Session 9

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, https://doi.org/10.5194/ecss2023-4, 2023.

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, https://doi.org/10.5194/ecss2023-163, 2023.

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, https://doi.org/10.5194/ecss2023-102, 2023.

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, https://doi.org/10.5194/ecss2023-115, 2023.

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, https://doi.org/10.5194/ecss2023-124, 2023.

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, https://doi.org/10.5194/ecss2023-130, 2023.

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, https://doi.org/10.5194/ecss2023-162, 2023.

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, https://doi.org/10.5194/ecss2023-170, 2023.