Assessing the Impact of Sensor Height on the Representativeness of Temperature-Monitoring Sites in a Dense Midrise Urban Development Using PALM-4U

In the context of ongoing global warming and urbanization, the need for reliable temperature monitoring in urban areas is increasing. Such monitoring serves multiple purposes, including assessing urban heat island (UHI) intensity, evaluating climate adaptation strategies, and supporting heat warning systems. This study utilizes high-resolution urban climate simulations with PALM-4U for calm, clear-sky summer weather conditions and an idealized model domain. The domain represents a dense midrise urban district in Dresden Neustadt, eastern Germany. Areas with air temperatures representative of the pedestrian level within the urban development are determined using a methodology based on a 24-h temporal moving representativity range defined by the temperature’s spatial median value and standard deviation. The method is extended by an evaluation of different temperature sensor heights, addressing practical considerations such as vandalism prevention and space availability. The results highlight the feasibility of representative pedestrian-level air temperature monitoring in densely built-up urban areas, particularly at elevated sensor heights between 2.5 and 6.5 m. It is found that higher sensor heights increase the area suitable for representative pedestrian-level temperature monitoring by up to about 50%. The sensitivity of the results to variations in wind speed and building height is also examined, demonstrating the robustness of the proposed method in clear, calm summer weather conditions.

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Publication Details

Journal
Atmosphere
Published
2025-08-31
DOI
https://doi.org/10.3390/atmos16091035
Citations
1
Primary Topic
Remote Sensing and Land Use
Type
article
Field-Weighted Citation Impact
1.30

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article

Assessing the Impact of Sensor Height on the Representativeness of Temperature-Monitoring Sites in a Dense Midrise Urban Development Using PALM-4U

Florian Steigerwald, Heike Schau-Noppel, Meinolf Kossmann, Astrid Eichhorn-Müller
1 citations
Atmosphere
Remote Sensing and Land Use
1.30
article

Assessing the Impact of Sensor Height on the Representativeness of Temperature-Monitoring Sites in a Dense Midrise Urban Development Using PALM-4U

Florian Steigerwald, Heike Schau-Noppel, Meinolf Kossmann, Astrid Eichhorn-Müller
article en
1 citations

Abstract

In the context of ongoing global warming and urbanization, the need for reliable temperature monitoring in urban areas is increasing. Such monitoring serves multiple purposes, including assessing urban heat island (UHI) intensity, evaluating climate adaptation strategies, and supporting heat warning systems. This study utilizes high-resolution urban climate simulations with PALM-4U for calm, clear-sky summer weather conditions and an idealized model domain. The domain represents a dense midrise urban district in Dresden Neustadt, eastern Germany. Areas with air temperatures representative of the pedestrian level within the urban development are determined using a methodology based on a 24-h temporal moving representativity range defined by the temperature’s spatial median value and standard deviation. The method is extended by an evaluation of different temperature sensor heights, addressing practical considerations such as vandalism prevention and space availability. The results highlight the feasibility of representative pedestrian-level air temperature monitoring in densely built-up urban areas, particularly at elevated sensor heights between 2.5 and 6.5 m. It is found that higher sensor heights increase the area suitable for representative pedestrian-level temperature monitoring by up to about 50%. The sensitivity of the results to variations in wind speed and building height is also examined, demonstrating the robustness of the proposed method in clear, calm summer weather conditions.

AtmosphereVol. 16(9)
Deutscher Wetterdienst (DE)
Gottfried Wilhelm Leibniz Universität Hannover, Leibniz-Gemeinschaft
Openalex Percentile: Top 19%
Remote Sensing and Land Use
1.30
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