The summer 2021 Switzerland hailstorms: weather situation, major impacts and unique ­observational data

Kopp, Jérôme; Schroeer, Katharina; Schwierz, Cornelia; Hering, Alessandro; Germann, Urs; Martius, Olivia

doi:10.1002/wea.4306

Cited by 21 publications

(16 citation statements)

References 17 publications

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“…The region is characterized by highly variable topography, ranging from flat plains to mountains over 4,500 m high. The activity of thunderstorms in the region is among the highest in Europe and they regularly cause large property losses, injuries and even deaths (Feldmann et al., 2021; Hilker et al., 2009; Kopp et al., 2022; Ozturk et al., 2018; Taszarek et al., 2019).…”

Section: Datamentioning

confidence: 99%

Thunderstorm Nowcasting With Deep Learning: A Multi‐Hazard Data Fusion Model

Leinonen

Hamann

Sideris

et al. 2023

Geophysical Research Letters

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Thunderstorms frequently endanger lives and cause damage to property through several distinct physical processes. Lightning are directly hazardous to humans, igniting fires and disrupting electric infrastructure and aviation (Holle, 2014(Holle, , 2016. The heavy rain frequently produced by these storms can cause flash floods and trigger landslides (Davis, 2001;Smith et al., 1996). Thunderstorms also often produce hail, which can damage buildings, vehicles, crops and forests and is particularly dangerous to flying aircraft (Battaglia et al., 2019;Hohl et al., 2002). They are furthermore frequently associated with strong, gusty wind (Allen & Allen, 2016;Dotzek, 2003;Kelly et al., 1985). Thunderstorms are relatively compact objects that develop rapidly, and consequently, the effects of these hazards are sudden and highly localized. Timely, accurate and actionable short-term predictions of them are needed to issue early warnings to the general population, emergency services and infrastructure operators.Numerical weather prediction (NWP) can forecast thunderstorm occurrence and their typical intensity level in a wider area, but have difficulty predicting the exact time and location where thunderstorms will appear (Sun et al., 2014). On the very short term of minutes to a few hours, one can instead use nowcasting: statistical prediction using the latest observational data. In contrast to NWP models that take hours to run, nowcasting models are typically designed to produce a prediction in seconds, making them suitable for time-critical short-term warning systems. The advantages of the two approaches can also be combined in a seamless forecast. As nowcasting systems tend to be accurate on the short term but lose predictive power faster than NWP models, seamless forecasts typically use nowcasting on the short term, NWP forecasts on the long term, and a combination of the two in between (Nerini, 2019;Nerini et al., 2019).In the recent years, deep learning (DL) has been increasingly adopted in thunderstorm nowcasting algorithms

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Section: Datamentioning

confidence: 99%

Thunderstorm Nowcasting With Deep Learning: A Multi‐Hazard Data Fusion Model

Leinonen

Hamann

Sideris

et al. 2023

Geophysical Research Letters

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“…25 , verified against insurance data and later established as the principal method in the Swiss hail climatology 13 . While radar-based hail detection is challenging and can differ from ground-based methods, it yields a high spatio-temporal coverage that is not achieved with crowdsourcing 94 or hail sensors 95 .…”

Section: Thunderstorm Classificationmentioning

confidence: 99%

Hailstorms and rainstorms versus supercells—a regional analysis of convective storm types in the Alpine region

et al. 2023

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The behaviour of severe thunderstorms, particularly supercells, in complex terrain is still poorly understood. Utilising 6 years of radar-, lightning- and radiosounding-based thunderstorm data in the domain of the Swiss radar network, we study different thunderstorm types in separate topographical regions. We classify the storms as ordinary thunderstorms, intense and severe rainstorms, hail and severe hailstorms and supercellular storms. After identifying the overlaps between the storm categories of rainstorms, hailstorms and supercells, the life cycles of several intensity metrics are investigated. This analysis allows the identification of predictors for intensification within severe storm life cycles. One of the most important predictors is the detection of a mesocyclone in a supercell before the onset or intensification of hail. We then divide the radar domain into sub-regions ranging from the Northwestern Po Valley, the Southern Prealps, main Alpine ridge, Northern Prealps, Swiss Plateau and Jura. This regional split separates storms in different terrain complexities. An investigation of the intensity distribution of storms in each region shows a clear intensity decrease over the main Alpine ridge, intermediate values over the moderately complex Prealpine regions and peaks for the flat Po Valley and Swiss Plateau. In contrast, the highest frequency of storms is found in the Prealpine regions on each side, with a lower frequency in the flat areas and a minimum in convective activity over the main Alpine ridge.

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“…The month of July 2021 was characterized by a persistent flow bringing moist and unstable air over Switzerland. Thunderstorms occurred regularly with heavy rainfall, hail and strong wind gusts that caused a fair amount of damage (Kopp et al., 2023; MeteoSwiss, 2021a). In the following, we give a short description of two case studies (CS) carried out for thunderstorm events which affected the city of Zurich in early summer 2021, and present results for the application of the detection algorithm outlined in Section 2.4.…”

Section: Convective Storm Case Studiesmentioning

confidence: 99%

“…On 28.06.2021, several supercell storms originated in Western Switzerland around 14:00 UTC and then moved along the northern flank of the Swiss Alps following southwest to northeast tracks. Some of these supercells merged and evolved in an intense mesoscale convective system which produced the second largest hail event in Switzerland since 2002 (MeteoSwiss, 2021b), and hailstones of up to 9 cm diameter in central Switzerland, southwest of Zurich (Kopp et al., 2023). This mesoscale convective system approached the city of Zurich and the ETHZ GNSS station from the southwest around 16:40 UTC (Figure 1).…”

Section: Convective Storm Case Studiesmentioning

confidence: 99%

Detecting Signatures of Convective Storm Events in GNSS‐SNR: Two Case Studies From Summer 2021 in Switzerland

Aichinger‐Rosenberger,

Aregger,

Kopp

et al. 2023

Geophysical Research Letters

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Global Navigation Satellite Systems (GNSS) are not only a state‐of‐the‐art sensor for positioning and navigation applications but also a valuable tool for remote sensing. Through the usage of L‐band carrier frequencies, GNSS acts as an all‐weather‐operation system, offering substantial benefits compared to optical systems. Nevertheless, severe weather can still have an impact on the strength of signals received at a ground station, as we show in this study. We investigate GNSS Signal‐to‐Noise Ratio (SNR) observations during two severe convective storm events over the city of Zurich, Switzerland. We make use of a GNSS‐SNR‐based algorithm originally developed for the detection of hail particles from volcanic eruptions. Results indicate that, although GNSS observations are considered to be fairly insensitive to the presence of hydrometeors, convective storm events are visible in SNR observations. SNR levels of affected satellites show a significant drop during event periods, which are determined by weather radar observations.

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The summer 2021 Switzerland hailstorms: weather situation, major impacts and unique observational data

Cited by 21 publications

References 17 publications

Thunderstorm Nowcasting With Deep Learning: A Multi‐Hazard Data Fusion Model

Thunderstorm Nowcasting With Deep Learning: A Multi‐Hazard Data Fusion Model

Hailstorms and rainstorms versus supercells—a regional analysis of convective storm types in the Alpine region

Detecting Signatures of Convective Storm Events in GNSS‐SNR: Two Case Studies From Summer 2021 in Switzerland

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The summer 2021 Switzerland hailstorms: weather situation, major impacts and unique ­observational data

Cited by 21 publications

References 17 publications

Thunderstorm Nowcasting With Deep Learning: A Multi‐Hazard Data Fusion Model

Thunderstorm Nowcasting With Deep Learning: A Multi‐Hazard Data Fusion Model

Hailstorms and rainstorms versus supercells—a regional analysis of convective storm types in the Alpine region

Detecting Signatures of Convective Storm Events in GNSS‐SNR: Two Case Studies From Summer 2021 in Switzerland

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The summer 2021 Switzerland hailstorms: weather situation, major impacts and unique observational data