Characterization and Analysis of Hailstorms in the Northern Great Plains

Hail is a meteorological occurrence that appears frequently during spring and summer across the Northern Great Plains. This paper first characterizes patterns associated with reported hail events occurring within a five-state region (North Dakota, Minnesota, South Dakota, Iowa, and Nebraska) from 2000-2006 using records taken from the National Climatic Data Center (NCDC) Severe Storms database. Patterns of interest include the seasonality of hail activity for each state and across the region, observational bias in the reporting of hailstone size, and temporal trends in the number of hail reports across the region and in each state. This paper then explores a possible link between the solar cycle and regional hail activity. Daily observations of solar radio flux (at 10.7 cm) and National Weather Service hail reports dating from 1956-2006 were compared. A chi-squared goodness of fit analyses showed possible associations exhibiting weaker significance in Solar Cycles (S.C.) 21 and 22, and higher significance during S.C. 23. The recent appearance of a significant linkage may be due to changes in reporting effort, weather patterns, or both. Further study is required to distinguish observational artifacts from geophysical effects. Because hail activity is common to the Northern Great Plains, a deeper understanding of the effects of the solar cycle on hail and also the spatial and temporal patterns associated with regional hail may prove beneficial for climate prediction, weather forecasting, and underwriting of crop-hail insurance

Precipitation growth in convective clouds

Analytical solutions to the equations of both the growth and motion of hailstones in updrafts and of cloud water contents which vary linearly with height were used to investigate hail growth in a model cloud. A strong correlation was found between the hail embyro starting position and its trajectory and final size. A simple model of the evolution of particle size distribution by coalescence and spontaneous and binary disintegrations was formulated. Solutions for the mean mass of the distribution and the equilibrium size distribution were obtained for the case of constant collection kernel and disintegration parameters. Azimuthal scans of Doppler velocity at a number of elevation angles were used to calculate high resolution vertical profiles of particle speed and horizontal divergence (the vertical air velocity) in a region of widespread precipitation trailing a mid-latitude squall line

Hail time series from radar proxies for decadal variability of hail in Switzerland

In Switzerland hail regularly causes substantial damage to agriculture, cars, and infrastructure. However, addressing hail damage is challenging, as hail is related to severe thunderstorms, one of the most complex atmospheric phenomena due to its small spatial scale, vigorous development, and intricate physical interactions. In a changing climate, hail frequency and its patterns of occurrence may change, with potentially negative ramifications, e.g. when considering agricultural losses. According to the new Swiss hail climatologies (Madonna et al. 2018; Nisi et al. 2016; Nisi et al. 2020) there is a significant difference between the interannual hail variability on the north and south sides of the Alps. Understanding the drivers of this variability is essential for possible adaptation strategies. In contrast to North America, where important drivers of interannual variability of severe convection are well studied (see Tippett et al. 2015 and Allen et al. 2020), a comprehensive analysis of the year-to-year variability of hail in Switzerland has only been done for the last 20 years (in preparation by Katharina Schröer2). A long-term analysis, however, is still missing. Therefore, this study presents a daily hail time series for Northern and Southern Switzerland from 1950 to today. The time series is produced from radar hail proxies and ERA-5 reanalysis data. Daily POH (Probability of Hail) data from MeteoSwiss is used to identify haildays in the region north and south of the Alps (plus 140km radar buffer) from 2002 to 2021 for the hail months April - September. The decision hailday yes/no is based on surpassing a POH ≥ 80 for a certain minimum footprint area of the domains. Then, a logistic regression model is constructed for each domain to predict the occurrence of a hailday depending on various environmental variables and indices. 70 different variables were tested. The predictors were chosen based on model performance, collinearity, and expert judgement. With the two best models, haildays are reconstructed back to 1950 for each region. The time series is then used to study the local and remote drivers of interannual variability, e.g. central European weather types, large-scale variability patterns, etc., as well as to investigate past changes or shifts in hailstorm seasonality. With this knowledge, we could improve our understanding of the meteorological-climatological variability, and, with the help of climate scenarios, infer about possible changes in the future

Assessment of Electrical Safety Beliefs and Practices: A Case Study

In this paper, the electrical safety beliefs and practices in Hail region, Saudi Arabia, have been assessed. Based on legislative recommendations and rules applied in Saudi Arabia, on official statistics regarding the electricity-caused accidents and on the analysis of more than 200 photos captured in Hail (related to electrical safety), a questionnaire composed of 36 questions (10 for the respondents information, 16 for the home safety culture and 10 for the electrical devices purchasing culture) has been devised and distributed to residents. 228 responses have been collected and analyzed. Using a scale similar to the one adopted for a university student GPA calculation, the electrical safety level (ESL) in Hail region has been found to be 0.76 (in a scale of 4 points) which is a very low score and indicates a poor electrical safety culture. Several recommendations involving different competent authorities have been proposed. Future work will concern the assessment of safety in industrial companies in Hail region

Drone-based photogrammetry combined with deep-learning to estimate hail size distributions and melting of hail on the ground

Hail is a major threat associated with severe thunderstorms and an estimation of the hail size is important for issuing warnings to the public. Operational radar products exist that estimate the size of the expected hail. For the verification of such products, ground based observations are necessary. Automatic hail sensors, as for example within the Swiss hail network, record the kinetic energy of hailstones and can estimate with this the hail diameters. However, due to the small size of the observational area of these sensors (0.2 m2) the estimation of the hail size distribution (HSD) can have large uncertainties. To overcome this issue, we combine drone-based aerial photogrammetry with a state-of-the-art custom trained deep-learning object detection model to identify hailstones in the images and estimate the HSD in a final step. This approach is applied to photogrammetric image data of hail on the ground from a supercell storm, that crossed central Switzerland from southwest to northeast in the afternoon of June 20, 2021. The hail swath of this intense right-moving supercell was intercepted a few minutes after the passage at a soccer field near Entlebuch (Canton Lucerne, Switzerland) and aerial images of the hail on the ground were taken by a commercial DJI drone, equipped with a 50 megapixels full frame camera system. The average ground sampling distance (GSD) that could be reached was 1.5 mm per pixel, which is set by the mounted camera objective with a focal length of 35 mm and a flight altitude of 12 m above ground. A 2D orthomosaic model of the survey area (750 m2) is created based on 116 captured images during the first drone mapping flight. Hail is then detected by using a region-based Convolutional Neural Network (Mask R-CNN). We first characterize the hail sizes based on the individual hail segmentation masks resulting from the model detections and investigate the performance by using manual hail annotations by experts to generate validation and test data sets. The final HSD, composed of 18209 hailstones, is compared with nearby automatic hail sensor observations, the operational weather radar based hail product MESHS (Maximum Expected Severe Hail Size) and some crowdsourced hail reports. Based on the retrieved drone hail data set, a statistical assessment of sampling errors of hail sensors is carried out. Furthermore, five repetitions of the drone-based photogrammetry mission within about 18 min give the unique opportunity to investigate the hail melting process on the ground for this specific supercell hailstorm and location

Analisis Citra Satelit Himawari 8/9 Terkait Kejadian Hujan Es Di Wilayah Klaten Tanggal 21 Oktober 2021

One of the extreme phenomena that often occurs is hail, hail can occur because when water vapor condenses it turns into ice particles due to the low air temperature factor. Hail includes local rain which covers an area of 5-10 km, and lasts a maximum of 10 minutes. On October 21, 2021, there was hail in Jogonalan Klaten. The incident occurred at around 14.18 WIB. By using Himawari-8 satellite data reference which was analyzed using the SATAID application and UV wind data obtained from the Copernicus web and processed with OpenGrADS software. Then as other supporting data used cloud top temperature data with band 13 himawari 8 which was processed using time series and also air lability data from the web weather.uwyo.edu. From the four data, it will be obtained that the formation of cumulonimbus clouds in the area around Klaten and Jogja as an indication of hail is strengthened by the convergence pattern in the northern region of the island of Java, precisely Central Java which causes a slowdown in the air mass that forms convective clouds which is the cause of hail in Klaten Regency. . This is reinforced by the results of the analysis of the cloud top temperature which reached -18°c

Study of 11 September 2004 hailstorm event using radar identification of 2-D systems and 3-D cells

The most important hail event recorded in the region of the Ebro Valley (NE Spain) in 2004 was the 11 September episode. Large hailstones (some of them with a diameter of over 30 mm) caused important damages in agriculture and properties. The hail event affected an area of 3848 ha and was caused by several multicellular systems. The aim of this paper is the analysis of the associated convective structures using the meteorological radar as well as the MM5 mesoscale model, thermodynamic data and a hailpad network. To achieve this end, the new hailstorm analysis tool RHAP (Rainfall events and Hailstorms Analysis Program) has been applied. It identifies tracks and characterises precipitation systems and convective cells, taking into account 2-D and 3-D structures. The event has also been studied with the TITAN software (Thunderstorm Identification, Tracking, Analysis and Nowcasting) in an attempt to compare both methods. Results show that the episode had a strong convective activity with CAPE values over 4000 J/kg and with hail-forming cells characterised by VIL (Vertical Integrated Liguid) exceeding 40 kg/m², VILD (VIL density) over 4 g/m³, HP (Hail Probability) of 100% and SHP (Severe Hail Probability) of over 75%. The hail cells evolved into multicellular systems that lasted between 70 and 90 min. Finally, the comparison of RHAP and TITAN has shown significant correlations between methods

Hail Variability in Supercell Storms and Response to Environmental Variables

Severe weather events in the United States including tornadoes, hail, and wind are often produced by supercell thunderstorms. These storms are characterized by complex hydrometeor distributions which can be influenced by environmental distributions of wind and moisture. Since the Weather Surveillance Radar-1988 Doppler (WSR-88D) network was fully upgraded to dual-polarimetric capabilities in 2013, dominant hydrometeor species such as hail have been inferable using fuzzy logic. In this study, time series of areal extent of the inferred hail signature at base scan level have been estimated for 145 supercell storms, including both tornadic and non-tornadic cases, across a variety of environments from February 2012-December 2014. Proximity soundings were gathered for environments representative of the supercells (e.g., on the same side of mesoscale boundaries, in a region representative of storm-relative inflow) using archived Rapid Update Cycle (RUC) and Rapid Refresh (RAP) model output from the National Operational Model Archive and Distribution System (NOMADS). Model sounding points were within ~80 km and the midpoint of the analysis period in order to spatiotemporally represent environments during the period in which storms were analyzed. Previous modeling and observational studies have shown that thermodynamic, moisture, and shear parameters influence the mean areal extent of hail at the base scan level and the temporal variability of inferred hail areal extent (HAE). Significant relationships were determined in this study between mean HAE/variability and several environmental parameters. Hail polarimetric radar signatures were also compared across environments; results showed that certain environments produce distinctive mean hail areal extent and hail variability. Correlations between HAE and environment variables are generally higher when the storm has a mean altitude greater than 1 km. An increase in some thermodynamic parameters is observed to produce an increase in mean HAE, while an increase in shear produces an increase in hail variability. Predictive equations for HAE and hail variability are also developed from the analyzed environmental variables. Advisor: Matthew Van Den Broek

A review of the scorpion fauna of Saudi Arabia

The scorpions of Saudi Arabia were surveyed in the major regions of Jazan, Al-Medina, Al-Baha, Hail, and Riyadh, in addition to nine provinces surveyed more superficially. Jazan (1,440 specimens) had 10 buthids and two scorpionid species and subspecies; Al-Medina (867) had seven buthid and two scorpionid species and subspecies, one of which, the scorpionid Scorpio maurus (palmatus?), needs further confirmation of identity. The Al-Baha region (2421 specimens) contained five buthids and two scorpionid species and subspecies; Hail (1,921) had eight buthid and two scorpionid species and subspecies - the most common subspecies here was Scorpio maurus kruglovi. Androctonus crassicauda and Leiurus quinquestriatus were only found in Hail and Al-Baha; Androctonus bicolor was newly recorded in Hail and Riyadh. Riyadh (4,164 specimens) had nine buthid, one scorpionid and at least two hemiscorpiid species and subspecies. The Saudi fauna was found to comprise at least 28 species and subspecies of the families Buthidae, Scorpionidae and Hemiscorpiidae.Keywords: Buthidae, Scorpionidae, Diplocentrida

A Crowdsourced Hail Dataset: Potential, Biases, and Inaccuracies

Hail is a substantial severe weather hazard in the USA, with significant damage to property and crops occurring annually. Traditional methods of forecasting hail size have limited accuracy, and despite improvements in remote sensing of precipitation, the fall characteristics of hail make quantification of hail imprecise. Research into hail is ongoing, but traditional hail datasets have known biases and low spatiotemporal resolution. The increased usage of smartphones creates the opportunity to use a crowdsourced dataset provided by the Precipitation Identification Near the Ground (PING) program, a program developed by the National Severe Storms Laboratory. PING data is compared to approximate ground truth in the form of preliminary Severe Prediction Center (SPC) hail reports, and National Weather Service (NWS) issued severe warning polygons. Biases and inaccuracies in the dataset are also explored through exploratory data analysis. While PING reports did not suffer from biases based on time of day or day of week, the location of PING reports was found to have a heavy bias towards high population density areas compared to SPC reports. Skill scores of PING reports, compared to SPC reports, were low, with a remarkably high False Alarm Rate (FAR), indicating false reports being a problem in the PING dataset. Comparing PING reports to severe polygons did not substantially improve the skill scores. The low number of severe PING reports prevented any meaningful analysis of size accuracy. While the number of SPC reports were mostly correlated with the number of warning polygons issued by each Weather Forecast Office, the PING reports were not well correlated, with an anomalously high number of reports in the Oklahoma City region. The inaccuracy of PING reports and strong population bias suggest that the PING hail database may not have high utility, and should only be used in conjunction with other databases in order to ensure quality