Assessment of severe hailstorms and hail risk using weather radar data

Transcription

1 METEOROLOGICAL APPLICATIONS Meteorol. Appl. 22: (2015) Published online 28 October 2015 in Wiley Online Library (wileyonlinelibrary.com) DOI: /met.1512 Assessment of severe hailstorms and hail risk using weather radar data Roxana Cică, a,b Sorin Burcea a * and Roxana Bojariu a a Climate Section and Laboratory of Nowcasting, National Meteorological Administration, Bucharest, Romania b Faculty of Geography, University of Bucharest, Romania ABSTRACT: Severe storms that produce hail of significant amount or size carry a high risk, being responsible for damage at the ground. Hail spawned from these storms can affect crops, automobiles and buildings. A total of 52 hail events that occurred in May 2013 in southern Romania are used to investigate the relation between radar-derived products and damage produced by hail. Two case studies are also presented to highlight the methodology used in this study. Three-dimensional single polarization weather radar data were used to derive composite reflectivity, echo top heights, vertically integrated liquid, vertically integrated liquid density and hail kinetic energy flux to detect the hail clouds. Time integration and spatial distribution of these radar products were produced in order to capture the swath, intensity and longevity of the hailstorms. Hail and damage reports were used to link the radar variables to the effects of hailstorms at the ground. Hail size information was arbitrarily broken down into bins of reported hail to investigate the relation between hail size and radar parameters. The results show that the areas where hail and damage were reported are well captured by the footprints and magnitude of the radar variables. The average values of the radar parameters corresponding to hail size bins increase with the increase of hail diameter. A steeper increasing trend characterizes the vertically integrated liquid density and hail kinetic energy flux. The results show a good agreement between weather radar data and surface reports. KEY WORDS weather radar; hailstorm; damage; hail risk Received 2 October 2014; Revised 19 March 2015; Accepted 20 March Introduction Convective storms occur almost exclusively in the summer and are usually accompanied by heavy rain, wind gusts, lightning and hail. Moreover, severe convective storms are of particular interest because the associated phenomena can have a direct impact on infrastructure and on humans and their activities. Severe hailstorms, which represent severe convective storms that produce hail of significant amount or size, carry a high risk especially for crops, vehicles and even buildings. As hailstorms are being recorded frequently during the warm season (May September), the exposure to their damaging potential is higher in this time of year. According to the hail climatology of Romania derived from ground observations made at the weather station, for the period , in most of the country, the annual number of hail days is, on average, <2 (Climate of Romania, 2008). The areas where the average annual number of hail days is the largest, >8, are the mountainous regions of western Romania. The smallest average annual number of hail days is found in southeastern Romania: Danube Delta (0.4 days at Sulina). According to the same climatology, the number of hail days increases from April and peaks in June, the most favourable period for hail being May July. During the last decades, damaging hailstorms have been reported and studied in numerous areas around the world including the United States (Changnon, 1999), South Africa (Visser and * Correspondence: S. Burcea, National Meteorological Administration, 97 Bucureşti-Ploieşti, Bucharest, Romania. sorin.burcea@meteoromania.ro van Heerden, 2000), Australia (Schuster et al., 2006) and Germany (Kunz and Puskeiler, 2010). These severe hailstorms can cause major losses, estimated for particular cases to tens of millions of euros. Kunz and Puskeiler (2010) reported a hailstorm that had occurred on 28 June 2006 in Germany, causing damage to buildings of approximately EUR 250 million. Recently, during the last days of May 2013, southern Romania was affected by hailstorms that caused damage to crops, evaluated at the order of more than EUR 2 million. Thus, one can classify severe hail as a natural hazard associated with high risk potential, being of particular interest to the insurance industry and essential for loss prevention and risk management. Because of the high variability in time and space of the convective events, the hailstorms are not always captured accurately and uniquely by a single observation system (e.g. a ground weather station). Since the spatial variability of hailfalls is large and their extent small, it is likely that localized events are not recorded by rain gauges or hailpads. The use of weather radars was found to be a feasible option in comparison to point measurements at the ground. In contrast to point measurements, the intensity, the track and the spatial extent of convective events can be derived from radar data. An additional advantage of radar data is provided by the 3D measurements performed at high temporal and spatial resolutions. Thus, various radar products and algorithms can be used to detect hail and assess some of its characteristics (e.g. size) (Amburn and Wolf, 1997; Holleman et al., 2000). To assess the damaging potential of hail, it is important to establish a relation between radar measurements and the damage data. Research studies emphasizing the usefulness of radar data on this topic have been published. Hohl et al. (2002a, 2002b), for example, established a relation between hail damage to automobiles and buildings and radar-derived hail kinetic energy (HKE) Royal Meteorological Society

2 Hail risk and weather radar data 747 The HKE was originally derived from hailpad observations by Waldvogel et al. (1978a, 1978b) and used afterwards in other studies that were focused on the damage produced by hail (Visser and van Heerden, 2000; Hohl and Schiesser, 2001; Hohl et al., 2002a, 2002b; Schuster et al., 2006). Sanchez et al. (2013) used hailpad measurements and radar data to build an algorithm to estimate the vertical component of HKE for the purpose of nowcasting. Since hail risk assessment and risk mapping are important to the insurance industry and to stakeholders in preventing and mitigating hail hazards, different approaches were developed. Egli (2007) developed hazard maps that relate hailstone diameter estimated from radar measurements to specific return periods. Kunz and Puskeiler (2010) presented a method for the assessment of the hail hazard for southwest Germany. They related radar reflectivity data to statistical return periods, their results confirming a high spatial variability of both track density of hail events and hail hazard. Busch et al. (2010) used radar imagery to define hailstorm footprints to be used in natural hazard models. Paraschivescu et al. (2011) evaluated a radar hail detection algorithm in the Muntenia region of Romania. They used hail observations recorded at the weather station and various radar products, for the period , to determine forecast and warning criteria for the hail events. The present study introduces supplemental data (e.g. hail damage reports) and radar products (e.g. HKE) to assess the detection of hail and, particularly, the relation between hailfalls and the surface damage. Also, the approach in the current study is intended not for the forecast but for the detection and assessment of the hail events. Thus, this research represents the first attempt to establish a connection between radar-derived parameters and the damage produced by hail within a region of Romania. Furthermore, the study aims to find the usefulness of radar-derived products in updating the hail climatology in Romania and in identifying the regions exposed to hail risk. The current Romanian hail climatology (Climate of Romania, 2008) is constructed using only data from weather stations. For a more reliable territorial representation of the hail events, high-resolution spatial observations are necessary. Radar data can help overcome this issue and can offer valuable information on the track and intensity of hailfalls, both needed for hail risk assessment. The present study is structured as follows: the data sets and study area are presented in Section 2. Section 3 describes the methods, particularly the data pre-processing and algorithms used to compute the radar products. Two severe hailstorm case studies are presented in Section 4, while Section 5 comprises a discussion and the analytic results. The last section gives a summary and concluding remarks. 2. Data Radar data, hail reports and damage surveys are used to investigate the severe hail events that occurred in May The study area is located in southern Romania and it is represented by the area covered by the C-band Doppler weather radar at Bucharest (Figure 1). Within this area, the terrain exhibits a certain complexity, with flat plains in the south and hills and mountains in the north. A consequence of the interaction of the topography with the westerly and southerly flows often produces convergence zones in southern Romania (Stan-Sion and Antonescu, 2006). The development of deep convection within this region is also underlined by Antonescu and Burcea (2010), who developed a cloud-to-ground lightning climatology for Romania and found the largest cloud-to-ground lightning density values in this area. Figure 1. Map of studied area showing the location of the Bucharest radar (the plus sign), orography and land sea borders. The range ring around the radar has 230 km radii. The radar is situated north of the city at an elevation of 90 m above mean sea level, operating on a magnetron transmitter and using 5 cm electromagnetic waves with 1 horizontal half-power beamwidth. The radar covers a range of 230 km, performing a complete volume scan every 6 min at 10 different elevation angles; the raw data have a range resolution of 1 km and 1 in azimuthal direction. The radar products used in this study are the composite reflectivity (CR), vertically integrated liquid (VIL), echo top heights (ET), vertically integrated liquid density (VILD) and HKE flux and were derived according to the methodologies presented in Section 3. To assess the damaging potential of hailfalls, surface observations of hail and damage reports are needed. Many researchers (e.g. Visser and van Heerden, 2000; Holleman, 2000; Hohl et al., 2002a, 2002b; Schuster et al., 2006) have used insurance claims as reference data for hail occurrence. While designing this study, several insurance companies were approached for records of hail damage, but such data were not available due to their information policy. Therefore, the only sources of data were weather station observations, reports from newspapers, spotters and damage surveys by local authorities and emergency situation institutions. 3. Methodology 3.1. Pre-processing of radar data As radar measurement errors and limitations exist, the data underwent several steps of processing. During radar measurements, the usual methods for data quality control, such as Doppler filtering to eliminate clutter and thresholding to minimize the noise, were applied. However, these processing algorithms cannot eliminate all the non-meteorological echoes such as electromagnetic interference and residual ground clutter. Therefore, additional algorithms were developed to pre-process the data before deriving the products used herein. The main goal of the pre-processing was the identification and removal of non-meteorological echoes, such as the residual ground clutter, isolated bins, anomalous propagation and external 2015 Royal Meteorological Society Meteorol. Appl. 22: (2015)

3 748 R. Cică et al. interferences. The algorithm used is similar in principle to that developed by Zhang et al. (2004). The main algorithm is actually composed from algorithmic modules that perform certain tasks specific to each type of non-meteorological echo Radar products After pre-processing the data, the radar products were derived from the 3D volume scan. The first product was the CR, which is the maximum echo intensity (in dbz) from the volume scan. Herein, it was used to reveal the strongest echo within the storm. To achieve this, each bin within the corresponding polar grids of each elevation was processed, and the maximum value was extracted. The resulting product is a single grid in which each bin is associated with this maximum value. The VIL (kg m 2 ) is a radar estimate of the total amount of liquid water in the column over a given location, and it is useful for the detection and assessment of severe thunderstorms, being correlated with updraft strength. Convective storms with strong updrafts are more likely to produce hail, as they can hold up more water into the hail growth region of the cloud. The 3D reflectivity data were converted into liquid water content followed by vertical integration (Equation (1)), resulting in a 2D product: VIL = [( Z i + Z i+1 ) 2 ] 4 7 Δh (1) where VIL has units of kg m 2 ;Z i and Z i+1 are reflectivity values (mm 6 m 3 ) at the bottom and top of the sampled layer, respectively; and Δh is the layer thickness (m). The output is represented on the original radar co-ordinates to mitigate the Cartesian smoothing. The ET is useful in identifying areas of strong updrafts and represents the estimate of the maximum height at which the radar detects an echo 18 dbz. Herein, the ET was computed using the algorithm described by Lakshmanan et al. (2013). They have proposed and evaluated the method of computing the echo tops by interpolating between elevations that bracket a reflectivity threshold, the results showing that this method performs better and gives smaller errors than the traditional method (i.e. determining the maximum elevation angle at which dbz 18 and computing the height assuming a 4/3 Earth model to account for standard atmospheric refractivity). The VILD can be used to identify the convective storms with high reflectivity relative to their height. As the VILD increases, high reflectivity cores that can be associated with hail tend to be deeper and more intense. Amburn and Wolf (1997) examined a total of 221 severe hailstorms; as the VILD increased, a substantial increase in severe hail diameter was observed. The VILD was computed by simply normalizing the VIL using the ET of a storm (Equation (2)): VILD = VIL ET (2) where VILD is the VIL density in g m 3, when multiplied by HKE and HKE flux are parameters derived from the reflectivity measurements and are used to assess the damaging potential of hailfalls. Waldvogel et al. (1978a, 1978b) obtained hailstone distributions using 175 hailstone spectra measured in severe hailstorms. This resulted in an empirical relationship of the following form: Ė = Z 0.84 (3) where Ė is the flux of the HKE (J m 2 s 1 )andz is the radar reflectivity factor (mm 6 m 3 ). Equation (3) assumes Rayleigh scattering, approximation of hailstones as water spheres, the effects of complex shapes and the different states of hailstones being ignored. Herein, the CR data were transformed into flux values of HKE by: Ė = Z 0.84 W (Z) (4) 0 for Z < 50 dbz W (Z) = (dbz 50) 0.2 for 50 dbz 55 1 for Z > 55 dbz where the additional term W(Z) is the weighting function used to define a transition zone between rain and hail reflectivities. A similar equation is known to be used by the WSR-88D radar system enhanced hail detection algorithm in the United States (Witt et al., 1998), and the same approach was used by Visser and van Heerden (2000) when comparing radar-based HKE with crop damage reports. The values in W(Z) are not the defaults and are adaptable. The WSR-88D algorithm uses 40 and 50 dbz as lower and upper thresholds, respectively, while Visser and van Heerden (2000) used 45 and 50 dbz. In the present study, the 50 and 55 dbz thresholds were chosen to bracket the 53 dbz value, the maximum considered to be associated with rain only by the WSR-88D rainfall estimation algorithm (Fulton et al., 1998). To obtain the total HKE per unit area, the flux of kinetic energy needs to be integrated over the time period during which each particular radar bin is covered by the measured Z values. Consequently, the total HKE, E (J m 2 ), is obtained by: t 1 E = Ė (t) dt (6) t 0 where t 0 is the time of the first and t 1 the last reflectivity measurement. E is the parameter most closely related to hail damage to cars and buildings (Hohl et al., 2002a, 2002b; Schuster et al., 2006; Kunz and Puskeiler, 2010) and to crops (Visser and van Heerden, 2000). 4. Case studies 4.1. Hailstorm from 25 May 2013 On 25 May 2013, convective storms started to develop in southern Romania on a southwesterly flow. At the synoptic scale, a large upper trough was present over central Europe, while a diffluent pattern persisted at mid and high levels of the atmosphere over Romania. Under these circumstances, moist and unstable air was advected towards the investigated region. The maximum CR distribution (Figure 2(a)) reveals the intensity and path of the hailstorm (south southwest of radar location). Large areas are characterized by reflectivities above 55 dbz, with cores exceeding 60 dbz. The thunderstorm was detected over Romania at 1230 UTC and lasted until 1530 UTC. The ET product gives an estimation of the vertical extent of the storms. A limitation of this product is the underestimation of the echo top height near the radar location. Storm tops near the radar cannot be accurately identified as the maximum elevation angle (i.e ) is not high enough for the beam to reach the top of the storm. The average estimated ET was around km, with a maximum of km (not shown here). The daily maximum VIL (Figure 2(b)) reveals the same path and the high quantity of liquid water available (maximum of 49 kg m 2 ). Very high values of VIL indicate very strong updrafts capable of holding (5) 2015 Royal Meteorological Society Meteorol. Appl. 22: (2015)

4 749 Hail risk and weather radar data Figure 2. Spatial distribution of the maximum composite reflectivity (CR) (a), maximum vertically integrated liquid (VIL) (b), maximum vertically integrated liquid density (VILD) (c) and maximum total hail kinetic energy (HKE) (d) for 25 May and transporting more water into the cold regions of the cloud, hence favouring the hail formation. Spatial distribution of maximum VILD is presented in Figure 2(c). Unlike VIL, which generally increases as the storm extends more vertically, the VILD increases mainly due to increases in target size. Therefore, as the VILD increases, hail cores inside the storm tend to be more intense, and the reported hail size at the surface tends to be larger. Amburn and Wolf (1997) found a significant increase in the number of severe events as the VILD values increased above 3.5 g m 3. Within the area where damage was reported, the maximum VILD reached values up to 5 g m 3, emphasizing the severe aspect of the storm. The total HKE flux adds value to the assessment of the severity of a hailstorm, being most closely related to the damage produced by the hail. Figure 2(d) illustrates the total HKE flux for this day, the average values in the affected area being in the range of J m 2. The maximum size of hailstones was reported to be around 2.5 cm in diameter. The significance of this event was the longevity of the hailfall producing a layer of hail on the ground measuring up to 15 cm in depth. The administrative boundaries of the villages that reported crop damage are depicted 2015 Royal Meteorological Society by the polygons within Teleorman County (Figure 2(d)). In the villages within the close proximity of the storms footprint, the damage was caused by heavy rain mixed with small hail. According to the report prepared and made available by the Teleorman County s Emergency Situation Committee, the overall damage, which was almost exclusively in the agricultural sector, was estimated to be more than EUR 1.3 million Hailstorm from 30 May 2013 At the European scale, a cut-off trough was slowly moving across the north Mediterranean, while a strong jet streak was present over the study area. Ahead of the jet streak, a cold front moved into Romania where low-level moisture was present. All these elements created the set-up for severe convection, which initiated at 1345 UTC and kept developing and moving towards northeast. About 1 h later, severe convection was triggered south, near radar location (storm B in Figure 3). The storms lasted until 2330 UTC. The field of maximum CR for 30 May 2013 is illustrated in Figure 3(a), where one can observe the paths followed by these severe storms. Across these paths, cores of very high Meteorol. Appl. 22: (2015)

5 750 R. Cica et al. Figure 3. Same as Figure 2, but for the event that occurred on 30 May For plotting reasons, only the name of Corabia city is illustrated in (d). reflectivities (>60 dbz) are depicted, indicating that large targets were interacting with the radar beam. From the maximum CR field, one can distinguish two major storms (labelled A and B in Figure 3(a d)) likely to produce severe hail. Although the estimated ET was over 10 km, for the storm A, the computed values exceeded 14 km (not shown). Maximum VIL (Figure 3(b)) also depicts these two storms, but storm A stands out through its very high values (>70 kg m 2 ), and through its spatial extent. The storm B had also high values of VIL, but its spatial extent was smaller. Very high values of VIL and ET indicate that the updrafts are very strong and the storm very likely will produce severe hail. The VILD product (Figure 3(c)) shows values >3.5 g m 3, reaching a maximum of 6 g m 3 for both storms. The convective system labelled A in Figure 3 was a severe hailstorm that produced damage to cars and buildings and destroyed the crops along its path. Infrastructure was affected mostly in the city of Corabia (Olt County) and in the villages situated in close vicinity of the town. The hailstones reached up to 3.5 cm in size, while the layer on the ground measured 10 cm in height. According to the evaluation performed by the Olt County s Emergency Situation Committee, hectares of crops (e.g. wheat, barley, oatmeal, corn and sunflower) and vegetables were affected. The total HKE field, illustrated in Figure 3(d), shows 2015 Royal Meteorological Society high average values ( J m 2 ) for this hailstorm, the maximum value exceeding 500 J m 2. The administrative boundaries of the localities that reported crop damage are depicted by the small area polygons in Figure 3(d). Many of the villages affected by the hailstorm on 25 May 2013 were also hit by the severe hailstorm on 30 May A swath of damaging hail is shown in the radar s total HKE flux for the storm near the radar location (B in Figure 3(d)). The storm appears as severe, with continuous regions of high CR, VIL and VILD. However, hail reports or damage surveys for this area were not found. One can speculate that the storm crossed over low populated or open areas with no crops. Also, the storm detected south of the Romanian border was not investigated. 5. Discussion and analytic results An approach based on single polarization radar data and damage reports that can be used to identify potentially damaging hailstorms has been presented so far, in the context of two severe weather events. The analysis was further extended to the month of May Information was gathered for 52 severe weather Meteorol. Appl. 22: (2015)

6 Hail risk and weather radar data 751 Figure 4. Spatial distribution of the maximum composite reflectivity (CR) for May 2013; hail reports are indicated by points. Figure 5. Same as Figure 4, but for maximum hail kinetic energy (HKE) flux. events that occurred in the studied area, consisting of reports of hail size and damage produced by hail. The damage amounts were not available for all the events, and were thus not considered in the statistical analysis. Some caveats regarding ground truth result from the fact that the hail size reports were most often given by comparison to common objects and not specific measurements, so they were converted to their associated sizes. Also, considering the storm scale variability of hail parameters, sampling may still not be consistent in representing hailfalls character. Tracks of hailstorms, their intensities and damaging potential can be determined using radar measurements. Studies published by Schiesser (1990); Hohl et al. (2002a, 2002b); Schuster et al. (2006) and Kunz and Puskeiler (2010) have shown that high reflectivity values (i.e. 55 dbz) are related to hail storms that produced damage, thus the threshold can be regarded as a potential indicator of hail occurrence. Radar reflectivity integrated in time can help define hailstorm footprints (spatial extent and intensity consistency over a time interval), offering the first indication of whether damage was possible or not. It is important that the radar measurements suggest hail, but it is equally important to have information on whether the hail is sufficiently strong or long-lasting to cause damage. The spatial distribution of maximum CR for May 2013 and the locations of the 52 events are illustrated in Figure 4. Storms paths and intensities are well captured, the footprints of high reflectivity being highlighted. The main variables responsible for hail damage are hailstorm intensity and wind speed. The storm intensity is dependent on the hailstone size, the swath of the hailstorm and the number of hailstones. Strong winds cause the hailstones to fall at inclined angles and can increase the damage amount. The HKE includes the intensity variables, is closely related to the damage and offers help in evaluating the damaging potential of hailstorms. Figure 5 depicts the maximum scan-to-scan HKE flux for May Footprints of high HKE values are observed in most of the areas where hail damage was reported. In some areas, the HKE values are not very large indicating that small hailstones were falling. Nevertheless, damage to young plants can still be caused. It is likely that severe hail occurred in locations not represented on the map, but this could not be verified as any reports were not available for those regions. Evaluation of the radar products in association to hail severity was performed using hail size ranges, due to the discontinuities in sample size for specific hailstone diameters across the spectrum of reported events. The hail size spectrum was arbitrarily divided into ranges of reported hail size. Figure 6 illustrates the hail size versus the average radar parameters calculated for each range. The average maximum CR, VIL, VILD and HKE flux increased with hail size range. A steeper increase is observed in the VILD and HKE distributions, these variables being well correlated with hail size (Amburn and Wolf, 1997) and hail damage (Hohl et al., 2002a). However, these averages do not reveal a predictive association, as the scatterplot patterns (not shown) illustrate a wider variation in all variables. Nevertheless, an increasing trend of the radar parameters with hail size is observed in the scatterplots. For all the 52 events, the CR ranged from 53 to 66.5 dbz, the VIL ranged from 30 to 83 kg m 2, the VILD ranged from 1.2 to 7.3 g m 3 and the HKE ranged from 84 to 750 J m 2. It was found that 94.2% (49 of 52) of the hail events occurred with a CR value 55 dbz, 67.3% (35 of 52) occurred with a VIL value 35 kg m 2, 61.5% (22 of 52) occurred with VILD values 3.5 g m 3 and 67.3% (35 of 52) occurred with HKE flux values 200 J m 2. Applying this methodology to longer data sets can help improve the hail climatology in a certain region of Romania through estimations of the number of hail days, by calculating the diurnal or seasonal variations of hailfalls or by assessing the regional differences of hailstorms. Saltikoff et al. (2010) produced hail climatologies using hail reports spanning 70 years from different sources and hail information derived primarily from radar data. The comparison produced roughly the same results, giving additional confidence in each of the methods. The average number of hail days and the diurnal distribution of the relative frequency were computed and presented by Skripnikova and Rezacova (2014) for Czech territory, using only radar data. Kunz and Puskeiler (2010) used radar data and insurance loss data to assess the hail hazard over complex terrain, underlining the usefulness of radar data to evaluate the number of hail days and the statistical return periods. All these studies, together with the approach from the current study, show that radar data sets are a valuable source in assessing the risk and the damaging potential of hailstorms Royal Meteorological Society Meteorol. Appl. 22: (2015)

7 752 R. Cică et al. Figure 6. Reported hail size versus average composite reflectivity (CR) (a), average vertically integrated liquid (VIL) (b), average vertically integrated liquid density (VILD) (c) and average total hail kinetic energy (HKE) flux (d) for May Conclusions The relationship between single polarization radar-derived parameters and the identification of severe hailstorms and surface damage are analysed for a region in southern Romania. This was done using the information gathered for 52 severe weather events that occurred in the coverage area of the Bucharest radar. The 3D radar data were used to compute the composite reflectivity (CR), vertically integrated liquid (VIL), echo tops height, vertically integrated liquid density (VILD) and hail kinetic energy (HKE) flux. Surface reports were used to investigate the correlation between radar data and the damage produced by hail. The main conclusions of this study are: Radar-derived parameters such as CR, VIL, VILD and HKE can be used to identify the areas where severe hail is likely to occur. This information is very valuable particularly in the regions were ground measurements of hail are not available. Time integration and spatial distribution of the above parameters offer information on the detection, swath, intensity and longevity of the hailstorms. The areas where hail and damage were reported are, overall, well captured by the spatial distribution of the radar products. A good agreement between radar data and severe hail reports and damage surveys has been found. For instance, increases in hail size resulted in increases of the average values of radar parameters. A steeper increasing trend was observed for VILD and HKE products. It is clear that radar-derived products and ground observations alone cannot solely fulfil the purpose of assessing the damaging potential of hailstorms. A combination between this type of data and comprehensive and detailed insurance loss data would significantly improve this type of research. The advantage of the high spatial and temporal resolutions of radar measurements should be exploited, but special attention should also be paid to radar data quality and limitations. Hail detection using dual polarization radars is more robust than using single polarization data. Taking advantage of the additional information from dual polarization, the precipitation can be categorized and a more precise mapping of hail can be achieved. Nevertheless, dual polarization data have their own limitations when used for detecting hail, for instance, explicit size estimation. The assessment of hail occurrence, damage and hazard using high-resolution data is a new and challenging scientific task with various applications. The results are useful in identifying the regions exposed to hail risk, where damage prevention and mitigation measures must be developed. Operational weather forecasting could be improved by adapting the early warnings to the hail hazard and identifying regions where severe weather warnings are of great importance. Also, additional 2015 Royal Meteorological Society Meteorol. Appl. 22: (2015)

8 Hail risk and weather radar data 753 radar products (e.g. HKE flux) can be developed to assist the operational nowcasting products. This study was the first step in evaluating the relationship between high-resolution 3D radar data and damaging hailstorms in Romania. A further development of this research is intended by using longer data sets, extension of the investigated areas as well as considering measurements from other types of radar systems (i.e. S-band radars). Storm structure can also play an important role in improving the identification of convective storms as hail-producing storms. A combination of storm structure with HKE could assist in generating improved hail damage assessment. Acknowledgements The authors thank the anonymous reviewers for their comments that improved this paper. This study was carried out in the frame of FP7 EURO4M project, with the financial support from European Commission. References National Meteorological Administration The climate of Romania. Editura Academiei Române, Bucharest, Romania; 368 pp. (in Romanian). ISBN: Amburn SA, Wolf PL VIL density as a hail indicator. Weather Forecast. 12: Antonescu B, Burcea S A cloud-to-ground lightning climatology for Romania. Mon. Weather Rev. 138: Busch S, Seltmann J, Otto M Using radar imagery to define hail footprints in natural hazard models. In Proceedings of 6th European Conference on Radar in Meteorology and Hydrology, 6 10 September 2010, Sibiu, Romania. Changnon SA Data and approaches for determining hail risk in the contiguous United States. J. Appl. Meteorol. 38: Egli T Elementarschutzregister Hagel. Technical report, Kantonale Gebaudeversicherungen, Rickli + Wyss: Bern; 35 pp. http: // Hagel_d.pdf (accessed 7 April 2015). Fulton RA, Breidenbach JP, Seo DJ, Miller DA, O Bannon T The WSR-88D rainfall algorithm. Weather Forecast. 13: Hohl R, Schiesser HH Cloud-to-ground lightning activity in relation to the radar-derived hail kinetic energy in Switzerland. Atmos. Res. 56: Hohl R, Schiesser HH, Aller D. 2002a. Hailfall: the relationship between radar-derived hail kinetic energy and hail damage to buildings. Atmos. Res. 63: Hohl R, Schiesser HH, Knepper I. 2002b. The use of weather radars to estimate hail damage to automobiles: an exploratory study in Switzerland. Atmos. Res. 61(3): Holleman I, Wessels HRA, Onvlee JRA, Barlag SJM Development of a hail-detection-product. Phys. Chem. Earth 25: Kunz M, Puskeiler M High-resolution assessment of the hail hazard over complex terrain from radar and insurance data. Meteorol. Z. 19: Lakshmanan V, Hondl K, Potvin CK, Preignitz D An improved method for estimating radar echo-top height. Weather Forecast. 28: Paraschivescu M, Stefan S, Bogdan M Verification of an algorithm (DWSR 2500C) for hail detection. Atmosfera 24(4): Saltikoff E, Tuovinen JP, Kotro J, Kuitunen T, Hohti H A climatological comparison of radar and ground observations of hail in Finland. J. Appl. Meteorol. Climatol. 49: Sanchez JL, Lopez L, Garcia-Ortega E, Gil B Nowcasting of kinetic energy of hail precipitation using radar. Atmos. Res. 123: Schiesser H Hailfall: the relationship between radar measurements and crop damage. Atmos. Res. 25: Schuster SS, Blong RJ, McAneney KJ Relationship between radar-derived hail kinetic energy and damage to insured buildings for severe hailstorms in Eastern Australia. Atmos. Res. 81: Skripnikova K, Rezacova D Radar-based hail detection. Atmos. Res. 144: Stan-Sion A, Antonescu B Mesocyclones in Romania characteristics and environments. In Proceedings of 23rd Conference on Severe Local Storms, 6 10 November 2006, St. Louis, MO. American Meteorological Society: Boston, MA. Visser P, van Heerden J Comparisons of hail kinetic energy derived from radar reflectivity with crop damage reports over the eastern Free State. Water SA 26: Waldvogel A, Federer B, Schmid W, Mezeix JF. 1978a. The kinetic energy of hailfalls. Part 2: radar and hailpads. J. Appl. Meteorol. 17: Waldvogel A, Schmid W, Federer B. 1978b. The kinetic energy of hailfalls. Part 1: hailstone spectra. J. Appl. Meteorol. 17: Witt A, Eilts MD, Stumpf GJ, Johnson JT, De Wayne ME, Thomas KW An enhanced hail detection algorithm for the WSR-88D. Weather Forecast. 13: Zhang J, Wang S, Clarke B WSR-88D reflectivity quality control using horizontal and vertical reflectivity structure. In Proceedings of 11th Conference on Aviation, Range, and Aerospace Meteorology, 4 8 October 2004, Hyannis, MA. American Meteorological Society: Boston, MA Royal Meteorological Society Meteorol. Appl. 22: (2015)