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Hail observations and hailstorm characteristics in Europe: A review

H.J. Punge, M. Kunz

PII: S0169-8095(16)30029-1DOI: doi: 10.1016/j.atmosres.2016.02.012Reference: ATMOS 3619

To appear in: Atmospheric Research

Received date: 6 October 2015Revised date: 10 February 2016Accepted date: 15 February 2016

Please cite this article as: Punge, H.J., Kunz, M., Hail observations and hail-storm characteristics in Europe: A review, Atmospheric Research (2016), doi:10.1016/j.atmosres.2016.02.012

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Hail observations and hailstorm characteristics in Europe: A review

H. J. Pungea,b, M. Kunza,b

aInstitute of Meteorology and Climate Research (IMK), Karlsruhe Institute of Technology (KIT), Karlsruhe, GermanybCenter for Disaster Management and Risk Reduction Technology (CEDIM), Karlsruhe Institute of Technology (KIT), Karlsruhe,

Germany

Abstract

Severe thunderstorms associated with large hail are among the most important perils in several European regions. Dueto the local-scale extent of hail-affected areas and a lack of appropriate observing systems in most regions, hailstorms arenot captured accurately and comprehensively, which makes statistical analysis of their frequency or climatology moredifficult. Various studies have been conducted so far to describe and analyze the frequency of hailstorms or relatedimpacts. These studies, however, refer to a wide range of spatial scales and consider different time periods, investigationmethods, or hailstone diameters.This article will give a comprehensive overview and review of the present state of knowledge on hail hazard and hailfrequency over recent decades up to centuries across Europe and is intended as a reference for future studies. Weattempted to summarize and synthesize the various prevailing studies with the objective to identify regions that aremost prone to hail hazard. Another focus is put on mechanisms that may explain spatial variability and inhomogeneitiesin hail frequency observed across various spatial scales.

Keywords: hail, hail frequency, hail observation, Europe, hail characteristics, hailstorm

1. Introduction

Over large parts of Europe, severe hailstorms frequentlycause considerable damage to buildings, crops, and auto-mobiles, resulting in large economic and insured losses.For example, two supercells on 27/28 July 2013 triggeredin the vicinity of the low pressure system Andreas causedan insured damage of 2.8 billion EUR in Germany, rep-resenting the costliest insured loss event worldwide inthe year of 2013 (SwissRe, 2014). One year later, hail-storms associated to the event Ela on 8-10 June 2014were responsible for insured damage of 2.3 billion EUR inFrance, Belgium, and Germany. Zimmerli (2005) estimatethe potential insured damage caused by a European hail-storm with a return period of 200-300 years to be around4 billion EUR. Despite the substantial damage associatedwith large hail, knowledge on local-scale hail frequency orprobability across Europe is still limited.

Hail is produced in deep convective storms that arecharacterized by strong updrafts, large supercooled liquidwater contents, high cloud tops, and a sufficient lifetime(Pruppacher and Klett, 2010; Houze, 2014). Hail-bearingthunderstorms are usually highly-organized convective sys-tems in terms of multicells, mesoscale convective systems(MCS), or supercells. A hailstone has a density compara-ble to that of solid ice and occurs in spheroidal, conical,or generally irregular shape. By convention, hail has a di-ameter of at least 5mm. Following WMO standards forrecording present and past weather at and around a station(WMO, 2011), significant hail is encoded by the numbers

96 and 99 (hail with thunderstorm). For aviation pur-poses, the WMO’s technical recommendations define thecode GR (from the French word: grêle) for significant hailwith diameters of 5mm or greater, notably to distinguishfrom other forms of solid precipitation including snow pel-lets, ice crystals and pellets, snow grains and snow, allrelated to processes such as freezing of rain or successivemelting rather than severe convection (WMO, 2008). Wegenerallly followed the WMO recommendations and referto hail for a diameter of 5mm or more on the ground, buta size limit is not always clearly defined in the reviewedstudies.

Knowledge on the frequency, characteristics, and inten-sity of hail is highly desirable for several reasons. Mete-orologists need climatological information for improvingtheir understanding of atmospheric processes that leadto hail formation. Geographers want to better under-stand the impact of surface features and properties on hail.Economists and insurers need to correctly estimate the riskthey take in hail insurance of a specific portfolio. Localhabits in the construction sector often imply decades tocenturies of experience with perils, but this may not holdfor new construction methods (e.g., subsequent heat insu-lation) or energy supply systems (e.g., solar thermal sys-tems), all highly vulnerable to hail. Similarly, farmers andwine growing enterprises require long-term information onhail hazard when deciding on crop protection measures orspending on hail suppression attempts, a much debatedissue (e.g., Mesinger and Mesinger, 1992; Simeonov, 1996;

Preprint submitted to Atmospheric Research February 10, 2016

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Dessens, 1998).Compared to the U.S. (e.g., Changnon et al., 2009), hail

in Europe tends to be less frequent and severe, mainlydue to a different orientation of large-scale orography (i.e.,the Alps) and related circulation patterns (Brooks et al.,2003). On the other hand, as a consequence of the hetero-geneous hail risk perception and management among Eu-ropean countries (Doswell and Bosart, 2003), the kind andamount of data availability on hail frequency and severityvaries largely within Europe. In general, information onhail frequency and intensity is difficult to obtain, above alldue to low occurrence probability of hailstorms at a certainlocation. The spatial extent of an area affected by hail,referred to as hailstreak, is usually smaller than 500 km2

in central Europe (Kleinschroth, 1999). Changnon (1970)and Changnon (1977) found for the U.S. that 80% ofa large number of observed hailstreaks affected areas ofeven less than 40 km2, and the median affected area was20.5 km2. It can be assumed that the same also appliesfor Europe. Together with a total hailstorm number of afew thousands per year in Europe, this gives an averagefrequency of much less than one hail event per year at anylocation.

Another critical limitation is the lack of widespread,standardized and operational observing systems. As nohail observation devices are usually installed at synop-tic weather stations (SYNOP), observations are availableonly from the few manned stations, most often solely dur-ing daytime. The density of the station network, how-ever, is too coarse to capture local-scale hail events reli-ably. Furthermore, observers may not strictly respect theWMO recommendations defining hail only for diametersgreater than 5mm, and cases of graupel or frozen rainmay be counted as hail. These restrictions make the eval-uation of hail frequency based on weather stations verydifficult and questionable. Regionally, hailpad networks(e.g., Changnon, 1970; Palencia et al., 2009) have been in-stalled to record the occurrence and intensity or size spec-trum of hail, often with the purpose to examine the effi-ciency of weather modification activities aimed at reducinghail damage to crops, in particular to vineyards and otherfruits (Dessens et al., 2015b). Such networks exist in sev-eral regions throughout Europe, including parts of France(western, central southern), northern Italy, eastern Aus-tria, parts of Spain, Greece, and Croatia (Dessens, 1998;Svabik, 1989; Giaiotti et al., 2003; Počakal et al., 2009;Berthet et al., 2011). Hailpad networks also existed in afew other countries in the past, for example in León inSpain (Sánchez et al., 2009). In addition, in several re-gions observer networks exist or have existed in the past,mostly relying on reporting of severe weather by farmers.

A second group of information sources on hail is pro-vided by single event-based, non-systematic hail records.These include damage claims from insurance companies, inparticular in the agriculture and real estate sectors. Thedata, however, is restricted to cultivated or inhabited ar-eas and is reliable only in regions with almost homogeneous

coverage and high insurance density. Only damage-causinghail events are captured, thus hail outside the growing sea-son for agriculture and small hail in case of building orautomobile insurance is not considered. Access is oftenlimited for several reasons, including data protection andtedious digitalization. Other event-based sources of infor-mation include newspaper reports (e.g., Tuovinen et al.,2009) or chronicles (e.g., Gudd, 2003). Nowadays, this in-formation combined with observations from trained stormspotters are integrated into severe weather archives such asthe European Severe Weather Database (ESWD; Dotzeket al., 2009), the Tornado and Storm Research Organiza-tion (TORRO) database, sturmarchiv.ch (Switzerland), orKeraunos (France). Although reporting in these sources isbiased towards population density and available spotters,it has become a major and valuable source of informationon large hail (and other convection-related) events and canbe used to validate hail proxies from radar or satellite ob-servations.

A third approach is to estimate hail occurrence fromproxy data obtained from remote sensing instruments. Inparticular weather radars having large areas under con-stants surveillance in high spatial and temporal resolutioncan give some indications for hail occurrences. Basically,hail signals can be estimated from the radar reflectivityabove a certain threshold (e.g., Atlas and Ludlam, 1961;Mason, 1971), from radar reflectivity at different eleva-tions in combination with other appropriate meteorologi-cal data (e.g., Waldvogel et al., 1979; Smart and Alberty,1985; Auer, 1994; Witt et al., 1998; Holleman, 2001), orfrom linear polarimetric observables measured by dual-polarization radars (e.g., Ryzhkov et al., 2013). Further-more, satellite observations based on cloud top brightnesstemperatures and the detection of overshooting cloud topsassociated with severe convection also have been used asproxy for hail (Setvák et al., 2003; Cecil, 2009; Bedka,2011; Mikuš and Strelec Mahović, 2013; Setvák et al., 2013;Merino et al., 2014a). In recent years, several hail clima-tologies based on proxies from radar (Kunz et al., 2009;Puskeiler, 2013; Skripniková and Řezáčová, 2014; Kunzand Kugel, 2015) or satellite observations (Bedka, 2011;Cecil and Blankenship, 2012; Punge et al., 2014) have beenpublished.

Finally, even if general circulation or regional climatemodels (RCMs/GCMs) are not capable of simulating hail,model output has been used by some authors to estimatehail probability, either based on convective indexes or acombination of different appropriate meteorological pa-rameters (Brooks et al., 2003; Hand and Cappelluti, 2011;Mohr and Kunz, 2013; Sanderson et al., 2015; Mohr et al.,2015; Gascón et al., 2015,?). Since the obtained quantitiesonly estimate the potential of the atmosphere for hailstormdevelopment and do not allow for reproduction of past hailevents, these approaches are not further considered here.

This article reviews available literature quantifying hailfrequency across Europe that were undertaken over recentdecades up to centuries. We considered peer-reviewed sci-

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entific papers, but also grey literature in terms of reportsfrom national organizations, insurances, as well as Mas-ter and PhD theses. Some literature sources are availableonly in the respective national language. In such cases,we tried to extract the essentials that were often providedby figures or tables. The investigation area considers theEuropean climatic zones including Russia west of the Uraland north of the Caucasus main ridges as well as Turkeyand Cyprus. The main objectives are to draw an overallpicture on the state of hail frequency research. We at-tempt to summarize and synthesize the various prevailingstudies and to identify regions that are most prone to thehail hazard. Another focus is put on mechanisms and pro-cesses that are most relevant for the spatial distribution ofhail across various spatial scales. In that sense our studymay serve as a reference for future studies.

This review paper is organized as following. In Sec-tion 2, we discuss the different standards and units ap-plied to quantify hail frequency and probability and pro-pose a conversion to a common standard. Section 3 brieflysummarizes European-wide hail analyses based on proxiesfrom remote sensing instruments. In Section 4 we dis-cuss in detail hail frequency obtained from point-basedmeasurements including weather stations and hailpads ona regional perspective for most of the European coun-tries. While Section 5 discusses main characteristics ofhail occurrence such as seasonality, daily cycle and ob-served trends, Section 6 synthesize the different prevailingstudies and discusses several factors that may be responsi-ble for the spatial distribution of hail probability. The lastSection 7 briefly summarizes the most important findingsand draws some conclusions.

2. Definitions and Conversions

2.1. Definitions of hail frequencyHail frequency can be defined in different ways. The

most straightforward definition is simply counting thenumber of hail events per year at a certain location, wherelocation is to be understood as an area much smaller thanan entire hailstreak, but large enough to be reliably hit bya passing storm. This is the case for hailpad observations,which are typically around 0.3×0.3m2 ≈ 0.1m2 in surfacearea (see Palencia et al., 2009, for a bibliometric review),but also for eye-witness reports and damage claims, whichrefer to scales from tens to several hundreds of square me-ters (e.g., Sioutas et al., 2009). Common to all these pointobservations is a low temporal sampling period, typicallyonce or only a few times per day. Multiple hail events perday for these small areas are sufficiently rare and, thus,can usually be neglected. Hail frequency defined in thatway is generally low (in most areas < 1 event per year),thus a high density of stations is required for obtainingrobust results.

Due to the small extent of hailstreaks, point hail fre-quencies often show large spatial variability. For this rea-son, the total number of hailstorms for a larger reference

area in terms of counts per area and year is often used.The reference area may be a district, a state or an entirecountry. Hailpad stations as well as observers are usuallygrouped together and operated in networks (e.g., ANELFAin France, or Friuli-Venezia Giulia network in Italy). Evenif the density of these networks is quite high, smaller hail-streaks with a typical width of 1–2.5 km (Changnon, 1970)may be missed. This is even more true for insurancerecords, which are limited to inhabited places in case ofreal estate and to insured and vulnerable crops in case ofagriculture.

For practical reasons, hail frequency is often quantifiedin terms of hail days per year for a certain region insteadof counting the number of all individual storms. In thisdefinition, a hail day is considered a day when hail oc-curred anywhere within the reference area. In contrast tohailstorm counts, the specified number of hail days willnot increase linearly with the extent of the reference area,as long as the latter is smaller or similar to the extent of ahailstreak. This makes comparisons of hail day counts forregions of different sizes a difficult task (see Sect. 2.3).

The insurance business also does not consider individualhailstorms, but rather the overall damage that occurredwithin a certain time period to a given portfolio. A dam-aging event is commonly defined by the extent of all settledclaims in the reference area over a time span of 72 hours,but the time span may depend on business sector and re-gional practice. Anyway, there is only little meteorologicalevidence for such a definition. Weather situations that fa-vor hailstorm occurrences over Europe in fact may persistover several days. But hailstorms are usually triggered bylocal-scale (flow) effects, which cannot directly be relatedto the prevailing synoptic situation. The insurance event-based counting evidently yields much lower event countsper year compared to the thunderstorm-based definition.

Demanding analysis is therefore required when combin-ing and synthesizing hail frequency estimates from differ-ent sources and for different areas. Due to the variablesizes and shapes of the reference areas considered in theliterature, the count of hail days per year at a small refer-ence area like a hailpad seems to be the most convenientmeasure.

2.2. Frequency and hail sizeEstimates of hail frequency depend on the minimum

hailstone diameter considered. While 5mm are often as-sumed as threshold for hailpads and agricultural losses,larger thresholds generally apply for other insurance typessuch as automobiles or buildings. Changnon (1971), for ex-ample, found damage to wheat, corn and soybeans to beclosely related to hailstones with a diameter of 6.35mm.Field and laboratory investigations (Stucki and Egli, 2007;Gessler and Petty, 2013), on the other hand, have proventhat buildings may become damaged by falling hailstoneslarger than approximately 25mm (∼ 1 inch), which is thustermed severe hail in the U.S. The amount of damage de-pends on the number of hailstones as much as on their

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size, so inferring hailstone size information from insureddamage is a very difficult, if not impossible undertaking.Finally, hail signals derived from data of remote sensing in-struments generally come with large uncertainties for hail-stone diameters, or no estimate at all.

Among the reports archived by the ESWD, the fractionof hail greater than 40mm (large hail) is 0.223. Estimat-ing entire hail events rather than considering individualreports and assuming an exponential distribution of max-imum hail size per storm, Punge et al. (2014) found that23.8% of all reported hail events include hailstones greaterthan 20mm, and 3.5% greater than 40mm. These esti-mates are probably biased towards central Europe, wherea large fraction of all ESWD entries were reported. Fur-thermore, they are biased towards larger hailstones, whichare more attractive for both the media and storm chasers.

As for the size distribution within a hailstorm, mostauthors assume an exponential or power law probabilitydistribution function (Ludlam and Macklin, 1959; Fraileet al., 1992), even if literature on the spatial variationof this distribution obtaining statistically significant re-sults is scarce (see, e.g., Sánchez et al., 2009). Accordingto the study of Simeonov (1996) hailstones hitting hail-pads within a sample of several hail days exhibit an in-verse Rayleigh distribution. Fraile et al. (1999) proposea gamma distribution to consider also melting processeseliminating part of the smaller stones.

2.3. Frequency and reference area

In any region with non-zero hail probability, the num-ber of observed hail events during a certain time periodwill generally increase with the size of the reference area.The increase, however, depends on the ratio α of the areaaffected by an hail event and the reference area (Klein-schroth, 1999). For example, assuming a region with con-stant hail probability and with a very small ratio α, dou-bling of the reference area will also double the event countin a certain time period. Conversely, if α is very high, onlylittle increase of the event count is to be expected. An al-ternative measure that avoids this problem would be touse the total area affected by hail with respect to the ref-erence area as a measure of the hail frequency. However,this approach has not been used in any of the availablestudies, mostly due to the difficulty of reliably assessingthe area affected by hail.

2.4. Hail events and hail days

While a single point is rather unlikely hit by several hail-storms within any given day or even a year, this changessubstantially when larger regions are considered. Dur-ing hail-prone weather situations (e.g., Eccel et al., 2012;Merino et al., 2013), several thunderstorms often developon the same day affecting neighboring regions. Insurancerecords do not distinguish among different hailstorms, butregister only hail damage that occurred on a whole daywithin a region.

3. Global- and continental-scale estimates of hailfrequency

Only a few studies have undertaken efforts to estimatehail frequency distributions on a global scale. A first mapof hail frequency based on station observations from thetropics was presented by Frisby and Sansom (1967), an-other one by Williams (1973) covering all continents. Ceciland Blankenship (2012) applied a satellite-based approachbased on cloud top temperatures from the Advanced Mi-crowave Scanning Radiometer-EOS (AMSR-E) instrumentaboard the Aqua mission as a proxy. While images areavailable for the whole earth, the disadvantage is that theyare available only for the up to four overpasses per day,thus biasing the results depending on the diurnal cycle ofconvection in the respective regions. Other global stud-ies on severe convective storms by Brooks et al. (2003)and Hand and Cappelluti (2011) rely on model data only.While the former used the well-known product of convec-tive available potential energy (CAPE) and vertical windshear as proxy for the storms, the latter applied the ap-proach of Fawbush and Miller (1953) that quantifies haildiameters from temperature and moisture at certain levels.A few other maps exist as products from insurance com-panies, relying at least partly on claims data (e.g., Berzet al., 2001; Munich Re, 2011). Further continental-scalemaps of hail frequency have been derived particularly forNorthern America (U.S.) (Collins and Howe, 1964; Brookset al., 2003; Doswell III et al., 2005; Changnon, 2009; Cin-tineo et al., 2012).

Ludlam (1980) states that intense convection with risk ofhail in Europe occurs most frequently in a long band fromFrance via the regions surrounding the Alps to the NorthCaucasus. According to the study of Cecil and Blanken-ship (2012), hail frequencies estimated from satellite im-ages are elevated over the Mediterranean countries andEastern Europe, but the maps are rather coarse and vague.The investigations of Bedka et al. (2012) indicate the high-est probability for severe convection as detected by over-shooting tops around the Alps, over North-Eastern (NE)Spain, and the Czech Republic. Key hail areas around theglobe as indicated by the model studies of Brooks et al.(2003) and Hand and Cappelluti (2011) are Central NorthAmerica, the southern parts of the Andes region in SouthAmerica, Eastern and Southern Africa, Central and South-ern Asia, and South-East Australia. Stronger discrepan-cies, however, are found for several other regions such asCentral Europe, Russia, or Central China, where Brookset al. (2003) estimate a very low number of days with favor-able severe parameters. From the insurance perspective,Munich Re (2011) found maxima in the central Mediter-ranean and from the Bay of Biscay to the Black Sea, withnotable impact of topography, whereas Berz et al. (2001)in an earlier study located the main hail frequency some-what further north.

Overall, the coarse resolution of the global hail assess-ments does not allow to estimate local-scale hail probabil-

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ity. However, numerous national- and regional-scale stud-ies are available that obtained detailed information on hailfrequency including seasonal and daily cycles. These re-gional studies also allow to study possible mechanism thatare most relevant for the hail climatology.

4. Regional and local hail frequency estimates overEurope

This section reviews hail frequency estimates primarilyobtained from point-based measurements such as weatherstations and hailpads, but also considers radar-derived hailsignals and insurance loss data. Such studies exist for mostof the European countries, but at different levels of accu-racy and with various aspects of hail. Even though theprobability of hailstorms is determined by large-scale cli-mate factors and topographic parameters rather than po-litical boundaries, the review of hail frequency estimatesis presented on a country level, where most of the liter-ature is available. Relations between hail frequency andpossible governing factors are discussed in detail in Sec-tion 6. The section is structured according to the politicaldefinition as proposed by the Permanent Committee onGeographical Names into: Central (C; Sect. 4.1), Western(W; Sect. 4.2), Southern (S; Sect. 4.3), Southeastern (SE;Sect. 4.4), Northern (N; Sect. 4.5), and Eastern Europe(E; Sect. 4.6).

For an overview of hail hazard in Europe, we reproduceFig. 4 of Punge et al. (2014) (Fig. 1). Seas were masked, aswe only treat land areas in this article. In addition, for bet-ter appreciation of the national- and regional-scale stud-ies, selected hail maps from the literature were composedto a single figure (Fig. 2). The main selection criteriawere spatial and temporal extent, and focus on large hail,where available. These figures are intended as a guidelinethrough the section. Unless otherwise specified, all num-bers given in the following refer to the number of hail daysper year either for a larger region or for a single station.

4.1. Central Europe

Central Europe is highly exposed to hail hazard, namelyGermany, Switzerland, and Austria, where several compre-hensive studies on hail frequency are available. It can beassumed that hail frequency decreases from west to eastand from south to north. Continentality is one of the gov-erning factors for hail frequency here, as it leads to lowermoisture contents and lesser frontal systems and there-fore less favorable conditions for convective activity. Onthe other hand, increasing insolation and decreasing windshear from north to south contribute to the latitudinalgradient.

4.1.1. GermanySome of Europe’s most costly hail events, including the

Munich hailstorm on 12 July 1984 (EUR 1.5 billion losses,converted to 2015 values; Heimann and Kurz, 1985; Höller

and Reinhardt, 1986; Kaspar et al., 2009) and the twohailstorms forming the event named Andreas on 27 and 28July 2013 (EUR 3.6 billion losses, also see Table 1; Swis-sRe, 2014), occurred in Germany. In the state of Baden-Württemberg in the SW, most of the (insured) damage tobuildings is related to large hail (Kunz et al., 2009). Ac-cording to an ESWD report, the largest hailstone recordedin Germany had a diameter of 141mm along the largestdimension and was found on 06 August 2013 near the cityof Reutlingen in Baden-Württemberg.

A pronounced north-to-south increase in hail frequencyis suggested by both insurance and radar data (Schwind,1957; Lindloff, 2003; Zimmerli, 2005; Otto, 2009; Mu-nich Re, 2011; Puskeiler, 2013). Hübner (1856) esti-mates a total hail loss in agriculture of about 1% of in-sured crop value in N Germany, but of up to 3% in theSouth, and Lindloff (2003) identified around 100 days peryear with agricultural hail damage (see Table 3). Klein-schroth (1999), who reconstructed more than 1 000 hail-storms from crop insurance claims during 15 years oversouthern Germany, found a hailstorms frequency of around1.0 km−2 yr−1 for S Germany. Note, however, that the vul-nerability of crops depends on the plant type and changessubstantially during the year.

For synoptic stations, Suwala and Bednorz (2013) re-port point hail frequencies ranging from 0.31 to 4.47 daysper year for the period 1966-2010. However, the highestvalues over mountainous regions may include other formsof precipitation. Point frequencies around 1.0 seem typ-ical for southern Germany, while hail frequencies furtherNorth are closer to 0.5.

The radar-based study by Puskeiler (2013) used the ver-tical distance between the freezing level and the 45 dBZecho top height as hail proxy (Waldvogel et al., 1979). Hefound up to 4 hail days per year and km2 in the center ofBaden-Württemberg, in the south of Bavaria, but also insouthern Saxony and northern Hesse (see Fig. 2), but 1 orless in the North. Combining 3D reflectivity from a singleradar and insured building damage in SW Germany, Kunzand Puskeiler (2010a) evaluated 65 hail days between 1997and 2007 and also found the highest hail frequency southof the city of Stuttgart, downstream of the Black ForestMountains. Using the Mason (1971) hail criterion (radarreflectivity in excess of 55 dBZ) they identified between 2.0and 4.3 hail days per year on a 10 × 10 km2 grid. Theseresults were confirmed by a subsequent study over the pe-riod 1997-2011 applying six different radar-based hail de-tection methods using 2D or 3D reflectivity (Kunz andKugel, 2015).

4.1.2. The Alpine countries - Switzerland, Austria, Slove-nia

Hail frequency is generally enhanced over the pre-Alpineregions of Switzerland, Austria, and Slovenia (for Franceand Italy, see Sects. 4.2.1 and 4.3.1, respectively). In con-trast, over the central Alps, in between the highest moun-tain peaks, large hail occurs only rarely (Stucki and Egli,

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2007; Svabik et al., 2013; Punge et al., 2014; Nisi et al.,2015).

Northern Switzerland is frequently affected by severehailstorms such as the supercells on 26 May and 23 July2009 causing insured damage of more than EUR 500 mil-lion (Hilfiker, 2009). Overall, the most hail-prone regionsare over the foothills both to the north and to the southof the Alps. Rough estimates of return periods for hail-stones larger than 20mm at a single point vary between 5years for the northern and central parts and 100 years forthe inner-Alpine Cantons of Graubünden (E) and Valais(Stucki and Egli, 2007).

Hail damage to agriculture in the portion north of theAlps occurs on 60 (Schiesser et al., 1997) to 71 days (5690hail days between 1920 and 1999; Schiesser, 2003). Esti-mates of hail frequency for the pre-Alpine districts (eachroughly 300 km2 in size) range from 4 and 7 days per year.

Analysing hail signals from 3D radar data between 2002and 2014, Nisi et al. (2015) determined the most appar-ent maxima over the Jura in the NW, the central Swissplateau between Bern and Lucerne, and the Canton of Ti-cino in the very south. Here, the frequencies are between2 and 3.5 hail days per km2 and year, comparable to hotspots in Germany (Puskeiler, 2013). For Ticino, this is inagreement with one of the first studies by Thom (1957),who found on average 1.35 hail days per year in Ticino inthe period 1917-1947, and 2.29 for Lugano.

Over the main Alpine chains, hail occurs less than onceper year (Nisi et al., 2015). This can be plausibly explainedby the lower temperatures at high elevations leading toreduced moisture content (see also Sect. 6.2).Austria counted on average 72 days with hail damage in

agriculture during the period 1990-2000 (Formayer et al.,2001). While there is more than one hail event with cropdamage per district and year in the south-eastern foothillsof the Alps, on the granite plateau in the North, and inCentral Tyrol, such events occur once in four years in thecentre and the west of the country Stroblmair (2003). Themost hail prone regions are in the states of Styria (southernparts) on the one hand and in Salzburg and Upper Aus-tria on the other hand (Unwetterstatistik Österreich, 2005;Zimmerli, 2005). The hail hazard map based on radar andpoint observations and recently published by Svabik et al.(2013, see Fig. 2) confirmed these hail prone regions. Aradar-based analysis of severe thunderstorms (Kaltenböckand Steinheimer, 2015) shows very similar patterns.

Several more detailed studies are available for the highlyexposed regions. The Climate Atlas for the State of Styria(Wakonigg, 2010), for example, estimated between 1.0 and5.5 hail days per year from 31 stations with direct hail ob-servation for the period 1971-2001, whereas for the regionsof Styria, Carinthia and upper Carniola (today in Slove-nia), Prohaska (1902) found an average of 2.3 hail reportsper year for a network of more than 300 stations and op-erating for 9 years. In a regional hailpad network Svabik(2004) found average point frequencies of up to 1.6 haildays per year, and in total, between 10 and 20 hail days

were observed each year (Giaiotti et al., 2003). In LowerAustria, a similar hailpad network covering 500 km2 reg-istered 4.2 hail days per year (mean point frequency of0.12, 1981-2000), but hailstones of 2 cm or greater fell onlyabout once per year in the area (Svabik, 2005).

Hail is a common phenomenon also in Slovenia, in par-ticular over the foothills of the Alps (Susnik and Zust,2005). Stations with more than one hail day per year werefound in all parts of the country (Rakovec et al., 1990).According to Kranjc (1981), hail fell on 21 days per yearon average in an area of 2550 km2 in the NE between 1972and 1980. Kajfez-Bogataj (1989) described a maximum tobe located over the Alpine-Dinaric ridge separating inlandSlovenia from the Adriatic sea (1951-1986).

This is confirmed by an evaluation of hail frequency from9 years of radar data (2002-2010, Klemencic et al., 2012),who found up to 1.5 hail days per year in this region,around 0.5 hail days in central parts of the country, and asecondary maximum with up to 1 hail day per year in theNE (also see Klemenčič et al., 2008; Skok et al., 2014).When Dolinar (2005) found the highest hail frequency overthe Alps with more than 3 days per year from station data,this certainly included other forms of precipitation.

4.1.3. Central Eastern EuropeIn Central Eastern Europe with the countries Poland,

Czech Republic, Slovakia and Hungary (the Baltic coun-tries are discussed in Sect. 4.5.2), the frequency of severehailstorms is most elevated in the South, where convec-tive activity increases (Punge et al., 2014), also due toorographic influence.

This applies in particular to Poland, where the coolinginfluence of the Baltic Sea reduces the overall risk of se-vere thunderstorms in the North during summer. Indeed,the evaluation of questionnaires collected in Poland during16 years (1960-1978) gives the highest frequencies in thesouthern provinces (Lesser Poland, Silesia, Świętokrzyskieand Opol, see Fig. 2; Kolkowska and Lorenc, 2012). Intotal, the study found on average 111 hail days per year.

Bielec-Bąkowska (2010) identified on average around 17hail days per year by analyzing observational data from23 polish weather stations (1966-2006), with a coverage ofone station per 13 600 km2. The point hail frequency inthe period April-September was found to range from 0.5in the very NW (Szczecin) to 2.1 in the Tatra mountainsin the S (Zakopane), whereas the mean at the 21 stationsover flat terrain was 1.08.

Suwala (2010) also evaluated station data, but for thewhole year (period 1973-2009), and obtained the highesthail activity at a station on the coast of the Baltic Sea (upto 10 hail days per year). This suggests that in particu-lar in winter, the WMO definition for hail is not observedstrictly at the stations. In a subsequent study focussingon the warm half year, Suwala and Bednorz (2013) reportan average point hail frequency of 1.12, which is close tothe estimation of Bielec-Bąkowska (2010). Detailed infor-mation are also available for some further stations, e.g. for

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Krakow in the SW (1.62 hail days; 1863-2008; Twardoszet al., 2010).

Due to the larger distance to the sea, the Czech Re-public is more exposed to hail compared to Poland. Overrecent decades, several severe storms are documented -those on 18 August 1986 (Pavlík et al., 1988), on 30 June1997 (Sálek, 1998), and on 6 July 1999 (Starostová, 2000;Sulan, 2000) caused substantial damage to buildings andcrops. Due to the commonness of hailstorms, multiplestudies on hail frequency estimates are available, someof them dating back more than hundred years. Bělohlav(1906) reports 94 hail days per year on average by ex-amining hail observations in NW Czechia (Bohemia) be-tween 1895 and 1902, whereas Koutny (1908) estimated33.3 days for the SE (former Moravia) from damage re-ports. Hrudička (1936) found an average value of 86 days,and estimated point frequencies exceed 2.0 hail days peryear for SE, SW and NE Bohemia.

Hrudička (1937) found hail damage in agriculture inmore than 20% of all years in W Moravia and more than10% in E Moravia and Czech Silesia. Annual loss rates of3-4% on a district level were common in S Bohemia andW Moravia (1926-1935; Hrudička, 1938).

Several more recent studies, including those of Brázdilet al. (1998), Heino et al. (1999), Chromá et al. (2005), orChromá (2006), estimated hail frequency from the networkof more than hundred meteorological stations in Moraviaand Silesia. Chromá (2006), for example, quantified ap-proximately 38 hail days from 155 stations in these regions,while Brázdil et al. (2014) found 31.4 days for SouthernMoravia, both in the range of the historic studies. In-terestingly, Chromá et al. (2005) were able to establish arelation in the number of hail days and station elevationfor Moravia and Silesia, also apparent in the atlas of Tolaszet al. (2007) (see Section 6.2 for discussion).

According to , hail frequency mostly follows topogra-phy. These station-based estimates may be biased high,as Skripniková and Řezáčová (2014, , also see Fig. 2) esti-mated from radar data an average number of 40 hail daysfor the period 2007-2011 for the entire Czech Republic.

Compared to the Czech Republic, Slovakia is much lessexposed to hail hazard (Punge et al., 2014). The only avail-able study by Líšková (2006) report on only 7.8 hail days(2000-2005) obtained from reports at 25 weather stations.Assuming large hail occurs only in the warm half year, theestimate has to be reduced to 6.8 hail days per year ina region of roughly 15 000 km2, which yields a rather lowmean point frequency of 0.27.

In Hungary, Seres and Horváth (2015) found on aver-age 42 extremely severe stormy days with a high probabil-ity of hail per year in the period 2004-2012. The highestvalues are found in the east with up to 2.3 extremely severestormy days (>55dBZ) per year. A secondary maximumexists in the SW with up to 2 days per year (see Fig. 2).The average values of 0.3-1.5 hail days per year are inreasonable agreement with recent report of OMSZ (2012)based on hail records at stations (1981-2010). This report

found point frequencies in the range between 0.5 and 1.5in more than 90% of the country with an average of 0.9hail days per year. Several hot spots with values up to 3days are particularly located in the Central North.

Hail is the second most important damage source tohungarian agriculture, next to drought, and accounts to adamage of 48 million EUR per year in this sector (Keményet al., 2012). Hail damage in agriculture affected on av-erage about 2.5% of the crop fields (1962-1982), with theprovince of Baranya in the SW being most severely affected(more than 4%; OMSZ, 2012).

4.2. Western Europe

Hail frequency across Western Europe is mainly in-fluenced by the proximity to the Atlantic ocean, whichleads to higher static stability due to the damped di-urnal and seasonal temperature amplitude. Thus, hailfrequency varies substantially in Western Europe (Pungeet al., 2014). Whereas France is frequently affected by se-vere hailstorms, hail occurs rarely in Benelux (Belgium,the Netherlands, Luxembourg) and the British Isles. Incontrast, these latter regions are characterized by a highdensity of low pressure systems and associated fronts, andhence convective storms generating graupel or small hailoccur quite frequently and throughout the year.

4.2.1. FranceSeveral regions of France, especially the SW and the

southern central parts around the Massif Central, are fre-quently affected by large hail causing huge damage tobuildings and agricultural products, especially in viticul-ture. Recent severe hailstorms such as those named Felix(25-26 May 2009), Andreas (27 July 2013), or Ela, a ma-jor hail outbreak in the greater Paris region between 08and 10 June 2014, caused damage between several hun-dred million and more than one billion Euros. The largesthailstone observed till today had a diameter of 150mmand a weight of 972 g (Strasbourg, 11 August 1958; Jalu,1959). Hailstones of a similar size were also reported nearTarbes in the SW (7 July 1936; Dauzère, 1936) or, morerecently, in the Loiret department in the NE and even inthe N (D ≈ 12 cm, 10 June 2014 and 25 June 2009, re-spectively; Keraunos, 2009).

A climatology of hail distribution throughout France(see Fig. 2) was published by Vinet (2000, 2002), whocombined data from hailpad networks with insurance claimdata of Malaval (1995) and Plumandon (1901). Accord-ing to these studies, the most hail-prone regions are theSW towards the Pyrenees, the Center-East of the Mas-sif Central, and the SW Alps with up to 0.66 occurrencesof hail >8 mm per year. Towards the Atlantic coast butalso towards the Mediterranean, this kind of hail is muchmore rare (below 0.05 cases per year). These findings areroughly in agreement with Hand and Cappelluti (2011) orPunge et al. (2014). In strict contradiction the hail haz-ard map of de la Floata et al. (1994), based on weather

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station reports, shows the highest frequency in Brittany -a striking example for misjudgment of the hail hazard inthe insurance industry.

Detailed knowledge on hail occurrence exists mainly forSW France, where a large region is equipped with a densehailpad network, thus being one of the best studied re-gions in Europe (Dessens, 1986a; Dessens et al., 2001, 2007;Sánchez et al., 2009; Berthet et al., 2011; Berthet et al.,2013; Hermida et al., 2015). Hailpads are also installedin some parts of central and SE France, but the networksare less dense, more recent, and fewer evaluations havebeen undertaken. Within SW France, the highest hail fre-quency was found in the Gers department with 1-2 hail-storms per year over a 900 km2 area (Dessens, 1986a), aresult also supported by the more recent work of Hermidaet al. (2013). The mean loss ratio for crops reaches 3.8% inthe neighbouring Lot department in the period 1944-1981(Dessens, 1986a), whereas the national average is close to1% (Dessens, 1986b). Berthet et al. (2011) found pointhail frequencies (D > 5mm) of 0.43 towards the Pyre-nees, 0.19 for the network in the Bordeaux region, 0.20 inthe center and 0.26 in the lower Rhone valley (all valuesin average number per year). According to Berthoumieu(2003), there were around 20 hail days per year in theirnetwork in SW France measuring 25 000 km2 during theperiod 1971-2002, which is roughly half the frequency ofthe Gers department (Dessens, 1986a).

4.2.2. BeneluxConvective activity for the Benelux countries, i.e. Bel-

gium, the Netherlands, and Luxembourg, is strongly influ-enced by the North Atlantic. A recent study for Belgium(area: 30 000 km2) identified 16.5 hail days per year fromradar data for the period 2003-2012 (Lukach and Delobbe,2013), using an adjusted version (Delobbe and Holleman,2006) of the Waldvogel et al. (1979) hail detection algo-rithm. In radar data from 2002-2012 Goudenhoofdt andDelobbe (2013) found storm frequency to decrease fromSE to NW (see Fig. 2). For the Netherlands, Holleman(2001) examined synoptic stations in combination with re-ports from precipitation observers and found on averageapproximately one report per station in the years of 1999and 2000. Based on eye-witness reports, Groenemeijer andvan Delden (2007) identified 143 events with severe hail(D > 20mm) between 1975 and 2003, or approximately 5events per year for the entire country, which is certainly alow estimate.

4.2.3. The British IslesDespite their rather maritime climate, significant hail-

storms can occur on the British Isles. Webb et al. (2009),for example, report on a hailstorm in 1697 in the UK,which reached the H8 TORRO scale (maximum diameterof 76-90mm), which is comparable to recent major hail-storms in Central Europe. Other examples are a hailstormwith a swath length of 255 km on 9 August 1843 (Jor-dan, 1843; Webb and Elsom, 1994) or the one on 28 June

2012, with hailstones diameters of up to 90mm, a lengthof 110 km between the West Midlands and Lincolnshire,and a width of 11 km (Clark and Webb, 2013).

Thunderstorm frequency in the UK estimated fromlightning data was found to be highest over SE England(Holt et al., 2001), which is reflected in the hail mapby Webb et al. (2009, see Fig. 2) relying on hail re-ports from weather stations and the TORRO database.Maps from the climatological surveys of hailstorms of theTORRO database alone show the highest rate of severehail events (H2; D 16-20mm) over the central-to-easternparts of England, with a clear decrease towards N Eng-land and Scotland, but also towards Wales and Cornwall(Webb et al., 2001, 2009). Rowsell (1956) estimate there isabout one damaging hailstorm per year in the SE of Eng-land (10 000 km2). Illustrating the problems of defininghail as opposed to other kinds of precipitation, a station-based hail climatology (UK Met Office, 2013) shows a pro-nounced maximum of up to 30 days per year over NWScotland. As already mentioned for France, these hailevents predominantly occur during winter and spring andmost likely include other forms of frozen precipitation.

The situation for Ireland is likely similar to that of theUK and, more or less, to other regions in the proximityof the Atlantic: Large hail is very rare, but small hail-stones occur much more frequently and can accumulate tosignificant depth. Walsh (2012), for example, report forthe period 1980-2010 on an extraordinary large number of11.3 days with hail per year for a station at the E coast(Casement Aerodrome) and even 22.8 days at the SW tip(Valentia Observatory). However, only a small fraction ofthese had audible thunder, and only 0.5 and 0.1, hail daysoccurred in the summer at these stations, respectively. Ofthe 14 stations on the Met Eireann website (www.met.ie,2014) with a climatological time series over the 1971-2000period, Kilkenny in the Center-South of the island hadthe highest hail frequency in summer with 0.8 hail daysper year. According to Webb et al. (2009), only three ma-jor hailstorms (their H5 class, max. size 41-50mm) weredocumented for Ireland in the past.

4.3. Southern EuropeThe climate of Southern Europe including Italy and the

Iberian Peninsular is dominated by the high insolation andproximity to the Mediterranean, where warm and moistair masses are advected from S to W directions. A fewregions, such as northern Italy, feature some of the highesthail frequency in Europe (e.g., Punge et al., 2014).

4.3.1. ItalyIn Italy, severe hailstorms occur more or less in all re-

gions according to records of the National Climatic DataCenter (NCDC; Baldi et al., 2014), but most frequentlyover the northern parts (e.g., Morgan, 1973; Punge et al.,2014).

The Po valley in the north regularly experiences sig-nificant economic damage: According to Morgan (1973),

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hail losses in agriculture were the highest worldwide atthe time. Roncali (1955) found annual crop losses of 4%or more in a large region north of the river Po, but alsoin adjacent regions of N Italy such as Veneto, Lombardy,parts of Friuli-Veneto Giulia (FVG), Piedmont and Emilia-Romagna. Morgan (1973) also found indications for excep-tionally large hailstorm-affected areas, long duration of thestorms and high stone’s density. Hailstones with diame-ters in excess of 100mm are reported to occur on a yearlybasis. Within the Po valley Prodi (1974) found the high-est hail occurrence in E Veneto and W Piedmont, withpoint frequencies of up to 3.5 hail days per year. Thesehail hot spots are confirmed by recent radar-derived hailclimatologies (Davini et al., 2011; Sartori, 2012). For theVeneto region, Tormena (2011) report an average numberof 13.8 hail days per year based on the Fondo Nazionaledi Solidarietà reimbursement data (1978-2005), and 3.9events per year for the province of Verona. In comparison,Morgan (1973) reported a point hail frequency of 2.5 haildays per year for the city of Verona based on 19 years ofobservations.

The longest lasting historical records of hail frequencyhave been published for the city of Padova in the NE,extending back to the 14th century (Camuffo et al., 2000).Approximately 3 to 4 hailstorms were reported per yearfor the period from 1750 to 1900, which is comparable tothe provincial average of 3.2 estimated for modern times(Tormena, 2011). The subsequent decrease of hail reportsis most likely due to a reduced reporting horizon, from theentire city and its surroundings in the past to the airportarea today. The record shows strong variations during the18th and during the 19th century, but these could as muchbe due to varying reporting practice as to changes in hailfrequency.

In Trentino (≈ 3 000 km2) Eccel et al. (2012) detectedaround 30 hail days per year by sustained observations over35 years, which is also quite high. The Friuli-Venezia Giu-lia is another hail prone region: For a hailpad network ofmore than 300 stations, Giaiotti et al. (2003) obtained 55hail days per year in an area of approximately 4,500 km2.The highest point frequencies were found near Udine in theNW, with around 2.0 hail days per year, compared to val-ues around 1.0 elsewhere (1992-2009, Manzato, 2012). Fora network consisting of 370 stations in Emilia-Romagnacovering an area of around 4 000 km2, Nanni (2004) reporta mean number of 26 hail days per year (1983-1998) witha mean point frequency of 0.7. Finally, Baldi et al. (2014),based on hail reports, claims and reanalysis, found a highhail frequency in the Campania and Potenza provinces inthe S (see Fig. 2).

4.3.2. The Iberian PeninsulaSpain is also among the countries with the greatest

losses by hail in the agricultural sector. According toPorras et al. (2013), the average annual compensation inthe sector was around EUR 240 million during the period2001-2009. Hail map based on agriculture damage show

maxima towards the Pyrenees and the Iberian System inthe NE (Burgaz, 2004, see Fig. 2), but also and on theeast coast (Saa Requejo et al., 2011).

In the Valencia community in the central-east, the meandamage ratio by hail is 2% for all crops, but 6% forfruits (without citrus) and vegetables (Olcina Cantos et al.,1998). Hail is most frequent in the NW of the regionaround Morella and Requena on the E flanks of the Iberanhighlands with about 2 days per year, but the highest cropdamage was in the S of the region around Elda.

Especially the Ebro Valley in the NE is well known forits high exposure to hail, mainly due to the geographi-cal situation (e.g., Castro and Sánchez, 1990). Approx-imately 10% of the harvest gets damaged by hail eachyear, amounting to a loss of EUR 100 million (Ceperu-elo Mallafré et al., 2009). Using radar detections and ob-servational reports, García-Ortega et al. (2014) detected32.6 days per year on average for the middle Ebro valley(≈ 60 000 km2) for the period 2001-2010.

Two operational hail monitoring networks consistingof voluntary observers and hailpads exist in NE Spain(Merino et al., 2013), in the Iberian System (Zaragozaprovince) and the Ebro valley (Lleida province). In the lat-ter, 12 hail days per year were reported for the period 1995-2007 in an area of 3500 km2 (Pascual, 2002; Farnell et al.,2009). For the Lleida observatory, a point hail frequencyof 1.4 hail days per year is estimated by Sousa (1987) forthe period 1953-1980. The agricultural hail losses in thisregion amounted to 5.6% of insured crop value over theperiod 2000-2009 (Aran et al., 2007).

Bernaldo (2009) compared hail days from meteorologicalstations and hail damage percentages in agriculture (1981-2007) for the provinces of Burgos, Cuenca, Valladolid andZaragossa. While Zaragossa in the Ebro Valley had thehighest average annual loss rate at 2.33%, on less than 8hail days, the higher elevated province of Burgos in Castillay León in the N with a similar number of stations had anaverage loss rate of only 1.85%, but distributed over 54days. This again illustrates the impact of altitude on hailfrequency and size in station data.

In Portugal, large hail is a very rare phenomenon,mainly due to the exposition to the Atlantic ocean as-suming prevailing Westerlies. Only 7 days with hailstonesize of 2 cm or greater over the last 10 years can be foundin the ESWD. However, small hail (presumably includinggraupel) appears to be more common in the N areas and athigher altitudes, with values over 5 days per year reportedat some stations (Font Tullot, 2000, see Fig. 2).

4.4. Southeastern Europe

In Southeastern Europe, including the countries of Ro-mania, Moldova, Bulgaria, Greece, Turkey, Cyprus, andthose of the Balkan Peninsular, the general climate isstrongly influenced by the Mediterranean and - for theeastern part - by the Black sea. Hail occurs frequentlyat several hot spots, for example in the NW of Romania.

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However, only limited scientific literature (in English lan-guage) is available, mainly based on hail observations atmeteorological stations, which may also include ice pelletsor graupel.

4.4.1. Romania and MoldovaDue to its climate influenced by both the Mediterranean

and the Black Sea, hail is quite common in Romania. Inthe East, more than 2% of all agricultural land (3.3% offruit and wine areas) is affected by hail each year (Machi-don, 2007), comparable to, e.g., S Germany.

While Bogdan (2005) report maximum hail frequencybetween the center and the S (see Fig. 2), Sandu et al.(2008) find the highest hail frequencies in the mountainousregions around the Transsylvanian Plateau. The evalua-tion of four years of radar data for the NW (2004-2007)yields elevated probabilities of severe hail in the ApuseniMountains, and a high frequency of small hail on the West-ern flanks of these mountains and in the E and S Carpathi-ans (Maier et al., 2010). For Oradea in the NW - locationof the largest reported hailstone with a diameter of 80mm(31 July 1991; Cristea, 2004) - Moza (2009) quantified 1.2hail days per year (1970-2005).

Paraschivescu (2010) report on 40 hail events per yearthat were reported at 142 places in the SE, specificallyin the region of Muntenia (period 2001-2008). Based onlong lasting station records (period 1961-2007), the aver-age point frequency is estimated to around one hail eventper year within this region. The hail frequency inreasesfrom the SE (0.65 in Constant,a at the Black Sea), to-wards the N and the W. Lower frequencies of 0.65 were re-ported for Constant,a at the Black Sea, but (2.0 for Câmp-ina S of the foothills of the Carpathian mountains) byParaschivescu (2010). In contrast, Lungu et al. (2010)state a hail frequency of 1.6 days per year for Constant,aover the period 1965-2005 and an increase towards theBlack Sea within the Dobruja region.

Over the Romanian part of the Moldavian Plateau inthe NE, mean annual hail day count for the period 1967-1998 ranged from 0.08 to 1.63 with the highest frequenciesrelated to higher station altitude and for urban centers(Apostol and Machidon, 2011). In the Barlad subregion,the average hail frequency was 0.6 days per year, but hailgreater than 20mm was observed only once in 10 years ateach station on average in the period 1961-2007 (Apostoland Machidon, 2009, 2010). For the Center-East, Machi-don (2007) found a high variability of hail occurrences andestimated annual point frequency in the range between 0.9and 1.5 hail days per year (1985-2004). He noted thatinsured damage in agriculture occurred on 0.65 days peryear at the stations considered on average. We note thatfor Romania only rather few events during the winter andearly spring seasons are documented in the station records,which increases the trustworthiness of these studies.

In Moldova, the most severe hailstorms occur over theN plateau and Central Uplands regions, where hail diam-eters of up to 100 mm have been observed (Potapov et al.,

2007). Germanyuk (1986) stated that the total affectedarea in agriculture per year was close to 12% of the cul-tivated land during 1955-1981, which would represent thehighest loss ratio reported across Europe. Potapov et al.(2007) estimated on average 30 hail days per year for thewhole country from April to September based on stationdata for the period 1966-2005. This estimation certainlycomprises also very small hail or even sleet. A Moldavianhail suppression service estimates on its web page (anti-grindina.md, 2013) that hail causes damage in agricultureon 21 days per year on average in the country. They alsoreport that 2% of all hailstorms spawn hailstones with di-ameters greater than 50mm.

4.4.2. BulgariaAccording to the OT-based climatology of Punge et al.

(2014), the convective situation in Bulgaria seem to be sim-ilar to that of Romania. Indeed, Bulgaria is highly exposedto hail; hail damage to agriculture occurs regularly, on 54-99 days per year according to insurance records datingback to 1888 (Simeonov, 1996). The variability is in partexplained in part by the variations of insured crop area. Amean of 65 hail days is given in another publication by thesame author (Simeonov et al., 2006). Overall, hail dam-age is reported to decrease from W to E. On average, of allannual hail days, 4 to 5 cause widespread damage to cropsand property in at least 4 of the 27 provinces (Simeonovet al., 2009). Giaiotti et al. (2003), on the other hand, es-timated only 30 hail days per year using information fromhailpad stations in the three main affected regions.

4.4.3. The Balkan PeninsulaOn the Balkan Peninsula hail occurs quite frequently,

especially in the interior. The largest recorded hailstoneshad a maximum diameter of 100 mm (Koceljeva/Serbia on4 June 2007).

Although severe hailstorms occur on a regular basis inCroatia, the available scientific literature is quite lim-ited. Interestingly, the largest hailstone, with a diameterof 80mm, was recorded on the island of Hvar offshore theDalmatian coast (28 August 2008). Počakal (2011) reporton a high number of 40 hail days per year (2002-2008),about half of them with damage, for a region in the NEthat is equipped with hailpad observations (≈ 26 800 km2).According to a follow-up study, point hail frequency, esti-mated from data for the period 1981 to 2006, reaches upto 2.4 hail days per year in this region (Počakal and Veče-naj, 2013, see Fig. 2). These estimates are comparable toparts of N Italy or E France.

Hail damage to the Serbian agriculture is on the orderof 3-4% (Mitic et al., 2009). In a network of 28 weatherstations with data for the period 1949-2012, the frequencyof hail greater than 2mm in the warm half year decreasesfrom 3.4 in the western mountains to values around 1.0in the E, and from about 2.0 days per year in the S val-ues below 1.0 in the NE (Ćurić and Janc, 2015). Gburciket al. (1993) found an average point hail frequency of 1.2

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based on the records of 39 weather stations during theperiod 1951-1980. Further studies support average pointhail frequencies in this range (Gavrilov et al., 2010; Drag-ićević et al., 2011; Gavrilov et al., 2013). The maximumin the hilly region of W Serbia around the Morava val-ley is confirmed by a hail reporting network of 235 sta-tions, where mean hail point frequencies of up to 2.4 wereobserved over a ten year period (Ćurić and Janc, 2012).This again demonstrates the important role of orographyfor hailstorm formation, that will be discussed in Section6.2.

For Bosnia and Herzegovina and the Vojvodinaprovince of Serbia, Mesinger and Mesinger (1992) esti-mated a hail point frequency of approximately one eventper year from station data (1949-1988), whereas for Cen-tral and S Serbia the quantities were slightly higher withvalues close to 1.2.

For Macedonia, the only available reference by Dim-itrievski (1983) report a huge average of 50 hail days peryear derived from radar observations in a 6-yrs period be-tween 1977 and 1982. In 4.1% cases of these days large hail(> 20mm) was documented, while the observed maximumdiameter for the period was 60mm.

No literature on hail frequency in Albania was found,but storms with large hail like the one in Maliq (up o100mm) on 19 May 2015 do occur. Satellite data suggestsa decrease of hail frequency towards the South (Pungeet al., 2014).

4.4.4. Greece

In contrast to other countries in SE Europe, hail is nota very common weather phenomenon in Greece, especiallyin the southern parts. In continental Greece, weather sta-tions report less than one hail day per year (Catsoulis andCarapiperis, 1984; Sioutas, 2011, see Fig. 2). A maximumin hail frequency is found over the western islands of theIonian Sea with 4-5 hail days per year, and also in theEastern Aegean Sea. The coastal and island areas appearto be affected by very small hail or sleet mainly in the coldseason.

More detailed studies exist for the region of CentralMacedonia in the N, where convective activity is higher ingeneral (Punge et al., 2014). Sioutas and Flocas (2003), forexample, report on 22.3 hail days per year in that regionbased on agriculture insurance and hailpad data that areavailable for the period 1976-2001. In a hailpad networkcovering an area of 2,400 km2 in the W of the region, inoperation from April to September, on average 8 hail daysoccurred in the period from 1984 to 2001 (Sioutas et al.,2009). The point hail frequency is reaches up to 0.88 inthe hillier NW and N of the hailpad area. About 10% ofthe hailstorms reach the TORRO category H2 (16-20mm)or higher and lead to significant damage, and about 2.4%of the storms were severe (H3 or H4, >20mm).

4.4.5. Turkey and CyprusAn analysis of hail in Turkey including 129 severe hail

events (maximum diameter >20mm) between 1929 and2010 mostly based on newspaper reports was presented byKahraman et al. (2011). They found higher concentrationnear the Mediterranean coast, in the Marmara region, aswell as in E and Central Anatolia, but these are likelybiased by population density. The largest hailstone docu-mented in ESWD had a diameter of 80mm and fell on 09April 2012, but Jéhan (1864) report on even larger stonesin Istanbul on 05 October 1831.

For the whole of Cyprus, 6.2 damaging severe hailevents per year were reported by the Meteorological Ser-vice of Cyprus during the period 1996-2005 (Michaelideset al., 2008). Station-based year-round hail frequency esti-mates are 1.3 days per year in the W (Paphos), 2.2 in theSE (Larnaka), and 2.9 in the S (Akrotiri). Based on agri-cultural insurance data, Nicolaides et al. (2009) estimateda higher value of 13.8 hail events per year for the period1996-2005. According to this study, highest frequencieswere found in the Troodos mountain range in the Center-West and their SW foothills, as well as at the South-Eastcoast. Given the political division of the island, these stud-ies focused only on the southern part of Cyprus.

4.5. Northern Europe

In Northern Europe, hail is less common compared tomost other parts of Europe, mainly due to the prevail-ing colder climate. In addition, the proximity to the seas,especially to the cool North Atlantic, inhibits strong con-vective activity. This applies especially for large hail. Bycontrast, ice pellets or graupel occur quite frequently andthroughout the whole year, dominating in several cases thestatistics, similar to the situation of the British Isles (cf.4.2.3).

4.5.1. ScandinaviaIn this article, Scandinavia is defined according to the

political definition and comprises the countries of Den-mark, Norway, Sweden, Finland, and Iceland. Accordingto Tuovinen et al. (2009), hail occurs infrequently both inDenmark and Norway. They also point out that withinNorway, hail frequency increases towards the SE, mainlydue to the (slightly) more continental climate. This regionalso featured the largest hailstone reported in Norway witha diameter of 100mm on 4 August 2014. In Denmark, thelargest stone reported in the ESWD had a diameter of40mm and fell in Jordrup in the Center-South on 5 May2015.

Compared to these two countries, hail is somewhat morecommon in Sweden, as it is shielded from oceanic influ-ence by the Scandinavian Mountains in westerly flow con-ditions. The largest hailstones reported to ESWD haddiameters of 80mm (4 July 1953, 80 km West of Uppsala,mass of 200 g). The most extensive study of hail in Swedenwas already conducted by Hamberg (1919), who evaluated

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24,863 hail reports from more than 600 stations between1865 and 1917. He estimated on average up to 6 hail eventsper year at the stations in the SW, almost 4 for the S (Gö-taland), 2.8 for the Centre (Svealand), 2.2 further north(Norrland), and less than 1.5 for parts of NW Sweden (seeFig. 2). However, as further stated by the author, only27.7% of these cases were accompanied by thunder. Thus,the majority of the events can be assumed to be of smallintensity and dimensions. A maximum hailstone size of10mm or more was reported for 258 cases in the period1879-1917. Large hail greater than 20mm was reported106 times (2.7 cases per year) in the entire country. Forcomparison, only 23 events with large hail were archivedby ESWD between 2000 and 2013.

The overall hail frequency in Finland is similar tothat of N Sweden due to similar climatological conditions(Saltikoff et al., 2010), and decreases from S to N (Tuovi-nen et al., 2009, see Fig. 2). The largest hailstone recordedand archived by the ESWD had a diameter of 90mm (8August 2010, near Tampere in the S). With the help ofradar data and ground verification, Tuovinen et al. (2015)estimate a total of 43 hail days per year for Finland inthe period 2008-2012, of which 17 are severe, on average.They also found 13 significant hail days with a maximumdiameter greater than 50 mm in the period 1999-2012.

In Iceland, large hail appears to be very rare, only threereports are listed in the ESWD and no further sources havebeen found.

4.5.2. The Baltic countriesEven if 12-30 thunderstorm days occur over the Baltic

countries each year (Enno et al., 2013), severe thunder-storms with large hail are rare. Therefore, only a fewstudies are available on hail frequency, namely for Estonia.However, in rare cases very large hail has been observed,for example hailstones with a diameter of up to 90mm anda weight of 150 g in Estonia (May 2000 at lake Peipus inthe E), and with a diameter of up to 70mm in Lithuania(15 June 1972; Vyriausioji enciklopediju redakcija, 1986).

In Estonia, hail was observed at weather stations on29 days per year on average during the period 1981-2005(Tammets, 2012). The point hail frequency per stationranged from 0.6 to 1.8 days per year. Since the hail dayestimate is close to the number of detected thunderstormdays (Enno, 2011), one can assume that a high percentageof the reported hail events produced only ice pellets andnot hail. In 2007 the hail frequency was highest in the SEof the country (Tammets, 2012).

4.6. Eastern Europe

Literature about hail events and frequency for East-ern Europe is scarce, even though several reports in theESWD with diameters of 80mm or more suggest a highhail hazard (e.g., near Minsk/Belarus on 19 May 2014, orNesterovka/Ukraine on 16/17 May 2014). Furthermore,hail is a major peril in Russia, affecting around 5 000 km2

of agricultural area each year (Abshaev and Malkarova,2006). The Northern Caucasus is sometimes consideredthe region with the highest hail hazard in Eastern Eu-rope (e.g., Abshaev et al., 2003). Malkarova (2011) foundnatural crop loss ratios of 2.5% in Crimea and 4.4% inthe region of Odessa (Ukraine), but more than 6% for theprovinces in the N Caucasus. According to Abshaev et al.(2009), the loss ratio in agriculture exceeds 8% over sev-eral parts of the Caucasus foothills, and 2% everywhereover the foothills. These ratios are in the range of thosefound for other highly exposed countries such as N Italyor NE Spain.

A map of hail frequency in Russia based on station datafrom 1958 to 2008 Abshaev et al. (2009, see Fig. 2) showsmore than two hail days per year and station over theN Caucasus but also in Western Russia and the VoronezhOblast, and several regions in the Asian part. The patternmatches relatively well with those estimated by Williams(1973). Most of the European part of Russia south of 62◦N experiences around 1-2 hail days per year. Out of 40-50 hail days per year in the Southern Federal District ofRussia (418,500 km2), 5-6 lead to widespread ’emergency’damage (Abshaev and Malkarova, 2002; Abshaev et al.,2006). Point hail day frequencies in S Russia and the NCaucasus reach from 0.5 for the dry steppe, to 0.5-1 forthe plain, 1 at the Black Sea Coast, and finally 1-2 in theCentral N foothills of the Caucasus (Abshaev et al., 2012).The 8 stations in the NW Caucasus flat-to-hilly district ofKrasnodar had hail frequencies of about 1.1 on average(Malkarova, 2011).

The more comprehensive radar-based analysis of Zha-rashuev (2012) identified 2250 hail cells per year ina 200 km radius around Stavropol in S Russia (≈125 000 km2) during the period 2002-2008, of which 8%were severe in the sense that they caused crop lossesgreater than 30%. For the Kirovske radar in the Center-East of the Crimea peninsula, they identified approxi-mately 2,900 hail cells per year within a 200 km radiusduring 5 years, and 17% of these cells were severe.

4.7. Conclusion of hail frequency estimatesVarious studies identified regions of increased hail inci-

dence. However, quantitative comparison is complicateddue to different analysis techniques (e.g., station data vs.damage estimates vs. radar data), different quantities eval-uated (large vs. small hail or even graupel), and differenttime periods taken into account. Given the large spatialand temporal variability of hailstreaks, latter is the mostsignificant restriction. Furthermore, the available studiesshow only little spatio-temporal overlap among each other.Despite these complications, we compiled the informationcontained in the various studies into the overview Tables1 - 3, and Figures 2 - 4.

Table 1 gives an overview of major hail loss events inEurope in the last decades. The list appears to be biasedtowards Central and Western Europe. This is more likelydue to the concentration of values and more widespread

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reporting rather than storm characteristics. However, thelarge damage sums highlight the importance of the hailproblem and indicates the high vulnerability of buildingsand vehicles.

The hazard distribution is somewhat better representedby maximum reported hailstone size documented in the lit-erature, as listed by country in Table 8. As some of theseevents, in particular the most ancient ones, lack detailedinformation and validation, we selected only the entriesof the ESWD database for the graphical representation inFigure 3. A notable finding is the occurrence of very largehail greater than 80mm in a large majority of Europeancountries. Despite a possible reporting bias, a maximumin Central Europe and decreasing maximum observed hail-stone size towards the SW, the NE and the SE are appar-ent.

The hail frequency references were summarized in Ta-ble 3. With the objective to group comparable numbers,study result were attributed to one of the three classesHail events per year, Hail Days per year, and Station meandays per year, subdivided into the size classes > 5mm -following the WMO definition - and > 20mm, the ESWDdefiniton for large hail. While the level of detail variesstrongly among the hail maps presented in Fig. 2), dis-tinct zones of greater hail risk can be identified in almostall countries, and appear to be related to orography (seesection 6.2).

5. Characteristics of hail occurrence

Hailstorm formation requires specific atmospheric con-ditions to prevail: (i) high convective energy to feed andmaintain the strong updraft, (ii) sufficient vertical windshear to support organized convective storms in terms ofmulticells, supercells, or MCS , and (iii) a trigger mecha-nism that lifts the air parcels over the convective inhibitionin the boundary layer (Markowski and Richardson, 2010;Houze, 2014). Whereas the latter two conditions are moreor less independent of the season, the former is determinedby the release of latent heat of condensation in combina-tion with the lapse rate, i.e. warm air at low levels and coldair aloft. Consequently, severe thunderstorms in Europe ingeneral have their highest probability and intensity in thesummer months during the afternoon and early evening.Daily and seasonal cycles, however, differ from region toregion. This issue together with hailstorm characteristicsfor different European regions is discussed in the following.

5.1. Seasonal cycle

None of the reviewed studies considered a clear defi-nition of the hail season based on predefined thresholdsor percentiles of the annual frequency distribution. Fur-thermore, whereas some of the studies differentiate amonghailstone diameters, others do not or even consider smallice pellets as hail. Consequently, hail season estimationsare subject to different hail classifications, approaches, and

data sets. This fact may partly explain the discrepanciesbetween the studies. Note that the same applies to thediurnal cycle, too (see next paragraph).

In the vast majority of European countries, the hail sea-son starts in April/May and lasts until August/September.This applies, for example, to Germany (Gudd, 2003;Deepen, 2006; Mohr and Kunz, 2013), France and Benelux(Dessens, 1986a; Vinet, 2002), Spain (Ceperuelo Mallafré,2008; González Martín, 2010), Italy (Nanni, 2004; Man-zato, 2012), or the Alpine countries (Admirat et al., 1985;Muriset, 2003; Wakonigg, 2010). On the other hand, hail isreported to be a round year phenomenon near the coast,where the climate is strongly influenced by the Atlanticocean or the Mediterranean (see next section). Hail dur-ing winter and early spring may dominate in the frequencyof occurrence, but the size of the hailstones in general arevery low. Furthermore, several studies using meteorologi-cal station data also consider ice pellets with diameters lessthan 5mm as hail. Thus, reports about winter hail haveto be interpreted very carefully. Significant amounts ofwinter hail are reported from the UK (especially over NWScotland; UK Met Office, 2013), Ireland (Walsh, 2012),Castilla-La Mancha in Spain (González Martín, 2010),parts of Romania (Lungu et al., 2010; Apostol and Machi-don, 2011), Greece (Sioutas, 2011), Turkey (Kahramanet al., 2011), or Cyprus (Michaelides et al., 2008).

Whereas the definition of the hail season is often some-what arbitrary, the peak hail month is a clearly definedproperty. However, it still depends on several factors: thequantity considered (number of hailstorms, affected area,damage), the minimum size of the hailstones (small vs.large hail), and the local convective climate (convectiveenergy, annual temperature cycle). Most of the studiesidentified the highest hail frequency in May/June whenthe number of hailstorms is counted, and between Juneand July when the affected area, total damage, or onlylarge hailstones are considered.

For central Germany, for example, Gudd (2003) foundthe highest hail frequencies in June and July at an al-most equal level. When considering only large hail (D >45mm), however, July becomes the peak month, whichis in agreement to the radar-based analyzes of hailstormsby Kunz and Puskeiler (2010a) and Puskeiler (2013). InN Switzerland, the maximum hail day probability esti-mated from a combination of radar data and insurancelosses spans from May to the beginning of July, whereasthe largest areas are affected in July (Admirat et al.,1985; Muriset, 2003). This is in agreement to the radar-derived hail frequency estimated by Nisi et al. (2015), whofound a pronounced seasonal cycle with a maximum duringJune/July and a minimum in April/September. In Aus-tria (Styria), the hail day maximum is in June, whereasthe number of hit hailpads per hail day is highest in July,indicating that storms in July cover larger areas than thoseoccurring in June (Svabik, 2004). For SW France, peaksof the seasonal cycle are found during the first half ofJune, the first decade of July, and mid-August (Dessens,

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1986a), whereas the number of hail days is greatest in May(Dessens et al., 2015a). Fraile et al. (2003) also foundthe highest hail kinetic energy in May, followed by July.This is more or less in agreement to the study of Her-mida et al. (2013), who observed a substantial increase inhail frequency in May, but again a decrease in the sum-mer months. The seasonal cycle in the UK was found topeak in June for hail larger than H2 on the TORRO in-tensity scale, but in July for events with destructive hail(>H5, maximum diameter 41-50mm). In the Netherlands,Botzen et al. (2010) found a clear maximum of hail damageto greenhouse horticulture in July, whereas outdoor haildamage was similar from May through July, also pointingtowards an increase of hailstorm severity from spring tosummer. According to Manzato (2012), the annual cyclein the Friaul-Venezia Giulia region in Italy peaks at theend of June for all hailstorms, and in the first half of Julyfor days with at least 16 hailpads hit. In Sweden and Fin-land, reports of large hail are most frequent in the secondhalf of July (Hamberg, 1919; Tuovinen et al., 2009).

Studies examining potential relationships between theseasonal cycle of hail and geographical factors such as el-evation, continentality, or the distance to the sea are veryrare. According to Kolkowska and Lorenc (2012), for ex-ample, hail in Poland peaks in April/May in regions nearthe Baltic Sea in the north, but in June/July in the SE. InGreece, the month with the highest hail frequency variesfrom late winter to spring as a function of continental-ity (Kotinis-Zambakas, 1989). Some regions influenced bythe ocean often feature a secondary maximum during fall.For parts of Spain (Castilla-La Mancha), an analysis ofhailstorms between 1855 and 1950 identified a pronouncedmaximum of the seasonal cycle in June and secondarypeaks in March and September (González Martín, 2010).For the region of Alicante in Eastern Spain, the hail seasonpeaks in June, but there is also a secondary maximum inAugust (Olcina Cantos et al., 1998). A few studies reportthat the peak month is shifted later in the year for loca-tions at higher altitudes. In Styria in Austria, for example,the peak month varying from May to July occurs latest atelevated stations (Wakonigg, 2010). For Romania, Lunguet al. (2010) identified from station data the hail maximumin April/May with a secondary maximum in autumn. Forstations at higher altitudes, however, they report on singlepeak occurrences in June or July.

Finally, some studies report on long-term changes of thepeak hail season. From the examination of historical se-vere hail reports, Sovadina (2002) estimated for E CzechRepublic (Moravia) the maximum in June during the 19thcentury, but in July during the 20th century. In Bulgaria,Simeonov et al. (2009) report on a decrease in the num-ber of hail days in May between 1991 and 2006, but anincrease in September during 1961-1990. In the long-termhistorical record for the city of Padova in NE Italy, thepeak hail months are June or July between the 14th and18th century, but shifted forward by one month in the18th-20th century (Camuffo et al., 2000). Whether such

a shift in seasonality is meteorologically-driven or due tochanges in the data source (e.g., station data vs. reportson damage to crops, changes of crop production, trainedspotters) remains unclear.

5.2. Diurnal cycleThe diurnal cycle of convection in general is mainly de-

termined by the diurnal cycle of the near-surface temper-ature and the resulting increase of the lapse rate. Im-portant trigger mechanisms, on the other hand, such aslow-level flow convergence due to thermally-induced circu-lations over complex terrain, inhomogeneities in land use,or land-sea-breeze circulations near the coast also developand intensify in connection with the diurnal temperaturecycle, but sometimes with a certain time shift. Therefore,hailstorms are expected to occur preferably in the after-noon and early evening in Europe, but with some shiftsdepending on the terrain characteristics and the distanceto the coast (Bedka, 2011). Unfortunately, the numberof studies with accurately and spatially resolved exami-nations of the diurnal cycle of hail is limited since bothhailpad data and insurance losses do not provide informa-tion on the hour of the day. However, the characteristicdaily cycle can be estimated from station records or fromremote sensing instruments.

Kaltenböck et al. (2009), for example, estimated thepeak hour of severe hail in Europe to be on average around15 UTC. However, since the local time (LT) across Eu-rope from Portugal in the W to the Caucasus in the Ehas a difference of approximately 3 hours, a time correc-tion is necessary, Therefore, we converted all given timesinto LT. Overall, most of the studies report on a diurnalpeak between 13 and 18 LT. In SW Germany, for exam-ple, the vast majority (>92%) of radar-derived hail eventsoccurred during that time window (Kunz and Puskeiler,2010b). The same is found for northern Italy, namely thePo valley (Morgan, 1973), or for Switzerland, where hourlyradar-derived hail frequency features a distinct diurnal cy-cle during all months and regions with a maximum in thelate afternoon and a minimum in the morning hours (Nisiet al., 2015). Interestingly, the hail peak occurs approxi-mately two hours earlier over the northern pre-alpine re-gion compared to the S, indicating that cold pools initiatedby the first convective cell over the mountains may act asan important trigger mechanism here.

Whereas in Spain the daily maximum occurs a bit lateron the day, for example around 17-18 LT in the Ebrovalley in the NE (Ceperuelo Mallafré et al., 2009) or be-tween 15 and 18 LT in the region of Castilla-La Mancha(González Martín, 2010), it is found earlier in several re-gions in Eastern Europe, for example between 1420 - 1520LT in Poland (Twardosz et al., 2010). In parts of Ro-mania, hail frequency peaks very early between 13 and14 LT in the W and central parts, but is delayed in theDanube Delta and on the littoral (Lungu et al., 2010).According to Apostol and Machidon (2010) for NE Ro-mania, the maximum show a strong variation among the

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stations considered and is between 14 and 19 LT, whichis in agreement to the situation in Moldowa (Germanyuk,1986). In Macedonia the peak is between 14 and 17 LT(Dimitrievski, 1983). The maximum of the diurnal cycle inthe North Caucasus is found to be between 16 and 18 LTfor Stavropol in the SW of Russia as well as for Moldovaand Tajikistan (Abshaev et al., 2009), and between 14 and16 LT in Kirovske in Ukraine (Zharashuev, 2012).

Three studies provided indications suggesting a relationbetween the diurnal cycle and the size of the hailstones.For Finland, Saltikoff et al. (2010) estimated the peak tooccur between 15 and 17 LT for all hail events, but withsome delay for large hail (>40mm). Hamberg (1919) de-termined from old hail records (which obviously includessmall ice pellets and graupel) a peak between 13 and 14LT in SE Sweden (Upsal). Noting that thunderstorm fre-quency is highest between 16 and 17 LT and graupel fre-quency between 11 and 12 LT, it is likely that the peakhour is significantly later for large hail. Dessens (1986a)estimated the peak hour in SW France around 15 LT be-tween April and June and around 16 LT between July andOctober. As hailstorms tend to be more severe in July,this is a further indication of a relation between hailstormpeak hour and severity. Whether this effect is significantand whether it applies to all countries cannot be concludedfrom the few studies.

5.3. Hailstreak dimensionsAlthough the hailstreak’s dimensions in terms of length,

width, and orientation are of paramount importance forapplication purposes (e.g., hail risk models), only a fewstudies presented some statistics on those aspects. Reli-able estimations of the dimensions require monitoring datahaving a large area under constant surveillance, such asradar data or highly resolved insurance loss data; the latterwith the restriction of being limited to insured areas andobjects. According to the few available studies, the meanhailstreak length is around 50 km with an exponential de-creases of the number with length. In very rare cases,hailstreaks may persist over several hundreds of kilometer(Puskeiler, 2013).

In SW France, (Dessens, 1986a) observed a mean hailtrack length of 86 km and a mean width of 6.3 km inrecords of 30 major hail storms between 1952 and 1981.In good agreement, Kleinschroth (1999) estimated a meantrack length of 82 km and a medium width of 6 km from in-surance data for S Germany. Both parameters were foundto decrease exponentially, yielding very large standard de-viations. Similar results were found by Puskeiler (2013),who reconstructed hailstreaks between 2005 and 2011 from3D radar data and appropriate post-processing. Accord-ingly, most of the 2,632 identified hailstreaks had a lengthof 20 km or less, whereas only 13 events reached a length of300 km or more. The mean (including standard deviation)and median were 48.0 ± 46.7 km and 40.0 km, respectively.For Spain, also Ceperuelo Mallafré et al. (2009) estimatedfrom radar data a mean length of 50 km for hailstorm

tracks against to 20 km for storms with no hail. Specif-ically for the region of León, Fraile et al. (1992) found anaverage hailstreak area of 44 km2. Severe thunderstorms inHungary in Central Eastern Europe had an average lengthof 70 km, the mode of the distribution being near 50 km(Horvath et al., 2015). For Moldova in Eastern Europe,Potapov et al. (2007) estimated a typical length of thehailstreaks of 20-25 km with a typical width of 0.2-4 km.

5.4. Trends in hail frequencyIn light of climate change and the substantial increase in

hail damage over recent years in several European regions,the question arise whether indications can be found thatsuggest an increase in the frequency and intensity of thehail events. Insufficient monitoring of hail events over along-term period, however, hamper statistical analysis andthe finding of robust observational evidence for changeson the regional scale. This paragraph briefly summarizesestimations of trends in hail damage, hail observations atweather stations, and atmospheric conditions.

Several studies using insurance data found a strong in-crease in related damage. For SW Germany, for example,Kunz et al. (2009) analyzed significant increases in both in-surance losses and the number of hail days between 1986and 2004. Also Schiesser and Schmid (2005) report fornorthern Switzerland a positive trend of hail days affect-ing more than 100 communes from 1980 to 1999 and an in-crease in hail-related weather constellations over the courseof the 20th century. According to Eccel et al. (2012), dam-age to crops have increased in N Italy within a 35-yearperiod from 1974, some at considerable rates.

Most of the studies based on hail observations at weatherstations found negative trends in the number of hail days,which, however, often lack statistical significance. Over NItaly, for example, a non-significant decrease in the hailfrequency has been observed. Specifically in the FVG re-gion, Eccel et al. (2012) analyzed - in agreement to thestudy of Manzato (2012) - no overall trend in hail prob-ability in a 35-yrs period based on hailpad observations.For different kinetic energy-related parameters, however,they found significant positive trends. Also for Styria inAustria hail events show a negative trend in the period1982-2001 according to Svabik (2004), which the authorattributed to cloud seeding activities in that region. Basedon hail reports from Poland, Kolkowska and Lorenc (2012)derived a negative trend of approximately 10% per decadein the number of hail events, which may be partly linkedto changes in reporting habits. For other regions in Polandnegative trends are estimated, for example for the stationof Krakow in the SW or for several stations during thelong-term period 1863-2008 (Twardosz et al., 2010). Inthe Czech Republic, Chromá et al. (2005) analyzed at sev-eral stations during the period 1961-2000 negative trendsof up to -2.3 hail days per decade. Losses in Baranya in theSW of Hungary are reported to have decreased to about1% during the period 1991-2011 (OMSZ, 2012) comparedto 3.3% for the preceding period 1976-1990. For Serbia,

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trends based on station data for the warm season weremostly negative (Ćurić and Janc, 2015; Gavrilov et al.,2010; Mesinger and Mesinger, 1992). Several other stud-ies based on station data also analyzed negative trends,in particular for Romania (Apostol and Machidon, 2011),Bulgaria (Simeonov et al., 2009), and the Caucasus in Rus-sia (Malkarova, 2011). Positive trends are found only forSlovenia in the Center-West (Dolinar, 2005) and in SWFrance in the hailpad network between 1990 and 2010 Her-mida et al. (2013). Note, however, that the trends esti-mated at weather stations are not reliable and robust dueto the stochastic nature of hailstorms.

A way overcoming the constraints and limitations ofdirect hail observations is an approach that links hail-storm occurrence to large-scale atmospheric conditions, forwhich homogeneous and long-term data from reanalysisand regional climate models are available. The forma-tion of severe convective systems is not only controlledby local-scale properties within the boundary layer, butalso by mesoscale thermal instability in combination withlarge-scale atmospheric circulation patterns. An extendedtrough located upstream of a certain location, for example,usually increases instability by warm and moist air advec-tion on the eastern flank, but also helps to overcome theconvective inhibition du to large-scale lifting. Long-termchanges in the frequency of deep convection therefore canbe studied by both large-scale weather patterns that favorthunderstorm development and thermal stability.

Kapsch et al. (2012), for example, identified fourweather patterns that favor the formation of hailstorms,and found a weak but significant increase of these pat-terns over the last three decades. Kunz et al. (2009) andMohr and Kunz (2013) estimated positive trends for vari-ous convective parameters and indices across Germany andEurope, respectively. However, changes in these proxies donot automatically affect the number and/or intensity of se-vere convective storms. CAPE or wind shear are predictorsmainly for severe convective storms and not for graupel-bearing convective clouds. This hypothesis is supported bythe findings of Eccel et al. (2012), who showed that for NItaly some hail metrics such as the number of hail days orthe cumulative hit surface decrease slightly and insignifi-cantly, whereas the most energetic indexes, especially their90th percentile measures, show considerable, albeit irreg-ular increases over a 35-year period. They concluded that,at least in Trentino, hail seemed to have become more ex-treme in its behavior. A similar result was obtained byBerthet et al. (2011) from hailpad analysis data in SWFrance. While no significant changes occurred for hail fre-quency, hail intensity increased by about 70% during theperiod 1989-2009. Dessens et al. (2015a) suggested an in-crease of hail intensity in this region for the future due arising freezing level.

However, questions still remain regarding the influenceof changes of moisture, which has increased significantlyat low levels (Kunz et al., 2009; Mohr and Kunz, 2013),changes of available moisture sources, changes of storm’s

organization (i.e., supercells or MCS), the possible rela-tion to wind shear, and changes in aerosol concentrations,especially in those acting as ice nuclei (IN).

6. Factors influencing hail frequency

Possible explanations for the spatial variability of hailfrequency requires detailed understanding of the complexinteraction between various processes and mechanisms ona wide range of temporal and spatial scales leading to theformation of hail within thunderstorms. In Europe, therelative importance of these processes and mechanisms ap-pears to differ from other hail-exposed parts of the world(see, e.g., the discussion in Ludlam, 1980). While a largenumber of papers investigate individual severe hailstormsor examine typical atmospheric conditions favoring thun-derstorm and hail formation, only a few studies evaluatedand suggested evidence for the impact of local and regionalgeographic and topographic features on hailstorm proba-bility in a systematic sense. From the spatial distributionof hail occurrences assessed across Europe, several conclu-sions with respect to hailstorm-favoring conditions can beidentified. These are discussed in this section, togetherwith other possible driving factors found in the literature.

6.1. Large-scale flow conditions

Thermal instability necessary for the formation andmaintenance of intense thunderstorms is often reinforcedby cold air advection at higher altitudes in combinationwith the advection of moist and warm air at low levels. InW and Central Europe, the synoptic situation that oftenprevails during severe hail events can be characterized byan extended trough with its center over the West EuropeanBasin or the Bay of Biscay, and a ridge downstream nearor over the Mediterranean. As a result, warm and moistair is advected to Central Europe by SW flow. This con-stellation is generally found to prevail during severe hailevents in several regions and countries including France(Vinet, 2001; Berthet et al., 2013), the Ebro valley (García-Ortega et al., 2011), Switzerland (Willemse, 1995; Muriset,2003; Schiesser, 2003), Germany (Kunz et al., 2009; Kap-sch et al., 2012), or Poland (Kolkowska and Lorenc, 2012).Berthet et al. (2013) further differentiate among a moresoutherly or more northerly location of the depression overthe Atlantic and identify SE flow as a third type of circu-lation with enhanced probability for severe hailstorms inSW France. In Germany, weather types with anticycloniccirculation in the middle troposphere (500 hPa) were foundto favor damaging hail as well (Kapsch et al., 2012). Bothfor Germany and Poland, weather types with influx of coolair aloft from the NW over previously established warmair masses related to a trough over Scandinavia were alsofound to be hail prone (Kapsch et al., 2012; Kolkowska andLorenc, 2012). Such a situation is also known to contributeto hailstorm formation in N Italy (Morgan, 1973; Giaiottiet al., 2003). The study of Kolkowska and Lorenc (2012)

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classified hail reports in Poland by circulation types as de-fined by Lityński (1969). They found 28% of the eventsto occur during four specific types with cold air flow aloftfrom the NE and frontal systems. According to Twardoszet al. (2010), cyclonic conditions under the influence of atrough and transformed maritime polar air masses werefound to favor hail occurrence at the station of Krakow inthe SW. The authors also estimated that 58% of the haildays were related to fronts. According to Brázdil et al.(1998), 90% of the hail days in the Czech Republic were as-sociated to cyclonic weather types as defined by the CzechMeteorological Service.

For Switzerland, Muriset (2003) found a ridge extend-ing from the Azores to Eastern Europe with high temper-atures and frontal perturbations from the NW as well as atrough over W Europe (Hess-Brezowsky classification BMand TRW, respectively, Gerstengarbe and Werner, 1999)to be the weather types that are most favorable for hail-storms; more than half of the investigated events are re-lated to this situation.

Several exceptional severe hailstorms in Central and NWEurope were found to be related to a well-mixed and hotcontinental air mass at mid-levels. Originally, the air masshas been formed over NW Africa or on the Central IberianPlateau and advected to the NE downstream of an ex-tended trough, a constellation often referred to as “Spanishplume” (Browning and Hill, 1984; Morris, 1986). Liftingof the air mass either by synoptic-scale processes down-stream of a trough or by flow over large-scale mountainscreates an elevated mixed layer (EML, Carlson and Lud-lam, 1968; Carlson et al., 1983), which is a well-mixed layerdecoupled from the ground at mid-levels (usually 700–500hPa). Several studies (e.g., Lanicci and Warner, 1991; vanDelden, 2001; Lewis and Gray, 2010) confirmed the EMLto be an important factor for the formation of the mostintense storms such as the Munich hailstorm or the super-cells termed Andreas in Germany, or Ela in France andBelgium. The advection of similar air masses formed overNorth Africa or in Central Europe are discussed by vanDelden (2001). It is possible that such a mechanism in-volving warm air formed over the Anatolian Plateau or theBalkans play a role in the formation of severe hailstormsin Eastern Europe.

Many studies, in particular those focusing on U.S.storms (Laing and Fritsch, 2000), found evidence that sug-gests a relationship between hail occurrence and convec-tive available potential energy (CAPE) times vertical windshear (Brooks et al., 2003; Craven and Brooks, 2004). Itmay be valid in particular for large hail, and indicationsfor such a relation were found in some studies in Europe(Púčik et al., 2015; Groenemeijer and van Delden, 2007;Kaltenböck et al., 2009; Eccel et al., 2012; Tuovinen et al.,2015). Púčik et al. (2015) identified moist unstable CAPEas the sounding parameter that hail severity is most sen-sitive to, and found a relation to wind shear in partic-ular for extremely severe hail (> 50mm) in Central Eu-rope. Similarly, for the Netherlands, Groenemeijer and

van Delden (2007) determined a strong association of hailto high CAPE values and moderately strong shear in par-ticular prior to events with large hail (> 30mm), whileweaker events (maximum diameter 20-30mm) were moreoften related to stronger shear and moderate CAPE.

In contrast, some studies showed that sounding- ormodel-derived shear is not among the most important pa-rameters for the prediction of sever thunderstorms in Cen-tral Europe giving the characteristic complex terrain whichper se causes flow variations with height. This may alsoexplain why the number of days with parameters favor-able for severe thunderstorms (mainly CAPE, shear, andlapse rate) estimated by Brooks et al. (2003), does notreflect convective activity across Europe in a proper way.Even for F2 tornadoes, shear derived from ERA-40 reanal-ysis turned out to be much weaker in Europe compared tothe U.S. (Graf et al., 2011). Using a logistic regressionapproach, Mohr et al. (2015) found the combination ofLifted Index, minimum and maximum temperature, andlarge-scale weather patterns to have the highest predic-tion skill for hailstorms in Germany. Again, shear turnedout to be not an important parameter in the model.

On the synoptic scale, low pressure systems and re-lated fronts pass over Europe mainly from west-to-easthaving formed and developed mainly over the North At-lantic. Over Western and Central Europe, severe hail-storms preferably occur either at a distance ahead of coldfronts by low-level flow convergence or at the front dueto a combination of increased instability in case of coldair advection aloft and convergence by cross-circulation.Fronts have been found to be important factors for trig-gering deep convection and hailstorms in several regions,for example, Spain and France (van Delden, 1998; García-Ortega et al., 2007), Poland (Kolkowska and Lorenc, 2012;Twardosz et al., 2010), but also in Bulgaria (Simeonov andGeorgiev, 2003) or Cyprus (Michaelides et al., 2008) inSoutheastern Europe.

6.2. Local-scale flow and orographyMountain ranges and resulting flow deflections in both

horizontal and vertical directions can be decisive for theestablishment of conditions favorable for triggering andmaintenance of thunderstorms including hailstorms. Forexample, de la Torre et al. (2015) provide a detailed dis-cussion of the role of flow anomalies caused by topographyfor the formation of severe thunderstorms in the clear-cutenvironment of the Southern Andes. Several studies havefound systematic relations between orography, flow condi-tions, and hailstorm frequency in Europe (e.g., Morgan,1973; Giaiotti et al., 2003; Kunz and Puskeiler, 2010b),but the situation appears to be more complex. Indeed, amap of major hail hazard zones (Fig. 4) provides evidencefor a relation between orography and hailstorm or hail fre-quency. This map has been created based on the review ofavailable studies on hail frequency presented in Section 4,including regional studies, but focusing on large hail whereavailable. It is obvious that - apart from general climatic

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conditions (i.e., distance to the sea or latitude) - orogra-phy has a strong influence on hail frequency. Several of theidentified hot spots are located over or near mountains, forexample the Black Forest range and the Swabian Jura inSW Germany, pre-alpine regions north and south of theAlps, the Ebro valley in Spain, the foothill region north ofthe Pyrenees and Massif Central in France, or the Apusenirange in Romania. Areas appearing unshaded in Figure 4indicate either low hail probability or a lack of appropriatestudies.

The dimensions of the mountains or mountain chains interms of height, lengths, exposition, and orientation in re-lation to ambient conditions (i.e., wind speed, stability) de-termine the predominant flow characteristics (Smith, 1979;Houze, 2014). Depending on the prevailing conditions, theflow may go over or around the mountains and may gen-erate different types of gravity waves aloft. These flowconditions are relevant for convective initiation; thus, con-vection can be triggered upslope or far upstream of themountain, directly over the crest, or on the lee side. It isfound that hailstorms preferably form downstream of hillyterrain (or in close proximity), whereas directly over themountains hail occurs infrequently (Doswell, 2001; García-Ortega et al., 2007; Kunz et al., 2009; Berthet et al., 2011;Cintineo et al., 2012; Eccel et al., 2012; Berthet et al., 2013;Merino et al., 2013), due to the mechanisms discussed byde la Torre et al. (2015). This configuration applies toboth large mountains such as the Caucasus, the Pyrenees(mainly the NE sides, Merino et al., 2014b), or the Alps(e.g., Kajfez-Bogataj, 1989; Giaiotti et al., 2003; Nisi et al.,2015), but also to low-mountain ranges such as the BlackForest in Germany (Kunz and Puskeiler, 2010b; Puskeiler,2013), the Iberian System in Spain (García-Ortega et al.,2012) or the Massif Central in France (Punge et al., 2014).

At the northern and southern Alpine foothills, warmand moist air at low levels originating from the plains orthe Mediterranean is forced to lift by low-level flow con-vergence. These convergence zones are caused either byflow deviations at the hills, by the outflow of previouslydeveloped thunderstorms cells in the Alpine valleys, or bythermally direct circulations. Furthermore, the Alps werefound to shield the influx of cool air from the NW nearthe ground, which in combination with the frequent moistair masses in N Italy leads to large instability, explain-ing the hail maxima found in that region (Morgan, 1973;Giaiotti et al., 2003). The mechanism is presumably alsoresponsible for the enhanced hail frequencies associatedwith NW flow in Styria (Wakonigg, 2010). Kleinschroth(1999) noted for Germany that the effects of the Alps canbe detected as far as 80 km north of the mountain range.In contrast, the inner-Alpine regions feature considerablyreduced convective activity as shown by van Delden (2001)using station data and by Nisi et al. (2014) based on light-ning detections. Those minima in convective activity con-cern particularly regions like the Valais, Aosta, Graubün-den, Haute-Maurienne and Susa valley, where the rarity ofhail has already been discovered by historical studies at the

very beginning of hail research (Marbach, 1836; Genevois,1838), but also parts of the Austrian Alps that are sur-rounded by high mountain (Svabik et al., 2013). Due tothe orographically-induced vertical lifting and related oro-graphic precipitation on both sides of the Alps, the inner-Alpine regions are relatively dry and convective energy isstrongly reduced.

Over lower mountain ranges with typical vertical ele-vations below 1,500m, hail occurs most frequently down-stream of the peaks. The presence of hills as a key driverfor hailstorm development was identified in several regions,for example by Puskeiler (2013) for Germany or by Ab-shaev et al. (2009) for the Stavropol Uplands in Russia.Ceperuelo Mallafré et al. (2009) identified convergence inthe wake of the Iberian system as one of the triggeringmechanisms for hail in the Ebro valley. Increased con-vective activity in the lee of secondary mountain rangeswas also found for the NE edge of the Massif Central(Vinet, 2001), in Macedonia (Spanos, 1993; Sioutas et al.,2009), Central Romania (Maier et al., 2010), Eastern Spain(Olcina Cantos, 1994), and the UK (Webb et al., 2009).Over the Massif Central, subsequent mountain ranges ori-ented in north-to-south direction may even amplify thiseffect. In situations with flow almost parallel to mountainridges, hailstorms are often found to form directly overthe foothills. This is the case over the N of the Pyrenees(Berthet et al., 2013), the Massif Central (Vinet, 2001),or in the piemontese foothills of the Alps (Davini et al.,2011). Some studies also suggest air masses channeledbetween mountain ranges lead to elevated hail frequency,for example in the Ebro valley in Spain (García-Ortegaet al., 2007), S Romania (Bogdan, 2005; Iliescu, 1983), orW Slovenia (Kajfez-Bogataj, 1989).

In SW Germany, hail frequency is highest downstreamof the Black Forest Mountains but lower directly over themountains and over the rolling terrain in northern partsas identified from 3D reflectivity of a single radar andof a composite of different radars (Kunz and Puskeiler,2010b; Puskeiler, 2013; Kunz and Kugel, 2015). The pre-convective conditions on hail days were found to be charac-terized by comparatively high static stability and low windspeeds (Kunz and Puskeiler, 2010b), yielding Froude num-bers below 1. Under these conditions, the flow is assumedto partly go around the mountains at low levels, causinga zone of horizontal flow convergence downstream, whichis area most favorable for hailstorm development. Theauthors further hypothesized that the prevailing SW flowmeets the Swabian Jura at a very sharp angle, reducing theFroude number, and forcing the flow parallel to the rangesas is the main direction of the hail tracks. Such flow effectsmay also explain the location of the hail frequency maximain other regions. Gudd (2003) and Puskeiler (2013) alsodocumented elevated hail frequencies in the wake of sec-ondary mountain ranges in Germany during certain flowdirections, for example of Rhön by SE flow or the Vogels-berg for NW flow.

Other studies also found frequent occurrences of hail

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upstream of low to medium mountain ranges such as theBohemian Forest (Kleinschroth, 1999), or the Vogelsbergand Spessart ranges in Germany (Gudd, 2003). The sameapplies for the foothills upstream of larger mountain rangeslike the Alps in the Drôme region of France, the Pyreneesand the Ebro valley (Ceperuelo Mallafré et al., 2009), orCatalonia (Porras et al., 2013). In these cases, the up-lift caused by the interaction of the flow with the terrainis presumably the most decisive triggering mechanism forconvection and hailstorms.

6.3. Geographical effect: Solar radiation and Moistureavailability

For the formation of severe thunderstorms, thermal orstatic instability is required. Hence, warming of near-surface layers by solar radiation and/or advection of warmair masses and/or cooling aloft favor convective develop-ments. When these effects are weak, as it is typically thecase during the winter in Central Europe or in the Arc-tic year-round, intense convective storms can not develop.Compared to, e.g., the US, the contribution of solar heat-ing appears to be more important (Ludlam, 1980). Infact, the long daytime during summer can create condi-tions that favor thunderstorm formation including largehail (>40mm) as far North as 68◦ N (Punkka and Bister,2005; Tuovinen et al., 2009). This far North such condi-tions prevail only during a short period of the year, andthe decrease of temperature due to decreasing solar radi-ation towards the N pole remains the major cause for thelow hail frequency in Northern Europe.

Another requirement for hail formation is the presenceof a layer of cold air in the middle and upper troposphere(e.g., Waldvogel et al., 1979), which increases the lapserate. On the other hand, a vertically extended layer ofwarm air can melt the hailstones, keeping them from reach-ing the ground. This effect mainly concerns small hailgiven that the relation between surface and volume de-creases inversely with the diameter (Pruppacher and Klett,2010). Thus, in areas or on days with high amounts ofsolar radiation, small hailstones can melt before reachingthe ground. This is one possible explanation for the com-paratively low hail frequencies estimated for Southern Eu-rope during midsummer (Olcina Cantos, 1994; Camuffoand Sturaro, 2001), or for the increased hail to thunder-storm ratio during night time (Manzato, 2007).

In addition to the lapse rate, the presence of sufficientmoisture at low levels is another basic prerequisite forthe formation of severe convective storms via the latentheat release by condensation (Markowski and Richardson,2010). Thus, the influx of moist air from the Mediter-ranean, the Black Sea, and, to a lesser degree, the At-lantic (Sánchez et al., 2003) contribute to favorable con-ditions for hail. This effect was documented for the Povalley in N Italy (Morgan, 1973; Giaiotti et al., 2003), NGreece (Sioutas, 2011), N Caucasus (Abshaev et al., 2009)or SW France (Berthet et al., 2013). Related hail maximaare also indicated in the composite hail hazard map (Fig.

4). Conversely, lower moisture contents prevailing duringthe summer months in continental climate can explain thedecrease in hail frequency towards Eastern Europe, espe-cially in Russia (Abshaev et al., 2009).

Furthermore, the large heat capacity of water damps thediurnal and seasonal cycle of near-surface temperature atcoastal areas, leading to a decrease of the lapse rate’s mag-nitude. In Northern Europe, the sea effect contributes tothe inhibition of large hail by avoiding a warmer climate,explaining the absence of large hail in these areas, for ex-ample in Iceland and parts of Scandinavia, in particularNorway. On the other hand, the heat storage of the sea orocean can lead to a secondary near-coastal hail maximumin fall as was observed for the Baltic Sea (Hamberg, 1919).

In several regions, the influx of moist air from the sealeads to frequent showers of graupel and small hail, in par-ticular during spring time. These are responsible for thehigh hail frequencies estimated at stations along the coastsof the Atlantic in Scotland and N England (UK Met Of-fice, 2013), Ireland (Walsh, 2012) and Brittany, the BlackSea coast in SE Romania (Bogdan, 2005), or the AdriaticSea in W Greece (Sioutas, 2011). Similar phenomena havebeen observed at the coast of the North Sea and elsewhere.

6.4. Land use and surface propertiesThere is generally little scientific evidence to support hy-

potheses on a possible relation between hail frequency andsurface properties, including the effect of lakes and landuse. Such an effect is hard to detect because these prop-erties usually vary on small scales in Europe and surfacefeatures like lakes are relatively small (compared to, e.g.,the U.S.). Consequently, relevant events for a given sur-face feature are rare, and data availability and resolutionare limiting factors. Furthermore, the convective stormsbecome more and more decorrelated from their initial lo-cation with increasing lifetime.

Historically, a study for Switzerland by Riniker (1881)suggested lower hail frequency in areas downstream oflarge forests compared to open landscape. While physi-cally plausible due to the lower thermal inertia of forestsand the local increase in moisture due to transpiration,this finding was supported by some authors, for example,by Sarrazin (1893), but disputed by numerous contempo-rary studies (Bühler, 1890; Heck, 1893; Plumandon, 1893).No recent study found direct evidence that suggest the im-portance of such an effect on hail probability. In fact, thetheory appears to be based on a misinterpretation of thelow frequency of hail damage in the forested inner-alpinevalleys of Savoy (Genevois, 1838). Only Vinet (2000) dis-cussed a possible impact of the Landes forest in SW Franceand mention that moistening as well as daytime coolingand night-time warming could modify thunderstorm andhail formation also downstream of the forests, but con-clude that evidence for such effects is insufficient in theirdata.

Local to regional temperature inhomogeneities causedby geographic features and resulting flow convergence are

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also thought to trigger or stimulate the formation of deepconvection including hailstorms. Studies analyzing OTdata indicate that the great African lakes modify the dis-tribution of severe convective storms in their surroundings(K. Bedka, pers. comm.). Also for the U.S. it was shownthat the Great Lakes influence the climatological distribu-tion of storms (e.g., Doswell, 2001). However, lakes of thisscale are absent on the European continent, and possibleeffects of Lake Constance or Lake Garda are necessarilymuch weaker. For Lake Constance, there is a discrepancybetween high damage in agriculture (Schwind, 1957) andlow signals in radar data (Puskeiler, 2013), possibly ex-plained by insufficient radar coverage. In Northern Italy,several studies (Prodi, 1974; Chiaudani et al., 2005; Po-liteo, 2008) found the largest hail frequencies of the Povalley in the region East of lake Garda. In both cases,hail frequency is elevated in areas downstream the mainwind direction on hail days. Enhanced hail frequency isalso reported for Lake Neusiedl in Austria (Svabik et al.,2013). On the other hand, Muriset (2003) and Nisi et al.(2015) did not find abnormal hail frequencies in the prox-imity of lakes in Switzerland. For Hungary, the numberof hail days is slightly enhanced to the E of Lake Balaton(OMSZ, 2012), but this could also be related to orographiceffects as discussed in the previous paragraph.

The impact of populated areas on hail frequency, whichcould be explained via the heat island effect (Lowry, 1998),is discussed by Changnon et al. (1979). However, due tothe rarity of hail observations and the limited spatial ex-tent of the cities, there is still little evidence for such aneffect in Europe. Kleinschroth (1999) found hail frequen-cies increased by 10-20% in the surroundings of southernGerman cities, reaching out 10-30 km from the city cen-ter. The comprehensive radar-based hail assessments forGermany of Puskeiler (2013) and Kunz and Kugel (2015)with a very high resolution (0.5 and 1 km) did not provideevidence for such an effect. A study on the initiation ofconvection by Thielen and Gadian (1997) for the Manch-ester region found that the heat island effect may amplifyorographically-related convection initiation.

7. Conclusions

Information on hail characteristics from various sourceshave been evaluated with the objective to present anoverview of the state of knowledge on hail hazard acrossEurope. A number of studies have estimated hail fre-quency on a continental scale, but these often have in-sufficient spatial resolution and coverage to reproduce hailhazard prone areas. While radar data has proven usefulfor convection-related purposes in some regions, such datasets are not available all over Europe. Thus, long-termhistorical records from weather stations and losses in agri-culture are often the only available sources to estimate hailfrequency. Weather station-based hail frequencies fromcoastal or mountainous regions often suggest high hail haz-ard. However, in most of the cases the distinction between

hail and other forms of precipitation such as graupel orfrozen rain is not explicit. Estimates based on damageclaims, on the other hand, may either under- or overrepre-sent hail hazard due to varying exposed value, vulnerabil-ity, and uncertainties in attribution. In few cases, histor-ical reports and studies lasting back more than 100 yearsare still unsurpassed, thanks to the tremendous efforts byearly hail researchers.

Our study shows that large hail can occur in any partof Europe, but particular hazard zones exist throughoutthe continent, and orography plays the most importantrole for the location of these zones. Quite often, hail fre-quency maxima match closely among studies with differingapproaches, giving further confidence in their reliability.Further small-scale variations on the order of 10 km likelyexist, but most methods are either inaccurate or cover in-sufficient time periods to prove them.

We also presented a short list of the costliest hail eventsof the past - but we should note that hail damage depends,besides the hazard, highly on exposure, and that hazarddata coverage for individual storms is still limited. Thus,a very severe hailstorm can still pass almost unnoticed ifit occurs in a rural area.

On the other hand, historic records still hide informationon a large number of very severe hailstorms, and the studyof these may be useful to determine the actual character-istics of extremes on time scales from decades to centuries.For example, relatively little is known on the typical ex-tents of these large hailstorms, their regional variationsand the relation to severity - these elements were beyondthe scope of this review.

A few studies identify long-term trends in hail frequencyand relate them to changing climate or the direct influ-ence of human activities, but only a low portion of thetrends are significant and attribution tends to be difficult.Longer time periods of both remote-sensing and grounddata may pave the way to future improvements in theseareas. A great majority of the reviewed hail research hasa national focus, both in geographic extent and methodsapplied. Enhanced international cooperation and coordi-nation of research efforts, in particular in areas like radarremote sensing, promises to yield an enhanced picture ofhail hazard in Europe.

8. Acknowledgments

H.J. Punge is a Willis Research fellow funded by WillisRe, London, while M. Kunz is a WRN Senior Academic.The authors thank all the researchers who performed thestudies on hail presented in this paper. In particular,the authors acknowledge the European Severe WeatherDatabase and sturmarchiv.ch provided their hail reports.We acknowledge the helpful comments from two anony-mous reviewers.

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Figure 3: Maximum size in mm of hailstones reported to the Euro-pean Severe Weather Database with defined location until September2015. Also see Table 8 for reference.

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Figure 1: Hail event frequency as estimated by (Punge et al., 2014).

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Figure 2: Map of Europe with composite of figures on hail incidence from the various studies referenced in this article. Sources: UK: Webbet al. (2009); Sweden: Hamberg (1919, color shading added); Russia: Abshaev et al. (2009, color shading added); Germany: Puskeiler(2013); Poland: Kolkowska and Lorenc (2012, Fig. 7); Czech Republic: Skripniková and Řezáčová (2014); France: Vinet (2001); Switzerland:Schweizer Hagel, 2015; Austria: Svabik et al. (2013); Hungary: Seres and Horváth (2015); Romania: Bogdan (2005); Portugal: (Font Tullot,2000, color shading added); Spain: (Fernández Toraño, 2010); Italy: (Baldi et al., 2014); Slovenia: provided by Skok, G., 2014; Croatia:(Počakal et al., 2009); Serbia: (Ćurić and Janc, 2015); Greece: (Sioutas, 2011, color shading added)

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Figure 4: Top: Map of Europe with shaded areas marking increased hail incidence identified in studies referenced in this article. Bottom: Zoomon the Alpine and Carpathian regions. Study domains are denoted by dashed white lines where they do not coincide with national borders.References by country in contour colors: UK: Webb et al. (2009), Sanderson et al. (2015); Sweden: Hamberg (1919); Finland: Tuovinenet al. (2009); Russia: Abshaev et al. (2009, Fig. 1), Abshaev et al. (2009, Fig. 2); Belgium: Goudenhoofdt and Delobbe (2013)*, Germany:Puskeiler (2013), Lindloff (2003); Czech Republic: Skripniková (2013, Fig. 11.21),Tolasz et al. (2007), Hrudička (1936), Chromá (2006);Poland: Kolkowska and Lorenc (2012, Fig. 7), Kolkowska and Lorenc (2012, Fig. 24); France: Vinet (2001), Malaval (1995), (Castet, 1974);Switzerland: Schweizer Hagel, hagelregister.ch, Swiss Mobiliar (2015); Austria: Svabik et al. (2013), Cehak (1978), Slovenia: Dolinar (2005),Skok et al. (2014)*; Croatia: Počakal et al. (2009); Hungary: OMSZ (2012), Seres and Horváth (2015*); Romania: Bogdan (2005), Machidon(2007),Maier et al. (2010)*,Apostol and Machidon (2009); Portugal: Font Tullot (2000); Spain: Fernández Toraño (2010), Font Tullot (2000)Olcina Cantos et al. (1998); Italy: Baldi et al. (2014), Davini et al. (2011), Sartori (2012), Prodi (1974), Giaiotti et al. (2003); Serbia: Ćurićand Janc (2015); Greece: Sioutas (2011), Sioutas et al. (2009); * includes regions in neighbouring countries marked by dashed white lines

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Table 1: Most significant and costly hailstorm events in EuropeNAME REGIONS DATE MAX. HAIL

SIZEECONOMICDAMAGE

INSUREDDAMAGE(2014)

REFERENCE

[mm] million EUR(2015)

million EUR(2015)

France 1788 France: Ile de France, Pi-cardie, Pays de la Loire

13 July 1788 80 ≈ 250 (Lachiver, 2000; Leroy,1790)

England1843

UK: Gloucester, Oxford-shire, Northamptom, Cam-bridge, Norfolk

9 August 1843 (Webb and Elsom, 1994)

Munich Hail-storm

Germany: Southern Bavaria 12 July 1984 95 (140) 1 500 (3 000billion DM)

3 000 Heimann and Kurz(1985); Höller and Rein-hardt (1986); Kasparet al. (2009), Munich Re

Felix Belgium: Western Belgium 26 May 2009 90 Hamid and Buelens(2009), ESWD

France: Picardie, Nord 26 May 2009 120 700 580 (Keraunos, 2009), WillisWolfgang Austria: Salzburg, Upper

Austria, Lower Austria23 July 2009 100 833 Willis

Switzerland: Berne,Lucerne,

Andreas Germany: Baden-Württemberg, LowerSaxony, NRW

27-28 July2013

80 (70) 3 600 2 800 Kunz et al. (Unpub-lished results) , MunichRE

Ela France: Ile-de-France, Cen-tre, Western Belgium

8-10 June 2014 125 2 300 Willis

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Table 2: Largest reported hailstones by country. In general, hail size was measured along the longest dimension of the stone. In case ofreports with similar size or inaccurate details (italic script), several reports are listed for each country.

COUNTRY PLACE DATE MAX. HAILSIZE [mm]

MAX. WEIGHT[g] REFERENCE

Germany Heidgraben, Schleswig-Holstein 10 August 1925 250* *250x140x120mm, Heidke (1925)Italy ? 1471 170 ESWDFrance Strasbourg, Alsace 11 August 1958 150 972 Jalu (1959)Austria Ottendorf/Rittschein, Styria 03 July 1897 150 1 100 Prohaska (1898, 1907)UK Stronsa/Orkneys 24 July 1818 150 230 Neill (1821)Czech Republic Kolín, Central Bohemia 1672 150 1 630 ESWDCzech Republic Dolni Cerekev, Vysoč 1574 150 ESWD

Spain Valencia region 1935 146* 1 480 * from mass, for sphere with density0.9, Rodes (1938)

Germany Undingen, Baden-Württemberg 06 August 2013 141 360 ESWDGermany Göstrup, North Rhine-Westphalia 01 July 1891 140 1 250 ESWDAustria Vienna 11 July 1689 140 Marbach (1836); von Cilano (1756)Russia Voronezh 09 July 1754 130 1 000 ESWDItaly Pozzuoli, Campania 05 September 2015 120 ESWD

France Raillencourt-Sainte-Olle, Nord-Pasde Calais 26 May 2009 120 ESWD

Spain Alcañiz,Teruel 16 August 2003 120 García-Ortega et al. (2007)Austria St Oswald, Lower Austria 13 July 1984 120 600 ESWDCzech Republic Plaňani and Ćeský Brod, Kolin 18 August 1986 120 ESWDCzech Republic Zlín 22 July 1937 120 750 ESWDBulgaria Gaytanevo, Sofia province 10 July 1891 120 Luterotti (1891)Russia Krasnogvardeyskoye, Stavropol 30 May 2015 100 ESWD; 5 other occasions)Albania Maliq, Kor cë county 19 May 2015 100 ESWDNorway Rena, Hedmark 04 August 2014 100 ESWDRomania Filia si, Dolj 11 August 2007 100 ESWD; 2 other occasions, same year)Poland Krzczeń, Lublin Province 22 July 2007 100 ESWD; 2 other occasionsSerbia Koceljeva, Mačva District 04 June 2007 100 ESWDSpain Marbella, Andalusia 21 September 2007 100 ESWDSpain Camprodon, Catalunya 05 August 2002 100 ESWDSwitzerland Bubikon, Zurich 21 June 1957 100 560 sturmarchiv.chNetherlands Nijmegen, Gelderland 25 July 1724 100 60 von Cilano (1756)UK Suffolk 1666 97* 500 *1 in diameter Ludlam (1980)Belgium Verrebroek, Oost-Vlaanderen 26 May 2009 92 149 Hamid and Buelens (2009), ESWDBulgaria Provadia, Varna province 29 May 2014 90 ESWDUkraine Nesterovka, Cherkasy region 16/17 May 2014 90 vk.com

UK Burbage, Leicestershire 28 June 2012 90 ESWD; one moreFinland Tampere, Pirkanmaa 08 August 2010 90 ESWD

Hungary Nyírtura and Nyírbogdány,Szabolcs-Szatmár-Bereg 07 June 2009 90 ESWD

Lithuania Rokiškis, Panevežys County 30 July 2005 90 ESWDEstonia Piibumäe, Jõgeva county May 2000 90 150 Tammets (2012)Turkey Istanbul 05 October 1831 90* 500 Jéhan (1864) *fist sizeCroatia Mursko Središće, Medimurje 08 July 2015 80 ESWDCroatia Hvar, Dalmatia 26 August 2008 80 ESWDBelarus Minsk, Minsk district 19 May 2014 80 ESWDTurkey Dutpinar, Maden, Elazig 09 April 2012 80 ESWDSlovenia Prgarje, Ilirska Bistrica 08 August 2008 80Bosnia and Herze-govina Gorazde 19 June 2007 80 ESWD

Netherlands Zeevolde, Flevoland 06 June 1998 80* Visser (1998)Sweden Ramnas, Västmanland 04 July 1953 80 ESWDIreland ? 1022 80 ESWDSlovakia Vyhne, Banskobystrický Kraj 19 July 2011 70 ESWDPortugal Braga, Norte 04 September 2004 70 ESWDMoldova Hînce sti 08 June 1984 70 ESWDMoldova Briceni 29 August 1969 70 ESWDKosovo Gjilan 24 May 2009 60 ESWDMacedonia 60 Dimitrievski (1983)Greece Kavala, East Macedonia and Thrace 11 June 2009 55 ESWDLatvia Vilani, Rezekne District 08 August 2010 50* ESWD; three other occasions)Ireland Ballinora, Cork County 08 July 1906 41* Webb et al. (2009) *H5 classIreland Kilbane, Clare County 04 January 1890 41* Webb et al. (2009) *H5 classCyprus Yeniceköy, Nicosia district 15 April 2013 40 ESWDLuxembourg Echternach 25 May 2007 40 ESWDMacedonia Dolno Kolichani, Studenichani 18 June 2014 35 ESWD; one more occasionDenmark Jordrup, Syddanmark 05 May 2015 35 ESWDMalta Gzira 24 December 2006 26 ESWD

Montenegro Sukurugi, Kotrabudan, Pothum;Podgorica 19 June 2014 20 ESWD; one more event

Andorra Pas de la Casa 09 July 2007 20 ESWDIceland Miðdalskot, Suðurland 27 July 2003 10 ESWD

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Table 3: Annual hail event and hail day estimates in the literature by country or regionCOUNTRY REGION METHOD AREA STUDY

PERIODHAILEVENTS /YEAR

HAILDAYS /YEAR

STATIONMEANDAYS/YEAR

REFERENCE

[km2] > >5mm 20mm 5mm 20mm 5mm 20mm

Austria agricult. 83 879 1990-2000 72 Formayer et al. (2001)Austria Lower Austria pads 500 1981-2000 4.2 1.05 0.121 0.024 Svabik (2005)Austria Styria pads 3 000 1988-2001 13 Giaiotti et al. (2003)Austria,Slovenia

Styria,Carinthia, Up-per Carniola

stations 36 000 1888-1900(10 years)

2.3 Prohaska (1902)

Belgium radar 30 791 2003-2012 16.5 Lukach and Delobbe(2013) * POH >60

Bulgaria stations 112 610 65 4-5* Simeonov et al. (2006)*major events

Bulgaria stations 112 610 1961-1990 4.2* 1.05 Simeonov et al. (2009)*>4 provinces

Bulgaria Center-West stations 5 000 1988-2001 28.71 Giaiotti et al. (2003)Bulgaria Center-West stations 5 000 1972-1981 35.5 Simeonov et al. (2006)Croatia North, East supp.

sites26 800 1981-2006 37 Počakal et al. (2009)

Croatia North, East supp.sites

26 800 2002-2008 41 0.91* Počakal (2011) *1981-2008

Croatia Zagorje area pads 600 2002-2009 10.4 0.32 Počakal (2012)Cyprus agricult. 5 707 1996-2005 13.8 Nicolaides et al. (2009)Cyprus stations 5 707 1996-2005 6.2 Michaelides et al. (2008)Czech Rep. radar 78 744 2007-2011 39 Skripniková and

Řezáčová (2014)Czech Rep. Bohemia reports 52 065 1895-1902 94 Bělohlav (1906)Czech Rep. Moravia reports 1896-1906 33.3 Koutny (1908)Czech Rep. Moravia stations 23 000 1961-2000 31.4 Brázdil et al. (2014)Estonia stations 45 699 1981-2005 29 Tammets (2012)Finland reports 332 610 1930-2006 3.11 2.09 Tuovinen et al. (2009)Finland radar,reports 332 610 2008-2012 57 43 17 Tuovinen et al. (2015)France ANELFA net-

workpads 1988-2003 0.244 0.0265 Dessens et al. (2007)

France ANELFA net-work

pads 66 500 1988-2009 0.27 Berthet et al. (2011)

France South-West pads 61 567 1989-1999 0.291 Dessens et al. (2001)France South-West pads 51 000 1997-2005 33.56 Sánchez et al. (2009)France South-West pads 51 000 1988-2001 39.08 Giaiotti et al. (2003)France SW - Atl. pads 33 700 1989-1999 15.54 0.133* Dessens et al. (2001) * >

10mmFrance South-West

Atl.pads 31 000 1988-2009 0.19 Berthet et al. (2011)

France South-WestPyr.

pads 27 900 1989-1999 16.55 0.285* Dessens et al. (2001) * >10mm

France South-WestPyr.

pads 20 000 1988-2009 0.43 Berthet et al. (2011)

France Centre pads 1990-2009 0.20 Berthet et al. (2011)France Mediterranean pads 1994-2009 0.26 Berthet et al. (2011)Germany agricult. 357 460 90 Lindloff (2003)Germany stations 357 460 1966-2010 1.04 Suwala and Bednorz

(2013)

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Table 3: Annual hail event and hail day estimates in the literature by country or region (ctd.)COUNTRY REGION METHOD AREA STUDY

PERIODHAILEVENTS /YEAR

HAILDAYS /YEAR

STATIONMEANDAYS/YEAR

REFERENCE

[km2] > >5mm 20mm 5mm 20mm 5mm 20mm

Germany Bavaria agricult. 65 923 1979-1996(15 yrs)

112.07 Kleinschroth (1999)

Germany Bavaria agricult. 65 923 1884-1984 65 Bay. Landeshagelver-sicherung, Deepen(2006)

Germany Stuttgart area 7 500 1908-1986 13 Müller (1987); Kajfez-Bogataj (1989)

Germany Stuttgart area reports 10 054 1969-1980 42.18 Müller (1982)Germany Central Hassia reports 6 700 1881-1980

(90 yrs)0.92 Gudd (2003)

Greece Macedonia agricult. 6 500 1976-2001 22.3 ELGA,Sioutas et al.(2009)

Greece Macedonia pads 2 400 1984-2001 8 0.32 Sioutas et al. (2009)Hungary stations 93 030 1981-2010 0.9 OMSZ (2012)Hungary radar 176 000 2004-2012 46* Seres and Horváth

(2015) *>55dBZItaly Friuli-Venezia

Giuliapads 4 500 1988-2001 55 Giaiotti et al. (2003)

Italy Trentino pads 1 088 1988-2001 30 Giaiotti et al. (2003);Tormena (2011)

Italy Trentino pads 1 088 1974-1993 30 Eccel et al. (2012); Tor-mena (2011)

Italy Emilia-Romagna

pads 4 000 1983-1998 26 0.54 Nanni (2004)

Italy Veneto agricult. 17 899 1978-2005 13.8 Tormena (2011)Italy Verona Prov. agricult. 3 096 1990-2004 3.9 Tormena (2011)Italy Padova Prov. agricult. 2 125 1990-2004 3.2 Tormena (2011)Italy Vicenza Prov. agricult. 2 723 1990-2004 2.4 Tormena (2011)Italy Treviso Prov. agricult. 2 477 1990-2004 1.9 Tormena (2011)Italy Rovigo Prov. agricult. 1 790 1990-2004 1.7 Tormena (2011)Italy Venezia Prov. agricult. 2 461 1990-2004 1.6 Tormena (2011)Italy Belluno Prov. agricult. 3 676 1990-2004 0.3 Tormena (2011)Italy Piemonte and

Liguriaradar 75 000 2005-2010 155* Davini et al. (2011) *

>56dBZMacedonia 25 401 1977-1982 50* 2** Dimitrievski (1983) *

from 2mm **4% of allevents

Moldova agricult. 33 208 1966-2005 21 Potapov et al. (2007)Netherlands reports 37 278 1975-2003 5.1 Groenemeijer and

van Delden (2007)Netherlands station 37 278 1999 60 Holleman (2001)Netherlands station 37 278 1999-2000 1.307 Holleman (2001)

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Table 3: Annual hail event and hail day estimates in the literature by country or region (ctd.)COUNTRY REGION METHOD AREA STUDY

PERIODHAILEVENTS /YEAR

HAILDAYS /YEAR

STATIONMEANDAYS/YEAR

REFERENCE

[km2] > >5mm 20mm 5mm 20mm 5mm 20mm

Norway Spitsbergen station 1898-1900 3 Lauscher (1976)Poland reports 313 770 1960-1978,

16 yrs111 Kolkowska and Lorenc

(2012)Poland stations 313 770 1966-2006 1.08 Bielec-Bąkowska (2010)Poland stations 313 770 1966-2010 1.12 Suwala and Bednorz

(2013)Romania Muntenia radar 52 900 2001-2008 40 Paraschivescu et al.

(2011)Romania Dobruja stations 15 500 1965-2005 1.12 Lungu et al. (2010)Romania Barlad stations 7 220 1961-2007 0.60 0.103 Apostol and Machidon

(2009, 2010)Russia South. Fed.

Dist.agricult. 418 500 45 Abshaev and Malkarova

(2002)Russia South. Fed.

Dist.agricult. 418 500 1998-2002 5.8* Abshaev et al. (2006) *

"emergency cases"Slovakia stations 15 000 2000-2005 6.8 Líšková (2006)Slovakia radar 15 000 1999-2005 96.7 Líšková (2006)Slovenia stations 20 245 2000-2008 150* Susnik and Pogacar

(2009) *station daysSlovenia stations 20 245 1996-2004 0.98 Susnik and Zust (2005)Slovenia stations 20 245 1951-1986 1.09 Kajfez-Bogataj (1989)Slovenia North-East 2 550 1972-1980 21 Kranjc (1981)Spain Aragon reports 47 719 2001-2010 32.6 García-Ortega et al.

(2014)Spain Ebro valley reports,

radar50 000 2001-2008 32.5 García-Ortega et al.

(2011)Spain Ebro valley pads, re-

ports2 700 1997-2001

(3 yrs)10 Sánchez et al. (2009)

(out of 116 days total)Spain León reports 6 825 1991-1995 8.6 Sánchez et al. (2009)Spain C. Valenciana agricult. 23 261 1980-1996 3.59* Olcina Cantos et al.

(1998) *hail episodesSpain Alicante Reg. agricult. 5 822 1965-1990 3.34* Olcina Cantos et al.

(1998) *hail episodesSpain Burgos stations 14 292 1981-2007 54.3 Bernaldo (2009)Spain Cuenca stations 17 141 1981-2007 19.3 Bernaldo (2009)Spain Valladollid stations 8 110 1981-2007 32.9 Bernaldo (2009)Spain Zaragossa stations 17 274 1981-2007 7.8 Bernaldo (2009)Sweden stations 445 650 1865-1917 2.79* 0.77** Hamberg (1919) *1879-

1917 **hail and thunderSwitzerland agricult. 41 316 1920-1999 71 Schiesser (2003)Turkey reports 780 480 1929-2010 1.57 Kahraman et al. (2011)UK reports 240 650 1930-2004 8.27* 4.08 Webb et al. (2009) *

TORRO H2 or greater

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