Hailstorm Event Research Articles

Assessing the performance of WRF model in simulating severe hailstorm events over Assam and Bihar, India

Assessing the performance of WRF model in simulating severe hailstorm events over Assam and Bihar, India

Bulk cloud microphysical properties as seen from numerical simulation and remote sensing products: case study of a hailstorm event over the La Plata Basin

Hailstorms develop over the La Plata Basin, in south-eastern South America, more often during later winter and early austral spring, between September and October. These systems have significant socioeconomic impacts over the region. Thus, a better understanding of how atmospheric drivers modulate the formation of hailstorms is important to improve the forecast of such phenomena. In this study, we selected a hailstorm event observed over the eastern La Plata Basin during 14–15 July 2016 to evaluate the performance of the Brazilian developments on the Regional Atmospheric Modelling System (BRAMS) model. The ability of the model in simulating cloud microphysical properties was evaluated by comparing simulations driven by different global forcings against in situ and remote sensing observations. The model results showed good skill in capturing the basic characteristics of the thunderstorm, particularly in terms of the spatial distribution of hydrometeors. The simulated spatial distribution of hail covers locations where hail fall was reported. The BRAMS simulations suggest that, despite relatively low values of the convective available potential energy (CAPE) (700–1000 J kg−1), environments with strong 0–8-km bulk shear (60–70 kt, ~30.9–36.0 m s–1) can promote the formation of ice clouds and hail fall over the eastern La Plata Basin. To be more conclusive, however, further research is needed to understand how different combinations of CAPE and shear affect hail formation over the region.

Major Role of Marine Heatwave and Anthropogenic Climate Change on a Giant Hail Event in Spain

AbstractA severe hailstorm that occurred in Spain on 30 August 2022, caused material and human damage, including one fatality due to giant hailstones up to 12 cm in diameter. By applying a pseudo‐global warming approach, here we evaluate how a simultaneous marine heatwave (and anthropogenic climate change) affected a unique environment conductive to such giant hailstones. The main results show that the supercell development was influenced by an unprecedented amount of convective available energy, with significant contributions from thermodynamic factors. Numerical simulations where the marine heatwave is not present show a notable reduction in the hail‐favorable environments, related mainly to modifications in thermodynamic environment. Our simulations also indicate that the environment in a preindustrial‐like climate would be less favorable for convective hazards and thus the hailstorm event would likely not have been as severe as the observed one, being possible to perform a novel attribution of such kind.

Observed northward shift of large hailstorms in the eastern United States since 2000

Given its high population density and degree of urbanization, the eastern United States (US) is a region vulnerable to the impacts from hailstorms. Small changes in hail activity may indicate large impacts on the potential hail risks faced by the region. While contrasting hailstorm-favorable environmental changes between the northeastern and southeastern US have been documented, the meridional shift of hail activity in the eastern US has not been directly revealed based on observed hailstorm records. In this letter, using the official hailstorm database, we find a significant northward migration of hail activity (+0.33° N decade−1) in the eastern US since 2000, which is mainly contributed by the increasing proportion of large hailstorm events (hail size 0.75–2.0 inch) hitting the northeast in July and August (+0.93° N decade−1). The spatially inhomogeneous climatic mean state changes over the past two decades contribute a leading role: the intensified Bermuda High and the eastward shift of upper-level jet stream over the central US tended to moisten (dry) the atmosphere over the northeastern (southeastern) US by enhancing the low-level poleward moisture transport. This not only provides more moisture for hailstorm formation in the northeast but also destabilizes (stabilizes) the atmosphere in the northeast (southeast) under an overall increase in dry instability over the eastern US. These factors together lead to a northward shift of large hailstorms toward the northeastern US, where hailstorms were relatively seldom reported. Incorporating this shift in knowledge may improve contingency and risk management strategies of both the public and private sectors in the future.

Life-cycle performance of aluminium cladding panels in resisting hailstorms

This paper delves into cumulative damage on aluminium cladding panels attributed to hailstorms throughout the lifespan of the installations. 40 gas gun tests subjecting the cladding panel to repeated impact were undertaken for the purpose of studying cumulative damage behaviour. Insights from these tests were integrated into a hail size distribution model to characterise the probabilistic distribution of permanent indentation resulted from multiple hailstorm events. A life-cycle analysis framework was subsequently introduced, incorporating the natural variability of hailstone sizes and dynamic response of claddings to repeated ice impact. Intervention criterion can be established based on knowledge of the accumulation of permanent indentation into the cladding panels. Proactive actions are recommended should the indentations become visible to prevent worsening damage. Randomness of hailstorm occurrences was considered using hazard function which can be inferred from historical observations. Practical application of the proposed model is illustrated through case studies of two Australian states, coupled with comparative analyses highlighting key factors influencing cladding performance. The ability to account for stochasticity distinguishes the presented framework from existing deterministic approaches.

A ten-year statistical radar analysis of an operational hail suppression program in Alberta

Hailstorms represent a significant natural hazard worldwide and can result in severe socioeconomic impacts. In Canada, the province of Alberta experiences the highest rate of hail occurrences causing tremendous damage to cities, properties, vehicles and crops. Cloud seeding is often adopted to reduce the hailstorm impacts; however, the efficacy of this mitigation measure is still unclear. In this study, a ten-year set of hailstorm events (2011−2020) in Alberta was analyzed using radar-based records to evaluate the potential effects of seeding. The radar completed a full volume-scan every four minutes, and a three-dimensional radar reflectivity threshold-based method was utilized to identify hailstorms. A single 3D storm is tracked over time and the ensemble is called a “storm track” in this contribution. 176 storm tracks persisting for more than one hour were considered for further analysis. We used two radar-derived metrics as proxies for hail damage potential, namely, VILmax (Vertically Integrated Liquid calculated from the maximum reflectivity profile in the storm) and hail mean coverage area (defined as the mean area with reflectivity greater than 60 dBZ). Seeding was performed by releasing silver iodide aerosols from aircraft, and seeded scans of the storm were identified based on the location and seeding time. The efficacy of seeding was evaluated by comparing the characteristics of the storms before, during and after seeding. Results show that for nearly 60% of cases, the median values of the largest (quantile 99) VILmax and mean area corresponding to the seeded scans are lower than those of the unseeded portions. For around 8% and 20% of cases, there is no change, while for approximately 17% to 30% of cases, the values are higher. The effects of seeding are more pronounced after 30 min than at the initiation of seeding. Furthermore, statistical analyses using Mann-Whitney and Wilcoxon's tests reveal that differences in VILmax and mean area between the seeded and unseeded scan groups are statistically significant. These findings suggest that cloud seeding could potentially influence the hail damage potential of storms, particularly those with high VILmax values.

A new four-stage approach based on normalized vegetation indices for detecting and mapping sugarcane hail damage using multispectral remotely sensed data

Hailstorms have increased in frequency and intensity over the past decade causing substantial losses in agriculture. Since hailstorms often hit a wide area with no detectable pattern, relying on traditional field-based methods to assess crop damage is difficult. The aim of this study was to develop and test a new four-stage normalized vegetation index approach for detecting the severity of hailstorm damage on sugarcane plants in a large estate in south eastern Zimbabwe. The following six spectral indices were computed for the period before and after a hailstorm event to assess the extent of hailstorm damage on sugarcane: Green Chlorophyll Index (GCI); Normalized Difference Vegetation Index (NDVI); Normalized Difference Senescent Vegetation Index (NDSVI); Red Edge Chlorophyll Index (RECI); Normalized Difference Tillage Index (NDTI); and Modified Soil Adjusted Vegetation Index (MSAVI2). Then, the spectral differences were computed for each index separately and the difference maps are reported as delta (Δ) indices. The results of this study show that within one week and even two weeks after a hailstorm, ΔNDTI, ΔNDVI and ΔRECI were consistently able to detect and characterise the severity of sugarcane damage. When used in partial least squares-discriminant analysis (PLS-DA), ΔNDTI performed best in mapping the severity of crop damage throughout the large estate. ΔNDTI was able to discriminate three different levels of sugarcane damage with an overall accuracy of 90% and a Kappa value of 0.85. Combined these results imply that ΔNDTI computed using multi-spectral datasets within a fortnight after a hailstorm is a promising tool for generating reliable information about the severity of sugarcane damage by hailstorms. Such spatially explicit information is useful for creating customised crop insurance packages sensitive to damage incurred by the farmer.

Effects of Microphysics Parameterizations on Forecasting a Severe Hailstorm of 30 April 2021 in Eastern China

On the evening of 30 April 2021, a severe hailstorm swept across eastern China, causing catastrophic gale and damaging hailstones. This hailstorm event was directly caused by two mesoscale convective systems associated with strong squall lines, with mid-level cold advection from the northeast cold vortex, and strong low-level convergence associated with the low-level vortex and wind shear line. Double nesting of the high-resolution weather research and forecasting model (9–1 km) is utilized to simulate this hailstorm with five microphysics schemes. The radar-based maximum estimated size of hail (MESH) algorithm, differential reflectivity and fractions skill scores were used to quantitatively evaluate the precision. All schemes basically captured the two squall lines that swept through eastern China, although they appeared one or two hours earlier than observation. Particularly, Goddard and Thompson performed better in the MESH swath and fractions skill scores among the five different schemes. However, Thompson most realistically captured the reflectivity pattern, intensity and vertical structure of mesoscale convective systems. Its high-reflectivity column corresponded to the maximum center of the hail mixing ratio within the updraft region, which is consistent with the characteristics of a pulse-type hailstorm in its mature phase.

Hail-caused greenfall leaves, litterfall, nutrients, and leaf decomposition in tropical cloud forest and a restoration planting

Abstract Greenfall leaves caused by hailstorms may represent a resource pulse of nutrients. We determined the contribution of greenfall versus senescent leaves to total litterfall production, carbon, nitrogen and phosphorus input to the system, and leaf decomposition rate. Litterfall was collected monthly for three years in two cloud forests (F1, F2) and a restoration planting area (R) in Veracruz, Mexico. Two fortuitous hailstorms occurred in the second year. Leaf decomposition rate was determined in all three sites but did not differ across them. Total annual litterfall, excluding greenfall, was 10.0, 10.1, and 7.7 Mg ha−1 y−1 for F1, F2, and R, respectively. Senescent leaves represented 65% of the litterfall, while greenfall leaves increased the annual leaf biomass component of the litterfall by 12%. Concentrations of carbon, nitrogen, and phosphorus were 2.3, 5.7, and 18.1% higher, respectively, in greenfall than in senescent leaves. Greenfall increased the annual input of C, N, and P by 12, 13, and 14%, respectively. Despite their short duration (approximately 70 minutes), the hailstorm events generated a substantial contribution of greenfall leaves and a source of extra C, N, and P, since these leaves decompose and are incorporated into the cloud forest system.

ARIES ST Radar: The First Central Himalayan Wind Profiler

Recently, a 206.5[Formula: see text]MHz Stratosphere Troposphere (ST) Radar system was successfully installed and made operationalized at Aryabhatta Research Institute of Observational Sciences (ARIES) (29.4N, 79.2E, 1793[Formula: see text]m amsl), Nainital, India. It is the first such unique observational facility located in the central Himalayan region and will play an important role in understanding the meteorological conditions of the region that has a vital role in atmospheric studies in South Asia. The entire ST radar system is indigenously built and installed in a compact 30[Formula: see text][Formula: see text][Formula: see text]m two-storey building, making maximum use of the available space in the hilly terrain. A metal fence of 3.5–4[Formula: see text]m height was designed and installed along the perimeter of the array to attenuate the clutter returns from the nearby mountains with the shielding efficiency 22–25[Formula: see text]dB. Since its operation, the radar has obtained useful data of neutral atmosphere, precipitation, convection, and hailstorm events for scientific research. The technical details of different sub-systems, radar integration and calibration methodology are presented here. A dedicated off-line GUI based data processing tool has been developed and is being used for the data analysis. A comparison of wind components derived from ARIES ST Radar with collocated GPS-radiosonde observations indicates a good agreement with correlation coefficients for zonal (0.92), meridional (0.76), wind speed (0.86), and wind direction (0.7). The change in wind patterns is demonstrated up to a height of about 31[Formula: see text]km amsl and the tropopause was marked to be at 16–17[Formula: see text]km on 2020 June 20. A dramatic reversal of winds from westerly (below the tropopause) to easterly (above the tropopause) was also observed. ARIES ST Radar could capture the signature of the precipitation in addition to neutral air in the same Doppler spectrum and the height of the starting point of precipitation is identified to be [Formula: see text][Formula: see text]km. This ability to detect atmospheric scattering from both neutral wind (Bragg) and precipitation (Rayleigh) in the same spectrum makes the 200[Formula: see text]MHz band radar a unique instrument in the wind profiler application for atmospheric research.

Sounding Data from Ground-Based Microwave Radiometers for a Hailstorm Case: Analyzing Spatiotemporal Differences and Initializing an Idealized Model for Prediction

Atmospheric physical sounding data from three ground-based microwave radiometers located in Xi’an were analyzed to explore the temporal and spatial differences of a hailstorm event and were initialized into an idealized Weather Research and Forecasting (WRF) model to predict the total evolution of the event, which occurred on 29 July 2019. Liquid water and relative humidity profiles revealed a consistent sequence of hailstorm intensity among observations from surface meteorological stations and the FY-4A satellite, where the precipitation and cloud top temperature intensified from north to south, corresponding to the locations of the ground-based microwave radiometers in Gaoling, Weiyang, and Chang’an. Compared with those of a similar storm without hail that occurred on 9 August 2018, the humidity profiles and heights at 0 °C and −20 °C exhibited more dramatic changes. The heights at 0 °C and −20 °C obviously increased with a low-value zone in the relative humidity profiles during the strongest stage of the hailstorm in Chang’an and Weiyang. Later, the heights sharply dropped in Chang’an when strong, downward ice-phased hydrometers occurred with hail production in the storm. A time-saving, idealized WRF simulation, initialized with pre-3-h sounding data from ground-based microwave radiometers, was designed to qualitatively predict this hailstorm. The simulations consistently showed a strong-to-weak intensity of storms from Chang’an to Weiyang to Gaoling. Although the first attempt at this model has uncertainties in both the observations and the model, it provides a potential new method for single-point fine hailstorm prediction.

Satellite-based assessment of hailstorm-affected potato crop for insurance purpose

Assessing the extent of hailstorm-affected crop is one of the thrust areas for quantifying mid-season adversities under crop insurance values chain. This study evaluated the pre- and post-hailstorm responses of potato crop, over different spectral bands and vegetation indices derived from Sentinel-2 data, towards assessment of damage severity classes. The potato crop was mapped using pre-event satellite data with overall accuracy of 88% (k = 0.82). Pair-wise Games–Howell t-test showed significant differences among the post-hailstorm potato severity classes in red, near infrared and short-wave infrared (SWIR) bands and normalized vegetation indices. Percentage change (from pre- to post-event) in band reflectance and vegetation indices showed a better sensitivity in differentiating damage severities. Differential behaviour of SWIR-1 (Band-11) and SWIR-2 (Band-12) was observed within severely affected potato crop under dry and wet soil conditions. Decision matrix based on percentage change in normalized difference vegetation index (ΔNDVI) and normalized difference tillage index (ΔNDVI) could able to capture the damage severity classes with an overall accuracy of 86.7%. Higher proportion of affected area was found to be associated with larger percentage of potato yield reduction based on measured yield data at insurance unit level. The proposed methodological approach could be adopted for operational assessment of the impact of hailstorm events on other crops with required modifications.

Simulation of a Hailstorm Event over Bangladesh using WRF Model

An attempt is made to simulate a pre-monsoon hailstorm event over Bangladesh using the high-resolution Weather Research and Forecasting (WRF) model. A severe hailstorm event embedded with multi-cell thunderstorm occurred over the North-central region of Bangladesh and adjacent West Bengal state of India on 30 March 2018, which moved towards the South-East direction up to its highest intensity stage during 0900-1030 UTC is selected for the study. The WRF model is used to investigate the dynamical and thermodynamical characteristics of the selected hail producing thunderstorm event. The model is run on a double nested domain at 9 and 3 km horizontal resolutions using the Kain-Fritsch (new Eta) cumulus and Mellor-Yamada Nakanishi and Niino Level 2.5 (MYNN2) planetary boundary layer parameterization schemes. The analysis of the accumulated hail, mean sea level pressure, vertical wind shear, vertical (w) component of wind, relative humidity along with low level wind, convective available potential energy (CAPE) and convective inhibition energy (CINE), rainfall (mm) parameters are investigated. Rainfall is compared with that of observed value. The high amount of relative humidity (>90%), CAPE (>3000 JKg-1) and vertical wind shear (>40 ms-1) help to form severe convective storm which vertically developed upto 150 hPa level. In addition, the high value of w component of winds (~0.4 ms-1) helps updraft process which is favorable for hail formation. The present combination of parameterization schemes of model is found suitable to capture the selected hailstorm event reasonably well, but more similar hailstorm events need to be studied to make any final conclusions. The Dhaka University Journal of Earth and Environmental Sciences, Centennial Special Volume June 2022: 123-130

Synoptic-thermodynamic analysis of hailstorm events (case study: Meshginshahr County, Iran, June 5 and 17, 2009 Hailstorms)

Synoptic-thermodynamic analysis of hailstorm events (case study: Meshginshahr County, Iran, June 5 and 17, 2009 Hailstorms)

Seasonal variability of hailstorm events over Delhi region: radar and model perspective

Seasonal variability of hailstorm events over Delhi region: radar and model perspective

Detection of Crop Hail Damage with a Machine Learning Algorithm Using Time Series of Remote Sensing Data

Hailstorms usually result in total crop loss. After a hailstorm, the affected field is inspected by an insurance claims adjuster to assess yield loss. Assessment accuracy depends largely on in situ detection of homogeneous damage sectors within the field, using visual techniques. This paper presents an algorithm for the automatic detection of homogeneous hail damage through the application of unsupervised machine learning techniques to vegetation indices calculated from remote sensing data. Five microwave and five spectral indices were evaluated before and after a hailstorm in zones with different degrees of damage. Dual Polarization SAR Vegetation Index and Normalized Pigment Chlorophyll Ratio Index were the most sensitive to hail-induced changes. The time series and rates of change of these indices were used as input variables in the K-means method for clustering pixels into homogeneous damage zones. Validation of the algorithm with data from 91 soybean, wheat, and corn plots showed that in 87.01% of cases there was significant evidence of differences in average damage between zones determined by the algorithm within the plot. Thus, the algorithm presented in this paper allowed efficient detection of homogeneous hail damage zones, which is expected to improve accuracy and transparency in the characterization of hailstorm events.

Microphysical Features of Unprecedented Hailstorms Over Central Region of India During February – March 2014

The central region of India was battered by prolonged, widespread and severe hailstorm events during the period of 15 February 2014 to 15 March 2014. The events were unprecedented not only in terms of time of occurrence, duration and frequency of hailstorms but also size of hailstones. In the present study, we have proposed a hypothesis which governs the mechanism for the development of severe hailstorms based on atmospheric instability, cloud microphysics and its feedback to large scale dynamics. The steep temperature lapse rate owing to warm (i.e. moist) south easterlies in the lower troposphere and cold (i.e. dry) westerlies in upper troposphere as a result of anomalous southward penetration of mid-latitude westerlies in the form of westerly trough fostered convective instability build up. The updraft associated with convectively unstable atmosphere invigorated growth of ice crystals by rapid collection of supercooled water droplets. This ultimately led to formation of graupel/hailstones through the microphysical mechanisms (e.g., riming process). The hypothesis is further tested with high resolution numerical model. The experiment pinpoints that mixed-phase hydrometeors were predominant in mid and upper troposphere and had bearing on the release of latent heat, further stronger the deep convective clouds (i.e., instability). Time evolution of cloud microphysical processes indicate dominant presence of graupel/hail which is consistent with formation of rain water, cloud ice and snow. The results underpin the interplay between large scale dynamical and cloud microphysical processes in the formation of anomalously widespread and persistent severe hailstorm events.

Hailstorm Formation Enhanced by Meso-γ Vortices along a Low-Level Convergence Line

During a hailstorm event, near-surface meso-γ vortices along a convergence line interact with hail cells. Herein we investigate this interaction by using observational data and a high-resolution simulation of a hailstorm that occurred over Taizhou (Zhejiang, China) on 19 March 2014. The 10-m surface wind data from automatic weather stations show that several meso-γ vortices or vortex-like disturbances existed over the convergence zone and played a vital role in the evolution of the hailstorm and the location of the hail. The model results agree with the observations and present a closer correlation between the hail and the low-level meso-γ vortices than those observed. The model simulation indicates that such low-level meso-γ vortices can be used to predict the next 10-min hail fallout zone. The low-level meso-γ vortices originated over the convergence zone and then fed back into the convergence field and provoked a stronger updraft. Vorticity was initiated primarily by stretching and was extended by tilting. A three-dimensional (3-D) flow analysis shows that the existence of low-level meso-γ vortices could help enhance a local updraft. Furthermore, the simulation reveals that the low-level meso-γ vortices formed in the bounded weak echo region (WER) at the front of the hail cell, enhancing convergence and strengthening updrafts. Graupel was broadly located between the 0°C isothermal line and the top of the clouds, roughly between the 0 and −20°C isothermal lines. Accordingly, the hailstones grew rapidly. The suitable environment and the positive effect of the meso-γ vortices on the updrafts enabled hailstorm formation.

Synoptic patterns of severe hailstorm events in Spain

A study of severe hailstorm (with size ≥3 cm) over Spain has been conducted using 8 years (2012–2019) of data. The 73 events of heavy hail selected were classified using the moisture flux at the 850 hPa pressure level (qv)850 and the lifted index (LI) as variables and a principal component analysis, coupled with a K-means clustering, as statistical technique. The application of this procedure provided four groups with their respective final cluster centre. We studied four cases, selected for being close to each final cluster centre. It was found that high values of low levels moisture flux (~ 89 g m/kg s on the average) are accompanied by static instability (SBCAPE ~600 j/kg and average LI ~ −4). The temperature values exceeded 20 °C at the 850 hPa pressure level and range between −10 °C and − 20 °C at the 500 hPa pressure level. The most typical configuration corresponded to geopotential and thermal trough at mean levels of the troposphere and low pressure at low levels. Likewise, high values of precipitable water (> 25 kg/m2) have been detected and the presence of storm relative helicity and shear was moderate or strong in all cases.

Quantifying hail size distributions from the sky – application of drone aerial photogrammetry

Abstract. A new technique, named “HailPixel”, is introduced for measuring the maximum dimension and intermediate dimension of hailstones from aerial imagery. The photogrammetry procedure applies a convolutional neural network for robust detection of hailstones against complex backgrounds and an edge detection method for measuring the shape of identified hailstones. This semi-automated technique is capable of measuring many thousands of hailstones within a single survey, which is several orders of magnitude larger (e.g. 10 000 or more hailstones) than population sizes from existing sensors (e.g. a hail pad). Comparison with a co-located hail pad for an Argentinian hailstorm event during the RELAMPAGO project demonstrates the larger population size of the HailPixel survey significantly improves the shape and tails of the observed hail size distribution. When hail fall is sparse, such as during large and giant hail events, the large survey area of this technique is especially advantageous for resolving the hail size distribution.