Unravelling the wind impact of clusters of storms, a case study over the French insurer Generali

Hasbini, Laura; Yiou, Pascal; Boissier, Laurent; Perringaux, Arthur

doi:10.5194/egusphere-2025-3138

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https://doi.org/10.5194/egusphere-2025-3138

Preprints

15 Jul 2025

| 15 Jul 2025

Unravelling the wind impact of clusters of storms, a case study over the French insurer Generali

Laura Hasbini, Pascal Yiou, Laurent Boissier, and Arthur Perringaux

Abstract. Winter windstorms cause extensive damage to infrastructure and represent the most significant natural hazard for Generali France in terms of insured losses. This study presents a method to systematically link physical storm events with observed insurance claims, enabling a better understanding of which storms, including weaker depressions, drive losses within Generali's portfolio. The proposed association represents a cornerstone for the calibration of insurance and reinsurance processes such as risk assessment, loss modelling and prevention. Beyond analysing individual events, we assess the impact of storm clusters, defined as multiple storms affecting the same region within a 96-hour window, consistent with reinsurance contract definitions. Our findings reveal that 85 % of windstorm-related losses since 1998 are attributable to clustered events. The most intense storms are frequently preceded or succeeded by smaller, yet damaging, depressions. This is illustrated by the case of Storms Anatol, Lothar and Martin in December 1999 and Storm Klaus in January 2009. Furthermore, we find that storms causing damage are more likely to occur as part of a cluster (50 %) compared to the overall population of depressions affecting France (29 %). These findings highlight the importance of accounting for storm clustering in risk modelling and reinsurance strategies.

Received: 01 Jul 2025 – Discussion started: 15 Jul 2025

Competing interests: The contact author has declared that neither of the authors has any competing interests. Authors LH, LB and AP are employed by Generali France.

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 paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.

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Journal article(s) based on this preprint

12 Jun 2026

Unravelling wind-driven impact of storm clusters, a case study for the insurer Generali France

Laura Hasbini, Pascal Yiou, Quentin Hénaff, Laurent Boissier, and Arthur Perringaux

Nat. Hazards Earth Syst. Sci., 26, 2817–2839, https://doi.org/10.5194/nhess-26-2817-2026,https://doi.org/10.5194/nhess-26-2817-2026, 2026

Short summary

Laura Hasbini, Pascal Yiou, Laurent Boissier, and Arthur Perringaux

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2025-3138', Anonymous Referee #1, 19 Aug 2025

Broadly I like this paper. The findings may be a bit specific to this work and I wonder about the scope for broader applicability but its a nice piece of work which solves a clear problem in working with loss data.

My more specific comments are therefore mostly minor:

17. This is a global figure? Seems very high for Europe, even if Economic rather than Insured loss.

25-29. I Don't entirely understand what this bit is saying.

45-49. Seems well justified.

Figure 1 caption - Should 'different by' be 'defined by'?

120-130 - Interesting discussion of clustering metrics and comments on how a clear definition has not been fixed, or at least not used within the insurance industry. From a loss perspective is it useful to split two clusters affecting France at the same time into separate clusters if they do not overlap?

155-159 Clearly a good dataset with locations recorded. Postcode level is surely fine for ERA5 resolution hazard.

164 - Good observation and an important problem to address.

174 - I can believe this but can you give a bit more explanation/justification. It reads slightly like you argue that the loss data over-represents big storms then remove the little storms yourself. From figure 2a I interpret that lowering N leads to more claims dates than storm dates which is a different problem.

203 - 'Enables the capture'

Section 2 - Overall I find this interesting. Loss data is often focused around extreme events for vulnerability development and this study is an opportunity to work with a sufficiently complete loss history that the impact of smaller events is observed.

246 - How typical Generali's portfolio is of the French market is crucial here. I expect the spread of risks cannot be shown but some commentary could be made as to whether the missing events were very focused on a particular region etc. Some commentary on the exposure comes in 271.

Fig 4 - Is there a relationship between total storm count and cluster counts? The grey bars corresponding to less clustered years appear to group in the 2000s which is also a period of low overall activity based on the red dots.

Section 4.1 seems like the most crucial results, using the dataset and methods discussed in much of the rest of the paper to draw conclusions.

Fig 5. I would expect b) cost per claim to be similar, as observed. But do a) and c) depend on the definition of a storm? If your catalogue contains more small storms the total cost and total claims would be lower, as is observed.

Fig 6. Could we see the spread of losses within clusters for different cluster lengths? For example in a 3 storm cluster is the second ranked loss still 26%?

Section 4.2 shows some value to the method. Often losses from these storms are indistinguishable.

Fig 7/8 what is the resolution of the loss contour maps? The scales go to 6k and 5k respectively but within what area are 5k claims observed? Also there are some white dots that look like they hit Paris that don't seem to be explained.

365-368 Losses are often due to debris, hence consecutive storms may impact losses. This is very difficult for the insurance industry to study and datasets such as this may enable that.

Section 4.3 is also interesting. As stated in 384 Klaus is often treated as a single event.

Citation: https://doi.org/10.5194/egusphere-2025-3138-RC1
- AC1: 'Reply on RC1', Laura Hasbini, 26 Sep 2025
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-3138/egusphere-2025-3138-AC1-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2025-3138-AC1
RC2:
'Comment on egusphere-2025-3138', Anonymous Referee #2, 20 Aug 2025

I read with great interest the paper by Hasbini et al. and found that it provides useful and insightful links between scientific research and industrial applications, particularly in the (re-)insurance sector. Nevertheless, I have several queries, especially regarding the presentation, methodology, and motivation. More specifically, I found it difficult to fully understand the methodological approach, and I have a few concerns about the procedure used to attribute impacts to unique storms. Despite these issues, I find the content very interesting and would therefore recommend a major revision.
Since my concerns mainly relate to the methodological approach, in the following, I focus primarily on the first half of the paper. Addressing these commentss could potentially lead to changes in the results or, at the very least, contribute to a better understanding of the conclusions.
1. Presentation

I found the use of language generally clear; however, I had some difficulty following the text overall. In my opinion, the introduction lacks a consistent narrative and does not make sufficiently meaningful use of technical language. I would strongly suggest revising it according to the comments below:
Line 21: Although I agree that tracking methods tend to show higher agreement on the tracks of well-developed storms, I would argue that tracking algorithms can still diverge in their results even for the most intense cyclones. Including a relatively brief review of tracking-method intercomparison studies would strengthen this point.
Lines 27–29: The terms “explosive cyclogenesis” and “strong jet stream” cannot be straightforwardly characterized as physical characteristics. Please revise this phrasing. Additionally, the purpose of these phrases is unclear. In fact, the second half of this paragraph appears to consist of loosely connected statements rather than a coherent argument. I suggest restructuring it for clarity. Probably discussing cyclone dynamics is not of strong concern for this paper.
Lines 30–31: The type of distribution may indeed vary depending on the tracking method used. However, I am not sure I understand how the distribution type justifies the varying number of ETCs “observed at a given location and for a given period.” Please clarify.
Lines 33–34: The statement is vague. Please be more precise.
Line 34: RWB typically occurs due to external forcing on the waveguide — for example, from the outflow of warm conveyor belts. I assume the authors intended to express a different idea here. Please revise, clarity or omit.
Lines 38–40: Dominate what? Which “dynamics” are being referred to? It seems that the term dynamics is being used as a generalization for “all the above.” Please be more specific and precise when using scientific terminology.
Lines 46–48: Why is a 4-day window necessary? Please provide justification for this choice (seems that this is done in the discussions section and thus it comes too late).
Lines 49–53: This section seems more like an introductory paragraph to clustering. Consider integrating it more clearly with the broader discussion.
Line 61: The meaning of “driving characteristics” is unclear. Please define the term more precisely.
Lines 62–63: I agree with the general point, but in some cases, a strict one-to-one attribution of impacts to individual storms is difficult, if not impossible. For example, if a new storm develops within the frontal region of a mature storm, then attributing damages exclusively to one or the other becomes completely arbitrary.
Lines 83–85: Reading the objectives, it seems that resolution-related issues are not thoroughly discussed earlier in the text. If necessary please do so. Additionally, while you mention the limitations of aggregated wind speed data in attributing impacts to a single storm, the importance of distinguishing between storms is not sufficiently developed (see also comments on methodological approach). I overall recommend formulating the introduction to better align it with the study’s stated objectives.
2a. Storm tracks approach
My impression is that cyclone tracking is naively taken for granted considering every track as "a storm". It is quite clear from many cyclone tracking papers that the number of "storms" can be easily tuned and lead to much different results. In the case of this paper, with some very simplistic calculations and supposing a rather constant number of storms per year, then the authors seem to conclude to 100 storms per 6-month season (4439 storms for 45 years), i.e. ~16 storms per month, or more naively, one storm every other day close to France. The potential conclusion of the important role of clusters in provoking damages is thus "predecided" by the storm tracking approach. I feel that the methodological approach should be better discussed and the reader should be provided with more insights about the tracked features (see specific comments below).
Specific comments

Line 109. The storms seem to be treated as "all of the same". For instance, the same radius of influence is used (1300 km) for all tracked features, but I would argue that this is a rather unrealistic assumption. From the perspectives of a morphological approach, the impacts are expected to take place close to the center and along the fronts. So a circular area with a radius of 1300 km seems to be overwhelming. Maybe not so overwhelming for capturing the extent of the fronts but for a small storm this would be certainly the case. This radius actually compares "rather too big" with the surface of France. This seems to further favor the collocation of storms and the conclusion of clusters' high importance. I would advise the authors either to use a dynamically varying effective area for the storms, e.g. change according to intensity, or better, to detect and attribute specific wind patterns to storm track points.
Line 96. I am not sure what is meant by "potential noise". As far as I know, the TRACK algorithm identifies and tracks local maxima of relative vorticity. Relative vorticity is a high frequency field. Even a relatively coarse resolution of 0.25x0.25 would pose a computational challenge due to numerous identified local maxima that need to be tracked in time. So smoothing is necessary (I would actually argue that noise is not even appropriate here). How much smoothing is applied and what is the native horizontal resolution of the input field matters greatly for the eventual number of tracked features. These aspects, plus the arbitrary choice of cyclones duration (line 101), are all important to conclude to a high number of tracks. My impression is that in the trascks dataset might be included features (i.e. local maxima of vorticity) that could be rather small, or nested within greater cyclonic structures (e.g. local maxima of vorticity nested in a cold front). Therefore, several (many?) of the tracked features could be hardly considered as "distinct storms".
This is not a critical comment, because bottom line, there is no right or wrong when it comes to the number of tracks, albeit the reader needs to have a good statistical knowledge of the characteristics and nature of tracked features. For instance, it is not rare for tracking algorithms to perceive a "large scale cyclone" with 2 or 3 distinct centers as a set of that many different storm tracks. Whether there is one "big" cyclone or 2-3 "smaller" ones is an arbitrary choice and depends on the tuning of the cyclone tracking method. However, if there are large heterogeneities in the intensities of tracked cyclones, or if many of these cyclones overlap in time, then one needs to adjust their approach when attributing specific impacts to "storms" (e.g. adjusting the radius of influence). Lines 113-115 suggest that cyclone tracking approach was designed to include heterogeneous storms. This needs to be shown, discussed and linked with an accordingly adjusted attribution methodology (see also below).
Section 2.2

except if I missed it, Figure 1 is not referenced in the text. Please also provide dates in the caption or within the figure (if these are the tracks of named storms, that would be also nice to know). Also it would be useful to provide a reference in the map for 7.5 degrees West.
Lines 127-128: Distance is 420 km (assuming that 70 km refers to 1 degree..). But is 96 hours a realistic timescale for a cluster of storms hitting the same place? Please elaborate. The 96h criterion is explained in section 5 but this comes too late.
Lines 130-134. While I appreciate the numbers provided here, could we have a visual impression of the frequency of areas affected by storm clusters in France. Also in connection with the above, could we have a figure with statistical information about the storms included in every cluster?
2b. Attribution procedure

Section 3.1 was rather difficult to understand. Probably I get the big picture but the text is rather complex, dense and in some points rather incomprehensible (at least to me). I would certainly appreciate having an illustrative example (maybe also a flow diagram?) where each step of the procedure is shown in a bit more detail. Maybe the examples in section 4 could serve this cause.
Lines 173-174 are quite cryptic to me. What does it mean "understanding of storm damage and Generali’s exposure"? And what would it mean "few claims"? Please elaborate. How does this affect the results in this study. How representative is the exposure of Generali for the total of assets in France? Can your results be generalized about storm impacts in France? Actually, why the minimum number of claims is important here? Maybe one claim alone in an area where Generalli has a low exposure still insinuates an impactful event(?).
I actually failed to understand several concepts in section 3.1. In a rather naive approach, I would say that since you already have the coordinates of the circular area influenced by every track point and since you already have the information on a claim, then you could simply say that if the claim spatially and temporally coincides with a storm-affected area (+/- a certain amount of time), then the claim is attributed to that track point. Of course this way, the claim could be attributed to more than one tracked features. In this case, the claim could be certainly attributed to a cluster of tracks (or storms). But I do not see the feasibility of the "..ultimate goal to associate each claim with a single storm" as stated in lines 171-172. Supposing that two track points are very close to each other and correspond to two different storms, or maybe they correspond to the tracks of a "bigger" storm with two centers. Then, the high wind gusts somewhere between two track points will be due to the interaction of these two storm centers (e.g. two distinct storm centers close to each other may result in higher pressure gradients and thus higher wind speeds). There is no really a reason to say that the high gusts are due to one or the other track point. Even in such a case, one could e.g. attribute the claim to the closest track point of either storm. Given that claims and track points are pinpointed and temporally well-defined, I am not very sure I follow the complexity of the approach here, the meaningfulness of the "dstorm" variable and of the cost function. As stated before, I would appreciate illustrative examples to better understand their necessity and use.
Few additional comments

In the examples of Dec 1999, Line 350 states that misattribution compromises the vulnerability curves. I am not an expert but if I understand correctly the field, the vulnerability curve links wind speed with losses. So this is indifferent of a storm attribution procedure(?). If not, could you please explain a bit more this point?
In your methodological approach, if I understood correctly, "dstorm" is defined by coordinates which are located over the Ocean (7.5W). So choosing Xb = 3 days means that the claim could take place while a storm is really far from France (I guess still within 1300 kms of radius). Could you show examples where evidently high wind speed is relevant to a storm with a far-reached center? Maybe I misunderstood this part. Could you please clarify.
As an additional note, please avoid using the verb "land" for stating that a track point is over continental areas (or a cyclone influences such). First of all, the coordinate of 7.5 W used here is over the sea, second, I presume that the use of this terminology is inspired by the field of tropical cyclones where the usual term is "landfall". But even so, "landfall" has a special weight because the highest wind speed is really close to the cyclone center. Therefore, time and area of landfalling cyclones is directly relevant to impacts, which is not -always- the case in ETCs.
Section 2.2 seems more like a "methods" rather than "Data" as stated in the title of section 2.
Section 3.1 Lines 161-167. This paragraph seems more adequate for the introduction.

Citation: https://doi.org/10.5194/egusphere-2025-3138-RC2
- AC2: 'Reply on RC2', Laura Hasbini, 26 Sep 2025
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-3138/egusphere-2025-3138-AC2-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2025-3138-AC2
RC3:
'Comment on egusphere-2025-3138', Anonymous Referee #3, 26 Aug 2025

The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-3138/egusphere-2025-3138-RC3-supplement.pdf

Citation: https://doi.org/10.5194/egusphere-2025-3138-RC3
- AC3: 'Reply on RC3', Laura Hasbini, 26 Sep 2025
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-3138/egusphere-2025-3138-AC3-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2025-3138-AC3

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2025-3138', Anonymous Referee #1, 19 Aug 2025

Broadly I like this paper. The findings may be a bit specific to this work and I wonder about the scope for broader applicability but its a nice piece of work which solves a clear problem in working with loss data.

My more specific comments are therefore mostly minor:

17. This is a global figure? Seems very high for Europe, even if Economic rather than Insured loss.

25-29. I Don't entirely understand what this bit is saying.

45-49. Seems well justified.

Figure 1 caption - Should 'different by' be 'defined by'?

120-130 - Interesting discussion of clustering metrics and comments on how a clear definition has not been fixed, or at least not used within the insurance industry. From a loss perspective is it useful to split two clusters affecting France at the same time into separate clusters if they do not overlap?

155-159 Clearly a good dataset with locations recorded. Postcode level is surely fine for ERA5 resolution hazard.

164 - Good observation and an important problem to address.

174 - I can believe this but can you give a bit more explanation/justification. It reads slightly like you argue that the loss data over-represents big storms then remove the little storms yourself. From figure 2a I interpret that lowering N leads to more claims dates than storm dates which is a different problem.

203 - 'Enables the capture'

Section 2 - Overall I find this interesting. Loss data is often focused around extreme events for vulnerability development and this study is an opportunity to work with a sufficiently complete loss history that the impact of smaller events is observed.

246 - How typical Generali's portfolio is of the French market is crucial here. I expect the spread of risks cannot be shown but some commentary could be made as to whether the missing events were very focused on a particular region etc. Some commentary on the exposure comes in 271.

Fig 4 - Is there a relationship between total storm count and cluster counts? The grey bars corresponding to less clustered years appear to group in the 2000s which is also a period of low overall activity based on the red dots.

Section 4.1 seems like the most crucial results, using the dataset and methods discussed in much of the rest of the paper to draw conclusions.

Fig 5. I would expect b) cost per claim to be similar, as observed. But do a) and c) depend on the definition of a storm? If your catalogue contains more small storms the total cost and total claims would be lower, as is observed.

Fig 6. Could we see the spread of losses within clusters for different cluster lengths? For example in a 3 storm cluster is the second ranked loss still 26%?

Section 4.2 shows some value to the method. Often losses from these storms are indistinguishable.

Fig 7/8 what is the resolution of the loss contour maps? The scales go to 6k and 5k respectively but within what area are 5k claims observed? Also there are some white dots that look like they hit Paris that don't seem to be explained.

365-368 Losses are often due to debris, hence consecutive storms may impact losses. This is very difficult for the insurance industry to study and datasets such as this may enable that.

Section 4.3 is also interesting. As stated in 384 Klaus is often treated as a single event.

Citation: https://doi.org/10.5194/egusphere-2025-3138-RC1
- AC1: 'Reply on RC1', Laura Hasbini, 26 Sep 2025
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-3138/egusphere-2025-3138-AC1-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2025-3138-AC1
RC2:
'Comment on egusphere-2025-3138', Anonymous Referee #2, 20 Aug 2025

I read with great interest the paper by Hasbini et al. and found that it provides useful and insightful links between scientific research and industrial applications, particularly in the (re-)insurance sector. Nevertheless, I have several queries, especially regarding the presentation, methodology, and motivation. More specifically, I found it difficult to fully understand the methodological approach, and I have a few concerns about the procedure used to attribute impacts to unique storms. Despite these issues, I find the content very interesting and would therefore recommend a major revision.
Since my concerns mainly relate to the methodological approach, in the following, I focus primarily on the first half of the paper. Addressing these commentss could potentially lead to changes in the results or, at the very least, contribute to a better understanding of the conclusions.
1. Presentation

I found the use of language generally clear; however, I had some difficulty following the text overall. In my opinion, the introduction lacks a consistent narrative and does not make sufficiently meaningful use of technical language. I would strongly suggest revising it according to the comments below:
Line 21: Although I agree that tracking methods tend to show higher agreement on the tracks of well-developed storms, I would argue that tracking algorithms can still diverge in their results even for the most intense cyclones. Including a relatively brief review of tracking-method intercomparison studies would strengthen this point.
Lines 27–29: The terms “explosive cyclogenesis” and “strong jet stream” cannot be straightforwardly characterized as physical characteristics. Please revise this phrasing. Additionally, the purpose of these phrases is unclear. In fact, the second half of this paragraph appears to consist of loosely connected statements rather than a coherent argument. I suggest restructuring it for clarity. Probably discussing cyclone dynamics is not of strong concern for this paper.
Lines 30–31: The type of distribution may indeed vary depending on the tracking method used. However, I am not sure I understand how the distribution type justifies the varying number of ETCs “observed at a given location and for a given period.” Please clarify.
Lines 33–34: The statement is vague. Please be more precise.
Line 34: RWB typically occurs due to external forcing on the waveguide — for example, from the outflow of warm conveyor belts. I assume the authors intended to express a different idea here. Please revise, clarity or omit.
Lines 38–40: Dominate what? Which “dynamics” are being referred to? It seems that the term dynamics is being used as a generalization for “all the above.” Please be more specific and precise when using scientific terminology.
Lines 46–48: Why is a 4-day window necessary? Please provide justification for this choice (seems that this is done in the discussions section and thus it comes too late).
Lines 49–53: This section seems more like an introductory paragraph to clustering. Consider integrating it more clearly with the broader discussion.
Line 61: The meaning of “driving characteristics” is unclear. Please define the term more precisely.
Lines 62–63: I agree with the general point, but in some cases, a strict one-to-one attribution of impacts to individual storms is difficult, if not impossible. For example, if a new storm develops within the frontal region of a mature storm, then attributing damages exclusively to one or the other becomes completely arbitrary.
Lines 83–85: Reading the objectives, it seems that resolution-related issues are not thoroughly discussed earlier in the text. If necessary please do so. Additionally, while you mention the limitations of aggregated wind speed data in attributing impacts to a single storm, the importance of distinguishing between storms is not sufficiently developed (see also comments on methodological approach). I overall recommend formulating the introduction to better align it with the study’s stated objectives.
2a. Storm tracks approach
My impression is that cyclone tracking is naively taken for granted considering every track as "a storm". It is quite clear from many cyclone tracking papers that the number of "storms" can be easily tuned and lead to much different results. In the case of this paper, with some very simplistic calculations and supposing a rather constant number of storms per year, then the authors seem to conclude to 100 storms per 6-month season (4439 storms for 45 years), i.e. ~16 storms per month, or more naively, one storm every other day close to France. The potential conclusion of the important role of clusters in provoking damages is thus "predecided" by the storm tracking approach. I feel that the methodological approach should be better discussed and the reader should be provided with more insights about the tracked features (see specific comments below).
Specific comments

Line 109. The storms seem to be treated as "all of the same". For instance, the same radius of influence is used (1300 km) for all tracked features, but I would argue that this is a rather unrealistic assumption. From the perspectives of a morphological approach, the impacts are expected to take place close to the center and along the fronts. So a circular area with a radius of 1300 km seems to be overwhelming. Maybe not so overwhelming for capturing the extent of the fronts but for a small storm this would be certainly the case. This radius actually compares "rather too big" with the surface of France. This seems to further favor the collocation of storms and the conclusion of clusters' high importance. I would advise the authors either to use a dynamically varying effective area for the storms, e.g. change according to intensity, or better, to detect and attribute specific wind patterns to storm track points.
Line 96. I am not sure what is meant by "potential noise". As far as I know, the TRACK algorithm identifies and tracks local maxima of relative vorticity. Relative vorticity is a high frequency field. Even a relatively coarse resolution of 0.25x0.25 would pose a computational challenge due to numerous identified local maxima that need to be tracked in time. So smoothing is necessary (I would actually argue that noise is not even appropriate here). How much smoothing is applied and what is the native horizontal resolution of the input field matters greatly for the eventual number of tracked features. These aspects, plus the arbitrary choice of cyclones duration (line 101), are all important to conclude to a high number of tracks. My impression is that in the trascks dataset might be included features (i.e. local maxima of vorticity) that could be rather small, or nested within greater cyclonic structures (e.g. local maxima of vorticity nested in a cold front). Therefore, several (many?) of the tracked features could be hardly considered as "distinct storms".
This is not a critical comment, because bottom line, there is no right or wrong when it comes to the number of tracks, albeit the reader needs to have a good statistical knowledge of the characteristics and nature of tracked features. For instance, it is not rare for tracking algorithms to perceive a "large scale cyclone" with 2 or 3 distinct centers as a set of that many different storm tracks. Whether there is one "big" cyclone or 2-3 "smaller" ones is an arbitrary choice and depends on the tuning of the cyclone tracking method. However, if there are large heterogeneities in the intensities of tracked cyclones, or if many of these cyclones overlap in time, then one needs to adjust their approach when attributing specific impacts to "storms" (e.g. adjusting the radius of influence). Lines 113-115 suggest that cyclone tracking approach was designed to include heterogeneous storms. This needs to be shown, discussed and linked with an accordingly adjusted attribution methodology (see also below).
Section 2.2

except if I missed it, Figure 1 is not referenced in the text. Please also provide dates in the caption or within the figure (if these are the tracks of named storms, that would be also nice to know). Also it would be useful to provide a reference in the map for 7.5 degrees West.
Lines 127-128: Distance is 420 km (assuming that 70 km refers to 1 degree..). But is 96 hours a realistic timescale for a cluster of storms hitting the same place? Please elaborate. The 96h criterion is explained in section 5 but this comes too late.
Lines 130-134. While I appreciate the numbers provided here, could we have a visual impression of the frequency of areas affected by storm clusters in France. Also in connection with the above, could we have a figure with statistical information about the storms included in every cluster?
2b. Attribution procedure

Section 3.1 was rather difficult to understand. Probably I get the big picture but the text is rather complex, dense and in some points rather incomprehensible (at least to me). I would certainly appreciate having an illustrative example (maybe also a flow diagram?) where each step of the procedure is shown in a bit more detail. Maybe the examples in section 4 could serve this cause.
Lines 173-174 are quite cryptic to me. What does it mean "understanding of storm damage and Generali’s exposure"? And what would it mean "few claims"? Please elaborate. How does this affect the results in this study. How representative is the exposure of Generali for the total of assets in France? Can your results be generalized about storm impacts in France? Actually, why the minimum number of claims is important here? Maybe one claim alone in an area where Generalli has a low exposure still insinuates an impactful event(?).
I actually failed to understand several concepts in section 3.1. In a rather naive approach, I would say that since you already have the coordinates of the circular area influenced by every track point and since you already have the information on a claim, then you could simply say that if the claim spatially and temporally coincides with a storm-affected area (+/- a certain amount of time), then the claim is attributed to that track point. Of course this way, the claim could be attributed to more than one tracked features. In this case, the claim could be certainly attributed to a cluster of tracks (or storms). But I do not see the feasibility of the "..ultimate goal to associate each claim with a single storm" as stated in lines 171-172. Supposing that two track points are very close to each other and correspond to two different storms, or maybe they correspond to the tracks of a "bigger" storm with two centers. Then, the high wind gusts somewhere between two track points will be due to the interaction of these two storm centers (e.g. two distinct storm centers close to each other may result in higher pressure gradients and thus higher wind speeds). There is no really a reason to say that the high gusts are due to one or the other track point. Even in such a case, one could e.g. attribute the claim to the closest track point of either storm. Given that claims and track points are pinpointed and temporally well-defined, I am not very sure I follow the complexity of the approach here, the meaningfulness of the "dstorm" variable and of the cost function. As stated before, I would appreciate illustrative examples to better understand their necessity and use.
Few additional comments

In the examples of Dec 1999, Line 350 states that misattribution compromises the vulnerability curves. I am not an expert but if I understand correctly the field, the vulnerability curve links wind speed with losses. So this is indifferent of a storm attribution procedure(?). If not, could you please explain a bit more this point?
In your methodological approach, if I understood correctly, "dstorm" is defined by coordinates which are located over the Ocean (7.5W). So choosing Xb = 3 days means that the claim could take place while a storm is really far from France (I guess still within 1300 kms of radius). Could you show examples where evidently high wind speed is relevant to a storm with a far-reached center? Maybe I misunderstood this part. Could you please clarify.
As an additional note, please avoid using the verb "land" for stating that a track point is over continental areas (or a cyclone influences such). First of all, the coordinate of 7.5 W used here is over the sea, second, I presume that the use of this terminology is inspired by the field of tropical cyclones where the usual term is "landfall". But even so, "landfall" has a special weight because the highest wind speed is really close to the cyclone center. Therefore, time and area of landfalling cyclones is directly relevant to impacts, which is not -always- the case in ETCs.
Section 2.2 seems more like a "methods" rather than "Data" as stated in the title of section 2.
Section 3.1 Lines 161-167. This paragraph seems more adequate for the introduction.

Citation: https://doi.org/10.5194/egusphere-2025-3138-RC2
- AC2: 'Reply on RC2', Laura Hasbini, 26 Sep 2025
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-3138/egusphere-2025-3138-AC2-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2025-3138-AC2
RC3:
'Comment on egusphere-2025-3138', Anonymous Referee #3, 26 Aug 2025

The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-3138/egusphere-2025-3138-RC3-supplement.pdf

Citation: https://doi.org/10.5194/egusphere-2025-3138-RC3
- AC3: 'Reply on RC3', Laura Hasbini, 26 Sep 2025
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-3138/egusphere-2025-3138-AC3-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2025-3138-AC3

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) (03 Oct 2025) by Joaquim G. Pinto

AR by Laura Hasbini on behalf of the Authors (19 Nov 2025) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (12 Dec 2025) by Joaquim G. Pinto

RR by Anonymous Referee #3 (19 Dec 2025)

RR by Anonymous Referee #2 (09 Jan 2026)

Suggestions for revision or reasons for rejection

I would like to thank the authors for the substantial effort to revise the paper and to reply to my comments. Unfortunately, I find that several of the points I raised were not adequately addressed. I would like to insist on the following:
- My overall impression is that the authors should make substantial revision of scientific language and make an effort for clearer organisation of the paper.
- Regarding the methods, I still do not fully understand the whole approach (see comments below).
- Finally, I have two general but critical comments:

A. The paper demonstrates results about the costliest storms, including famous cases, but we gain almost no knowledge about the storm characteristics that produce the damages. For instance, we gain no insights into claims’ distance from storms’ center, storms’ intensity and wind speed at claims’ location, seasonality of the clusters and storms, what kind of cyclones form the clusters, whether these clusters are multi-centered cyclones, or distinct storms (like in the case of Lothar). Figure 11 seems to summarize all new knowledge, but it only generally quantifies impacts because of clusters. Still, apart from the obvious (clusters are important for impacts), there is no information that someone could use in a subsequent study (see comment below on Fig. 11).

B. The paper insists too much on the 96h claims window and Generali’s terms. This is fine because it connects to the claims dataset, which seems to be very important in linking cyclones with economic impacts. However, this dataset is not open access, and the 96h clause is Generali-specific (maybe I am wrong). I am aware that the method is adjustable to other temporal claim windows, but this would make the paper only attractive to other insurances with their own contract specificities and, of course, their own proprietary datasets.

Combining these two critical comments together, I believe that the broader impact of the paper is rather weak, especially for the scientific community. I am thus inclined to recommend the rejection of the paper, except if the authors are willing to provide a more detailed association of claims to storms’ physical characteristics so that results are useful to bridge the scientific community with the industry.

Specific comments

All line numbering refers to the manuscript with tracked changes.

1. Presentation

I am afraid I have to repeat the first comment of my original review. While the authors adequately addressed every single comment that I had in my first review, in my view the manuscript suffers from use of plain language and lack of focus on the narrative. The whole manuscript demands important editing rather than addressing single phrases. I provide several examples below.

Line 23: automated scheme -> cyclone tracking method. Please be consistent throughout the manuscript on this terminology.

Line 30: I appreciate this discussion on cyclone tracking schemes, but it seems a bit disconnected from your motivation and inadequate for the beginning of the article. You could move it to the methods (?).

Line 34: I am not sure what makes SSI a “robust framework”. If I am not mistaken, SSI is a proxy for high-impact windstorms, not a framework. But even so, I am not sure what makes it “robust” here. Please elaborate, rephrase, or put into context.

Line 36: “restricted vision” is informal language. Anyway, SSI is anyway irrelevant to the existance or not of a storm.

Line 37: A jet stream can be an atmospheric feature or a large-scale atmospheric condition. “Background dynamical environment” seems a bit uncommon, if not odd.

Lines 40–41: I think that the severity of impacts only depends—from the hazard perspective—on the wind speed intensity. This is irrelevant to the underlying physical conditions. Please rephrase. Actually, the next and last phrase of the paragraph seems to be adequate.

Lines 58–59: Is the reference to Mailler et al. suggesting that “This metric was introduced based on the observation that storm occurrences do not follow a Poisson distribution with constant intensity”? If not, please provide a reference, or else move the citation to the end of this phrase.

Line 66: The article has many phrases like this one which linguistically seem to make sense but, at least to me, I feel that I do not gain much insight into the issue. First of all, “threat” is informal language; second, we do not understand if extreme cyclones are clustered or are part of clustering; and third, “pronounced” is not really giving much qualitative or quantitative information.

Lines 66–70: Awkward use of parentheses.

Lines 75–77: These two phrases seem paradoxical. The first one reads: “claims falling outside an event definition are either not reimbursed..” and the next phrase reads: “..event definitions are typically based on claim date”. If an event is defined by the claim, then how can a claim fall outside of an event?

Lines 72–85: My impression is that in crucial parts of the text, the authors use plain language; therefore, I miss some important aspects of the rationale and motivation of this study. This new paragraph has many examples of inaccurate text. For instance, in the first phrase of the paragraph we read that insurances are interested in the “representation of hazard”. I guess “hazard” is some kind of a measure for wind intensity. Then, to what does the term “event”, which is introduced right after, refer? Does it refer to the date when high wind speeds caused some damages, or to a cyclone itself? If it is the former (and this is why an “event” is defined by the claim), then why do they “lack physical coherence”? What does it mean that events are “adjusted to maximise.. reimbursable claims”? Then it is mentioned “consistent hazard-based definition”: consistent with what? And so on.

Line 81: This phrase seems to suggest that without linking high wind speed to physical drivers (the storms, in our case), it is not possible to anticipate potential impacts in the future. I guess that a 200-year return period of impacts due to windstorms can be estimated by multi-year simulations of wind speed alone.

Lines 83–85: This phrase is cryptic to me. “Hazard representation” in what? In atmospheric modelling? I guess that “accuracy” refers to the minimization of high wind speed bias (provided that the hazard is high wind speed). If this is the case, then how does bias correction strengthen the “physical consistency of event definition”? What do “efficiency”, “reliability”, and “processes” refer to here? Why is wind speed accuracy relevant to this study?

Lines 86–97: This whole paragraph comes too late. It gives the motivation and reasoning of this study. All the above paragraphs make sense to me after I read this one.

Line 90: I am not sure why non-inclusion of storms’ physical characteristics in risk calculation is presented as a limitation. I think that it is simply irrelevant to the risk since hazard is only defined by wind speed.

Line 101: I am not really sure that C3S was designed primarily for reinsurance purposes.

Line 107: What kind of biases? What is the limitation that is covered by this study?

Line 110: “Meteorology” is not a sector.

Lines 111–114: Most of the content of this phrase is not relevant to a “meteorological standpoint” but to risk calculations (i.e., “consequences”, “disentangling impacts”, “conditions contributing to damages”).

2. Methods

Line 137: Please change the reference according to the dataset used. C3S is the service hosting them.

Line 140: Sect. C -> Appendix C

Line 142: If I got it right, the time of dstorm is not necessarily related to impacts, so “impacts date” is not a good way to name the date that a storm reaches 7.5°W. Please revise.

Line 140: What is meant by “well-developed”? Please revise the whole manuscript to avoid plain language and give physical content to the terms you use.

Line 152: I am not sure I understand this ±12h window around cyclones’ center. Does this mean that the gust footprint is defined as the maximum gusts within 1300 km of each track point and within ±12h? That would mean that for some storms there will be an overlap sharing the same maximum values, right? For instance, in Fig. 1 the track points of the two storms are really close to each other (temporally and spatially) and probably they share the same maximum gusts. How often does it happen for at least two storms to share the same wind-gust maximum?

Line 157: Relaxed relative to what? Apologies if I missed it, but I did not find the criteria used to retain or reject a track.

Line 178: are -> area?

Lines 175–180: I am not sure I understand the rationale here. If we consider that a gust footprint is calculated within 1300 km, this is because we believe that important impacts can take place within this radius. However, line 176 suggests that within 700 km one might get more intense winds. Is there a relationship between wind speed and radius? If so, please elaborate. Thereafter, lines 176–179 seem to suggest that this radius is chosen to tune the ratio between individual storms and clusters, while the 4-day window is chosen according to Generali’s contracts. Is there an actual physical reason for the 4-day window and the 700 km radius? If yes, please elaborate. If not, I strongly suggest changing your approach so that clustering is based on physical criteria. Then, you can check how many storm clusters might match the claims.

I appreciate the inclusion of Fig. 2, but I still find it hard to understand. First of all, Fig. 2 refers to an example which comes much later in the text (Section 4.2). Then, line 229 starts the description of the figure from the second histogram. Trying to figure out the first histogram, I deduce from it that there is a considerable number of claims. However, on the right-hand side it reads “1 Claim”. Actually, I am not sure I understand the length of the red lines. In the caption it reads “storm events and their associated number of claims”. Does this mean that the length of a red line corresponds to the number of claims from a single storm? I actually thought that the example here is to associate claims per storm. If so, then the red vertical lines already show this information (?!). Also, I do not understand how red lines are placed between the grey bars. I guess that a red line is placed according to the “dstorm” date? If so, this is not clear. Also, if this is the case, then I do not understand the comment about “good alignment” in line 231. If the claims are declared with a lag-time difference with respect to dstorm, then a “good alignment” might be just a matter of coincidence, right? Finally, I guess that the claims shown in the histogram correspond to the whole of France(?). If so, is it correct to assume that part of the histogram is due to claims in eastern France while the dstorm has to do with the storm being located over the Atlantic Ocean? Actually, if a storm moves, then instead of a red line there should be a “red area”, since while the storm moves towards the east, it captures within its radius more and more regions, right?

I do not understand the last phrase of the caption.
Schematic -> Example
Plots -> histograms?

Line 232: Storms are mentioned here as “large-scale events” but previously it is argued that the methods are tuned to retain small-scale systems.

Line 234: Is there a reference or a basis for the assumption that a storm will result in at least 50 claims?

Line 241: storm date -> dstorm

Line 243: “The performances of the association are evaluated with three metrics, comparing the identified storms to the claim’s local maxima. The local maxima are identified by peaks over the time series of claim count gathering at least 10 claims.” This is an example of phrasing that gives me a hard time understanding the methods. How is the storm defined so that it is compared to a local maximum of claims? What is meant by peaks in the time series of claims? Are time series of claims for all France? I guess only for claims overlapping with the 1300 km radius. If different, please revise.

Lines 244–245: I do not understand these phrases. What is meant by major physical event? Is the physical event the storm? If so, this phrase suggests that there are also secondary or minor storms, and the tuning should capture the “major” one. Why is this assumed and not shown?

The paragraph continues with the definition of the metrics, but actually none is really clear to me (especially the last one in line 251).

Bottom line: how and why is the date that a storm is close to the reference longitude of 7.5°W important to define impacts? It seems completely arbitrary. Why is there no spatial criterion explicitly mentioned in this section? Do claim attributions with spatial criteria with respect to the storms’ area of influence? In fact, I am not really sure that the authors replied to my relevant comment in the previous review that reads “I actually failed to understand several concepts in section 3.1... better understand their necessity and use.”

I also do not understand the rationale of the reply to my question in the previous review: “In your methodological approach,... Could you please clarify.” So regarding the claims, a person might be mistaken by ±1 day on the date of impacts. So would it be reasonable (and much simpler) to simply consider that if an area within 1300 km from a storm center overlaps with a claim’s location within ±1 day, it could be attributed to that storm? In fact, the metrics used in the paper are not adequately explained, the conceptual diagram is quite difficult to understand, and I still lack examples or a good explanation about how a storm being 3 days before reaching the longitude of 7.5°W can be related to claims in France.

Section 3.3, and especially lines 305–310: I understand that the 40 costliest storms are consistent with the strongest ones in a Meteo-France open public dissemination (Meteo-France, 2023), but this only validates that major storms are part of your dataset. This is certainly good, but your method should be expected to include the costliest storms in your datasets anyway (else it would be worrisome). I think the interest here is to see all the weaker storms and the role of clusters in enhancing impacts. Unfortunately, so far we gain no particular insight into the representativity of the captured storms from the whole methodology, or into the weaknesses and advantages of your methods regarding clustered storms. We also gain no insight into the role of smaller storms in producing damages. I also have a bit of a hard time understanding Fig. 6. Actually, “global damage statistics of storms”, “distribution of the total cost per storm, the mean cost per claim and the number of claims per storm”, and “aggregating policy-based costs at the storm level” are not explained. So far, the methods were done to associate Generali’s claims to storms, but apparently things get a bit perplexed with two additional monetary metrics (“policy-based costs” and “cost per claim”).

Section 4

Basically, Sections 4.1 and 4.2 focus on known cases that have been addressed thoroughly in the past. Using known cases is good to support the methodological approach, as it is done here. Thereafter, however, there is just a small section (4.3) to provide new insights into storms’ relationship to impacts, which is rather weak. In fact, results in this section are summarized by Figure 11. This is a rather interesting figure, but it is hard to make use of it for any purpose other than quantifying the importance of clusters. As of now, we do not know the representativity of clusters in claims with respect to individual storms. We have no specific knowledge of the physical characteristics of the storms that produce the damages. We do not know, for instance, whether a loss rank 3 or 4 has a small percentage just because a very intense storms are only marginally affecting France. We also do not have knowledge about the lag dates between the events, the wind intensities that produce the damages, and whether, for instance, claims are collocated in clusters (at least this would provide some insight into recovery).

Line 405: Awkward phrasing. Pretty much all extratropical storms are formed due to wave activity driven by the jet. Please rephrase or remove.

Lines 421–422: This phrase is not really clear. I am sorry for the naive question, but if I got it right, any person will claim insurance within 4 days from the damages. Then, methodologically, a specific storm can be associated to a claim if the claim takes place within +4 days from the first time that the claim’s location is within 1300 km radius of this specific storm. Probably this comment connects to the methodological approach, but I think that this is a much simpler and accurate approach than the one in Section 2.

Line 638: There are no experiments in this paper. Please revise.

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ED: Reconsider after major revisions (further review by editor and referees) (11 Jan 2026) by Joaquim G. Pinto

AR by Laura Hasbini on behalf of the Authors (20 Feb 2026) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (21 Mar 2026) by Joaquim G. Pinto

RR by Anonymous Referee #2 (25 Mar 2026)

RR by Anonymous Referee #4 (13 Apr 2026)

ED: Publish subject to minor revisions (review by editor) (30 Apr 2026) by Joaquim G. Pinto

AR by Laura Hasbini on behalf of the Authors (12 May 2026) Author's response Author's tracked changes Manuscript

ED: Publish as is (17 May 2026) by Joaquim G. Pinto

AR by Laura Hasbini on behalf of the Authors (19 May 2026) Manuscript

Journal article(s) based on this preprint

12 Jun 2026

Unravelling wind-driven impact of storm clusters, a case study for the insurer Generali France

Laura Hasbini, Pascal Yiou, Quentin Hénaff, Laurent Boissier, and Arthur Perringaux

Nat. Hazards Earth Syst. Sci., 26, 2817–2839, https://doi.org/10.5194/nhess-26-2817-2026,https://doi.org/10.5194/nhess-26-2817-2026, 2026

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Laura Hasbini, Pascal Yiou, Laurent Boissier, and Arthur Perringaux

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Winter windstorms are the main natural hazard for Generali France. We present a method linking storm events to insurance claims, with a focus on clustered events (multiple storms hitting the same region within 96 h). These account for 85 % of losses since 1998 and include major events like Lothar and Klaus. Damaging storms are twice as likely to occur in clusters, underlining the need to account for their impact in risk, loss, and reinsurance modelling.


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