Session 9 | Storm microphysics, electrification, lightning and hail

Session 9

Storm microphysics, electrification, lightning and hail
| Fri, 12 May, 09:00–10:45 (EEST)|Main Conference Room
| Attendance Tue, 09 May, 14:30–16:00 (EEST) | Display Mon, 08 May, 09:00–Tue, 09 May, 18:30|Exhibition area
Orals |
Fri, 09:00
Tue, 14:30

Orals: Fri, 12 May | Main Conference Room

keynote presentation
Matthew Kumjian, Kelly Lombardo, Cameron Nixon, and John Allen

Vertical wind shear (or more precisely, bulk wind vector magnitude differences between specified altitudes) has long been used for severe convective storm science and forecasting, in part owing to its relative success in correlating to various storm behaviors and hazards. However, theoretical, modeling, and observational work has suggested that this success may arise because vertical wind shear is associated with and/or a proxy for other, more dynamically relevant environmental characteristics.

Recent research has explored various environmental controls on hail sizes in supercell storms, and found that vertical wind shear (and, by extension, hodograph shape) is an important determinant on a storm’s proclivity for hail production. Idealized numerical simulations of supercells showed that, as deep-layer shear increases (specifically, zonal shear in the 2-6 km AGL layer; this also means increased 0-6-km shear), the resulting broader updrafts increased hailstone residence time and thus size. Paradoxically, increasing the 0-2-km shear (predominantly, but not entirely, in the meridional direction) broadened the updraft but decreased hail size: increased southerly winds within the hail growth zone increased residence times along the main growth pathway. These studies left a lingering question: what is the underlying driver to changes in supercellular hail production, the low-level shear magnitude, its orientation relative to the deep-layer shear, or neither?    

To answer this question, we present the results of simple idealized numerical modeling experiments in which the low-level (0-2 km) vertical wind shear magnitudes and directions are systematically and independently varied, keeping all other environmental factors the same. The resulting storms are used to drive the Kumjian & Lombardo (2020) hailstone growth trajectory model. The simulations lead to differences in hail sizes produced, despite having identical 0-2-km shear values. Rather, the differences in storm motion and hodograph shape lead to markedly different low-level storm-relative wind profiles. As a consequence of varied low-level storm-relative wind speeds and directions, the mesocyclonic flow speeds and directions within the hail growth zone differ amongst the experiments, which directly affects residence times and thus hail growth.

How to cite: Kumjian, M., Lombardo, K., Nixon, C., and Allen, J.: Does Low-level Vertical Wind Shear Matter for Hail Production?, 11th European Conference on Severe Storms, Bucharest, Romania, 8–12 May 2023, ECSS2023-91,, 2023.

Yuzhu Lin and Matthew R. Kumjian

Numerical modeling is valuable in hail research and forecasting. Physical assumptions regarding hailstones’ shape, tumbling behavior, fall speed, and thermal energy transfer are applied in this process, be it explicit or inexplicit. However, many of these physical assumptions are uncertain. In this study, we investigate the effect of applying different physical assumptions in hail modeling using Cloud Model 1 to simulate supercell storms, coupled with the detailed 3D hail growth trajectory model by Kumjian & Lombardo (2020; hereafter KL20). We then examine the reason behind the variability in hail statistics produced with these assumptions. 

Most microphysics schemes and hail models assume hailstones are spherical (e.g., Morrison et al. 2005; Adams-Selin & Ziegler 2016; KL20). Using hailstone shape data from Heymsfield et al. (2020) and Shedd et al. (2021), we establish a relationship between the hailstones’ mass or equivalent spherical diameter and the largest, smallest, and intermediate dimensions with reasonable degrees of randomness in consideration of hailstones’ shape variability, capturing the observed distribution of tri-axial ellipsoidal shapes. We also incorporated explicit, random 3D tumbling of individual hailstones during each timestep of their growth to simulate the behavior of free-falling, non-spherical particles (Bagheri & Bonadonna 2016) and the resultant changes in cross-sectional area (which affects collection of cloud droplets). These physical attributes are then incorporated in calculating the hailstone’s terminal velocity, using either empirical relationships such as that derived in Heymsfield et al. (2020), or analytical relationships from Bagheri & Bonadonna (2016) based on each hailstone’s Best number and Reynolds number. Options for drag coefficient modification are added to characterize the hailstone’s rough surface with varying degrees of “lobiness.” The hailstone’s shape and “lobiness,” in turn, modify its thermal energy transfer coefficient (Macklin 1963; Bailey & Macklin 1968). We find the choice of hailstone diameter-mass relation, and terminal velocity scheme to have the strongest influence on final hail size. Using non-spherical, tumbling hailstones tends to reduce the number of large hail produced in our simulated supercell storms; applying shape-specific thermal energy transfer coefficients tends to increase final hail size by a small amount; the effect of lobes varies depending on the terminal velocity scheme used. We show that many of these physical assumptions, albeit adding complexity to hailstone growth modeling, can be parameterized efficiently and potentially used in bulk microphysics schemes.

How to cite: Lin, Y. and Kumjian, M. R.: Implementing physical assumptions about nonspherical hailstone shapes, 11th European Conference on Severe Storms, Bucharest, Romania, 8–12 May 2023, ECSS2023-22,, 2023.

Antonio Giordani, Michael Kunz, Kristopher M. Bedka, Heinz Jürgen Punge, Tiziana Paccagnella, and Silvana Di Sabatino

Among severe weather events generated by deep convection, hail represents one of the most hazardous perils for agriculture, buildings, and properties. Moreover, in response to global warming, hailstorm frequency and severity are expected to increase in Europe. Therefore, a better understanding of hail hazards and risks is becoming increasingly crucial. However, major hurdles are posed by direct observations, by being heterogeneous, temporally limited, and scarce, as well as by numerical simulations, which generally lack sufficient detail to represent hailstorm dynamics properly. As a possible solution, hail likelihood can be indirectly assessed by combining multiple data sources, such as remote-sensing detections and ambient numerical predictors.

The new high-resolution reanalysis SPHERA (High rEsolution ReAnalysis over Italy), developed at ARPAE, is considered to describe hail-favoring environments over Italy and nearby countries. SPHERA is dynamically downscaled from ERA5 and driven by the model COSMO at the convection-permitting resolution of 2.2 km. A set of predictors is selected and combined with Overshooting cloud Top (OT) satellite detections, constituting a reliable proxy for hail. OTs are automatically detected with a probabilistic algorithm (NASA) from geostationary Meteosat Second Generation SEVIRI infrared images. However, not all OTs are linked to hail. Hence, a filter based on their surrounding ambient conditions is developed to retain only OT occurrences with the potential for hailstorm formation. For this purpose, ESWD (European Severe Weather Database) crowdsourced hail reports are coupled with SPHERA proxies to characterize hailstorm environments.

The analysis is performed over 2016-2020. Hence, the primary intent is to present the methodology rather than a comprehensive hail frequency characterization. More than a quarter of non-hailing OTs are removed, mainly over the Mediterranean sea and complex-topography areas. Maximum hail likelihood characterizes pre-Alpine regions and the northern Adriatic sea around 15 UTC in June-July, agreeing with previous European hail climatologies. The hit rate of ESWD reports with OTs exceeds 60%, i.e., ~20% more than the previous coupling of non-probabilistic OT detections with ERA-Interim over Europe. Different ambient characteristics are revealed by separating hit/miss reports for small/large hail events. Most hits present hailstones with diameters ≥3 cm, suggesting a better suitability of the method in case of severe hailstorms. Further, missed small-hail reports show peculiar environmental signatures characterized by lower instability, less wind shear, and colder atmospheric profiles.

These results suggest a promising avenue to enhance hailstorm events identification. In addition, further extension of the analysis should shed more light on the climatology of hailstorms.

How to cite: Giordani, A., Kunz, M., Bedka, K. M., Punge, H. J., Paccagnella, T., and Di Sabatino, S.: Combining convection-permitting reanalysis with satellite overshooting top detections for investigating hailstorm environments over south-central Europe, 11th European Conference on Severe Storms, Bucharest, Romania, 8–12 May 2023, ECSS2023-30,, 2023.

Vincent Forcadell, Clotilde Augros, Kevin Dedieu, and Olivier Caumont

In 2022, convective storms producing large hailstones were responsible of significant damages in Europe, particularly in France, where the insurance sector was severely hit. As a result, prices for 2023 might rise significantly compared to 2022, leaving a lot of pressure on the sector. Thus, the need to correctly assess and measure hail damage in real time has become crucial to assist communities in Europe and help them recover from such events.

The potential of radar data, when it comes to the detection of hail, is not to be challenged anymore: radar remains since decades the best remote sensing tool to detect such hazard. As a result, a lot of computational methods using radar data were developed to detect hail and estimate its size in real-time. Probability of hail based algorithms, hydrometeor classification using fuzzy logic or new methods using machine learning were explored to locally quantify the presence of hail in storms. Recent studies even suggest that studying the structure of the parent storm when there is hail could be of interest. Deep Learning algorithms built to analyze images and detect features in multi-dimensional data appear to be a good lead for estimating the occurrence and the size of hail in storms and on the ground. Applied to radar data, such techniques could detect signatures specific to hail and enhance our understanding on why one storm can produce 2cm hailstones and another 10cm hailstones.

This work aims to present a novel approach using Convolutional Neural Networks (CNNs) on radar images to detect hail occurrence and estimate its size. Based on multiple hail cases in France from 2017 to 2022 and hail reports on the ground, a CNN is trained and tested. Comparisons are made with existing sizing algorithms such as Fuzzy-Logic Hydrometeor Classification or Maximum Estimated Size of Hail (MESH). First results show great perspectives, even with really simple network architectures.

How to cite: Forcadell, V., Augros, C., Dedieu, K., and Caumont, O.: Deep Learning for Hail Size Estimation Using Polarimetric Radar Data, 11th European Conference on Severe Storms, Bucharest, Romania, 8–12 May 2023, ECSS2023-63,, 2023.

Oscar van der Velde, Joan Montanyà, Jesús López, and Ted Mansell

Low-light cameras installed in different locations in Colombia have recorded a total of 70 Gigantic Jets (GJ), mainly between 2016 and 2022. These are spectacular cloud-to-ionosphere lightning discharges reaching 90 km altitude and are mostly a tropical phenomenon. However, there are many more thunderstorms in the tropics that do not produce them.

We summarize the results of a statistical study of ERA5 profiles of 48 gigantic jet nights and 83 null cases in northern and western Colombia in a work published in Atmospheric Research (van der Velde et al. 2022). The main conclusion was that the environment of gigantic jet producing storms is characterized by greater warm cloud depth (to -10°C), weak low-level shear, weak lapse rates and reduced updraft and downdraft buoyancies, while no evidence was found for cloud top shear effects. It was hypothesized that warm cloud processes and secondary ice processes may result in a temporary low rime accretion rate, which could reverse the charging polarity and temporarily cause a highly negatively charged balance in the storm.

Mean vertical profiles have been constructed for null events and GJ events. They have minimal CIN (-10 J/kg) and were not modified. These are used to initiate Cloud Model 1 simulations at 0.25 km grid spacing or better and the NSSL microphysics scheme (other models and schemes may be considered). A sensitivity study was done to see the effect of initial lifting mechanism on the storm evolution. With default settings a very fragile storm was simulated. Here, we compare against satellite evolution of GJ producing storms, which tend to be rather strong. A setting of updraft nudging was selected that reproduces the storm well, while not causing unrealistically strong converging winds at the surface in response. The effect of cloud condensation nuclei is also tested.

The purpose of the simulations is to find differences to the microphysical particle distributions and densities, between GJ and null and in GJ case ERA5 profiles, which can affect the electrical charging efficiency and polarity. In a preliminary study with CM1+NSSL microphysics, we confirmed that there are indeed differences e.g. in graupel – ice/snow density overlap, but effects on charging and typical timing of the gigantic jets relative to the storm evolution are in need of further investigation.

How to cite: van der Velde, O., Montanyà, J., López, J., and Mansell, T.: Microphysical evolution of thunderstorms in tropical environments associated with gigantic jet production using CM1, 11th European Conference on Severe Storms, Bucharest, Romania, 8–12 May 2023, ECSS2023-165,, 2023.

Woomi Jung, Bo-Young Ye, MyoungJae Son, Mi-Kyung Suk, and Ik-Hyun Cho

Hail is generated within strong convective cells. In particular, hail from strong convective cells that develop rapidly can occur within 30 minutes. In that case, hail is difficult to be predicted and can cause a lot of damage in many part such as agriculture, aviation fields, and human life, etc. Pre-signal detection of developing hail will contribute to predicting hail and reducing damage. Therefore, Weather Radar Center of KMA (Korea Meteorological Administration) analyzed the characteristics of hail and developed an algorithm to detect hail pre-signal using the results of analysis.
In this study, 3-dimensional gridded radar reflectivity and temperature data were used to detect hail pre-signals. The results of pre-signal detection have 2 types of areas: “hail possible” and “hail warning”. The “hail possible” area represents the area where hail can develop before hail exists in the upper atmosphere. The “hail warning” area represents the area with a possibility of hail size of 2cm or larger. And the “hail warning” area is included in the “hail possible” area. For signal classification, 3-dimensional data based VIL (Vertically Integrated Liquid), UVIL (Upper-level VIL, -10~-40 degrees Celsius), 35 dBZ Echo top, freezing level height, and hail ground observation information such as hail size, observed time and location were used. Hail ground observation cases for 5 years were analyzed to determine thresholds for signal classification variables and verify this algorithm.
As a result of analyzing hail cases observed on 78 days, at 231 sites through this algorithm, all cases but 4 days were detected (ACC=0.81, POFD=0.22), and the hail signals were detected in advance of an average of about 40 minutes. And all 18 cases in which hail with a size of 2 cm or larger was observed were detected as “hail warning” signals. Comparing with the severe hail indices (SHI, POSH, MEHS) of previous studies, the “hail warning” areas in most of the cases were similar.


This research was supported by “Development of integrated radar analysis and customized radar technology (KMA2021-03021)” of “Development of integrated application technology for Korea weather radar” project funded by the Weather Radar Center, Korea Meteorological Administration.

How to cite: Jung, W., Ye, B.-Y., Son, M., Suk, M.-K., and Cho, I.-H.: Analysis of Hail Characteristics and Detection of Hail Pre-Signal based on 3-dimensional Radar Data, 11th European Conference on Severe Storms, Bucharest, Romania, 8–12 May 2023, ECSS2023-74,, 2023.

Posters: Tue, 9 May, 14:30–16:00 | Exhibition area

Display time: Mon, 8 May 09:00–Tue, 9 May 18:30
Dieter Poelman

The Royal Meteorological Institute of Belgium (RMI) has been operating a lightning detection network since 1992. Over the past years, the network has undergone upgrades at the level of soft- and hardware. The present-day Belgian Lightning Location System (BELLS) consists out of 14 LS7002 sensors, out of which five are owned by RMI and positioned within Belgium. The sensors detect low frequency (LF) electromagnetic signals generated by lightning in the 1-350 kHz range. Combined with Vaisala’s latest Total Lightning Processor (TLP), the network uses a combination of magnetic direction finding and time-of-arrival for detecting (intra)cloud (IC) lightning pulses and cloud-to-ground (CG) lightning strokes. 
Based on BELLS data of the last 10 years, i.e., 2013-2022, the temporal and spatial distribution of CG lightning will be discussed. In addition, while the CG detection efficiency can be fairly accurately determined through the use of ground-truth video data, this is not as straightforward in case of IC lightning. Hence, in order to evaluate the performance of the cloud lightning detection, the ratio of the number of cloud lightning pulses to the number of CG lightning return strokes can serve as a measure of change in the network’s cloud lightning detection efficiency. The behavior of this ratio is examined with specific attention to the influence of the length of the baseline between the sensors. Finally, estimated peak currents for negative first and subsequent CG strokes and cloud pulses are reviewed.

How to cite: Poelman, D.: The Belgian Lightning Location System (BELLS), 11th European Conference on Severe Storms, Bucharest, Romania, 8–12 May 2023, ECSS2023-4,, 2023.

John Allen, Cameron Nixon, Matthew Kumjian, and Mateusz Taszarek

Expected hail size is frequently poorly forecast. Hail predictions have been explored on a variety of timescales through both modeling and statistical approaches, though relatively few skillful predictors have been identified. The lack of applicable predictors has led to challenges in understanding why a given parameter may inform the expected occurrence of hail, or its absolute diameter. Previous approaches have focused around bulk metrics or coarse parameterizations of storm processes. However, these efforts have been hampered by a failure to focus on the underlying quality control of the data, including lack of  consideration of the representativeness of hail size observations, inappropriate use of nulls, and failure to look beyond existing parameters. This presentation will focus on new insights and parameters identified through modeling, discriminant analyses, self-organising maps and clustering, and leverages a data-informed approach to better characterise the relationship between the ambient storm environment and hail characteristics. We find that key to addressing this problem is the addition of consideration of synoptic regime, regional differences, seasonality and storm mode.


Parameter importance is tested through ERA-5 pseudo-proximity soundings to hail profiles from multiple datasets, including the Storm Prediction Center (SPC) Storm Data, the SPC Storm Mode Dataset, Community Collaborative Rain, Hail and Snow Network (CoCoRAHS) and Meteorological Phenomena Identification Near the Ground (MPING). Through use of buddy-checking, and a KD-tree approach for spatial independence, these sources in combination yield 80,000 reliable and independent cases over the past 25 years. Using these data allows assessment of the relationship between hail and its environment for a variety of sizes ranging from 6.4mm to >100.2 mm in maximum diameter. Results show that the null dataset being used to predict hail is important, and prior approaches have likely failed due to strong parameter overlaps for existing parameters. Through this analysis, we find that predictability exists for whether hail will be severe (>2.5 cm) or not, but with existing parameters the predictability becomes more challenging for discriminating larger categories. These results also highlight that many common associations between parameters (e.g. CAPE and hail size) are improperly-posed, as hail production exhibits multiple environmental pathways or requires different parameters, or can be driven by storm dynamics. These suggest that a multi-model and profile-aware approach is necessary to obtain reliable environmental-based predictions of hail size.  


How to cite: Allen, J., Nixon, C., Kumjian, M., and Taszarek, M.: Will hail be severe? Elusive Environmental Predictors Generating Large Hail, 11th European Conference on Severe Storms, Bucharest, Romania, 8–12 May 2023, ECSS2023-163,, 2023.

Georgios Papavasileiou, Vasiliki Kotroni, Konstantinos Lagouvardos, and Theodore M. Giannaros

Since 2021, the METEO unit of the National Observatory of Athens (NOA) in Greece implemented a multi-model operational hail forecasting system. In our forecasting system we make use of four high-resolution numerical weather prediction models, namely BOLAM, WRF-ARW, MOLOCH, and ICON. BOLAM, WRF-ARW and MOLOCH are operated by NOA and run twice per day at 00Z and 12Z, while ICON is operated by the German Weather Service (DWD) and runs eight times per day. For the purpose of our application here we use the 00Z and 12Z initialization times for all four models and we focus our analysis over the domain of Greece extending from 18˚ to 30˚E and from 34˚ to 43˚ N. Here we apply the newly developed Hail Size Index (HSI) as an offline diagnostic tool in the four models for forecasting the expected maximum hail size. We choose HSI against other commonly used indices (e.g. significant hail parameter) because by construction it is more suitable for estimating hail size in European thunderstorms. The computation of HSI takes into account the atmospheric instability, wind shear, freezing level height, lifting condensation level, equilibrium height and temperature lapse rate. Here we present preliminary results of a validation of our hail forecasting system using observations from the European Severe Weather Database (ESWD) provided by the European Severe Storms Laboratory (ESSL). Our analysis primarily focuses on the predictability of large hail (> 2.5 cm) events which can lead to significant economic losses. In addition, we also attempt to assess the predictability of high accumulation of small hail (< 2.5 cm) events which can still pose a threat to agriculture and travel safety.

How to cite: Papavasileiou, G., Kotroni, V., Lagouvardos, K., and Giannaros, T. M.: Operational hail forecasting in Greece, 11th European Conference on Severe Storms, Bucharest, Romania, 8–12 May 2023, ECSS2023-102,, 2023.

Nikolaos Papaevangelou, Ulrike Lohmann, and Sylvaine Ferrachat

Abstract Ice Nucleating Particles (INPs) are known to affect the microphysical properties of hailstorms. However, their role on surface precipitation amount and hail size distribution is still unclear. The reason is that hailstorms are rare events that consist of many non–linear interacting processes. In this study, we are analyzing the effect of INPs perturbations on hailstorms observed on 6th of July 2019 over the Swiss plateau in terms of precipitation and hail size distribution. The simulations are performed by the regional weather and climate model COSMO(Consortium for Small-Scale Modeling) (Steppeler et al., 2003; Baldauf et al., 2011) on a rotated latitude-longitude grid with 0.01° horizontal resolution (which corresponds to approximately 1.1km grid spacing), 80 hybrid vertical levels with the upper limit being at approximately 23 km and  6 s temporal resolution. One prerequisite of our experiment is to create a thunderstorm that is in agreement with one of the observed thunderstorms, using initial and boundary conditions of the given day. We define this agreement when the following two conditions are met: i) the simulated cell is within 40 km and 3 hours of the observed cell and ii) when the hail diameter of the simulated cell is larger than 1 cm at the cloud base in one or more grid points. When this is achieved, we seed these hailstorms the time period that they are at the cumulus until the early mature stage (onset of precipitation). The seeding is conducted in the updraft close to the cloud base. The additional INPs from the seeding are treated as a prognostic variable following the studies by Possner et al. (2017) and Eirund et al. (2018). In order to go back in time and locate the wider area in which the hailstorm was at these stages, we use the mean updraft 700-500 hPa and precipitation rate. The mean updraft indicates the area where there is upward motion until 3 m/s while the precipitation rate assures the existence of a deep convective cloud which doesn’t precipitate heavily -- higher than 5 mm/hr.  The whole process is repeated 10 times following the time-lagged ensemble approach (Vogel et al. 2013), in order to analyze the effect of seeding and its significance in the context of model variability.

How to cite: Papaevangelou, N., Lohmann, U., and Ferrachat, S.: Selective simulated seeding on hailstorms – a summertime case study over Switzerland, 11th European Conference on Severe Storms, Bucharest, Romania, 8–12 May 2023, ECSS2023-115,, 2023.

Tomas Pucik, Pieter Groenemeijer, Mateusz Taszarek, and Francesco Battaglioli

Both 2021 and 2022 broke records in terms of the amount of large (≥ 2 cm) and very large (≥ 5 cm) hail reports across Europe. 24 June 2021 featured the highest number of large hail reports per day (860) in the history of the European Severe Weather Database and giant (≥ 10 cm) hail was reported in three countries. In 2022, the insured damage exceeded € 4 billion in France alone while 215 people were injured that year. Furthermore, the Catalonian hailstorm on 30 August 2022 caused the first direct hail fatality in Europe since 1997.  

In this work, we studied storm-scale processes of severe hailstorms and their larger-scale environment in relation to the maximum observed hail diameter and hailstorm lifetime. The storm-scale properties include storm type, the occurrence of storm mergers, and the type of storm dissipation.  The larger-scale environment was addressed using CAPE-shear parameter space, hodograph properties (shape, longest segment in the hodograph, and storm-relative winds), and the presence of boundaries near the storms. We selected the most impactful hailstorms of 2021 and 2022, all of which featured very large hail and caused considerable damage to property or agriculture, or caused injuries. 79 hailstorms were selected from both years, spanning maximum hail diameters of 5 to 14 cm and hailstorm lifetimes of 10 to 420 minutes.

We found that most hailstorm hodographs had a straight shape with the longest segment between 1 and 3 km and storm-relative inflow typically exceeded 10 m/s. Hodograph properties and the amount of CAPE had no relation to the duration of the hailstorm. Hailstorms forming near boundaries had average lifetimes twice as long as hailstorms forming elsewhere. For hail > 5 cm, CAPE had the strongest correlation with the observed diameter, even higher than the CAPE-shear product. Hodographs suggest that the inflow magnitude into the deviant moving storms stays almost the same (around 10 m/s) for 10 to 22 m/s of 0-6 km bulk shear. In some cases, very large hail occurred in marginally favorable environments only after a storm merger occurred. This shows that storm-scale processes (merger, deviant motion of the storm) and interaction with boundaries can be as important as the background environment.

How to cite: Pucik, T., Groenemeijer, P., Taszarek, M., and Battaglioli, F.: Pre-storm environments and storm-scale properties of the major hailstorms of 2021 and 2022 in Europe., 11th European Conference on Severe Storms, Bucharest, Romania, 8–12 May 2023, ECSS2023-124,, 2023.

Markus Schultze, Tabea Wilke, and Christian Berndt

Hail is a pronounced natural hazard in Germany. Nevertheless, major hail events are quite rare and there is a lack of information in hail occurrence and size and its spatiotemporal distribution. Measurement sensors that are able to detect hail (e.g. disdrometers) are in principle available in Germany, but the spatial density of those stations is far lower than the typical spatial extent of hail events. Furthermore, sensors for hail size estimation are still in evaluation stage and currently only located at a few selected places. Hail reports based on professional and particularly amateurish eyewitness become increasingly important. But besides a certain degree of subjectivity in the reported hail size, highly populated areas might be overrepresented compared to rural and sparsely populated areas. Areal information from weather radar networks can overcome this issue with a high spatiotemporal resolution. Because of the high update frequency and fast availability of radar data, an automatic hail detection and hail size estimation might provide valuable hints to forecasters and supports the warning decision process.       
The Deutscher Wetterdienst (DWD) utilizes a C-Band dual-polarimetric weather radar network consisting of 17 radar stations that provide ten volume scans and a terrain-following low-elevation scan every five minutes. The operationally used hydrometeor classification algorithm HYMEC processes data of reflectivity, differential reflectivity and co-polar correlation coefficient to distinguish between hail and other hydrometeors. With this classification a hail distribution over Germany can already be derived. For the analysis of hail sizes, the Maximum Expected Size of Hail (MESH) and a method based on Vertical Integrated Ice (VII) are used. The latter method is motivated by a linear relation between maximum hail size and VII proposed by our forecasters based on their practical experience.        
This contribution will give an overview on the statistics of hail occurrence and hail size using the aforementioned algorithms in Germany during the convective seasons 2021 and 2022. Also, selected case studies are discussed in more detail. The results are compared against hail observations from manned and automatic weather stations, reports from the European Severe Weather Database and user reports from DWD’s WarnWetter-App.

How to cite: Schultze, M., Wilke, T., and Berndt, C.: Radar-based Hail Detection and Hail Size Estimation at DWD, 11th European Conference on Severe Storms, Bucharest, Romania, 8–12 May 2023, ECSS2023-130,, 2023.

Simona Andrei, Răzvan Pîrloagă, Mariana Adam, Alexandru Tudor, Adrian Piticar, and Bogdan Antonescu

Understanding the macrophysical and microphysical processes within a convective environment is highly important to strengthen the resilience and adaptation to severe weather. The ACCuReSy project targets a topic a topic under debate within the international scientific community but less tackled in Romania: the aerosol-cloud interactions. By using state-of-the-art remote sensing instruments from new exploratory platforms placed within relevant two locations chosen by their relevance for the environmental factors that meet the conditions for observing convective development either thermal and dynamical, the project aims to perform advanced research on the atmospheric environment before, during and shortly after the convective events and to investigate the aerosol-cloud interactions with special attention on the factors that contribute to the hail formation. Here we present briefly the work performed during the first phase of the project and the future steps in achieving our goals.

Key words: aerosol-clouds interactions, cloud macro- and microphysics, convective clouds, severe weather

Acknowledgment: This work is funded by a grant of the Ministry of Research, Innovation and Digitization, CCCDI - UEFISCDI, project number PN-III-P2-2.1-PED-2021-1938, ctr. No. 713PED, and partially supported by the Romanian National Core Program Contract No.11N/03.01 2023 and by the Romanian Ministry of Research, Innovation and Digitalization, through Program 1- Development of the national research-development system, Subprogram 1.2 - Institutional performance - Projects to finance the excellent RDI, Contract no. 18PFE/30.12.2021

How to cite: Andrei, S., Pîrloagă, R., Adam, M., Tudor, A., Piticar, A., and Antonescu, B.: ACCuReSy Project- targeting the aerosol-cloud interactions within convective environments in Romania, 11th European Conference on Severe Storms, Bucharest, Romania, 8–12 May 2023, ECSS2023-162,, 2023.

Julian C. Brimelow, Gregory A. Kopp, and David M.L. Sills

With ever-rising losses caused by extreme weather, the majority of which are associated with severe convective storms (SCS), it is of national importance to advance our understanding of hail occurrence and damage impacts across Canada and improve the means for managing our risk and vulnerability to hail. It has been almost 40 years since the Albert Hail Project (AHP) wrapped up. Although the AHP left a valuable legacy for future research initiatives to build on, the break in substantive hail research since its cessation has led to critical observational and knowledge gaps related to SCS occurrence and their impacts in Canada. It is thus essential that we address the key data and knowledge gaps and provide opportunities to train the next generation of scientists and engineers to work on SCS and their impacts. To this end, the University of Western Ontario has recently launched the Northern Hail Project (NHP). We will provide the background, rationale and expected outcomes for the NHP and also present observations from our first field season in Alberta. The philosophy of the NHP is to undertake world-class and transformative research that is data driven and has real-world applications. This includes deploying Canada's first hail disdrometer and weather station network in Calgary (located in Canada's Hail Alley), installing hailpad networks, sampling and preserving hail collected from hail swaths for analysis, monitoring vegetation health following hailstorms using multi-spectral cameras mounted on UAVs and super-high-resolution satellite imagery. Valuable data will also be collected from a portable testbed comprising a dual-polarization Doppler C-band radar, a lightning mapping array, a surface station mesonet, a mobile mesonet, upper-air soundings and remote-controlled cameras. One of the foci of the testbed is to improve the detection and quantification of hail (and tornadoes) using radar and other remote sensing tools. Collectively these data, and the analysis thereof, are expected to have significant benefits for Canada and internationally. In the long term, detailed data for damaging-hail events over both agricultural land and urban areas will help stakeholders mitigate damage. Our climatology of damaging hail events will allow improved risk and catastrophe models for the insurance sector. The improved understanding of how hailstorms form will improve warning systems. In the short term, our data will be useful for stakeholders, such as emergency managers, who will have access to our damage maps and surveys. Here we will share some preliminary results from our 2022 inaugural field program

How to cite: Brimelow, J. C., Kopp, G. A., and Sills, D. M. L.: The Northern Hail Project: A renaissance in hail research in Canada, 11th European Conference on Severe Storms, Bucharest, Romania, 8–12 May 2023, ECSS2023-170,, 2023.