusphere-2025-144-RC1
Model code and software
SanDyPALM code repository Julian Vogel and Sebastian Stadler https://gitlab.cc-asp.fraunhofer.de/upm/sandy-palm
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