Preprints
https://doi.org/10.5194/egusphere-2025-1819
https://doi.org/10.5194/egusphere-2025-1819
07 May 2025
 | 07 May 2025
Status: this preprint is open for discussion and under review for Weather and Climate Dynamics (WCD).

Observation based precipitation life cycle analysis of heavy rainfall events in the southeastern Alpine forelands

Stephanie J. Haas, Andreas Kvas, and Jürgen Fuchsberger

Abstract. Heavy thunderstorms are a typical weather phenomenon during summer in southeast Austria. These fast-developing high-impact rainfall events often result in serious damage and are hard to predict. A profound understanding of the life cycle of these events, from formation to dissipation, is therefore crucial to increase resilience and improve forecasting skills. High-resolution observation datasets, like the one of the WegenerNet 3D Open-Air Laboratory for Climate Change Research (WEGN3D) in Feldbach (Austria), provide unique insights and are especially well suited for these important use cases. Consisting of 156 ground stations, an X-band radar, two radiometers, and 6 global navigation satellite system (GNSS) stations, the WEGN3D delivers highly resolved data, in both space and time, of key atmospheric parameters that enable a detailed investigation of small-scale weather phenomena, such as heavy rainfall events. Here we follow the different stages of the life cycle of 94 heavy rainfall events by investigating multiple atmospheric parameters in WEGN3D and global reanalysis data. Beginning with the event formation stage (i.e., the 8 h before the event), where temperatures are usually already quite high and continue to rise, while the first clouds begin to form, before wind speeds pick up and the sky darkens. Connected to these characteristics, we find an increase in \unit[2]{m} air temperature anomaly, integrated water vapor (IWV) anomaly, liquid water path (LWP), convective available potential energy (CAPE), and wind speed. Also, a decrease of cloud base height (CBH) can be observed, in accordance with the deepening of the convective cloud system. During the precipitation stage, we find an increase in the spatial variability of precipitation amount, temperature, LWP, and cloud cover, which represents the highly localized character of these events. After a few minutes to hours of intense rainfall, the event is over and has reached the dissipation stage. The parameters that increased during the event formation stage experience a drop during this last stage (i.e., the 16 h after the event), while CBH again reaches its pre-event levels. Our study gives insights into the physical processes connected to the life cycle of heavy rainfall events, by using the WEGN3D's distinct capability to capture characteristic features of such small-scale events, which illustrates the dataset's high potential for improving and verifying weather and climate models.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
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Stephanie J. Haas, Andreas Kvas, and Jürgen Fuchsberger

Status: open (until 18 Jun 2025)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2025-1819', Anonymous Referee #1, 08 May 2025 reply
    • AC1: 'Reply on RC1', Stephanie Haas, 15 May 2025 reply
Stephanie J. Haas, Andreas Kvas, and Jürgen Fuchsberger

Data sets

Preprocessed WegenerNet 3D Open-Air Lab data Stephanie J. Haas et al. https://cloud.uni-graz.at/s/cXMi33bfTNHQA8S

Stephanie J. Haas, Andreas Kvas, and Jürgen Fuchsberger

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Short summary
In southeast Austria, summer thunderstorms often cause severe damage but are very hard to accurately forecast. With data from the WegenerNet 3D Open-Air Laboratory, we study these storms from beginning to end in multiple atmospheric parameters, like temperature, cloud properties, and wind speed. The characteristic features we find in these parameters expand our understanding of intense storms and can improve their prediction.
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