Accurate estimates of hail risk to exposed assets, such as crops, infrastructure, and vehicles, are required for both insurance pricing and preventive measures. Here we present an event catalog to ...describe the hail hazard in South Africa guided by 14 years of geostationary satellite observations of convective storms. Overshooting cloud tops have been detected, grouped, and tracked to describe the spatiotemporal extent of potential hail events. It is found that hail events concentrate mainly in the southeast of the country, along the Highveld, and around the eastern slopes. Events are most frequent from mid-November through February and peak in the afternoon, between 13:00 and 17:00 UTC. Multivariate stochastic modeling of event properties yields an event catalog spanning 25 000 years, aiming to estimate, in combination with vulnerability and exposure data, hail risk for return periods of 200 years.
Predictions of thunderstorm‐related hazards are needed in several sectors, including first responders, infrastructure management and aviation. To address this need, we present a deep learning model ...that can be adapted to different hazard types. The model can utilize multiple data sources; we use data from weather radar, lightning detection, satellite visible/infrared imagery, numerical weather prediction and digital elevation models. We demonstrate the ability of the model to predict lightning, hail and heavy precipitation probabilistically on a 1 km resolution grid, with a temporal resolution of 5 min and lead times up to 60 min. Shapley values quantify the importance of the different data sources, showing that the weather radar products are the most important predictors for all three hazard types.
Plain Language Summary
Thunderstorms are hazardous to both people and property through various extreme weather phenomena. Predicting these hazards allows individual people, infrastructure managers and emergency services to take action in advance. To serve their needs, we use a model based on an artificial intelligence (AI) to predict the probability of the hazards occurring at a given time and place during the next 60 min. The model uses multiple sources of weather observations and predictions to construct the predictions, and can be adapted to function also when some of these sources are unavailable, increasing its reliability. We show that the model can predict the occurrence of lightning, hail and heavy precipitation, detecting and predicting the motion of thunderstorms as well as whether they are increasing or decreasing in severity. We use explainable AI methods to determine how much each of the data sources contributes to the predictions, showing that weather radar observations are the most important source of data.
Key Points
We present a deep learning model for nowcasting thunderstorm hazards, and demonstrate it for lightning, hail and heavy precipitation
The model can provide probabilistic warnings of these hazards on a two‐dimensional grid
We analyze the importance of the different data sources used in the model using explainable artificial intelligence methods
Due to the increasing interest in highbush blueberry (Vaccinium corymbosum L.) among consumers, together with the problems of climate change and specific substrate requirements, a novel approach to ...intensive blueberry production is required. Here, ‘Duke’, ‘Aurora’, and ‘Brigitta’ blueberry cultivars were planted under the protective environments of a high tunnel and black hail net, each using ridge and pot planting systems. The high tunnel increased the maximal air temperature on average by 7.2 °C compared to the hail net. For all three cultivars, harvest began 6 to 18 days earlier under the high tunnel than under the hail net; however, lower yields and individual phenolics contents were obtained for the fruit. In ‘Aurora’ and ‘Brigitta’, environmental conditions under the high tunnel also reduced plant volume and fruit sugar/organic acid ratio. Growing blueberry plants in 60 L pots had no negative effects on plant volume and fruit ripening time, yield, firmness, color, and chemical composition. This study represents the first to compare highbush blueberry grown under the high tunnel and hail net protective environments using ridge and pot planting systems across three different cultivars. Here, we can conclude that optimal highbush blueberry production of ‘Duke’, ‘Aurora’, and ‘Brigitta’ under the climate conditions of the study provides earlier ripening times under the high tunnel. However, according to fruit yield and quality, all three cultivars benefit from the hail net over the high tunnel, while ‘Duke’ and ‘Brigitta’ also benefit in particular from the hail net combined with growth in pots.
Hailstorms in subtropical South America are known to be some of the most frequent anywhere in the world, causing significant damage to the local agricultural economy every year. Convection in this ...region tends to be orographically forced, with moisture supplied from the Amazon rainforest by the South American low-level jet. Previous climatologies of hailstorms in this region have been limited to localized and sparse observational networks. Due to the lack of sufficient ground-based radar coverage, objective radar-derived hail climatologies have also not been produced for this region. As a result, this study uses a 16-year dataset of TRMM Precipitation Radar and Microwave Imager observations to identify possible hailstorms remotely, using 37-GHz brightness temperature as a hail proxy. By combining satellite instruments and ERA-Interim reanalysis data, this study produces the first objective study of hailstorms in this region. Hailstorms in subtropical South America have an extended diurnal cycle, often occurring in the overnight hours. Additionally, they tend to be multi-cellular in nature, rather than discrete. High-probability hailstorms (≥ 50% probability of containing hail) tend to be deeper by 1-2 km and horizontally larger by greater than 15,000 km
than storms having a low-probability of containing hail (< 25% probability of containing hail). Finally, hailstorms are supported synoptically by strong upper- and lower-level jets, anomalously warm and moist low levels, and enhanced instability. The findings of this study will support the forecasting of these severe storms and mitigation of their damages within this region.
Abstract
A powerful winter storm affected the south-central United States in early March 2014, accompanied by elevated convective cells with hail and high rates of sleet, freezing rain, and snow. ...During portions of the event the thermal profile exhibited a shallow surface cold layer and warm, unstable air aloft. Precipitation falling into the cold layer refroze into ice pellets and was accompanied by a polarimetric refreezing signature and numerous crowdsourced surface ice pellet reports. Quasi-vertical profiles of the polarimetric variables indicated an enhanced reflectivity factor
Z
HH
below the melting layer bright band and enhanced low-level differential reflectivity
Z
DR
values coincident with surface ice pellet reports. Freezing rain rate was highest in areas with high
Z
HH
and specific differential phase
K
DP
values at low levels. High snow rates were most closely associated with 1- and 1.5-km
Z
HH
values, though
K
DP
and
Z
DR
also appeared to show some ability to distinguish high snow rate. Numerous elevated convective cells contained rotating updrafts that appeared to contribute to storm longevity and intensity. Most contained well-defined
Z
DR
maxima or columns and relatively high base-scan
Z
DR
values. Several contained polarimetric signatures consistent with heavy mixed-phase precipitation and hail; social media reports indicated that large hail was produced by some of the storms.
The impact of hail ice cubes on composite structures (such as solar cells) causes actual defects. This article presents a series of tests, in which solar cell modules were exposed to hail simulation ...testbed balls, allowing to assess the following: the impact energy, which causes the major defects in solar cells; the formed micro-cracks in the structure of solar cells, resulting in the loss of power generated by a solar cell; and the solar cell parameters necessary for modelling. In addition, this article presents a digital analysis of hail simulation. Information received from the digital analysis was used to optimize the structure of solar cells in order to improve its resistance properties. The aim of this study was to present a simple method for experimental hail simulation. The proposed hail impact estimation method can be successfully applied to study the influence of the mechanical–dynamic impact of photovoltaic (PV) modules of different structures on the technical characteristics of these modules (structural stability, power generation, etc.). The study showed that PV modules are subjected to an irreversible effect of the excitation force (i.e., micro-cracking) and it can reduce the generated power by 2.33% to 4.83%.
Climate oscillations are becoming more extreme, and mangroves may be more susceptible to changes in physical conditions that can lead to mass diebacks. The current study analysed the impacts of an ...extreme weather event in the Cananéia-Iguape Coastal System, southeast Brazilian mangroves and the condition of the area over three years. We used a multiproxy approach, including analyses of climatic attributes, structural vegetation, and vegetation indices. Damage caused by a strong storm and hail damage had a severe impact on mangrove areas. A meteorological station installed in the mangrove since 2008 recorded a maximum wind gust of 58 km·h−1 on 30 May 2019. On the Beaufort scale, this speed is classified as strong wind. After the extreme weather event, there were catastrophic impacts on the mangrove, with more than 90% dead trunks. Vegetation indices were reduced, indicating intense changes. The NDVI of the mangroves was reduced from 0.72 to 0.35. The LAI confirmed this premise, with a reduction from 4.25 to 0.63. After three years, natural recovery had not occurred. Extreme weather events have continued to occur along coasts, drastically altering the landscape. Mangroves have been affected by these events, and depending on the state of health of the forests, may have difficulties in recovery.
Agriculture involves multiple risks of which environmental and production threats are major ones. Farmers’ risk attitudes and risk perceptions have a significant role in dealing with their decisions, ...farm-relevant practices and management exposure to risk. Developing countries have carried out limited research work on the variety of risk management issues. This research work quantifies farmers’ attitudes and perceptions of different types of risks, such as which wheat crop is grown. The study relies on a survey of six wheat-producing districts containing household farms with 402 wheat-producing farmers in Punjab, Pakistan. To discover farmer’ attitudes toward risks, the Equally Likely Certainty Equivalent approach has been employed, with the ranking of farmers’ perceptions of four disastrous risk sources, storm rainfall and hail, drought, high input prices and wheat diseases, using a risk matrix. A probit model was employed to analyze the empirical estimation of factors affecting farmers’ attitudes and perceptions. The findings of the study indicate that the majority of the farmers have a risk-averse nature and consider storm rainfall and hail, drought, high input prices and wheat diseases as major threats to their wheat crops. Empirical findings of the study show that gender, religion, age, farming experience, education, credit, farming area, livestock numbers and off-farm income have significant (positive or negative) effects on farmers’ attitudes toward risk and risk perceptions. The study provides a convenient analysis for farmers, researchers, extension services, the agricultural insurance sector, and agriculture policy makers. Policy makers and researchers need to understand farmers’ risk attitudes and risks, providing them with precise knowledge regarding risks and refined risk management tools, and ensuring the provision of agricultural financing and contemporary agricultural extension services.
Diabetes Mellitus is a serious and expanding health problem, together with the issues of health- related quality of life (HRQoL). This further puts pressure on the government to allocate more funds ...for public healthcare.
This study was devised to evaluate the health-related quality of life of people living with diabetes in Hail region of Saudi Arabia.
This cross-sectional research was carried out at eight locations in the Hail region of Saudi Arabia between 21st March-20th May 2022 using the adapted version of the Euro QoL-5 dimension (EQ-5D-3L) questionnaire. A multistage random sample approach was used to choose the diabetes clinics, and data collectors approached the participants in the waiting areas to collect the information. The data were analyzed using logistic regression analysis, Mann-Whitney test, and Kruskal-Wallis tests in IBM SPSS statistics 21.0.
The mean HRQoL score was 0.71±0.21 with a visual analog score of 68.4±16.2. Despite having much higher levels of quality of life in terms of self-care (85.8%), regular activity (73.8%) and anxiety (71.8%), nearly one half of the people reported moderate pain or discomfort, and more than one third reported having moderate mobility issues. In general, the quality of life for women was poorer than for men. Individuals with diabetes who were unmarried, young, educated, financially secure, and taking only oral medication had much improved HRQoL. The Euro QoL of people with diabetes patients were significantly influenced by gender, marital status, age, education, employment and treatment modality (p-values < 0.05), whereas only treatment modality had a significant impact on the patients' visual analogue measures (p-values < 0.05).
The HRQoL of people with diabetes in Hail region was moderate in general, with pain and mobility issues being particularly prevalent. Gender, marital status, age, education, employment and type of medication therapy are significant predictors of HRQoL of patients with diabetes. Hence, planning and programs to enhance the HRQoL of people with diabetes, especially women is recommended.
Abstract
Real polarimetric radar observations are directly assimilated for the first time using the ensemble Kalman filter (EnKF) for a supercell case from 20 May 2013 in Oklahoma. A double-moment ...microphysics scheme and advanced polarimetric radar observation operators are used together to estimate the model states. Lookup tables for the observation operators are developed based on T-matrix scattering amplitudes for all hydrometeor categories, which improve upon previous curved-fitted approximations of T-matrix scattering amplitudes or the Rayleigh approximation. Two experiments are conducted: one assimilates reflectivity (Z) and radial velocity (Vr) (EXPZ), and one assimilates in addition differential reflectivity (ZDR) below the observed melting level at ~2-km height (EXPZZDR). In the EnKF analyses, EXPZZDR exhibits a ZDR arc that better matches observations than EXPZ. EXPZZDR also has higher ZDR above 2 km, consistent with the observed ZDR column. Additionally, EXPZZDR has an improved estimate of the model microphysical states. Specifically, the rain mean mass diameter (Dnr) in EXPZZDR is higher in the ZDR arc region and the total rain number concentration (Ntr) is lower downshear in the forward flank than EXPZ when compared to values retrieved from the polarimetric observations. Finally, a negative gradient of hail mean mass diameter (Dnh) is found in the right-forward flank of the EXPZZDR analysis, which supports previous findings indicating that size sorting of hail, as opposed to rain, has a more significant impact on low-level polarimetric signatures. This paper represents a proof-of-concept study demonstrating the value of assimilating polarimetric radar data in improving the analysis of features and states related to microphysics in supercell storms.
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