the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 18 Dec 2024
Saharan dust linked to European hail events
Abstract. Saharan dust significantly influences hail occurrence in Europe. Using Copernicus Atmosphere Monitoring Service (CAMS) and reanalysis data, crowd-sourced hail reports, lightning data, and radar measurements, we find a strong correlation between elevated dust loading and hail events. Hail coverage exceeding 28 % of 1°×1° grid cells only occurs when dust loading surpasses 2.4 mg m−2, while on hail days the median dust load is 1.82 times higher than on non-hail days (7σ difference). This effect is particularly strong along the Alpine crest, central France, eastern Germany, Austria, and Eastern Europe, where median dust loads more than double on hail days.
By grouping data according to synoptic weather patterns, we confirm that hail days consistently exhibit higher dust concentrations regardless of prevailing synoptic conditions, supporting the robust link between dust and hail. Peak hail activity occurs at 38 mg m−2 or a dust optical depth of 0.033, suggesting enhanced cloud and ice nucleation. Above this range, hail frequency declines, likely due to microphysical or radiative constraints.
Crowd-sourced reports show significantly more hail events on high-dust days, with up to 10 times more reports for hail >20 mm. Statistical hail models, including a logistic regression model (LRM) and a generalized additive model (GAM), rank dust as one of the top three predictors. Its inclusion increases the critical success index (CSI) by 5 % (LRM) and 12 % (GAM), and boosts explained variance in the GAM by 6 %. These findings identify Saharan dust as a key modulator of European hail activity.
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Status: open (until 23 Apr 2025)
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RC1: 'Comment on egusphere-2024-3924', Anonymous Referee #1, 23 Feb 2025
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Dust aerosols have an important influence on cloud formation and development. This manuscript analyzes the influence of Saharan dust on hail in Europe, which has important scientific significance. Nevertheless, the manuscript leaves much to be desired. Here are some specific comments:
1. Lines 43-44: It’s inappropriate to cite unpublished papers.
2. How did the authors determine that all the dust came from the Sahara? Relevant weather pattern analysis is required.
3. Lines 81-84: The author declared that they mainly focus on to investigate the influence of dust aerosol on hail occurrence, but only local days with lightning were included. Can it be understood that hail and lightning occur simultaneously? It should be described in more detail to make it easier for readers to understand.
4. When the availability of the OPERA data is less than 100% in a 1°×1° grid, how is the hail area fraction calculated?
5. Lines 106-109: Do you mean that there are only 140 grid-points are available using EURADHAIL for determine hail events? I found that it conflicts with Figure 2.
6. Lines 114-17: This sentences “POH is an empirical hail detection algorithm estimating ground-level hail probability (0 – 100%) based on the vertical distance between the 45 dBZ echo top height and the freezing level height, following Waldvogel et al. (1979). This approach is more accurate in capturing hail events than EURADHAIL, since it does not include the freezing level.” confused me. Freezing level height is used to judge hail events, why does the author claim that this algorithm is more accurate than EURADHAIL because it does not include freezing level?
7. Many of the labels on the horizontal and vertical axes of the figures are incomplete and need to be carefully modified.
8. Line 145: Thunderstorm day or hail day, which one is right? The same question in the title of Figure 1 and Figure 4.
9. More detailed description about the Q should be added in Figure 1. In addition, how to calculated the fraction of hail days in Figure 3?
10. In figure 1, the mass of dust concentration is only divided into 2 groups. If the dust mass concentration is divided into three groups, does the maximum hail area fraction change with the dust mass concentration group as described in the manuscript？
11. Lines 156-158: Such analysis does not make sense, since aerosols of different scales co-exist in hail days.
12. Lines 180-184 and Lines 190-192: How does the author determine the optimal number of clustering centers?
13. Lines 225-226: “availability” should be “variable”. Why are different moisture variable used in LRM and GAM models.
14. Lines 226-229: This sentence confused me. Which variable is most important for hail event prediction, dust loading or CAPE？Citation: https://doi.org/10.5194/egusphere-2024-3924-RC1 -
AC1: 'Comment on egusphere-2024-3924', Killian Brennan, 26 Feb 2025
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After some of the comments from the first reviewer, we checked the preprint which was posted on the 18th of December, and realized that the figure axis labels of most figures are messed up. The figures were intact in the initial submission, however it appears that some fonts were not properly embedded.
The missing figure elements make the figures and, by extension, the manuscript difficult to follow, we apologize for this inconvenience.Following suggestions from the reviewer, we've included rasterized versions of the figures in the manuscript in this comment.
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