1. Introduction
Hail annually causes billions of dollars [U.S. dollars (USD)] in damage worldwide to crops and property combined. Damage from individual hailstorms can exceed $1 billion USD, as shown by individual cases from Europe (Höller and Reinhardt 1986; Kunz et al. 2018), the United States (Changnon and Burroughs 2003; Brown et al. 2015), and Australia (Yeo et al. 1999; Schuster et al. 2005). While small hail (i.e., less than 2 cm in diameter) can severely impact agriculture (Changnon et al. 2009), it rarely causes damage to property. To address the risk hail poses to property, it is necessary to look specifically at the occurrence of larger hail. Unfortunately, there is a lack of temporally and spatially homogeneous records of large hail over Europe. Most climatological studies about hail over Europe (see the recent review by Punge and Kunz 2016) do not concern large hail, with exceptions of Dessens (1986) for France, Burcea et al. (2016) for Romania, Tuovinen et al. (2009) for Finland, and Kahraman et al. (2016) for Turkey. This is because European weather stations typically do not record hail sizes, unlike in China, where the hail size is observed at all stations (Li et al. 2018). Hailpad station networks, often installed in hail-prone areas such as northern Spain, southern France, or northern Italy, have facilitated analyses of the spectrum of observed hail sizes (Fraile et al. 1992; Fraile et al. 2003; Sánchez et al. 2009; Palencia et al. 2010), but they often miss the largest hailstones due to the gaps between the hail pads (Changnon 1977; Smith and Waldvogel 1989).
Besides weather stations and hailpad networks, observations of large hail can be collected from various sources, such as voluntary observers, storm spotters, or media. Collecting such information has been standard practice since the 1950s in the United States (Tippett et al. 2015; Allen and Tippett 2015) and Australia (Allen and Allen 2016), though questions have been raised regarding the lack of homogeneity of these datasets (Allen and Tippett 2015). In Europe, the European Severe Weather Database (Dotzek et al. 2009; Groenemeijer et al. 2017), established in 2006 and managed by the European Severe Storms Laboratory (ESSL), collects reports of large hail.
Remote sensing provides more spatially homogeneous observations of (severe) convective hazards than storm spotter reports. Hail size metrics derived from the vertical profiles of radar reflectivity have been used to develop climatologies of large hail in the United States (Cintineo et al. 2012) and the Alpine region (Kunz and Puskeiler 2010; Nisi et al. 2016; Nisi et al. 2018). However, several studies have pointed out limitations of radar in inferring hail size (Edwards and Thompson 1998; Cică et al. 2015; Ortega 2018). Satellites cover larger areas than the radar observations (Cecil and Blankenship 2012), but the results tend to overestimate hail occurrence in tropics (Allen et al. 2015; Ferraro et al. 2015). Observations of overshooting tops as indicators of storm severity were used to develop hail models over Europe (Punge et al. 2017) and Australia (Bedka et al. 2018). In addition, the frequency of large hail on global (Prein and Holland 2018), continental (Rädler et al. 2018), and national (Allen et al. 2015) scales has been estimated by identifying favorable large-scale environments in reanalysis datasets.
Due to the large economic losses hail can cause, the relationship between inflicted damage and hailstorm intensity is another branch of hail-related research, which can help in terms of increasing structural resilience. A frequently used measure for hailstorm intensity is hail kinetic energy, which can be derived from both radar and hailpad data (Waldvogel et al. 1978; Hohl et al. 2002; Sánchez et al. 2013). However, Schuster et al. (2006) and Brown et al. (2015) noted weak correlation between the radar-derived hail kinetic energy and the property losses. This metric is not a reliable indicator of the observed hail size, as it depends on the concentration of hail stones, which tends to get smaller for large hail (Cheng and English 1983). Hail size observations are thus still necessary to complement the radar data in these types of studies.
Studies of inflicted damage with respect to observed or radar-derived hail sizes have been done either by detailed investigations of individual hail events (Brown et al. 2015) or in a laboratory setting (Marshall et al. 2002). There is a lack of studies on the relationship between hail size and damage covering multiple cases or multiple years of data, except for crops (Sánchez et al. 1996; Changnon et al. 2009). The Institute for Business and Home Safety is currently using hail observations from the Community Collaborative Rain, Hail and Snow Network (CoCoRaHS) to create such a dataset in the United States (Giammanco et al. 2014; Reges et al. 2016).
In addition to structural damage, very large hailstones (≥5 cm) may cause injury or death to animals or humans. Individual cases of injuries or fatalities of people have been reported in the literature (Calianese et al. 2002), including large death tolls for historical hail cases in southern Asia (Cerveny et al. 2017). A comprehensive assessment of the injuries or deaths caused by large hail has not been published so far for any region.
This paper uses archived hail reports, hail loss insurance data, and a probabilistic hail model to fill existing gaps in the knowledge of 1) large hail incidence, 2) the societal and economic impacts of large hail across Europe, and 3) the relationship between hail size and damage. Section 2 describes the data and methodology used to analyze hail occurrence and its impacts, section 3 discusses the spatiotemporal characteristics of large hail, section 4 deals with the relationship of hail size with the impacts and section 5 with the injuries and economic losses caused by hail. Section 6 briefly summarizes the main results.
2. Data
Quality controlled large hail reports were obtained from the ESWD. The quality control system was described in detail by Dotzek et al. (2009) and Groenemeijer et al. (2017). Only the reports that have passed the plausibility check were included for further analysis, comprising a total of 39 537 reports by 31 December 2018 (including the historical reports entered in the database retrospectively). Each report of large hail contains information on event location, location accuracy, event time, event time accuracy and the source of the information. Optionally, a number of fields can be filled out, such as maximum hail diameter, hail weight, depth of a hail layer, number of injured/killed people or a description of the event. Unlike Storm Data in the United States, maximum hail diameter is not a mandatory field in the ESWD. In this paper we will refer to large hail as hail with diameter ≥2cm, very large hail as hail with diameter ≥5 cm and giant hail as hail with diameter ≥10 cm (Blair et al. 2011). Information about the damage inflicted by hail was acquired from the description field of the report. To match the particular hail sizes with specific types of damage, we searched for object keywords contained in the description field such as “vegetation,” “vehicle,” “window,” “windshield,” “roof,” or “greenhouse” and then checked for the damage description associated with that object. The descriptions submitted in languages other than English were not taken into consideration to prevent the incorrect interpretation. The fraction of entries in the description field in other languages was 4.5%. Such entries were typically entered in German or French and pertained historical reports from before 2006.
Data on the hail losses were acquired from the NatCatSERVICE database of Munich RE (Kron et al. 2012), which contains information from more than 30 000 datasets. Each loss entry includes its type, location, date, monetary losses, and the description. We considered only events, where either the type or the description of the event suggested that the loss was at least partly caused by hail. Individual loss events often spanned multiple days and comprised multiple severe convective storms. In such cases, the coordinates of the loss location point to the place of the greatest loss during the event. When calculating the number of hail loss days per year, we took into consideration the whole duration of the loss event. Due to inflation, urban growth and regional wealth differences, we used the estimated overall losses normalized to 2018 levels of destructible wealth. Normalization is done by considering the Gross Domestic Product discretized on a 1° × 1° latitude and longitude grid. Kron et al. (2012) state that the accuracy of the loss estimates in the NatCatSERVICE depends on the insurance penetration in a given country for a specific hazard. In Europe, the best insurance penetration is in Germany, BENELUX, Austria, and Switzerland.
To match the hail-related losses and number of hail loss days to the frequency of favorable environments for damaging hailstorms, we used the additive regression hazard model ARCHaMo (Rädler et al. 2018). ARCHaMo was developed for central Europe using ERA-Interim (Dee et al. 2011), EUCLID lightning detection network (Schulz et al. 2016), and the ESWD dataset between 2008 and 2016. The model predicts the probability of a severe hazard as a product of two components: the probability that a thunderstorm occurs and the probability of the severe hazard, given the storm is present. Annual losses and annual number of hail loss days were compared to the probability of large and very large hail based on the ARCHaMo applied to the ERA-Interim dataset. The annual frequency was calculated as a sum of the 6-hourly probabilities of large and very large hail, averaged over a specific area in the 0.75° × 0.75° latitude and longitude grid in the ERA-Interim dataset.
3. Spatiotemporal climatology of large hail reports according to the ESWD
a. Number of (very) large hail days across Europe
The annual number of large and very large hail reports (Fig. 1) shows a steep increase in the 30-yr period between 1989 and 2018. Since the ESSL was founded in 2006, the annual number of ESWD large hail reports has tripled, from 750 reports in 2006 to 2400 reports in 2018. Since 2012, the number of reports has consistently exceeded 2000, whereas prior to 2000 it never reached 100. A similar pattern is visible for very large hail reports. The trend in the number of hail reports reflects an increase in the reporting rate due to the increased cooperation between the ESSL and voluntary observer networks or national (hydro)meteorological institutes across Europe. Thus, reports cannot be used to investigate the trend in the large hail occurrence, similar to the record of large hail in Storm Data for the United States (Allen and Tippett 2015).
Number of large (blue line) and very large (red line) hail reports between 1990 and 2018.
Citation: Monthly Weather Review 147, 11; 10.1175/MWR-D-19-0204.1
To perform spatial analysis, we selected reports in the period from 2006 onward, when a coordinated effort to collect reports across the whole of Europe started. Large hail is most frequently reported in the pre-Alpine areas of central Europe and northern Italy, with up to 4 large hail days per year (Fig. 2a). A large hail day is defined as a 24-h period, between 0000 and 0000 UTC, with at least one report of large hail. Reports were counted in a grid spanning 0.5° longitude × 0.5° latitude. Central Europe has the smallest reporting bias and thus also shows the highest frequency of large hail days, compared to southern or eastern Europe (Groenemeijer and Kühne 2014). Outside central Europe, local maxima can be found over southern Bulgaria, the Caucasus mountains, and the Moscow metropolitan area. Very large hail occurs less frequently, but the location of maxima shows similar patterns to large hail with the highest incidence over southeastern Austria, where very large hail is reported almost every second year (Fig. 2b). A local maximum between the Black Forest and Swabian Jura in southwestern Germany was also detected by Kunz and Puskeiler (2010). Other maxima were found over the Po Valley in Italy, over Ore mountains on the border of the Czech Republic and Germany, and Silesia on the border of the Czech Republic and Poland. These areas experience on average at least one day with very large hail every 5 years.
Mean annual number of (a) large and (b) very large days in the 0.5° latitude × 0.5° longitude grid, smoothed by a Gaussian filter.
Citation: Monthly Weather Review 147, 11; 10.1175/MWR-D-19-0204.1
The locations of hail maxima based on studies from individual countries (i.e., France, Germany, Poland, Austria, Croatia, or Serbia) shown in Punge and Kunz (2016) coincide with the maxima based on the ESWD reports. Underreporting in the areas outside of central Europe do not allow for a country to country comparison of large hail incidence. Furthermore, because most hail climatologies listed in Punge and Kunz (2016) did not focus exclusively on large hail, a quantitative comparison to our study is difficult. Rädler et al. (2018), Taszarek et al. (2018) and Púčik et al. (2017) show that environmental conditions supporting severe thunderstorms (or large hail) occur as frequently over eastern Spain, southern France and parts of the Balkan peninsula, as they do over southern Germany or southeastern Austria. The discrepancy between the frequency of favorable environments and the ESWD reports has also been noted by Taszarek et al. (2019), and the observations of overshooting tops (Punge et al. 2017) associated with convective storms corroborate the negative bias of ESWD outside the central Europe.
Hailpad network data (Fraile et al. 2003) show that hail larger than 3 cm in diameter occurs every year somewhere in the hail pad network of southern France, spanning 811 stations over 34 000 km2, again suggesting underreporting in the current state of ESWD. On the other hand, hailpad data tend to underestimate the largest hail sizes (Fraile et al. 2003). For example, the hailpad network over northeastern Italy collected a maximum hail size of 3.5 cm over an 11-yr period (Palencia et al. 2010), while ESWD reports suggest that the area experienced very large hail on average at least once every 5 years. Gridding the dataset to 0.5° of latitude and longitude and smoothing with a Gaussian filter has decreased the strong gradients in the large hail activity between the main Alpine ridge and pre-Alpine areas. This strong gradient in large hail activity between the mountains and premountain areas was found using the radar data over Switzerland (Nisi et al. 2016; Nisi et al. 2018).
b. Annual cycle
Peak month was calculated similar to Groenemeijer and Kühne (2014), over a grid of 2° of latitude and longitude and a running 3-month mean. Large hail is reported most frequently in the summer months across much of Europe and the frequency peaks in June over three quarters of the continent (Fig. 3). Frequency peaks in July over northeastern Spain, southeastern France, northern Italy, United Kingdom, parts of Estonia, or Russia. Slightly more variation can be found over southern Europe with large hail occurring most frequently in September over Corsica, February over Crete and the southern Ionian Sea, and in May over southern Greece, southern Turkey, and Cyprus. Continental areas experience the peak thunderstorm season in months with peak heating, while the coastal maritime areas experience their peak in the autumn to winter months as the Atlantic subtropical ridge shifts southward and deep low pressure systems develop over the Mediterranean Sea.
Month with most frequent large hail occurrence calculated over 2° latitude × 2° longitude grid. Grid points with less than 5 events are not displayed.
Citation: Monthly Weather Review 147, 11; 10.1175/MWR-D-19-0204.1
Splitting Europe into two parts, south and north of the 46° latitude, shows some differences in the annual cycle of large hail incidence between the two regions (Fig. 4). The 46° latitude was chosen as it is the first latitude to be completely north of the Mediterranean Sea. Both the southern and northern part experience a rapid increase in large hail activity between April and May with the activity peaking around June. The most pronounced difference is the activity in July, which remained on the level of June across northern Europe but decreased markedly over southern Europe by almost 50%. This can be attributed to the migration of the polar frontal zone, separating polar and tropical air mass, northward and the dominance of the subtropical high pressure system inhibiting convection over southern Europe. Another difference is that the hail activity remained more frequent in the cooler months over southern part of Europe. The annual cycle of very large hail (not shown) is similar to the activity of large hail, but the peak month shifts from June to July over northeastern Spain and parts of central Europe. This shift has also been noted by Punge et al. (2017).
The annual cycle of large hail over Europe north of 46°N latitude (blue bars) and south of 46°N latitude (red bars).
Citation: Monthly Weather Review 147, 11; 10.1175/MWR-D-19-0204.1
The annual cycle of large hail does not exactly match the annual cycle of thunderstorms or other severe convective storm phenomena, such as tornadoes. Tornado activity peaks one month later than large hail, in July, over continental Europe. Over the Mediterranean, tornadoes occur most frequently between August to October, including the inland areas (Groenemeijer and Kühne 2014), where hail typically occurred in May or June. This discrepancy has already been addressed by Antonescu et al. (2016) and can be noted when comparing the climatologies of tornadoes and large hail over Turkey (Kahraman and Markowski 2014; Kahraman et al. 2016). Taszarek et al. (2019) noted two peaks of thunderstorm activity over the Mediterranean area: one in late spring, and the other in autumn (particularly near the coast and over the sea). This suggests that the large-scale environment in which storms form over this area must differ between the two peak seasons, as autumn activity produces large hail less frequently than spring.
c. Diurnal cycle
The diurnal cycle of all large hail reports (Fig. 5) over Europe shows three distinct peaks at 0000, 1200, and 1500 UTC. Because the ESWD reports also contain the information on the time accuracy of the reports, subsequent filtering for reports that have an accuracy within one hour yielded only one peak of 1500 UTC. The peaks at 0000 and 1200 UTC represent the events that occurred at night and day, with exact time unknown (time accuracy between 6 and 24 h), rather than the true peaks in the large hail occurrence. It is important to take time accuracy into consideration when performing time sensitive studies. For instance, only 37.71% of 39 531 large hail reports used in this study had time accuracy higher than one hour. These filtered reports are used for subsequent analysis of peak hourly occurrence of large hail over Europe.
Diurnal cycle of large hail over Europe for all reports (blue bars) and for hail reports with time accuracy higher than one hour (red bars).
Citation: Monthly Weather Review 147, 11; 10.1175/MWR-D-19-0204.1
Large hail typically occurs in the afternoon over most of Europe (Fig. 6). Due to the shift in time of the maximum diurnal heating, the peak hour was later over western Europe compared to eastern Europe, which is corroborated by the findings of Punge and Kunz (2016). For example, the peak time of occurrence was between 1600 and 1800 UTC over much of France and Spain, compared to 1300–1700 UTC over much of central Europe. However, differences also existed within the western and eastern half of Europe. The peak time of occurrence for Great Britain was two hours earlier compared to France, suggesting the differences in the usual time that convection is initiated over these areas. The same was true for the Alpine region, as large hail occurred most frequently between 1200 and 1500 UTC north of the ridge and between 1600 and 1800 UTC south of the ridge. The diurnal cycle of very large hail (not shown) was similar to large hail, but the peak was shifted by 1–2 h toward the evening over central Europe and the Balkan Peninsula.
Peak hour (in UTC) of large hail activity on a 2° latitude × 2° longitude grid. Grid points with less than 5 events are not displayed.
Citation: Monthly Weather Review 147, 11; 10.1175/MWR-D-19-0204.1
d. Hail size distribution
Analyses of hail size distributions have been performed using hailpad networks (Sánchez et al. 2009), observations from manned stations (Li et al. 2018) or from the Storm Data (Allen et al. 2017). Because the ESWD collects severe hail events, most of the hail sizes involve hail over 2 cm in diameter. 60.79% of large hail reports submitted to ESWD include information on maximum hail diameter. The remaining reports contain only information about the hail damage or mention that large hail did occur, but size could not be estimated. The hail size of 2–2.9 cm is the most common hail size category reported, comprising almost 40% of reports, where hail size was given (Fig. 7). Only 7% of these reports included maximum hail size larger than 5 cm and less than 1% of reports involved hail equal or larger than 8 cm in diameter. Allen and Tippett (2015) analyzed the changes in the hail size distribution in Storm Data since 1955 and found a decrease in the relative frequency of very large hail sizes. Between 2005 and 2014, very large hail represented 2%–3% of all large hail reports, less than half of the relative frequency found for Europe.
Cumulative relative frequency of hail sizes sorted into bins of 1–1.9 cm, 2–2.9 cm up to 9–9.9 cm category. Hail sizes of 10+ cm were not considered here.
Citation: Monthly Weather Review 147, 11; 10.1175/MWR-D-19-0204.1
Giant hail is very rare but has been observed across many areas over Europe (Fig. 8). Hail of such size was reported 91 times in the database, 49 times before and 42 times after 1 January 2006. Overall, giant hail encompassed 0.38% of the reports where a hail size was provided. Most of the pre-2006 reports of giant hail came from Germany, Czechia and northern Italy. Since 2006, giant hail has been reported from regions north of the Caucasus mountains, surrounding the Alps, and from Serbia and Romania. The largest reported hail sizes in recent years are 15 cm on 20 June 2016 at Sânandrei in western Romania and 14.1 cm on 6 August 2013 at Undingen in southwestern Germany.
Very large hail and giant hail reports over Europe. Giant hail reports occurring before (after) 1 Jan 2006 are denoted by orange (red) triangles, while very large hail corresponds to the small blue triangles.
Citation: Monthly Weather Review 147, 11; 10.1175/MWR-D-19-0204.1
e. Other measures of hail severity
Besides the maximum hail size, which is by far the most common characteristic submitted to the database, information on the average hail size, maximum hail weight or the thickness of the hail cover was included in some of the reports. Average hail size was present in 8.55% of the reports and was discontinued from the ESWD in 2017. Thickness of the hail cover can still be reported to the ESWD, and hail cover reaching over 2 cm on the flat surface is considered severe. A total of 5.11% of the submitted reports involved presence of deep hail cover. Significant hail cover can cause major problems in traffic and may increase the flash flood threat. For example, during the severe hailstorm in Stuttgart on 15 August 1972, hail accumulations on the order of tens of centimeters clogged the drainage system and the subsequent flooding killed 6 people. While hailstorms producing significant hail accumulations receive less attention in the literature than the hailstorms including very large hail sizes, first efforts have been undertaken to address this issue (Kalina et al. 2016; Kumjian et al. 2019). Hailstone weight was included only in 0.6% of all severe hail reports submitted to the ESWD. This metric was more typically used for historical records as the ratio of reports containing hail weight dropped to 0.13% after 2006.
4. Comparison of hail size to inflicted damage
The ESWD allows for a description of the type of hail damage. While the type of hail damage is not described for every single report, it is possible to use this information to analyze their association with a particular hail size. The number of instances when damage of particular type was reported along with the given hail size is shown in Table 1. Damage to property was reported less frequently than damage to vegetation. For example, damage to crops was mentioned 1584 times whereas damage to house windows was mentioned 166 times. Animal and human casualties were reported less frequently than other types of damage. Human injuries were reported 65 times and animal deaths were reported 37 times.
Number of reports containing description of damage to different types of objects and with specified hail size.
These data can be used to study the relationship between the hail sizes and resulting damage. The first question that is possible to answer is “At what hail sizes does a particular type of damage typically occur?” Damage to vegetation (crops and trees) occurred most frequently with a hail size of 3 cm (Fig. 9). Damage to vehicle bodies, house windows and roofs was most common with a hail size of 5 cm, and to vehicle windows with a hail size of 7 cm. In contrast to other types of damage, car window damage was only damage category to occur almost exclusively with hail exceeding 5 cm. Injuries were reported most frequently with hail size of 5 cm, but some cases involved smaller hail, including a few cases where the reported diameter was only 2 cm. In these cases, other factors, such as the strong wind, may have exacerbated the hail impact.
Relative frequency of impact type as a function of maximum hail diameter.
Citation: Monthly Weather Review 147, 11; 10.1175/MWR-D-19-0204.1
The second question that we can pose is: “Given the hail size, what is the probability that particular type of damage will be reported?” Because the frequency of hail decreases with increasing size (Fig. 7), the hail size which corresponds to the highest probability of impact may not coincide with the hail size at which the impact is most frequently observed. The probability of damage occurrence climbed steadily toward the largest hail sizes for most types of damage, except for crops, trees and greenhouses that can be damaged by smaller hail sizes that dominate the dataset (Fig. 10). Giant hail had the highest likelihood to damage roofs (34%), windows (22%), car bodies (30%), car windshields (27%), and also to injure humans (22%). The values of relative frequency and conditional probability of impact given the hail size may be biased due to a number of reporting issues. First, observers do not consistently report damage even if it occurs. Second, they may feel more motivated to report the damage with increasing hail size because it is less common and is more impressive. Third, hail reports often do not sample the largest hailstone. Blair et al. (2017) compared the hail size reports in the Storm Data to their in situ high-resolution measurements and found that the largest hail size per hail event in Storm Data was on average negatively biased by 2 cm.
Conditional probability of different types of hail damage as a function of maximum hail diameter for living and nonliving objects.
Citation: Monthly Weather Review 147, 11; 10.1175/MWR-D-19-0204.1
Due to the damage reporting biases mentioned above, real conditional probabilities of hail damage for a particular hail size are likely higher than presented here. For example, Marshall et al. (2002) found that most roof types suffered almost complete destruction when impacted by spherical hail stones with 5 cm diameter in a laboratory setting. Yet, conditional probability of roof damage by 5 cm hail is only 10.4%. Yeo et al. (1999) also found higher probability of damage occurrence for the Sydney hailstorm of 1999. 80% of roofs were damaged by hail between 5 and 7 cm and all roofs were damaged by hail exceeding 7 cm in diameter. Broken windshields in vehicles were reported in 30% and 40% of cases for 5–7 and 7+ cm hail, compared to 10% and 20% in our study. While such studies likely represent a more accurate account of the relationship between hail size and impact compared to our study, it is based on a single case. Furthermore, roofing materials used in the United States and Australia are different from Europe, which does not allow a direct comparison with our results.
Hailstone weight would likely yield better correspondence to the potential hail impact than hail diameter alone, as it can be converted to the kinetic energy of the hailstone upon its impact. Hail weight can be inferred from the diameter, assuming a perfect sphere, but field observations show that large hailstones are irregular in shape (Knight and Knight 2005; Giammanco et al. 2014; Giammanco et al. 2017). For example, Knight and Knight (2005) documented very large hailstone measuring 18 cm across, but weighing only around 500 g in contrast to the expected weight of 2500 g if the hailstone was a perfect sphere. Therefore, for a given hail size, large range of kinetic energies is possible, depending on the shape and the density of the hailstone (Heymsfield et al. 2014) Large, spongy hail may produce less damage upon impact than denser, but smaller hail. Laboratory tests (Marshall et al. 2002), performed with the perfectly spherical hailstone, then provide the upper-end damage estimate that hail can cause upon impact, as natural hailstones with the same diameter will typically weigh less.
5. Injuries and economic losses associated with hailstorms
a. Injuries
Injuries associated with large hail were reported 96 times. The severity or the circumstances of such injuries is rarely mentioned in the reports, but the text description most typically mentions “light injuries,” “head injuries,” or “persons taken to hospital.” Serious injuries were reported only once, for a hailstorm event in Marbella and Malaga, southern Spain, which occurred in the morning hours of 21 September 2007. The 8–10-cm large hail injured 30 people and three persons were rushed to the hospital with serious head injuries. Reports with injuries are concentrated in areas with a high incidence of very large hail, such as southern Germany, northern Italy, the Balkans and southern Russia (Fig. 11a). Surprisingly, almost no injuries were reported from southern Poland, where the frequency of very large hail is also high. On the other hand, injuries were also reported in areas where the ESWD suggests that very large hail is infrequent. This difference may be related either to varying degree of vulnerability to large hail or to how media report on weather-related injuries across Europe.
Hail-related injuries over (a) Europe, based on ESWD, and (b) the United States, based on Storm Data. Color and size of the triangle correspond to the number of injuries.
Citation: Monthly Weather Review 147, 11; 10.1175/MWR-D-19-0204.1
Hailstorms with larger hail sizes cause more injuries. For three hail size categories (2–5, 5–8, and 8+ cm), the mean (median) value of the number of injuries per report, given that at least one injury occurred, was 1.77 (1), 4.37 (2), and 38.79 (10), respectively. Number of hail injury reports associated with the three hail size categories was 13, 30, and 22. There were 17 reports with 10 or more injuries and 9 reports with at least 20 injuries. The 10 events with most injuries are listed in Table 2. The hailstorm with the highest number of injuries was a hailstorm in Munich, Germany, on 12 July 1984 with 400 injuries, followed by a hailstorm in Reutlingen, Germany, on 28 July 2013 that caused 74 injuries.
Ten hailstorms with most injuries across Europe.
Injuries related to hail can also be found in the Storm Data for the United States. As the database was established in 1955, the number of hail events with injuries (291) is higher than in Europe (96). Most hail-related injuries were concentrated in the Midwest, east of the Rocky Mountains (Fig. 11b), where very large hail occurs most frequently (Cintineo et al. 2012; Allen and Tippett 2015; Allen et al. 2017). The highest number of injuries in a single storm event (109) was associated with a hailstorm in Fort Worth, Texas, on 5 May 1995. Similar to Europe, larger hail sizes were associated with more injuries. The mean (median) number of injuries per report, given at least one injury, was 2.72 (1) for 2–5, 3.59 (1) for 5–8, and 8.34 (2) for 8+ cm hail sizes. Number of hail injury reports associated with the three hail size categories was 132, 115, and 44. The higher average number of injuries per hail injury event in Europe may result from the higher population density over hail-prone areas in Europe compared to the United States.
While many injuries associated with hail were reported to the ESWD, deaths directly caused by hail were extremely rare with only one such event since 1990. Allegedly, 4 people were killed by hail on 20 June 1997 in Apele Vii, Dolj, Romania (Marinică 2003). The same event also ranks as the hailstorm with third most injuries. Witnesses of the event mention giant hail size (ostrich egg), but no photographs have been found to confirm the actual hail size. A number of indirect deaths due to hail have been reported with other hail events, such as road accidents due to slippery road conditions or deaths during the repair of damaged roofs.
b. Financial losses
Besides harm to persons, hail has a large monetary impact across Europe. NatCatSERVICE database lists 669 losses involving hail with a normalized value exceeding $1 million (USD) between 1980 and 2018 across Europe. The majority of those loss events were located within Germany, Austria and Switzerland (Fig. 12). In particular, urban centers of southern and western Germany had a high incidence of normalized hail losses, including several events with loss exceeding $1 billion (USD). Six out of the top 10 normalized losses (Table 3) occurred in Germany, including the Munich hailstorm of 12 July 1984 and the hailstorms of 27–28 July 2013. Both events also rank as the hailstorms with most injuries (Table 2). The 2013 event loss comprises losses from two separate damaging hailstorms occurring on consecutive days first over northern and then over southwestern Germany. All events listed in Table 3 involved hail exceeding 5 cm and 5 of the top 10 loss events included hail reaching 10 cm in diameter.
Hail-related losses exceeding $1 million (USD) from 1980 to 2018.
Citation: Monthly Weather Review 147, 11; 10.1175/MWR-D-19-0204.1
Ten highest hail-related loss events across Europe. Location denotes the place that experienced the highest loss during the event.
Comparing the distribution of very large hail reports (Fig. 8) to the distribution of loss events (Fig. 12) suggests that the loss database likely underestimates number of events in countries where the incidence of very large hail events is similar to southern Germany, but the number of loss events is much smaller. Besides the underreporting of losses, the density and the value of vulnerable assets differ across Europe. Thus, it is likely that database currently underestimates the number of loss events over regions such as southern France, northern Italy, southern Poland, central Serbia, Bulgaria, Romania, and southern Russia. Over eastern Europe the only loss to exceed $500 million (USD) is the Sofia hailstorm that occurred on 8 July 2014 and the Istanbul hailstorm on 28 July 2017.
c. Trend in hail losses and its relation to the changes in environments capable of large hail
According to the NatCatSERVICE, the majority of losses associated with hail were reported in Germany. Thus, we focus on Germany for which we assume the data are more homogenous compared to other European countries. Because Germany was divided between the German Federal Republic and the German Democratic Republic, and no reports are available from the latter, the analysis is confined to the time period after 1990. Both the number of hail loss events and the normalized losses show an upward trend. The annual number of the hail loss days over Germany has increased substantially, from around 7.5 days in the early 1990 to around 15 days in recent years (Figs. 13c,d). Using Mann–Kendall test (Hussain et al. 2019), the trend was found to be statistically significant (p < 0.001). The trend in the annual hail losses (Figs. 13a,b) also seems positive, with the average annual hail loss between 1990 and 1999 reaching $527.7 million (USD) and between 2006 and 2018 reaching $1398.24 million (USD), but was found to be statistically insignificant (p > 0.05). The time series of hail losses over Germany is dominated by two outliers, corresponding to the major hail events of 12 July 1984 and of 27 and 28 July 2013, respectively.
Relation of mean annual probability of hail, calculated for a 0.75° ERA-Interim grid box and averaged over the area of Germany with each grid point having the same weight, to the hail-related annual losses (in millions of USD) for (a) large and (b) very large hail and to the number of hail loss days for (c) large and (d) very large hail across Germany between 1990 and 2018.
Citation: Monthly Weather Review 147, 11; 10.1175/MWR-D-19-0204.1
The upward trend of hail loss days and number of losses can be attributed to changes in exposure, vulnerability and hazard. Increasing population in the major urban centers as well as the increase in the vulnerability of assets has increased potential hail losses. This effect is similar to the impact of increasing population size on the potential impact of tornado outbreaks that was discussed by Strader et al. (2017), Ashley and Strader (2016) and Antonescu et al. (2018).
Besides increasing vulnerability, the frequency of severe storm environments capable of producing large hail has increased since 1990, as demonstrated by the ARCHaMo models developed by Rädler et al. (2018) using the ERA-Interim data (Fig. 13). The mean annual probability of large hail per ERA-Interim grid point, averaged over Germany, has almost doubled since 1990. The mean annual probability of very large hail has increased as well. Increasing trend in both large and very large hail probabilities is statistically significant (p < 0.001). The correlation coefficient between the number of hail loss days and the mean annual probability of large hail and very large hail reaches 0.65 and 0.69, respectively. Strong, positive correlation is also found using Spearman’s correlation coefficient (0.647 and 0.65, p < 0.001) The correlation between annual hail losses and mean annual probability of large/very large hail was lower than with hail loss days, 0.24 and 0.22, respectively. Spearman’s correlation coefficient yields values of 0.405 (p = 0.029) and 0.327 (p = 0.084). Low correlation is also due the strong outlier in 2013, which was not reflected in the correspondingly high frequency of favorable large hail environments in given year. For the United States, Sander et al. (2013) shows that high loss events are correlated with environments characterized by climatologically very rare combinations of high CAPE and vertical wind shear. Over Germany, the years with the highest losses did not feature correspondingly elevated values of mean annual probability of large/very large hail. This suggests the highest losses in this region represent unfortunate events of severe storms impacting major urban areas rather than prolonged presence of high CAPE and strong vertical wind shear resulting in multiple outbreaks of severe weather. The study domain of Sander et al. (2013) was larger (United States east of the Rocky Mountains compared to Germany), so in their case largest losses were driven by widespread outbreaks of convective storms, spanning multiple states, rather than single local events as in our study. Furthermore, they considered losses from all severe convective storm hazards and did not limit their study only to the losses driven mainly by hail.
6. Conclusions
By 31 December 2018, 39 537 quality-controlled reports of large hail had been submitted to the ESWD and the annual number of reports had tripled since the founding of the ESSL in 2006. The highest number of annual large hail days were found over the pre-Alpine areas including southeastern Austria and southern Germany. Large hail activity peaked in June and July in the afternoon hours across much of Europe, except in several Mediterranean areas, where the peak was shifted to the cooler season. However, due to the temporal and spatial inhomogeneities in reporting, it is currently not possible to use the database to construct a reliable large hail climatology across Europe. ESSL is improving the reporting homogeneity by establishing formal cooperation with national meteorological institutes and voluntary observer networks across Europe.
Some of the reports contained description of damage caused by large hail. Damage to crops and trees was reported most frequently with the hail sizes of 2 and 3 cm, while damage to roofs, cars and windows was typically reported with larger hail size of 4 to 6 cm. The probability of damage to roofs, vehicles and windows steeply increased with increasing hail size, reaching highest values for giant hail, but remained relatively constant for damage to vegetation and greenhouses. To streamline the study of impacts reported to the ESWD, the online submission form has been substantially reworked in 2018. Injuries caused by hail were reported 96 times in the database, compared to the 291 cases over United States listed in Storm Data. Larger hail sizes were associated with greater number of injuries over both regions. Since 1990, only one case with directly caused hail fatalities was reported in Europe.
Large economic losses due to the hailstorms have been reported to the NatCatSERVICE database of Munich RE and 8 losses exceeded $1 billion (USD). Both annual hail losses and annual number of days with hail losses have increased between 1990 and 2018 over Germany. Part of this increase may be attributed to the increase in the exposure, but it is shown that the large hail risk has also increased based on the ERA-Interim dataset. As the hail losses increase over Europe, the future work on hailstorms should further explore the impacts of these events to increase the resilience of communities. The authors foresee an update to this study as the reporting homogeneity improves in future and more reports include the specification of an impact caused by large hail.
Acknowledgments
The contributions of Tomáš Púčik, Christopher Castellano, Pieter Groenemeijer, Anja T. Rädler, and Eberhard Faust were carried out within the project Analysis of Changes in the Risk of Severe convective Storms in Europe (ARCS), by Munich Re and the German Ministry of Education and Research (BMBF) under Grant 01LP1525A. The contribution of Thilo Kühne was carried out as part of the project Severe Thunderstorm Evaluation and Predictability in Climate Models (STEPCLIM), part of the MiKLIP research program, supported by the German Federal Ministry of Education and Research (BMBF) under Grant 01LP1117A. The contribution of Bogdan Antonescu was carried under funding from the Romanian STAR Program Ctr. 162/20.07.2017b and from the National Core Programe Contract PN 33/2018. The authors would like to thank three anonymous reviewers for their comments that helped to improve the manuscript.
REFERENCES
Allen, J. T., and M. K. Tippett, 2015: The characteristics of United States hail reports: 1955–2014. Electron. J. Severe Storms Meteor., 10 (3), http://www.ejssm.org/ojs/index.php/ejssm/article/viewArticle/149.
Allen, J. T., and E. R. Allen, 2016: A review of severe thunderstorms in Australia. Atmos. Res., 178–179, 347–366, https://doi.org/10.1016/j.atmosres.2016.03.011.
Allen, J. T., M. K. Tippett, and A. H. Sobel, 2015: An empirical model relating US monthly hail occurrence to large-scale meteorological environment. J. Adv. Model. Earth Syst., 7, 226–243, https://doi.org/10.1002/2014MS000397.
Allen, J. T., M. K. Tippett, Y. Kaheil, A. H. Sobel, C. Lepore, S. Nong, and A. Muehlbauer, 2017: An extreme value model for U.S. hail size. Mon. Wea. Rev., 145, 4501–4519, https://doi.org/10.1175/MWR-D-17-0119.1.
Antonescu, B., D. M. Schultz, F. Lomas, and T. Kühne, 2016: Tornadoes in Europe: Synthesis of the observational datasets. Mon. Wea. Rev., 144, 2445–2480, https://doi.org/10.1175/MWR-D-15-0298.1.
Antonescu, B., J. G. Fairman Jr., and D. M. Schultz, 2018: What is the worst that could happen? Reexamining the 24–25 June 1967 tornado outbreak over western Europe. Wea. Climate Soc., 10, 323–340, https://doi.org/10.1175/WCAS-D-17-0076.1.
Ashley, W. S., and S. M. Strader, 2016: Recipe for disaster: How the dynamic ingredients of risk and exposure are changing the tornado disaster landscape. Bull. Amer. Meteor. Soc., 97, 767–786, https://doi.org/10.1175/BAMS-D-15-00150.1.
Bedka, K. M., J. T. Allen, H. J. Punge, M. Kunz, and D. Simanovic, 2018: A long-term overshooting convective cloud-top detection database over Australia derived from MTSAT Japanese advanced meteorological imager observations. J. Appl. Meteor. Climatol., 57, 937–951, https://doi.org/10.1175/JAMC-D-17-0056.1.
Blair, S. F., D. R. Deroche, J. M. Boustead, J. W. Leighton, B. L. Barjenbruch, and W. P. Gargan, 2011: A radar-based assessment of the detectability of giant hail. Electron. J. Severe Storms Meteor., 6 (7), http://www.ejssm.org/ojs/index.php/ejssm/article/viewArticle/87.
Blair, S. F., and Coauthors, 2017: High-resolution hail observations: Implications for NWS warning operations. Wea. Forecasting, 32, 1101–1119, https://doi.org/10.1175/WAF-D-16-0203.1.
Brown, T. M., W. H. Pogorzelski, and I. M. Giammanco, 2015: Evaluating hail damage using property insurance claims data. Wea. Climate Soc., 7, 197–210, https://doi.org/10.1175/WCAS-D-15-0011.1.
Burcea, S., R. Cică, and R. Bojariu, 2016: Hail climatology and trends in Romania: 1961–2014. Mon. Wea. Rev., 144, 4289–4299, https://doi.org/10.1175/MWR-D-16-0126.1.
Calianese, E. J., J. K. Jordan, E. B. Curran, A. R. Moller, and G. Woodall, 2002: The Mayfest high-precipitation supercell of 5 May 1995—A case study. Preprints, 21st Conf. on Severe Local Storms, San Antonio, TX, Amer. Meteor. Soc., P3.4, https://ams.confex.com/ams/SLS_WAF_NWP/techprogram/paper_47099.htm.
Cecil, D. J., and C. B. Blankenship, 2012: Toward a global climatology of severe hailstorms as estimated by satellite passive microwave imagers. J. Climate, 25, 687–703, https://doi.org/10.1175/JCLI-D-11-00130.1.
Cerveny, R. S., and Coauthors, 2017: WMO assessment of weather and climate mortality extremes: Lightning, tropical cyclones, tornadoes, and hail. Wea. Climate Soc., 9, 487–497, https://doi.org/10.1175/WCAS-D-16-0120.1.
Changnon, S. A., 1977: The scales of hail. J. Appl. Meteor., 16, 626–648, https://doi.org/10.1175/1520-0450(1977)016<0626:TSOH>2.0.CO;2.
Changnon, S. A., and J. Burroughs, 2003: The tristate hailstorm: The most costly on record. Mon. Wea. Rev., 131, 1734–1739, https://doi.org/10.1175//2549.1.
Changnon, S. A., D. Chagnon, and S. D. Hilberg, 2009. Hailstorms across the nation: An atlas about hail and its damages. Contract Rep. 2009-12, 95 pp.
Cheng, L., and M. English, 1983: A relationship between hailstone concentration and size. J. Atmos. Sci., 40, 204–213, https://doi.org/10.1175/1520-0469(1983)040<0204:ARBHCA>2.0.CO;2.
Cică, R., S. Burcea, and R. Bojariu, 2015: Assessment of severe hailstorms and hail risk using weather radar data. Meteor. Appl., 22, 746–753, https://doi.org/10.1002/met.1512.
Cintineo, J. L., T. M. Smith, V. Lakshmanan, H. E. Brooks, and K. L. Ortega, 2012: An objective high-resolution hail climatology of the contiguous United States. Wea. Forecasting, 27, 1235–1248, https://doi.org/10.1175/WAF-D-11-00151.1.
Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553–597, https://doi.org/10.1002/qj.828.
Dessens, J., 1986: Hail in southwestern France. Part I: Hailfall characteristics and hailstrom environment. J. Climate Appl. Meteor., 25, 35–47, https://doi.org/10.1175/1520-0450(1986)025<0035:HISFIH>2.0.CO;2.
Dotzek, N., P. Groenemeijer, B. Feuerstein, and A. M. Holzer, 2009: Overview of ESSL’s severe convective storms research using the European Severe Weather Database ESWD. Atmos. Res., 93, 575–586, https://doi.org/10.1016/j.atmosres.2008.10.020.
Edwards, R., and R. L. Thompson, 1998: Nationwide comparisons of hail size with WSR-88D vertically integrated liquid water and derived thermodynamic sounding data. Wea. Forecasting, 13, 277–285, https://doi.org/10.1175/1520-0434(1998)013<0277:NCOHSW>2.0.CO;2.
Ferraro, R., J. Beauchamp, D. Cecil, and G. Heymsfield, 2015: A prototype hail detection algorithm and hail climatology developed with the Advanced Microwave Sounding Unit (AMSU). Atmos. Res., 163, 24–35, https://doi.org/10.1016/j.atmosres.2014.08.010.
Fraile, R., A. Castro, and J. L. Sanchez, 1992: Analysis of hailstone size distributions from a hailpad network. Atmos. Res., 28, 311–326, https://doi.org/10.1016/0169-8095(92)90015-3.
Fraile, R., C. Berthet, J. Dessens, and J. L. Sánchez, 2003: Return periods of severe hailfalls computed from hailpad data. Atmos. Res., 67–68, 189–202, https://doi.org/10.1016/S0169-8095(03)00051-6.
Giammanco, I. M., T. M. Brown, M. R. Kumjian, and A. J. Heymsfield, 2014: Observations of hailstone sizes and shapes from the IBHS Hail Measurement Program: 2012–2014. 27th Conf. on Severe Local Storms, Madison, WI, Amer. Meteor. Soc., 16B.2., https://ams.confex.com/ams/27SLS/webprogram/Paper255294.html.
Giammanco, I. M., B. R. Maiden, H. E. Estes, and T. M. Brown-Giammanco, 2017: Using 3D laser scanning technology to create digital models of hailstones. Bull. Amer. Meteor. Soc., 98, 1341–1347, https://doi.org/10.1175/BAMS-D-15-00314.1.
Groenemeijer, P., and T. Kühne, 2014: A climatology of tornadoes in Europe: Results from the European Severe Weather Database. Mon. Wea. Rev., 142, 4775–4790, https://doi.org/10.1175/MWR-D-14-00107.1.
Groenemeijer, P., and Coauthors, 2017: Severe convective storms in Europe: Ten years of research at the European Severe Storms Laboratory. Bull. Amer. Meteor. Soc., 98, 2641–2651, https://doi.org/10.1175/BAMS-D-16-0067.1.
Heymsfield, A. J., I. M. Giammanco, and R. Wright, 2014: Terminal velocities and kinetic energies of natural hailstones. Geophys. Res. Lett., 41, 8666–8672, https://doi.org/10.1002/2014GL062324.
Hohl, R., H. H. Schiesser, and D. Aller, 2002: Hailfall: The relationship between radar-derived hail kinetic energy and hail damage to buildings. Atmos. Res., 63, 177–207, https://doi.org/10.1016/S0169-8095(02)00059-5.
Höller, H., and M. E. Reinhardt, 1986: The Munich Hailstorm of July 12, 1984—Convective development and preliminary hailstone analysis. Beitr. Phys. Atmos., 59, 1–12.
Hussain, and Coauthors, 2019: pyMannKendall: A python package for nonparametric Mann Kendall family of trend tests. J. Open Source Software, 4, 1556, https://doi.org/10.21105/joss.01556.
Kahraman, A., and P. M. Markowski, 2014: Tornado climatology of Turkey. Mon. Wea. Rev., 142, 2345–2352, https://doi.org/10.1175/MWR-D-13-00364.1.
Kahraman, A., Ş. Tilev-Tanriover, M. Kadioglu, D. M. Schultz, and P. M. Markowski, 2016: Severe hail climatology of Turkey. Mon. Wea. Rev., 144, 337–346, https://doi.org/10.1175/MWR-D-15-0337.1.
Kalina, E. A., K. Friedrich, B. C. Motta, W. Deierling, G. T. Stano, and N. N. Rydell, 2016: Colorado plowable hailstorms: Synoptic weather, radar, and lightning characteristics. Wea. Forecasting, 31, 663–693, https://doi.org/10.1175/WAF-D-15-0037.1.
Knight, C. A., and N. C. Knight, 2005: Very large hailstones from Aurora, Nebraska. Bull. Amer. Meteor. Soc., 86, 1773–1782, https://doi.org/10.1175/BAMS-86-12-1773.
Kron, W., M. Steuer, P. Löw, and A. Wirtz, 2012: How to deal properly with a natural catastrophe database—Analysis of flood losses. Nat. Hazards Earth Syst. Sci., 12, 535–550, https://doi.org/10.5194/nhess-12-535-2012.
Kumjian, M. R., Z. J. Lebo, and A. M. Ward, 2019: Storms producing large accumulations of small hail. J. Appl. Meteor. Climatol., 58, 341–364, https://doi.org/10.1175/JAMC-D-18-0073.1.
Kunz, M., and M. Puskeiler, 2010: High-resolution assessment of the hail hazard over complex terrain from radar and insurance data. Meteor. Z., 19, 427–439, https://doi.org/10.1127/0941-2948/2010/0452.
Kunz, M., U. Blahak, J. Handwerker, M. Schmidberger, H. J. Punge, S. Mohr, E. Fluck, and K. M. Bedka, 2018: The severe hailstorm in southwest Germany on 28 July 2013: Characteristics, impacts and meteorological conditions. Quart. J. Roy. Meteor. Soc., 144, 231–250, https://doi.org/10.1002/qj.3197.
Li, X., Q. Zhang, T. Zou, J. Lin, H. Kong, and Z. Ren, 2018: Climatology of hail frequency and size in China, 1980–2015. J. Appl. Meteor. Climatol., 57, 875–887, https://doi.org/10.1175/JAMC-D-17-0208.1.
Marinică, I., 2003: Fenomene Meteorologice extreme în Oltenia (Extreme Meteorological Phenomena in Oltenia) (in Romanian). MJM Press, 280 pp.
Marshall, T. P., R. F. Herzog, S. J. Morrison, and S. R. Smith, 2002: Hail damage threshold sizes for common roofing and siding materials. 21st Conf. on Severe Local Storms, San Antonio, TX, Amer. Meteor. Soc., P3.2, https://ams.confex.com/ams/SLS_WAF_NWP/techprogram/paper_45858.htm.
Nisi, L., O. Martius, A. Hering, M. Kunz, and U. Germann, 2016: Spatial and temporal distribution of hailstorms in the Alpine region: A long-term, high resolution, radar-based analysis. Quart. J. Roy. Meteor. Soc., 142, 1590–1604, https://doi.org/10.1002/qj.2771.
Nisi, L., A. Hering, U. Germann, and O. Martius, 2018: A 15-year hail streak climatology for the Alpine region. Quart. J. Roy. Meteor. Soc., 144, 1429–1449, https://doi.org/10.1002/qj.3286.
Ortega, K. L., 2018: Evaluating multi-radar, multi-sensor products for surface hailfall diagnosis. Electron. J. Severe Storms Meteor., 13 (1), http://www.ejssm.org/ojs/index.php/ejssm/article/viewArticle/163.
Palencia, C., D. Giaiotti, F. Stel, A. Castro, and R. Fraile, 2010: Maximum hailstone size: Relationship with meteorological variables. Atmos. Res., 96, 256–265, https://doi.org/10.1016/j.atmosres.2009.08.011.
Prein, A. F., and G. J. Holland, 2018: Global estimates of damaging hail hazard. Wea. Climate Extremes, 22, 10–23, https://doi.org/10.1016/j.wace.2018.10.004.
Púčik, T., and Coauthors, 2017: Future changes in European severe convection environments in a regional climate model ensemble. J. Climate, 30, 6771–6794, https://doi.org/10.1175/JCLI-D-16-0777.1.
Punge, H. J., and M. Kunz, 2016: Hail observations and hailstorm characteristics in Europe: A review. Atmos. Res., 176–177, 159–184, https://doi.org/10.1016/j.atmosres.2016.02.012.
Punge, H. J., K. M. Bedka, M. Kunz, and A. Reinbold, 2017: Hail frequency estimation across Europe based on a combination of overshooting top detections and the ERA-INTERIM reanalysis. Atmos. Res., 198, 34–43, https://doi.org/10.1016/j.atmosres.2017.07.025.
Rädler, A. T., P. Groenemeijer, E. Faust, and R. Sausen, 2018: Detecting severe weather trends using an Additive Regressive Convective Hazard Model (AR-CHaMo). J. Appl. Meteor. Climatol., 57, 569–587, https://doi.org/10.1175/JAMC-D-17-0132.1.
Reges, H. W., N. Doesken, J. Turner, N. Newman, A. Bergantino, and Z. Schwalbe, 2016: CoCoRaHS: The evolution and accomplishments of a volunteer rain gauge network. Bull. Amer. Meteor. Soc., 97, 1831–1846, https://doi.org/10.1175/BAMS-D-14-00213.1.
Sánchez, J. L., R. Fraile, J. L. de La Madrid, M. T. de La Fuente, P. Rodríguez, and A. Castro, 1996: Crop damage: The hail size factor. J. Appl. Meteor., 35, 1535–1541, https://doi.org/10.1175/1520-0450(1996)035<1535:CDTHSF>2.0.CO;2.
Sánchez, J. L., and Coauthors, 2009: Characterization of hailstone size spectra in hailpad networks in France, Spain, and Argentina. Atmos. Res., 93, 641–654, https://doi.org/10.1016/j.atmosres.2008.09.033.
Sánchez, J. L., L. López, E. García-Ortega, and B. Gil, 2013: Nowcasting of kinetic energy of hail precipitation using radar. Atmos. Res., 123, 48–60, https://doi.org/10.1016/j.atmosres.2012.07.021.
Sander, J., J. F. Eichner, E. Faust, and M. Steuer, 2013: Rising variability in thunderstorm-related U.S. losses as a reflection of changes in large-scale thunderstorm forcing. Wea. Climate Soc., 5, 317–331, https://doi.org/10.1175/WCAS-D-12-00023.1.
Schulz, W., G. Diendorfer, S. Pedeboy, and D. R. Poelman, 2016: The European lightning location system EUCLID–Part 1: Performance analysis and validation. Nat. Hazards Earth Syst. Sci., 16, 595–605, https://doi.org/10.5194/nhess-16-595-2016.
Schuster, S. S., R. J. Blong, R. J. Leigh, and K. J. McAneney, 2005: Characteristics of the 14 April 1999 Sydney hailstorm based on ground observations, weather radar, insurance data and emergency calls. Nat. Hazards Earth Syst. Sci., 5, 613–620, https://doi.org/10.5194/nhess-5-613-2005.
Schuster, S. S., R. J. Blong, and K. J. McAneney, 2006: Relationship between radar-derived hail kinetic energy and damage to insured buildings for severe hailstorms in Eastern Australia. Atmos. Res., 81, 215–235, https://doi.org/10.1016/j.atmosres.2005.12.003.
Smith, P. L., and A. Waldvogel, 1989:On determinations of maximum hailstone sizes from hailpad observation. J. Appl. Meteor., 28, 71–76, https://doi.org/10.1175/1520-0450(1989)028<0071:ODOMHS>2.0.CO;2.
Strader, S. M., W. S. Ashley, T. J. Pingel, and A. J. Krmenec, 2017: Observed and projected changes in U.S. tornado exposure. Wea. Climate Soc., 9, 109–123, https://doi.org/10.1175/WCAS-D-16-0041.1.
Taszarek, M., H. E. Brooks, B. Czernecki, P. Szuster, and K. Fortuniak, 2018: Climatological aspects of convective parameters over Europe: a comparison of ERA-Interim and sounding data. J. Climate, 31, 4281–4308, https://doi.org/10.1175/JCLI-D-17-0596.1.
Taszarek, M., and Coauthors, 2019: A climatology of thunderstorms across Europe from a synthesis of multiple data sources. J. Climate, 32, 1813–1837, https://doi.org/10.1175/JCLI-D-18-0372.1.
Tippett, M. K., J. T. Allen, V. A. Gensini, and H. E. Brooks, 2015: Climate and hazardous convective weather. Curr. Climate Change Rep., 1, 60–73, https://doi.org/10.1007/s40641-015-0006-6.
Tuovinen, J. P., A. J. Punkka, J. Rauhala, H. Hohti, and D. M. Schultz, 2009: Climatology of severe hail in Finland: 1930–2006. Mon. Wea. Rev., 137, 2238–2249, https://doi.org/10.1175/2008MWR2707.1.
Waldvogel, A., B. Federer, W. Schmid, and J. F. Mezeix, 1978: The kinetic energy of hailfalls. Part II: Radar and hailpads. J. Appl. Meteor., 17, 1680–1693, https://doi.org/10.1175/1520-0450(1978)017<1680:TKEOHP>2.0.CO;2.
Yeo, S., R. Leigh, and I. Kuhne, 1999: The April 1999 Sydney Hailstorm. Aust. J. Emergency Manage., 14 (4), 23–25.