Hail events in Germany, rare or frequent natural hazards?

Wilke, Tabea; Lengfeld, Katharina; Schultze, Markus

doi:https://doi.org/10.5194/egusphere-2024-2507

Preprints

https://doi.org/10.5194/egusphere-2024-2507

Preprints

14 Aug 2024

| 14 Aug 2024

Hail events in Germany, rare or frequent natural hazards?

Tabea Wilke, Katharina Lengfeld, and Markus Schultze

Abstract. Hail in Germany is a natural hazard that is not in everyone's focus, even though it can cause great damage. In this study we focus on hail frequency, sizes and spatial distribution in Germany based on crowd sourcing and weather radar data. We derive hail sizes from radar reflectivity through the use of vertically integrated ice (VII) and maximum estimated size of hail (MESH). With that we create a hail climatology for Germany out of 6 years radar data. We found that hail can occur over whole Germany, but is much more probable in the south. The size of hail depends heavily on the storm, as we see hail tracks with large hail sizes. June is the month with the most and largest hail events. The mountainous areas are hit more frequently by hail than the lower parts. We analyzed crowd data in a short study to obtain how well people can estimate sizes especially hail sizes. In summary, the mean of a crowd is a quite good fit, but individual estimates can be very wrong. In comparison to radar data we found that MESH overestimates the hail size clearly, VII is in our case study a good fit.

Received: 08 Aug 2024 – Discussion started: 14 Aug 2024

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Journal article(s) based on this preprint

09 Sep 2025

Hail events in Germany: rare or frequent natural hazards?

Tabea Wilke, Katharina Lengfeld, and Markus Schultze

Nat. Hazards Earth Syst. Sci., 25, 3141–3159, https://doi.org/10.5194/nhess-25-3141-2025,https://doi.org/10.5194/nhess-25-3141-2025, 2025

Short summary

Tabea Wilke, Katharina Lengfeld, and Markus Schultze

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2024-2507', Anonymous Referee #1, 23 Sep 2024

Wilke et al. "Hail events in Germany; rare of frequent natural hazards" manuscripts presents the first study attempting to provide an in-depth comparison of MESH and VII for hail events in Germany and the first paper globally to look at recalibrating MESH from reports for C band radar. These results are of significant interest for the natural hazard and weather radar community; however, I wasn't able to complete a review due to many (minor) copyediting issues throughout the text, which made it difficult to focus on the science content. I'd strongly encourage the authors revise the manuscript (e.g., after a copyediting and structure review) and resubmit asap.
Here are some suggestions for the abstract
Abstract

line 2: "crowd sourcing" should be "crowd sourced"

line 4: "out of 6 years radar data" should be "using 6 years of radar data"

line 4-5: "over whole Germany" should be "over the whole of Germany"

line 5: The sentence "The size of hail depends heavily on the storm..." isn't clear to me? What properties of the storm are in question

abstract in general: The abstract at present doesn't read well, it feels more like a collection of short dot points rather than a summary of the paper. Please look at rewording it to improve the readability. It should provide an engaging summary of the motivations and findings of the paper, written to tie everything together.

Citation: https://doi.org/10.5194/egusphere-2024-2507-RC1
- AC1: 'Reply on RC1', Tabea Wilke, 10 Nov 2024
  
  Thank you very much for your review of our manuscript. We sincerely apologize for the minor copyediting issues that hindered your review process. Your feedback is very helpful, and we appreciate your recognition of the importance of our study to the natural hazard and weather radar community. We acknowledge that the abstract currently lacks clarity, and we are committed to improving it by incorporating your suggestions.
  We believe that the structure of the manuscript follows the common guidelines for scientific publications with an introduction, a description of the used data sets and methods, followed by the results and a concluding section including an outlook. However, we will carefully revisit the overall structure of the manuscript to improve its readability and coherence e.g. by deleting the sub-subsection and merging them into one section and choosing more appropriate titles for some of the sections and subsections.
  Thank you again for your feedback. We look forward to implementing these revisions and resubmitting our manuscript for your consideration.
  
  Citation: https://doi.org/10.5194/egusphere-2024-2507-AC1
RC2:
'Comment on egusphere-2024-2507', Anonymous Referee #2, 01 Oct 2024
The manuscript “Hail events in Germany, rare or frequent natural hazards?” presents a strong and innovative analysis of hail events in Germany, utilizing advanced radar techniques and a diverse set of data sources. However, its impact is limited by a relatively short timeframe, over-reliance on radar data without sufficient validation or correction mechanisms, and a somewhat superficial analysis of auxiliary data sources such as crowd-sourced observations and insurance claims. There is significant potential for improvement.
Strengths:
Comprehensive Data Integration: The study stands out for its integration of diverse data sources, including radar, crowd-sourced reports, and insurance claims. This multifaceted approach provides a well-rounded perspective on hail events in Germany, ensuring a more complete understanding than relying on any single data type.

Innovative Use of Radar Technology: The application of advanced radar methods like MESH and VII demonstrates the authors’ technical proficiency. By using modern radar data to estimate hail sizes and occurrences, the study pushes the boundaries of traditional meteorological research.

Detailed Case Study and Real-World Application: The case study of the August 2021 hail event effectively highlights the strengths and limitations of crowd-sourced data, providing practical insights into how well lay observations compare with radar measurements. This adds a valuable real-world dimension to the analysis.

Directions for improvements:
Short Timeframe and Lack of Trend Analysis: The six-year period (2018–2023) used in the radar analysis is too brief to establish meaningful long-term trends. As hail events vary significantly year-to-year, a longer dataset or a more in-depth discussion of the limitations imposed by the short timeframe would enhance the study’s credibility.

Over-Reliance on Radar Data with Limited Corrections: While radar data is central to the study, its known issue of overestimating hail sizes is acknowledged but not adequately corrected. This over-reliance, without stronger validation or adjustment methods, weakens the conclusions and leaves room for potential inaccuracies.

Superficial Treatment of Crowd-Sourced Data and Insurance Claims: Though crowd-sourced data and insurance claims are included, the analysis does not fully explore their potential biases (e.g., urban reporting bias) or offer solutions to mitigate them. The insurance data, in particular, is not sufficiently explored for regional or structural factors, making this section feel underdeveloped relative to the overall scope of the study.
Citation: https://doi.org/10.5194/egusphere-2024-2507-RC2
- AC2: 'Reply on RC2', Tabea Wilke, 10 Nov 2024
  
  Thank you for your thoughtful review of our manuscript on hail events in Germany. We appreciate your insights and the opportunity to clarify and enhance our work based on your feedback. Below, we address the key points raised in your review.
  Response to Review Points
  Short Timeframe and Lack of Trend Analysis: The six-year period (2018–2023) used in the radar analysis is too brief to establish meaningful long-term trends. As hail events vary significantly year-to-year, a longer dataset or a more in-depth discussion of the limitations imposed by the short timeframe would enhance the study’s credibility.
  We acknowledge that long-term trend analysis is a valuable aspect of meteorological studies, but we agree with the reviewer that six years of data is not sufficient for a trend analysis. Therefore, a trend analysis was not the primary goal of our research. Our focus was on utilizing advanced radar techniques to analyze recent hail events. However, we recognize the importance of understanding trends over time. In the revised manuscript, we will include an outlook discussing potential data sources that could be leveraged for future trend estimation, such as historical weather records and climate models. Additionally, we are committed to reanalyzing the data continuously as new data will be made available.
  Over-Reliance on Radar Data with Limited Corrections: While radar data is central to the study, its known issue of overestimating hail sizes is acknowledged but not adequately corrected. This over-reliance, without stronger validation or adjustment methods, weakens the conclusions and leaves room for potential inaccuracies.
  
  We are aware that radar data has its limitations, particularly regarding overestimation of hail sizes. To address this, we undertook our own calibration of the MESH (Maximum Estimated Size of Hail) methodology as part of our study. This calibration effort aims to improve the accuracy of our hail size estimations. We will add a paragraph about how other studies deal with these uncertainties to the introduction e.g. the empirical correction done by Brook et al. (2024) and threshold-based optimization derived by CNNs by Forcadell et al. (2024).
  Brook, Jordan P., et al. "A Radar-Based Hail Climatology of Australia." Monthly Weather Review 152.2 (2024): 607-628.
  Forcadell, Vincent, et al. "Severe hail detection with C-band dual-polarisation radars using convolutional neural networks." EGUsphere 2024 (2024): 1-43.
  Superficial Treatment of Crowd-Sourced Data and Insurance Claims: Though crowd-sourced data and insurance claims are included, the analysis does not fully explore their potential biases (e.g., urban reporting bias) or offer solutions to mitigate them. The insurance data, in particular, is not sufficiently explored for regional or structural factors, making this section feel underdeveloped relative to the overall scope of the study.
  We truly appreciate your insightful suggestion to explore the implications of bias in crowd-sourced data within our analysis. To enhance this aspect, we plan to provide a clearer comparison between population density and crowd-reported observations. We recognize that our reliance on insurance data, which is derived solely from postal code areas, may not provide a complete picture of hail events (see Figure 1). This limitation is why we chose to leave spatial analysis out of our examination of insurance data. By focusing primarily on larger hail events, we may inadvertently overlook occurrences of smaller hail, which are equally significant.
  
  Figure 1: Loss [€] / Insured Value [€]
  
  We are grateful for your constructive feedback, which will help us improve the quality and depth of our manuscript. By addressing these points, we aim to provide a more robust analysis of hail events in Germany while acknowledging the complexities involved in interpreting radar data and auxiliary sources. Thank you once again for your valuable insights. We look forward to submitting a revised version that addresses your concerns.
  
  Citation: https://doi.org/10.5194/egusphere-2024-2507-AC2

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2024-2507', Anonymous Referee #1, 23 Sep 2024

Wilke et al. "Hail events in Germany; rare of frequent natural hazards" manuscripts presents the first study attempting to provide an in-depth comparison of MESH and VII for hail events in Germany and the first paper globally to look at recalibrating MESH from reports for C band radar. These results are of significant interest for the natural hazard and weather radar community; however, I wasn't able to complete a review due to many (minor) copyediting issues throughout the text, which made it difficult to focus on the science content. I'd strongly encourage the authors revise the manuscript (e.g., after a copyediting and structure review) and resubmit asap.
Here are some suggestions for the abstract
Abstract

line 2: "crowd sourcing" should be "crowd sourced"

line 4: "out of 6 years radar data" should be "using 6 years of radar data"

line 4-5: "over whole Germany" should be "over the whole of Germany"

line 5: The sentence "The size of hail depends heavily on the storm..." isn't clear to me? What properties of the storm are in question

abstract in general: The abstract at present doesn't read well, it feels more like a collection of short dot points rather than a summary of the paper. Please look at rewording it to improve the readability. It should provide an engaging summary of the motivations and findings of the paper, written to tie everything together.

Citation: https://doi.org/10.5194/egusphere-2024-2507-RC1
- AC1: 'Reply on RC1', Tabea Wilke, 10 Nov 2024
  
  Thank you very much for your review of our manuscript. We sincerely apologize for the minor copyediting issues that hindered your review process. Your feedback is very helpful, and we appreciate your recognition of the importance of our study to the natural hazard and weather radar community. We acknowledge that the abstract currently lacks clarity, and we are committed to improving it by incorporating your suggestions.
  We believe that the structure of the manuscript follows the common guidelines for scientific publications with an introduction, a description of the used data sets and methods, followed by the results and a concluding section including an outlook. However, we will carefully revisit the overall structure of the manuscript to improve its readability and coherence e.g. by deleting the sub-subsection and merging them into one section and choosing more appropriate titles for some of the sections and subsections.
  Thank you again for your feedback. We look forward to implementing these revisions and resubmitting our manuscript for your consideration.
  
  Citation: https://doi.org/10.5194/egusphere-2024-2507-AC1
RC2:
'Comment on egusphere-2024-2507', Anonymous Referee #2, 01 Oct 2024
The manuscript “Hail events in Germany, rare or frequent natural hazards?” presents a strong and innovative analysis of hail events in Germany, utilizing advanced radar techniques and a diverse set of data sources. However, its impact is limited by a relatively short timeframe, over-reliance on radar data without sufficient validation or correction mechanisms, and a somewhat superficial analysis of auxiliary data sources such as crowd-sourced observations and insurance claims. There is significant potential for improvement.
Strengths:
Comprehensive Data Integration: The study stands out for its integration of diverse data sources, including radar, crowd-sourced reports, and insurance claims. This multifaceted approach provides a well-rounded perspective on hail events in Germany, ensuring a more complete understanding than relying on any single data type.

Innovative Use of Radar Technology: The application of advanced radar methods like MESH and VII demonstrates the authors’ technical proficiency. By using modern radar data to estimate hail sizes and occurrences, the study pushes the boundaries of traditional meteorological research.

Detailed Case Study and Real-World Application: The case study of the August 2021 hail event effectively highlights the strengths and limitations of crowd-sourced data, providing practical insights into how well lay observations compare with radar measurements. This adds a valuable real-world dimension to the analysis.

Directions for improvements:
Short Timeframe and Lack of Trend Analysis: The six-year period (2018–2023) used in the radar analysis is too brief to establish meaningful long-term trends. As hail events vary significantly year-to-year, a longer dataset or a more in-depth discussion of the limitations imposed by the short timeframe would enhance the study’s credibility.

Over-Reliance on Radar Data with Limited Corrections: While radar data is central to the study, its known issue of overestimating hail sizes is acknowledged but not adequately corrected. This over-reliance, without stronger validation or adjustment methods, weakens the conclusions and leaves room for potential inaccuracies.

Superficial Treatment of Crowd-Sourced Data and Insurance Claims: Though crowd-sourced data and insurance claims are included, the analysis does not fully explore their potential biases (e.g., urban reporting bias) or offer solutions to mitigate them. The insurance data, in particular, is not sufficiently explored for regional or structural factors, making this section feel underdeveloped relative to the overall scope of the study.
Citation: https://doi.org/10.5194/egusphere-2024-2507-RC2
- AC2: 'Reply on RC2', Tabea Wilke, 10 Nov 2024
  
  Thank you for your thoughtful review of our manuscript on hail events in Germany. We appreciate your insights and the opportunity to clarify and enhance our work based on your feedback. Below, we address the key points raised in your review.
  Response to Review Points
  Short Timeframe and Lack of Trend Analysis: The six-year period (2018–2023) used in the radar analysis is too brief to establish meaningful long-term trends. As hail events vary significantly year-to-year, a longer dataset or a more in-depth discussion of the limitations imposed by the short timeframe would enhance the study’s credibility.
  We acknowledge that long-term trend analysis is a valuable aspect of meteorological studies, but we agree with the reviewer that six years of data is not sufficient for a trend analysis. Therefore, a trend analysis was not the primary goal of our research. Our focus was on utilizing advanced radar techniques to analyze recent hail events. However, we recognize the importance of understanding trends over time. In the revised manuscript, we will include an outlook discussing potential data sources that could be leveraged for future trend estimation, such as historical weather records and climate models. Additionally, we are committed to reanalyzing the data continuously as new data will be made available.
  Over-Reliance on Radar Data with Limited Corrections: While radar data is central to the study, its known issue of overestimating hail sizes is acknowledged but not adequately corrected. This over-reliance, without stronger validation or adjustment methods, weakens the conclusions and leaves room for potential inaccuracies.
  
  We are aware that radar data has its limitations, particularly regarding overestimation of hail sizes. To address this, we undertook our own calibration of the MESH (Maximum Estimated Size of Hail) methodology as part of our study. This calibration effort aims to improve the accuracy of our hail size estimations. We will add a paragraph about how other studies deal with these uncertainties to the introduction e.g. the empirical correction done by Brook et al. (2024) and threshold-based optimization derived by CNNs by Forcadell et al. (2024).
  Brook, Jordan P., et al. "A Radar-Based Hail Climatology of Australia." Monthly Weather Review 152.2 (2024): 607-628.
  Forcadell, Vincent, et al. "Severe hail detection with C-band dual-polarisation radars using convolutional neural networks." EGUsphere 2024 (2024): 1-43.
  Superficial Treatment of Crowd-Sourced Data and Insurance Claims: Though crowd-sourced data and insurance claims are included, the analysis does not fully explore their potential biases (e.g., urban reporting bias) or offer solutions to mitigate them. The insurance data, in particular, is not sufficiently explored for regional or structural factors, making this section feel underdeveloped relative to the overall scope of the study.
  We truly appreciate your insightful suggestion to explore the implications of bias in crowd-sourced data within our analysis. To enhance this aspect, we plan to provide a clearer comparison between population density and crowd-reported observations. We recognize that our reliance on insurance data, which is derived solely from postal code areas, may not provide a complete picture of hail events (see Figure 1). This limitation is why we chose to leave spatial analysis out of our examination of insurance data. By focusing primarily on larger hail events, we may inadvertently overlook occurrences of smaller hail, which are equally significant.
  
  Figure 1: Loss [€] / Insured Value [€]
  
  We are grateful for your constructive feedback, which will help us improve the quality and depth of our manuscript. By addressing these points, we aim to provide a more robust analysis of hail events in Germany while acknowledging the complexities involved in interpreting radar data and auxiliary sources. Thank you once again for your valuable insights. We look forward to submitting a revised version that addresses your concerns.
  
  Citation: https://doi.org/10.5194/egusphere-2024-2507-AC2

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

ED: Reconsider after major revisions (further review by editor and referees) (16 Nov 2024) by Brunella Bonaccorso

AR by Tabea Wilke on behalf of the Authors (10 Jan 2025) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (12 Jan 2025) by Brunella Bonaccorso

RR by Anonymous Referee #1 (28 Feb 2025)

Suggestions for revision or reasons for rejection

Review of
"Hail events in Germany, rare or frequent natural hazards?"
Feb 2025
Tabea Wilke, Katharina Lengfeld, and Markus Schultze

Thank you for the opportunity to read this paper regarding a new hail climatology of Germany. It's promising to see a more detailed evaluation of potential report biases, and refitting of the MESH relationship. I will have to recommend a major review though, as I believe some work is needed in the results to better support the use of VII and evaluate it's skill. I hope you can take on these comments and I'm looking forward to seeing the revisions.

major comments:

line 262: Would it be possible to provide some quantitative evidence of the MESH and VII comparison to back up statements like "MESH overestimates the sizes"? This could involve using a sample of multiple events not included in the fitting and directly comparing reported hail size and radar estimates using the same neighbourhood rules. I'm a little worried that an graphical evaluation based on one event might not provide robust evidence to support this claim. Furthermore, if MESH75 was overestimating hail size, why not try a lower percentile threshold and see what is optimal? Would the mean fitted MESH work better? A statistical evaluation would also let you look at biases as a function of reported hail size... Perhaps VII works well for smaller sizes, but works poorly, compared to MESH, for larger sizes. To reject MESH and focus on VII based on the analysis of one event seems a little rushed, especially given the work you've done to refit MESH.

Section 3.1 VII: Would it be possible to investigate the performance of this hail size estimate from VII using the reports dataset, and maybe even refitting it, making it more consistent with the analysis performed on MESH?

General comment: Would it be possible to integrate your findings about how the "mean of many hail reports is usually very good" with the radar based climatology fitting/evaluation? Perhaps rather than training/evaluating using one SHI value for every hail observation, it could be worthwhile using the local neighbourhood of hail observations (as a mean etc) to correlate with the SHI value. This could certainly reduce the noise in observations, as you've indicated, and provide a direct benefit to your radar analysis from this study of report biases.

Section 4.7: Can you please relate this back to monthly trends shown in figure 8 and 9, and explore the differences? I think it's important to put this insurance information in context with the observations.

General comment: Did you apply the correction developed by Brook et al. 2024 to the C band data here? I'm still not clear why your C band MESH is overestimating hail size to such an extent given you fitted a new relationship, and the performance seems poor even for smaller sizes. It's also not clear why VII can perform so well with no refitting!

minor comments:

line 15: what year were these figures normalised for? That's important if we're comparing them.
line 26-27: The rich-poor (disparity in wealth) bias can be normalised for though if we know the total sum insured. Is this possible with your datasets?
line 32: It's not clear why 90% damages was selected here for discussion. Can you provide more context?
line 48: Brook et al. 2024 found that C band overestimated hail with respect to S band estimates, not hail reports (Which this could imply). Please clarify that this w.r.t. S band.
line 52: "was expected to improve" reads like it failed to improve estimation of hail size. I would disagree with this, as the HSDA retrieval has provided a robust way to size hail using polarimetric information (but it's only for S band) and is used in operations.
lines 57-60: Can you please link how ZDR/kdp signatures of liquid water in updrafts relate to hail size? I'm not aware of any hail size estimates that have been developed from polarimetric signatures like this. Some references would be great.
line 62-63: For the Junghänel et a. 2016 study, can you please comment on their findings and how it relates to hotspots?
line 66: Can you please add any background or literature on VII? There hasn't been any discussion of it in the literature review, but it's only of the main retrievals used by this paper! Perhaps you can comment on how it differs from MESH, and why it has not been used in many climatology studies?
line 135: Why was a minimum threshold of 7.5 mm used? Do you have any past studies which selected this? Or was it something you found suitable through experimentation?
line 161: Can you please introduce the DWD app hail size categories here?
lines 161-162. This sentence, starting with "Therefore, …" was not clear to me. Please look at reworking.
Figure 3: I'm curious to why the full hail reports dataset (from the 3 sources) was not applied to develop your own calibration of MESH. This may have helped increase the number of larger hailstones in the sample.
3.2: MESH: Would it be possible to report on the number of samples in each category when fitting SHI with reports, which is important to better understand the potential skewness of the fit.
Figure 3: Please describe the features in the plot: vertical bars, dots and shaded area.
line 189: "larger diameter" could be improved with "maximum dimension/axis", as we're not measuring spheres.
Section 4.2: would it be possible to justify why results where analysed by age groups? What hypothesis are you testing here?
Figure 4: Can you please list the sizes (maximum dimension) and type (oblate vs spherical) of hailstones shown here. This would be help the reader to make better use of this image.
Figure 5: Again, please details all the features of the plot for the reader.
line 225: Can you comments on how this bias changes with hail size? It seems larger hailstones have a smaller relative bias?
Figure 8 : Please comment on the total number of hail reports used in this analysis for context.
line 235: Is it possible to normalise this dataset to reduce the effects of the much larger DWD starting in 2021? I'm concerned these stats shown in figure 8 and 9 might be impacted by the strong annual variability of hail for the years when the much larger DWD app datasets started.
line 240: What is the "h3 grid of level 6"?
Figure 10: Did you investigate using a larger grid size? There are many grids points with no data which make it difficult to see any hotpots, especially with the colourmap used (maybe it's just me!). Using a larger grid might help bring out the underlying trends.
lines 257-259: Please reword, the meaning of this sentence is not clear to me.
line 282: While it is mostly likely that supercells provided the hail larger than 5cm, it would be good to see evidence, such as mesocyclone tracks.
Figure 11: For the colorbar used in panels (a) and (b), can the colourbar ticks, and ideally a discrete colourmap, match the 4 levels used to plot hail observations. This would greatly improve readability.
Figure 16 caption: I'm not sure what the authors mean by "ascending over" and "descending from". It might be simpler to put the month on each panel and leave any commentary to the manuscript (to avoid repeating it).
Line 337-338: I would advise not using "perfectly" unless there has been some statistical evaluation.

Hide

ED: Reconsider after major revisions (further review by editor and referees) (28 Feb 2025) by Brunella Bonaccorso

AR by Tabea Wilke on behalf of the Authors (24 Apr 2025) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (25 Apr 2025) by Brunella Bonaccorso

RR by Anonymous Referee #1 (17 Jun 2025)

ED: Publish subject to technical corrections (17 Jun 2025) by Brunella Bonaccorso

AR by Tabea Wilke on behalf of the Authors (24 Jun 2025) Author's response Manuscript

Journal article(s) based on this preprint

09 Sep 2025

Hail events in Germany: rare or frequent natural hazards?

Tabea Wilke, Katharina Lengfeld, and Markus Schultze

Nat. Hazards Earth Syst. Sci., 25, 3141–3159, https://doi.org/10.5194/nhess-25-3141-2025,https://doi.org/10.5194/nhess-25-3141-2025, 2025

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Tabea Wilke, Katharina Lengfeld, and Markus Schultze

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Short summary

Hail in Germany is a natural hazard that is not in everyone's focus, even though it can cause great damage. In this study we focus on hail frequency, sizes and spatial distribution in Germany based on crowd sourcing and weather radar data. We compare different algorithms based on weather radar data with crowd sourced data and show the annual and diurnal cycle of hail in Germany.


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Hail events in Germany, rare or frequent natural hazards?

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Interactive discussion

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Suggestions for revision or reasons for rejection

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