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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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 -
RC2: 'Comment on egusphere-2025-144', Anonymous Referee #2, 28 Mar 2025
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Overview of the manuscript:
This manuscript introduces a novel and user-friendly toolkit, SanDyPALM, specially developed for preparing static and dynamic driver input data for the PALM model. In particular, OSM2PALM and LCZ4PALM are highlighted as two novel automated methods for static driver preparation. The authors presented the tutorials, workflow, and algorithms for their developed tools. The work presented includes four different static datasets with different origins and details provided in them, their comparisons, and the preparation of the input files for the PALM simulations. It compares generated static driver files in terms of their representation of the simulated urban environment. In addition, it evaluates the presented static-diver-differing ensemble of microscale outputs, i.e., meteorological variables, against the measuring stations.
This toolkit enhances and simplifies the preprocessing stage in the PALM modeling framework. Such tools are appreciated within the field of urban microclimate analysis in general, especially when high-resolution models such as PALM are employed, since large efforts are needed to collect, prepare, and process both dynamic and static input data.
Generally, the manuscript is structured nicely and includes a lot of details. Methodology is sound, and the evaluation provided can be considered trustworthy. The manuscript fits the GMD scope. Before final publication, I would suggest a careful review of the language and sentence structures. Finally, it would be appreciated if the authors would address the following comments before final publication.
Major comments:
- Connecting the research questions in the introduction with the conclusions. In particular, the conclusion does not provide a clear answer to the questions you posed in the Introduction. Especially questions 2) and 3) on L106-107. Please, try to connect the two (introduction and conclusions) and delve into more detail, if possible, for questions 2 and 3, and try to answer one by one in more detail, concerning your contribution with the presented input data toolkit.
In addition to this, the statement on the L720 in the conclusion is too general in terms of “refining these automated tools”. Refining in what way? Can you be more specific about this concerning your toolkit?
- At the beginning of the manuscript, you suggested UHI as a phenomenon enhancing the urban climate problems and evaluated a heat-wave episode as a validation episode. So, what influence do you expect on the UHI evaluation, for example, from microscale simulations with different static drivers in these cases? How much “trust” can we put into our evaluations of such phenomena, depending on the level of detail the input data provides in the case of your datasets? The accuracy of the static driver data has a major influence on the simulated urban climate processes (e.g., radiative processes and consequently the energy balances, etc.). I see that this manuscript is technical and of a development nature, and you provided the validation of the model outcomes to justify the tools. However, one of the PALM’s purposes and superiority over other available tools lies in its details and potential use for urban mitigation strategies and the overall microclimate assessment. Your statement on L124-126 indicates that the proposed toolkit contributes to more reliable urban climate simulations. I would elaborate on this in the discussion/conclusion section a bit more.
- I would suggest including a separate dedicated section addressing the current limitations of the toolkit and potentially comparing the toolkit with the other available sources in terms of their shortcomings and strong sides.
In addition, regarding limitations, can you elaborate on how compatible the static data file input from your toolkit is with dynamic drivers created by other tools? Can the static driver created by this toolkit only be used with the one created from the WRF toolkit you described? Can it be utilized and combined with different mesoscale models whose drivers are created by alternate tools for their utilization (e.g., COSMO)?Specific comments:
Manuscript title:
I would suggest removing or modifying the word “realistic” from the title since it can mislead in terms of overstating the accuracy. It is quite subjective, and the word itself needs to be taken with caution.
L1-21: At the beginning of the manuscript, the authors discuss the issues related to the urban climate. However, this is quite scarce and not anyhow connected to the rest of the introduction. I would suggest rewriting this part and incorporating it with the rest of the text. The impression I got while reading it is that the authors discuss the general urban climate modeling and jump into the PALM specifics.
L17: Please provide a reference for this statement.
L18-20: I suggest including adequate example studies incorporating/testing the mentioned mitigation/development strategies.
L23: Please provide a reference for this statement.
L24: The NS solved equations include the Boussinesq approximation as well, please be more precise in your text (though with an anelastic approximation available too if necessary).
L28-30: I would suggest including the chemistry module, as well as the BIO module, in your list of key components, especially since you discuss the urban microclimate and UHI’s impact at the beginning of the manuscript. These two play quite an important role when assessing urban microclimates in terms of chemistry and human thermal comfort. I have noticed that you mentioned the PCM module and utilized it in your simulations, so please consider including it in the list as well.
L48: Please provide a reference for this statement or rewrite the section. I would also strongly suggest going through the manuscript and filling in all the citation gaps.
L67: QGIS, explain this abbreviation.
L80-81: Please provide a reference for the WRF model.
L81: Provide the name of the developed tool.
L85: WRF model repetitive (see line 81). Provide one full explanation of the abbreviation and use only the abbreviation afterward.
Figure 1: Indicate stations clearly in the figure. Use an asterisk or some other symbol for stations. I would also suggest using colors to distinguish between the parent/child domains.
L162: OSM has already been introduced by the full name at L111, please go thoroughly through the manuscript and check the abbreviations not to repeat them (L186), this applies to other abbreviations, LAD, LAI, etc. Please go through the manuscript carefully and unify all. The same goes for L417, L437, and similar cases. This should all be fixed, and references should be placed there accordingly in cases where they are not provided.
L230-235: What do you consider to be a “coarse grid” in terms of resolution and resolving the urban features? In what aspect can that provide a reasonable (realistic) contribution to microclimate/urban climate studies? I have noticed this term “coarse grid” across the manuscript. Can you maybe add an explanation about the resolution assumed under this term throughout the text (in cases when you don't refer to the to the 20 m parent domain)?
L394: HPC, abbreviation without the full name.
L312-325: Even though this information is known, I would suggest citing either the PALM website or the PALM description paper from Maronga et al., 2020. This part of the text could also be condensed, and the link to the PIDS needs to be provided.
L441-453: I would suggest removing the explanation and keeping Table 3 with all the relevant information regarding the domain setup.
478: Add reference for the STG, please.
L722: Please check the ZENODO link once more, it does not lead directly to the repository. Also, regarding ZENODO, I would suggest adding a brief description on the ZENODO webpage about its contents.
Section 2.2.3: I would suggest rewriting and condensing information in this section concerning the quality of data OSM provides. On the one hand, you have L187-188, but on the other, L197-201.
L414-417: I would say that the statements you provided here about WRF schemes and resolutions cannot be considered universally true, and that it depends on the study case one is testing. In addition, using a “double” representation of the urbanized areas (in WRF’s more sophisticated schemes and PALM’s high-resolution data) can potentially affect the microscale outputs. Could the authors elaborate on this? Did you try researching this topic or testing sensitivities for different domain setups, cities, etc.?
Figures 8 and 9: Please consider moving them to the supplements or appendix sections.
L476: Provide reference for the plant canopy model (PCM).
Table 2: Please consider moving this to the supplements or appendix section.
Table 4 and Table 5: I would suggest moving the tables to the appendix or supplement about the statistical comparison of the static drivers. I would also suggest maybe condensing information about their differences (dominant types, “realness “, overestimating and underestimating certain urban morphological characteristics) into a table if possible.
L495: I would suggest including a comparison with other available studies (if any) in the discussion section in terms of the static driver data accuracy and selection, as well as its impact on the modeled outputs.
L580: Can you specify what you mean by “keeping the dynamic driver consistent”? Are dynamic drivers synchronized with static drivers while preparing them? Considering you had 4 different static drivers, each of which had different information, the dynamic drivers could not be consistent among the 4 cases. The schemes used in the WRF setup, I suppose yes, but the boundary conditions derived from it would depend on the static driver information that is provided, and would have a further influence on the microscale simulation. Can you comment on this, please, and be clear in the manuscript about your statement on this line?
L617: and onwards: Can you describe how you took the PALM data for evaluation in case considering the domain grid box of 20 m?
L691-694: The authors state that: “this research indicates that the outcomes of realistic urban microclimate simulations depend strongly on input data.” However, in the upcoming sentences, they state that the overall features of the microclimate simulations are similar. I suggest clarifying the initial claim on strong dependency on input data or adjusting it.
General question about observation measurements: Is the data from the stations assimilated anyhow to the ERA5 data used for driving the WRF model? Is there a bias of such a type? If so, please indicate that in the text.
A suggestion for the authors: As far as the PALM/PALM-4U naming, I recommend using “PALM”/ “PALM model” throughout the manuscript since, technically speaking, PALM-4U modules/components are a part of PALM’s framework.
Citation: https://doi.org/10.5194/egusphere-2025-144-RC2
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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