EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Statistical modeling of fire brigade operations with respect to extreme precipitation events over Berlin

Alexander Pasternack, Ines Langer, Henning W. Rust, and Uwe Ulbrich
Alexander Pasternack et al.
  • Freie Universität Berlin, Meteorology, Berlin, Germany (

Large cities and urban regions are highly sensitive to impacts caused by extreme meteorological events (e.g. heavy rainfall). As problems caused by hazardous atmospheric events are expected to intensify due to the anthropogenic climate change, planning of adequate adaptation measures for urban infrastructure is needed. Planning adaptation measures does not only require further research on potential impacts in a changing climate as a basis, but also a check of the practical feasibility for stakeholders. 

Under the BMBF research program “Urban Climate Under Change” ([UC]²), we relate heavy precipitation events over Berlin to the respective fire brigade operations. Here, the precipitation data are based on temporally high resolved radar data. The fire brigade operation data are available on time and location, but the number of recorded events is small, and their distribution is highly overdispersive compared to a Poisson model. To account for this problem we apply a two part hurdle model with one part modeling the probability of the occurrence of fire brigade operations and one part modeling the actual number of operations given that at least one operation occurs. In the corresponding statistical models the parameters of the distributions are described by additive predictors, which are based on precipitation duration and intensity as well as building density. With a fire brigade dataset covering the years 2002 - 2013 we already could show with a cross validation setup that both the occurrence model and the model for the number of operations significantly outperform the reference forecast of the climatology for certain areas over Berlin. For this study we are able to investigate the behaviour of both statistical models for an extended dataset including the years 2018 - 2020. Morevover we examine the effects of the orography as additional predictor on the statistical models, since sinks may have an importent influence on fire brigade operations w.r.t. water damage.

How to cite: Pasternack, A., Langer, I., Rust, H. W., and Ulbrich, U.: Statistical modeling of fire brigade operations with respect to extreme precipitation events over Berlin, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8558,, 2022.


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