the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 30 Oct 2025
ImpactETC1.0: Impact-oriented tracking of extratropical cyclones with global optimisation and track reconciliation
Abstract. Extratropical cyclones (ETCs) play a critical role in shaping extreme weather events in the Nordic region, often driving storm surges, heavy precipitation, and high winds that can lead to significant socio-economic and environmental impacts. However, traditional cyclone tracking methods focus primarily on large-scale atmospheric dynamics without explicitly linking cyclone characteristics to their regional impacts. To address this gap, we introduce ImpactETC1.0, a novel framework designed to identify and track ETCs with a specific focus on their impacts, here illustrated for the case of storm surges. The framework includes several novel algorithmic features, including global optimisation for the correspondence problem, BLOB analysis techniques for track fragmentation issues arising from surface-level tracking over complex terrain, and automated calibration of post-processing parameters. Applied to the CERRA reanalysis dataset and with a focus on the Nordic region, ImpactETC1.0 successfully reconstructed ETC tracks across complex terrain and during periods of rapid storm evolution, while keeping computational costs low. Compared with a standard nearest-neighbour heuristics, the global optimisation reduced suboptimal connections by up to one-third, at negligible additional runtime. The track reconciliation step was essential in preventing track fragmentation and premature termination of tracks, producing storm tracks that were, on average, twice as long over complex land-ocean boundaries and mountain ranges. As the post-processing step is extremely quick to perform, a sensitivity analysis could be done, and a score named the Single Storm Score for automated calibration of filtering parameters was developed. A key strength of ImpactETC1.0 is the use of global optimisation and track reconciliation, as the need for these in storm tracking will only grow with the increasing resolution of data sets. Together, these results demonstrate that ImpactETC1.0 enables accurate and impact-relevant ETC tracking.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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RC1: 'Comment on egusphere-2025-4466', Anonymous Referee #1, 19 Nov 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-4466/egusphere-2025-4466-RC1-supplement.pdfCitation: https://doi.org/
10.5194/egusphere-2025-4466-RC1 -
AC2: 'Reply on RC1', Niels Agertoft, 02 Feb 2026
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-4466/egusphere-2025-4466-AC2-supplement.pdf
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AC2: 'Reply on RC1', Niels Agertoft, 02 Feb 2026
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RC2: 'Comment on egusphere-2025-4466', Anonymous Referee #2, 13 Dec 2025
In this paper, the authors introduce ImpactETC1.0, software that tracks extratropical cyclones (ETCs). They specifically focus on linking detected/tracked storms to observed regional impacts such as storm surges. The authors argue three key novelties to this work: (1) the application of the Hungarian Algorithm to globally optimize the "correspondence problem" of connecting storm centers across timesteps, which reduced "suboptimal connections" compared to traditional greedy algorithms; (2) a "BLOB" analysis technique for track reconciliation that merges broken tracks when storms cross orographically complex regions, and (3) an automated calibration procedure for post-processing parameters using a new metric they termed "Single Storm Score." Applied to historical storm surge events in Denmark using the high-resolution CERRA reanalysis dataset (1991-2020), the code successfully identified a series of impact-relevant ETCs.
In general, the paper is interesting and well-written (some minor typos are noted below). The material is well-suited for GMD. I would contend that the authors slightly overemphasize certain aspects of the work, and there is some additional room for contextualization and framing. More details regarding that are below. However, this critique aside, the ideas are worth considering, and the field of storm tracking more broadly lacks papers that discuss the "nuts and bolts" algorithmic performance and combine that with a discussion of tradeoffs, which this paper does. I would suggest some form of revisions based on the itemized feedback below. However, pending those revisions, I think the paper could be suitable for publication (and relevant for interested researchers) in GMD with some additional work.
Note, I have not tested the software/data, but I have verified that the DOI linking to the Zenodo is valid, and it contains code that appears sound, so this would adhere to EGU's data requirements.
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Major Theme #1, tracking mechanics during ETC trajectory building
The Hungarian algorithm implementation is interesting, and the argument for potential use cases for it over a greedy nearest neighbor is compelling computationally. As the authors do note, most of the wall clock time of their software is tied up in data ingestion, so even an order of magnitude slower during the "stitching" step is unlikely to have deleterious consequences on the overall workflow.
Regarding the physical motivation for the algorithm application. From a synoptic meteorology perspective, where the greedy algorithm seems to fail (Table 2) is for small pruning radii. This makes intuitive sense because there are far more potential "local minimum" low-pressure centers with a smaller radius with which to merge/eliminate nearby ones.
However, the realized physical spatial scales for ETCs are ~1000km (if not larger, this can be deduced from observation or scale analysis of the Navier-Stokes equations). Given this, I would struggle to find a practical reason why an ETC tracker would need to have such a small pruning radius. If we agree on this, the Hungarian algorithm doesn't objectively buy a lot of skill, because with larger pruning radii (physically consistent with ETC scales), the deviation between the nearest neighbor and the Hungarian Algorithm narrows significantly. However, as noted by the authors, it doesn't cost a lot more, and I think they do a good job of arguing that, without the cost being prohibitive, it is a physically defensible choice. The action item here may be for the authors to discuss how the scales of motion in weather phenomena dictate hyperparameter settings such as the pruning radius. I could imagine a situation where such an algorithm may be more beneficial for "noiser" fields (e.g., tracking individual thunderstorms, cloud tracing, etc.), so perhaps this can be noted as a target for future application.
Finally, the authors discuss "higher-resolution for ETCs" in the final paragraphs. While local scale impacts (e.g., precipitation, specific winds in coastal channels, etc.) may be better resolved, I'd argue that ETC tracking doesn't benefit from high-resolution (and may be better served by coarsening data anyways).
While not necessary for the paper, it would be interesting to see how the algorithm compares to a more established ETC tracker such as TRACK (Hodges) or TempestExtremes (Ullrich). Given the synoptic scales mentioned above, I would assume there is likely minimal difference, particularly for well-defined tracks, between the methods.
Major Theme #2, tracking variable choices and broken track evaluation
For the BLOB analysis, I interpret the code to work such that if I have a point that exists at t = t_1 and another point exists at t = t_3 (or thereabouts) with a large spatial gap, the BLOB area operator allows them to be "glued" together as a single track based on an overlap strategy. Frankly, in many ways, this seems to behave somewhat like Ullrich et al. (2021) and Peréz-Alarcón et al. (2024), both of which the authors mention. Can the authors comment as to the specific, unique benefits of applying such analysis versus allowing time-varying gaps in the "gluing" stage? I.e., does the BLOB method provide materially different results than the simpler logic during stitching of "any ETCs within nhrs of model time and (nhrs * max_travel_dist/hr) in space are considered the same system?"
The reasoning behind the BLOB logic is to deal with the common challenge of the land surface and orography influencing near-surface quantities (particularly problematic for mean sea level pressure (MSLP), as it's a derived quantity based on the elevation and surface pressure in the model). It is my understanding that the design choices of alternative tracking fields (i.e., see the contributions to the Neu BAMS paper that the authors cite) are chosen in large part because of some of the orography-induced challenges the authors point out here. The authors then go on to use MSLP as a core tracking variable (in conjunction with 500mb vorticity). It appears this was done because low-level vorticity fields are noisy (aside: this is not unexpected in high-resolution data, although this can be smoothed as in many of Hodges' papers). However, this does introduce a bit of what I would consider semi-circular logic ("we have a problem of broken tracks to solve, but we choose a tracking variable that has been shown to be prone to broken tracks"). I'll admit I'm playing a bit of devil's advocate here since I prefer tracking on MSLP myself (and MSLP is probably most tied to surface wind/wave forcing relevant for surge, which may be worth pointing out more aggressively in the manuscript), but some context in the manuscript might help smooth out the rationale.
Major Theme #3: Impacts and compound events
It is my understanding that the core of the ETC (i.e., the sea level pressure minimum) must be in the AOR for it to be classified as an impactful storm. However, it is well known that mid-latitude cyclones can have impacts extending far from the core of the storm (e.g., cold fronts). While I assume the authors are most concerned with the surge associated with the wind field near the comma head, it would be good to discuss this a bit more. For example, such a technique may struggle if applied to ETCs that produce precipitation impacts, as such impacts can be rather disconnected from the storm's dynamical core (e.g., see atmospheric rivers tied to mid-latitude cyclones).
Along the same line, I was a bit disappointed that an impact-focused paper didn't really discuss the hazards themselves. E.g., in this high-resolution model, one could easily analyze the wind field that is the driver of the surge in the region and see how this is tied to the ETC centers that are detected. The authors wouldn't necessarily need to run a hydrodynamical model, but just evaluate the magnitude and spatial extent of the wind impinging on the shoreline. In fact, I would argue that this is a key drawback of pointwise Lagrangian analysis; 1-D tracks lack information about the spatiotemporal evolution of the 3D atmosphere, land, and ocean during relevant extreme weather events. I think it would be a nice addition to the paper (a qualitative case analysis, not unlike what was taken with a handful of events) and could improve the "impact" (no pun intended) the work makes on the community. However, if the authors do not wish to undertake such an endeavor, it should be discussed more at the end of the manuscript.
Major Theme #4: Semantical accuracy
I have slight issues with phrasing like "several innovative components." A definition of "innovative" is "introducing new, valuable ideas, methods, products, or services that are original, solve problems, and improve upon the status quo, often by creatively combining existing elements or challenging norms to deliver fresh solutions and create impact." I will admit, I find the Hungarian optimization the most novel component of the work. I consider the BLOB analysis (see above) to be a distinctive slant on previous strategies aimed at addressing track gaps in space and time. The post-processing optimization can broadly be considered a basic grid-search hyperparameter tuning that prioritizes having a singular event in the impacts domain (i.e., it's computationally efficient because it's only modifying a small subset of hyperparameters). I'll also point out that, tied to the above, the hypothesis implicit in the optimization is to enforce the notion that a singular event leads to specific hazards. For very local scales, that may be the case, but there is a large volume of literature surrounding compound extremes, including multiple events/stressors amplifying a hazard (e.g., flooding).
While I think all of the above are worth publishing, I think the authors should take care not to deemphasize existing work in the space via the use of what I would consider somewhat loaded terms like "novel" and "innovative."
---
Minor comments:
In general, I found the paper to be relatively free of typographical errors. I did see some spellings like "artefacts" that I think are less common usage, but Google assures me they are valid. As always, I encourage the authors to do a thorough read-through after revision, but I commend them on the attention to detail in the initial submission.
Figure 1. Some more geographic information (e.g., lat/lon ticks) would help in the left panel.
Line 137. Figure 3 should be capitalized.
Is Fig. 5a missing some of the inland points over Norway? (e.g., black 16/17 from Fig. 6)
There are many examples where the possessive is applied to plural acronyms/words (e.g., ETC's in lines 380-384). These should not have an apostrophe (apostrophe only used for possessive or contractions).
In Figs. 5 and 6, the colormap is inappropriately washed out on the strong cyclone in the NE corner. Adjust the color scale.
For Figs. 5 and 6, I also wonder if a different colormap would be beneficial (perhaps a perceptually uniform colormap to avoid color-blind issues). Red-blue implies biases or differences visually.
Fig 10. The inset is a bit small; I would try to make it larger. Also, it may be worth trying to add some more color contrast (e.g., coloring the land mass in the inset brown instead of gray or something to highlight it's a distinct figure from the parent it overlays.
Citation: https://doi.org/10.5194/egusphere-2025-4466-RC2 -
AC1: 'Reply on RC2', Niels Agertoft, 02 Feb 2026
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-4466/egusphere-2025-4466-AC1-supplement.pdf
-
AC1: 'Reply on RC2', Niels Agertoft, 02 Feb 2026
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2025-4466', Anonymous Referee #1, 19 Nov 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-4466/egusphere-2025-4466-RC1-supplement.pdf
-
AC2: 'Reply on RC1', Niels Agertoft, 02 Feb 2026
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-4466/egusphere-2025-4466-AC2-supplement.pdf
-
AC2: 'Reply on RC1', Niels Agertoft, 02 Feb 2026
-
RC2: 'Comment on egusphere-2025-4466', Anonymous Referee #2, 13 Dec 2025
In this paper, the authors introduce ImpactETC1.0, software that tracks extratropical cyclones (ETCs). They specifically focus on linking detected/tracked storms to observed regional impacts such as storm surges. The authors argue three key novelties to this work: (1) the application of the Hungarian Algorithm to globally optimize the "correspondence problem" of connecting storm centers across timesteps, which reduced "suboptimal connections" compared to traditional greedy algorithms; (2) a "BLOB" analysis technique for track reconciliation that merges broken tracks when storms cross orographically complex regions, and (3) an automated calibration procedure for post-processing parameters using a new metric they termed "Single Storm Score." Applied to historical storm surge events in Denmark using the high-resolution CERRA reanalysis dataset (1991-2020), the code successfully identified a series of impact-relevant ETCs.
In general, the paper is interesting and well-written (some minor typos are noted below). The material is well-suited for GMD. I would contend that the authors slightly overemphasize certain aspects of the work, and there is some additional room for contextualization and framing. More details regarding that are below. However, this critique aside, the ideas are worth considering, and the field of storm tracking more broadly lacks papers that discuss the "nuts and bolts" algorithmic performance and combine that with a discussion of tradeoffs, which this paper does. I would suggest some form of revisions based on the itemized feedback below. However, pending those revisions, I think the paper could be suitable for publication (and relevant for interested researchers) in GMD with some additional work.
Note, I have not tested the software/data, but I have verified that the DOI linking to the Zenodo is valid, and it contains code that appears sound, so this would adhere to EGU's data requirements.
---
Major Theme #1, tracking mechanics during ETC trajectory building
The Hungarian algorithm implementation is interesting, and the argument for potential use cases for it over a greedy nearest neighbor is compelling computationally. As the authors do note, most of the wall clock time of their software is tied up in data ingestion, so even an order of magnitude slower during the "stitching" step is unlikely to have deleterious consequences on the overall workflow.
Regarding the physical motivation for the algorithm application. From a synoptic meteorology perspective, where the greedy algorithm seems to fail (Table 2) is for small pruning radii. This makes intuitive sense because there are far more potential "local minimum" low-pressure centers with a smaller radius with which to merge/eliminate nearby ones.
However, the realized physical spatial scales for ETCs are ~1000km (if not larger, this can be deduced from observation or scale analysis of the Navier-Stokes equations). Given this, I would struggle to find a practical reason why an ETC tracker would need to have such a small pruning radius. If we agree on this, the Hungarian algorithm doesn't objectively buy a lot of skill, because with larger pruning radii (physically consistent with ETC scales), the deviation between the nearest neighbor and the Hungarian Algorithm narrows significantly. However, as noted by the authors, it doesn't cost a lot more, and I think they do a good job of arguing that, without the cost being prohibitive, it is a physically defensible choice. The action item here may be for the authors to discuss how the scales of motion in weather phenomena dictate hyperparameter settings such as the pruning radius. I could imagine a situation where such an algorithm may be more beneficial for "noiser" fields (e.g., tracking individual thunderstorms, cloud tracing, etc.), so perhaps this can be noted as a target for future application.
Finally, the authors discuss "higher-resolution for ETCs" in the final paragraphs. While local scale impacts (e.g., precipitation, specific winds in coastal channels, etc.) may be better resolved, I'd argue that ETC tracking doesn't benefit from high-resolution (and may be better served by coarsening data anyways).
While not necessary for the paper, it would be interesting to see how the algorithm compares to a more established ETC tracker such as TRACK (Hodges) or TempestExtremes (Ullrich). Given the synoptic scales mentioned above, I would assume there is likely minimal difference, particularly for well-defined tracks, between the methods.
Major Theme #2, tracking variable choices and broken track evaluation
For the BLOB analysis, I interpret the code to work such that if I have a point that exists at t = t_1 and another point exists at t = t_3 (or thereabouts) with a large spatial gap, the BLOB area operator allows them to be "glued" together as a single track based on an overlap strategy. Frankly, in many ways, this seems to behave somewhat like Ullrich et al. (2021) and Peréz-Alarcón et al. (2024), both of which the authors mention. Can the authors comment as to the specific, unique benefits of applying such analysis versus allowing time-varying gaps in the "gluing" stage? I.e., does the BLOB method provide materially different results than the simpler logic during stitching of "any ETCs within nhrs of model time and (nhrs * max_travel_dist/hr) in space are considered the same system?"
The reasoning behind the BLOB logic is to deal with the common challenge of the land surface and orography influencing near-surface quantities (particularly problematic for mean sea level pressure (MSLP), as it's a derived quantity based on the elevation and surface pressure in the model). It is my understanding that the design choices of alternative tracking fields (i.e., see the contributions to the Neu BAMS paper that the authors cite) are chosen in large part because of some of the orography-induced challenges the authors point out here. The authors then go on to use MSLP as a core tracking variable (in conjunction with 500mb vorticity). It appears this was done because low-level vorticity fields are noisy (aside: this is not unexpected in high-resolution data, although this can be smoothed as in many of Hodges' papers). However, this does introduce a bit of what I would consider semi-circular logic ("we have a problem of broken tracks to solve, but we choose a tracking variable that has been shown to be prone to broken tracks"). I'll admit I'm playing a bit of devil's advocate here since I prefer tracking on MSLP myself (and MSLP is probably most tied to surface wind/wave forcing relevant for surge, which may be worth pointing out more aggressively in the manuscript), but some context in the manuscript might help smooth out the rationale.
Major Theme #3: Impacts and compound events
It is my understanding that the core of the ETC (i.e., the sea level pressure minimum) must be in the AOR for it to be classified as an impactful storm. However, it is well known that mid-latitude cyclones can have impacts extending far from the core of the storm (e.g., cold fronts). While I assume the authors are most concerned with the surge associated with the wind field near the comma head, it would be good to discuss this a bit more. For example, such a technique may struggle if applied to ETCs that produce precipitation impacts, as such impacts can be rather disconnected from the storm's dynamical core (e.g., see atmospheric rivers tied to mid-latitude cyclones).
Along the same line, I was a bit disappointed that an impact-focused paper didn't really discuss the hazards themselves. E.g., in this high-resolution model, one could easily analyze the wind field that is the driver of the surge in the region and see how this is tied to the ETC centers that are detected. The authors wouldn't necessarily need to run a hydrodynamical model, but just evaluate the magnitude and spatial extent of the wind impinging on the shoreline. In fact, I would argue that this is a key drawback of pointwise Lagrangian analysis; 1-D tracks lack information about the spatiotemporal evolution of the 3D atmosphere, land, and ocean during relevant extreme weather events. I think it would be a nice addition to the paper (a qualitative case analysis, not unlike what was taken with a handful of events) and could improve the "impact" (no pun intended) the work makes on the community. However, if the authors do not wish to undertake such an endeavor, it should be discussed more at the end of the manuscript.
Major Theme #4: Semantical accuracy
I have slight issues with phrasing like "several innovative components." A definition of "innovative" is "introducing new, valuable ideas, methods, products, or services that are original, solve problems, and improve upon the status quo, often by creatively combining existing elements or challenging norms to deliver fresh solutions and create impact." I will admit, I find the Hungarian optimization the most novel component of the work. I consider the BLOB analysis (see above) to be a distinctive slant on previous strategies aimed at addressing track gaps in space and time. The post-processing optimization can broadly be considered a basic grid-search hyperparameter tuning that prioritizes having a singular event in the impacts domain (i.e., it's computationally efficient because it's only modifying a small subset of hyperparameters). I'll also point out that, tied to the above, the hypothesis implicit in the optimization is to enforce the notion that a singular event leads to specific hazards. For very local scales, that may be the case, but there is a large volume of literature surrounding compound extremes, including multiple events/stressors amplifying a hazard (e.g., flooding).
While I think all of the above are worth publishing, I think the authors should take care not to deemphasize existing work in the space via the use of what I would consider somewhat loaded terms like "novel" and "innovative."
---
Minor comments:
In general, I found the paper to be relatively free of typographical errors. I did see some spellings like "artefacts" that I think are less common usage, but Google assures me they are valid. As always, I encourage the authors to do a thorough read-through after revision, but I commend them on the attention to detail in the initial submission.
Figure 1. Some more geographic information (e.g., lat/lon ticks) would help in the left panel.
Line 137. Figure 3 should be capitalized.
Is Fig. 5a missing some of the inland points over Norway? (e.g., black 16/17 from Fig. 6)
There are many examples where the possessive is applied to plural acronyms/words (e.g., ETC's in lines 380-384). These should not have an apostrophe (apostrophe only used for possessive or contractions).
In Figs. 5 and 6, the colormap is inappropriately washed out on the strong cyclone in the NE corner. Adjust the color scale.
For Figs. 5 and 6, I also wonder if a different colormap would be beneficial (perhaps a perceptually uniform colormap to avoid color-blind issues). Red-blue implies biases or differences visually.
Fig 10. The inset is a bit small; I would try to make it larger. Also, it may be worth trying to add some more color contrast (e.g., coloring the land mass in the inset brown instead of gray or something to highlight it's a distinct figure from the parent it overlays.
Citation: https://doi.org/10.5194/egusphere-2025-4466-RC2 -
AC1: 'Reply on RC2', Niels Agertoft, 02 Feb 2026
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-4466/egusphere-2025-4466-AC1-supplement.pdf
-
AC1: 'Reply on RC2', Niels Agertoft, 02 Feb 2026
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Jonas Wied Pedersen
Ida Margrethe Ringgaard
Morten Andreas Dahl Larsen
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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