EMS Annual Meeting Abstracts
Vol. 18, EMS2021-152, 2021
https://doi.org/10.5194/ems2021-152
EMS Annual Meeting 2021
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Assessing urban climate model results with crowd-sourced data

Claudia Hahn, Sandro Oswald, Brigitta Hollosi, Robert Goler, Astrid Kainz, and Maja Zuvela-Aloise
Claudia Hahn et al.
  • Zentralanstalt für Meteorologie und Geodynamik (ZAMG), Urban Modelling, Austria (claudia.hahn@zamg.ac.at)

Climate change impacts are amplified in cities due to the urban heat island effect and the high population density. Information about the intra-urban temperature patterns is therefore crucial to support resilient city planning. Within the ACRP funded project LUCRETIA, the intra-urban temperature patterns in Vienna, Austria, are investigated using urban climate models (MUKLIMO_3, PALM-4U) and data from citizen weather stations.

While the density of conventional weather station networks is usually too low to capture the temperature patterns in cities and to assess urban climate model results, citizen weather stations provide a dense monitoring network, especially in cities. In Vienna, more than 1000 citizen weather stations from the company Netatmo are available for our study period in August 2018, after the quality control. First investigations showed, that air temperature measurements from citizen weather stations are in good agreement with measurements from conventional stations. The observed differences are attributed to the different locations of the stations and micro-scale effects. A preliminary comparison of citizen weather station data with urban climate model results from MUKLIMO_3 for Vienna revealed for some of the stations similar patterns as the comparison between conventional stations and model results: a reasonably good agreement during the day, after model initialization, and a temperature overestimation at night. Within LUCRETIA we are assessing in more detail the model results (MUKLIMO_3, PALM-4U) for a three day period in August 2018, thereby looking at the effect of the different land-use classes within the city. In addition, we will investigate whether similar spatial temperature patterns are identified when using urban climate models and data from citizen weather stations.

How to cite: Hahn, C., Oswald, S., Hollosi, B., Goler, R., Kainz, A., and Zuvela-Aloise, M.: Assessing urban climate model results with crowd-sourced data, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-152, https://doi.org/10.5194/ems2021-152, 2021.

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