Severe Weather Research Articles

Ship coping strategies for hurricane-induced port disruptions

PurposeDisruptions at ports may destroy the planned ship schedules profoundly, which is an imperative operation problem that shipping companies need to overcome. This paper attempts to help shipping companies cope with port disruptions through recovery scheduling.Design/methodology/approachThis paper studies the ship coping strategies for the port disruptions caused by severe weather. A novel mixed-integer nonlinear programming model is proposed to solve the ship schedule recovery problem (SSRP). A distributionally robust mean conditional value-at-risk (CVaR) optimization model was constructed to handle the SSRP with port disruption uncertainties, for which we derive tractable counterparts under the polyhedral ambiguity sets.FindingsThe results show that the size of ambiguity set, confidence level and risk-aversion parameter can significantly affect the optimal values, decision-makers should choose a reasonable parameter combination. Besides, sailing speed adjustment and handling rate adjustment are effective strategies in SSRP but may not be sufficient to recover the schedule; therefore, port skipping and swapping are necessary when multiple or longer disruptions occur at ports.Originality/valueSince the port disruption is difficult to forecast, we attempt to take the uncertainties into account to achieve more meaningful results. To the best of our knowledge, there is barely a research study focusing on the uncertain port disruptions in the SSRP. Moreover, this is the first paper that applies distributionally robust optimization (DRO) to deal with uncertain port disruptions through the equivalent counterpart of DRO with polyhedral ambiguity set, in which a robust mean-CVaR optimization formulation is adopted as the objective function for a trade-off between the expected total costs and the risk.

Ocorrência de ondas de frio no Rio Grande do Sul no final do século XVIII e no século XIX e a produção do conhecimento climatológico

The state of Rio Grande do Sul experienced intense cold at the end of the 15th century and heavy snowfalls in the 19th century, specifically in 1858, 1862, 1867, 1870, 1871, 1873, 1875, 1879 and 1885. Due to the social problems caused by these severe weather events, the need arose to create a meteorological service. There were several initiatives in the 19th century, most notably the service organized by the Commissão de Obras do Porto de Rio Grande, the Public Works station in Porto Alegre and the Escola de Artes e Officio station in Pelotas.

Examining the roles of visualizations in people's understanding of uncertain storm surge forecasts, interpretation of risk, and decision-making

Maps and other forms of geovisualizations play a central yet complex role in communicating weather hazard risks and threats to the public. Through designing storm surge risk communication prototypes and testing these visualizations in focus groups with residents of coastal communities in Georgia and South Carolina, this study investigates how different aspects of geospatial visualization influence individuals' understanding and relatability of storm surge information and how different elements of geospatial risk visualization influence people's intent to take protective actions. The focus group discussions revealed common themes and unique perspectives in the interpretation of hurricane storm surge flooding forecasts and illustrated the complexity of communicating risk and uncertainty to diverse populations. This study found a sequential pattern of visual information processing, beginning with understanding the content and its perceived accuracy and uncertainty, followed by personalizing the information and deciding on protective action. Showing storm surge forecast maps at multiple spatial scales was valuable, combined with realistic visualizations in the form of images or 3D animations of flooded buildings. Maps with recognizable places and labels helped study participants locate themselves and assess storm surge risks, although residents of urban, barrier island, and rural coastal communities interpreted the information in their local and personal context. Additional research is needed to understand how people interact with different types of storm surge forecast information and how to improve visual risk communication in ways that enhance people's understanding and responses to storm surge risk in the context of their local environment and prior experiences.

An RTK UAV-Based Method for Radial Velocity Validation of Weather Radar

The quality of weather radar affects the reliability and effectiveness of monitoring severe convective weather. Therefore, rigorous calibration and validation are the foundation for the quantitative application of weather radar. Among the available methods, radial velocity validation is of great significance for reducing the false alarm rate in the identification of tornadoes and thunderstorms. Based on the traditional method that utilizes internal and external instrument radar velocity measurements, we propose a weather radar radial velocity validation method that uses RTK UAV to simulate external targets. In addition, according to the characteristics of the UAV application scenarios, we introduce the evaluation parameter of optimal absolute accuracy to supplement the original parametric system. The experimental results show that the evaluation parameter of optimal absolute accuracy can effectively reduce the interference caused by the systematic deviation of the UAV due to the internal and external environment, which can affect the validation results. When the UAV velocity is not greater than 10 m/s, the optimal absolute accuracy of the radial velocity validation is less than 0.05 m/s, which is essentially consistent with the external instruments’ measurement results. This method can be effectively applied to the procedural handling of weather radar radial velocity validation. It is significant for ensuring the accuracy and quality of weather radar radial velocity measurements and improving the effectiveness of radar velocity data applications.

Energy, exergy, economic, and environmental (4E) analysis of SAHP water heaters in very cold climatic conditions

Solar-assisted heat pumps (SAHPs) are emerging as promising solutions for space/water heating. Compared to conventional air-source heat pumps (ASHPs), SAHPs show superior thermal performance in cold climates where outdoor air temperatures are below freezing most of the heating season. However, given the inherent variability of solar energy, SAHPs have poor thermal performance to meet the load requirements during periods of weak solar radiation. To enhance the reliability of SAHPs, an air-source evaporator can be integrated with a SAHP, forming a dual SAHP – ASHP system with better performance. In this research, the energy, exergy, environmental, and economic performance of a dual-source solar/air source heat pump, which provides hot water for a typical Canadian household, is investigated. The performance of the system is compared with a solar-assisted direct-expansion heat pump (DX-SAHP), an ASHP, and conventional gas/electric water heaters. A thoroughly validated mathematical model based on heat transfer and thermodynamics fundamentals is developed and implemented in MATLAB® coupled with the CoolProp® library, which gives the refrigerant's properties. Results highlight the superior performance of the dual-source heat pump, particularly in the summer, where it surpasses the DX-SAHP. Among the examined heat pump configurations, the dual-source heat pump demonstrates the lowest annual power consumption. The system attains the maximum monthly average COP of 3.94 and achieves a minimum of 2.4 during the severe winter season in Calgary, Alberta. Compared to a conventional ASHP, the energy consumption of the system was reduced by 25%. The second-law analysis shows that the ASHP obtains the highest monthly average exergy efficiencies, followed by the DX-SAHP and the dual-source heat pump. Furthermore, results show that the solar/air source heat pump water heater can reduce CO2 emissions by 1450 kg/year, while the system's payback period is 19 years.

Resistance of Gastrointestinal Nematodes to Anthelmintics in Sheep Production in Zimbabwe

Introduction: Sheep play a crucial role in enhancing the socio-economic welfare of individuals in numerous nations. Within sub-Saharan Africa, productivity levels are hindered by various factors, such as prevalent diseases, inadequate nutrition, climate variations, predators, severe weather, as well as infestations of both external and internal parasites. The purpose of the present study was to assess the extent of anthelmintic resistance (AR) observed on a sheep farm operating for commercial purposes in Zimbabwe. Materials and methods: Forty female weaner Dorper sheep, averaging 43 kg in body weight, were selected from Ballineety commercial sheep farm in Mashonaland Central, Zimbabwe. These sheep were then divided into four groups of ten each, receiving different treatments including ivermectin, albendazole, levamisole, and no treatment as a control. Each treatment was administered according to the manufacturer’s recommendations. Pre-treatment faecal samples (day zero) and post-treatment (day 14) were collected and subjected to the Faecal Egg Count Reduction Test (FECRT). Anthelminthic resistance was suggested (AR) by the presence of faecal eggs after treatment. Results: All the drugs indicated a decrease in egg counts by less than 90%. The effectiveness of ivermectin, albendazole, and levamisole was measured at 58.5%, 70.1%, and 85.8% respectively. These findings indicate that nematodes have developed resistance to all treatments. The larval cultures of the samples before and after treatment indicated a significant presence of Haemonchus spp. in the pooled samples. In contrast, the quantities of Trichostrongylus spp. and Oesophagostomum spp. were low but present in all treatments. Cooperia spp. was recovered in post-treatment larval cultures of groups that received anthelmintic. The present study reports lower-than-expected efficacy for ivermectin, albendazole, and levamisole based on the reduction of egg excreted post-treatment. Conclusion: A common worms population in sheep are AR in the current study area. Therefore, comprehensive nematode management programs along with complementary eradication strategies are essential for sustainable sheep production.

Anticipating Climate Catastrophe: The Impacts of Diminished Snowfall in Gilgit-Baltistan and Azad Jammu and Kashmir

Climate change poses a significant threat to regions like Azad Jammu & Kashmir (AJ&K) and Gilgit-Baltistan (GB), where vulnerabilities are pronounced. Decreased snowfall alters water supplies and agriculture, exacerbating climate-related challenges. Integrated strategies, drawing on research, policy, and community efforts, are crucial for addressing these impacts effectively. Scientists agree on human-induced global warming, impacting precipitation patterns, temperatures, and glaciers in GB and AJ&K. Such changes affect water availability, agriculture, and increase the frequency of severe weather events, endangering local livelihoods. Of particular concern is the accelerated melting of glaciers in the Hunza Basin, heightening the risk of Glacial Lake Outburst Floods (GLOFs) and posing a threat to downstream communities. Addressing climate change requires comprehensive approaches. This includes infrastructure resilience, community preparedness, sustainable agriculture, and improved early warning systems. Collaboration across sectors, investment in climate science, and empowering local communities are essential. Urgent action, at local, national, and global levels, is vital to mitigate climate change impacts and secure a sustainable future for all vulnerable regions like GB and AJ&K.

Analysis of Factors Affecting the Severity of Injuries in Electric Two-Wheeled Vehicle Crashes with or without Violation: A Random Parametric Logit Model considering Heterogeneity of Means and Variances

Electric two-wheeled vehicle is one of the main commuting tools in China, but they are also more likely to have violations of the road group. In order to study the effect of the presence of violation on the severity of road traffic crashes among electric two-wheeler riders, in this study, the effects of rider characteristics, road characteristics, collision characteristics, and environmental characteristics on the severity of injuries of electric two-wheeled vehicle riders were considered separately analyzed based on the data of 6403 two-wheeled electric vehicle traffic crashes in a region of Shandong Province from 2015 to 2021, and a random parametric logit model considering the heterogeneity of the mean and the variance (RP-HMV logit) was established based on the presence or absence of violation behaviors of riders, respectively, in order to explore unobserved heterogeneity. In order to test the validity of the model for modeling the injury severity of pedestrians riding electric two-wheelers, multinomial logit (MN-logit model), and random parameter logit model (RP-logit) were estimated, and the results showed that the RP-HMV logit model was significantly superior in terms of goodness of fit. The study showed that some of the factors differed somewhat between the two scenarios, such as gender, while the factors that were significant in both scenarios were >60, broken pavement, street lights at night, no street lights at night, mixed motorized and nonmotorized lanes, sidewalks, other angles, no control, severe weather, and visibility <200 m, where the severe weather and visibility <200 m were random parameters obeying normal distributions, there is a significant difference between having street lights and no control at night in both scenarios, and the difference is significant. The results of the study can provide a reference for the development of targeted countermeasures to improve the traffic safety of electric two-wheeled vehicles in China.

Nonlinear dynamic modelling and analysis of a rotating composite thin-walled beam considering ice coating

For rotating structures operating in severe weather conditions, ice coating phenomenon is an established issue that impairs the reliability and effectiveness of the structures by changing the dynamic behaviors. To explore the influence mechanisms of ice coating on the dynamic behaviors of rotating structures, this work presents an analytical dynamic model for nonlinear forced vibration of a composite thin-walled beam under both rotating and ice coating conditions. The ice coating is considered as C-shaped configuration which can affect the system parameters of the beam, especially aerodynamic performance. With effect of out-of-plane warping taken into account, the nonlinear dynamic equation of the beam is established using Hamilton's principle. The Galerkin discrete method together with the incremental harmonic balance method (IHB) as well as pseudo arclength continuation technique are adopted to obtain the periodic response of the nonlinear system. Then, a detailed parametric study on natural frequency, frequency-response and bifurcation behaviors is numerically conducted. Special attention is paid to the effects of ice coating, rotating angular velocity, inflow velocity and design parameters on the linear and nonlinear dynamic behaviors of the beam. Results reveal a prominent change in natural frequency, vibration amplitude and cusp bifurcation of multivalued region under the combined effects of ice coating and rotating motion. Such ice-induced changes exhibit the requirement of vibration control and optimization design consideration for rotating structures operating in icing coating conditions.

Intraguild interactions and abiotic conditions mediate occupancy of mammalian carnivores: co‐occurrence of coyotes–fishers–martens

The widespread eradication of large carnivores and subsequent expansion of top mesopredators has the potential to impact species and community interactions with ecosystem‐wide implications. An example of these trophic dynamics is the widespread establishment of coyotes following extirpation of wolves and mountain lions in eastern North America. Here, we examined occupancy of three carnivores in northern New York considering both environmental/habitat factors and interspecific interactions. We estimated the co‐occurrence of coyotes, fishers, and martens from a landscape‐scale winter camera trap survey repeated annually for three years. Martens occurred independently of both coyotes and fishers, while fishers and coyotes displayed positive intraguild interactions that were constant across the landscape. Both marten and fisher first‐order occupancy were driven by a combination of biotic and abiotic factors, with both species displaying positive associations with forest cover but antithetical responses to average snow depth. The integral and antithetical role of snow depth in driving the occurrence of martens (positive) and fishers (negative) in the landscape indicates that future climatic warming could reduce the availability of current spatial refuges for martens created by severe winter conditions. Climate‐driven alterations to established competitive interactions and co‐existence patterns between marten and fisher have critical implications for the species survival and conservation. We provide correlational evidence consistent with the potential for positive top–down effects of dominant mesocarnivores on subordinate species, with fisher occupancy increasing conditional on the presence of coyotes across the landscape. These findings align with the hypothesis that under certain conditions, coyotes may facilitate certain subordinate carnivores. The evidence produced here is consistent with hypotheses on the dynamic nature of trophic niches. We demonstrate the need to consider the interplay between climate, habitat, and interspecific interactions to understand wildlife occupancy patterns and inform wildlife management in a rapidly changing world.

Rapid Warming Exacerbates Winter Drought Stress in Trees at High-Altitude Areas in Northeast China

Against the background of global warming, trees in high-latitude and high-altitude areas are more sensitive to rapid warming. Revealing the response patterns of trees at different altitudes to rapid warming in typical alpine mountain environments can help to predict the future distribution of forests in the region and the associated changes in the timberline. This study used tree-ring width data (band sampling) from Dahurian larch (Larix gmelinii Rupr.) along an altitudinal gradient (970–1409 m) on Oakley Mountain to establish 10 chronologies and to analyze the growth–climate response of larch to warming and altitudinal changes along a gradient. The results showed that before rapid warming, larch growth was strongly influenced mainly by precipitation in July–August, while after warming, the growth was controlled mainly by precipitation (snowfall) in winter (October–April) and showed a significant positive correlation with the SPEI in winter and a negative correlation with temperature in February–April (early spring). This indicates that the rapid warming event led to a drastic change in the water heat balance during the pre-growth period of the trees in the study area, which shifted the tree growth from being restricted by drought in the growing season before warming to being restricted by winter drought (i.e., the lagged effect of snowfall in the pre-growing season) after warming and that the trees at low altitudes suffered from more severe winter drought. In the future, as the global climate warms further, the growth of mountain larch at low altitudes (below 1200 m) will continue to decrease, and the coupling of winter snowfall and seasonal (February–April) warming will increase the upper altitude limit at which winter drought occurs for larch in the study area; additionally, larch at higher altitudes will be able to cope with this ecological process better.

Prevalence and genome features of lake sinai virus isolated from Apis mellifera in the Republic of Korea.

Lake Sinai Virus (LSV) is an emerging pathogen known to affect the honeybee (Apis mellifera). However, its prevalence and genomic characteristics in the Republic of Korea (ROK) remain unexplored. This study aimed to assess the prevalence of and analyze the LSVs by examining 266 honeybee samples from the ROK. Our findings revealed that LSV exhibited the highest infection rate among the pathogens observed in Korean apiaries, particularly during the reported period of severe winter loss (SWL) in A. mellifera apiaries in 2022. Three LSV genotypes- 2, 3, and 4 -were identified using RNA-dependent RNA polymerase gene analysis. Importantly, the infection rates of LSV2 (65.2%) and LSV3 (73.3%) were significantly higher in colonies experiencing SWL than in those experiencing normal winter loss (NWL) (p < 0.03). Furthermore, this study provides the first near-complete genome sequences of the Korean LSV2, LSV3, and LSV4 strains, comprising 5,759, 6,040, and 5,985 nt, respectively. Phylogenetic analysis based on these near-complete genome sequences demonstrated a close relationship between LSVs in the ROK and China. The high LSV infection rate in colonies experiencing a heightened mortality rate during winter suggests that this pathogen might contribute to SWL in ROK. Moreover, the genomic characteristic information on LSVs in this study holds immense potential for epidemiological information and the selection of specific genes suitable for preventing and treating LSV, including the promising utilization of RNA interference medicine in the future.

MPC-based framework incorporating pre-disaster and post-disaster actions and transportation network constraints for weather-resilient power distribution networks

Enhancing power system resilience to extreme weather requires an effective coordination of pre-disaster preventive response actions and post-disaster repair and restoration services. In this context, this article presents a two-stage framework to enhance resilience of power distribution networks to severe weather events. It considers operating and resource constraints in interdependent power and transportation networks based on official guidance from transport appraisal studies. It also incorporates sources of information available in a smart city context, such as energy measurements, traffic flows, and geographical information systems. In the first stage, a pre-disaster operational planning strategy is defined to prepare the grid for a high-risk outage scenario with the objective of minimizing the value of lost loads, formulated as a mixed-integer quadratic programming model. In the second stage, a post-disaster corrective strategy is implemented over a receding time horizon to compensate for deviations between the actions computed in the first stage and the actions required to minimize power outages, embedded into a model predictive control scheme. The effectiveness of the proposed framework is demonstrated on a real-world large-scale distribution network in the United Kingdom over a range of outage scenarios. Simulation results show that the proposed framework is capable of effectively minimizing first-stage operational costs and second-stage deviations between the projected and actual load demand supply. Numerical results obtained with the proposed framework indicate that the load energy unserved is at least twice smaller than with typical practices adopted by distribution network operators and computational times take two minutes or less. Therefore, it can be effectively used by distribution network operators to ensure an appropriate level of preparedness to power outages caused by extreme weather along with prompt restoration and repair services.

Predictive modeling of severe weather impact on individuals and populations using Machine Learning

In this work, Machine Learning (ML) techniques are used to develop tools capable of accurately predicting the impact of severe weather events. We use readily accessible predictors, including daily meteorological data, basic demographics, geographic and terrain features, along with the number of daily meteorological incidents reported to the emergency services. The model was built using disaggregated data from the 947 municipalities from the region of Catalonia between January 1, 2015 and June 30, 2021. Catalonia’s region is situated in the northeastern part of Spain along the Mediterranean Basin, and frequently experiences storms with strong winds and intense rainfall. In 2020, such weather events resulted in 64 injuries and damages amounting to approximately 70 million USD. The ML-based model presented in this study shows a predictive capacity for extreme weather risk superior to that of the daily meteorological warning system of the Meteorological Service of Catalonia. In addition, the model has been used to estimate how urbanization modifies the extreme weather impact. Given the expected increase in the intensity, frequency, and duration of extreme weather events in the context of global warming, the methodology presented in this work could be helpful in developing tools to assist emergency service managers and policy makers in making rapid and effective decisions.

Impact of Severe Winter Weather on Operations of a Radiation Oncology Department

IntroductionDuring winter of 2022, Western New York faced two major storms with blizzard conditions and record-breaking snowfall. The severe weather resulted in power outages, and travel bans. This study investigates the impact of these conditions on patient adherence to radiotherapy. Combining data from a large academic center and its satellite clinic, this single-center study is shedding light on the challenges faced by cancer care facilities during severe weather and proposing suggestions to prevent and mitigate harm done by severe weather. MethodsIn this study, data was collected using the MOSAIQ Record and Verify system (v. 2.81) to generate deidentified reports of scheduled and treated patients. The treatment adherence rate was calculated by dividing the number of patients treated by the total number of patients scheduled. Data was specifically collected for patients undergoing treatment on linear accelerators at a primary academic center and a satellite facility. The study focused on working days from November 1, 2022, to March 31, 2023, excluding weekends and holidays (as treatments are not routinely scheduled). Severe weather days were identified using advisories from the National Weather Service and local institution, including specific periods in November, December, and January. ResultsIn the study, 15,010 scheduled treatment visits were recorded across the academic center and the satellite clinic. The mean daily treatment adherence rate was 91.7%. Severe weather conditions led to a significant reduction in adherence, with rates dropping to 77.8%. Adherence rates during non-severe weather days were notably higher at 93.9%. Statistical analysis confirmed the substantial impact of severe weather on adherence (p<0.001). Severe weather had a more pronounced impact on the satellite clinic during periods of severe weather with absolute reduction in adherence rates of 21.9% vs. 15% in primary hospital). Moreover, adherence rates at the satellite clinic vs. primary hospital site was lower even under standard operating conditions (92.2% vs 94.0%, p<0.001). ConclusionAs a part of operational planning, it is important to be aware how severe weather can impact treatment adherence. The study findings underscore the importance of proactive measures to ensure patient access to healthcare services during adverse weather events and highlight the broader significance of incorporating Social Determinants of Health (SDOH) considerations into contingency planning for maintaining treatment continuity.

Characteristics of PM2.5 Chemical Species in 23 Chinese Cities Identified Using a Vehicular Platform

PM2.5 pollution remains a significant concern in China due to its adverse environmental and health implications. This study aims to explore in depth the differences in the causes of PM2.5 pollution between some regions in China based on high temporal resolution PM2.5 component information. We used a particulate matter chemical composition vehicle (PMCCV) as a mobile monitoring platform which travelled among 23 cities in China from March 2018 to December 2019 to collect PM2.5 concentrations and chemical composition data. Observations revealed that PM2.5 concentrations were notably higher in northern cities compared than their southern counterparts. Seasonal variation was evident, with peak concentrations during winter and troughs during summer. In regions experiencing severe winter pollution, such as Hebei and Shanxi (HB/SX), organic matter (OM) emerged as the dominant contributor (47.3%), escalating with increasing PM2.5 concentrations. OM significantly impacted PM2.5 levels during autumn in Jiangxi and Anhui (AH/JX) and across the monitoring period in Liuzhou, Guangxi (GX), with the former related to vehicle emissions and the latter related to bagasse reuse and biomass burning emissions. Conversely, nitrate (NO3−) made the highest contribution to PM2.5 during winter in the AH/JX region (34.4%), which was attributed to reduced SO2 levels and favorable low-temperature conditions conducive to nitrate condensation. Notably, nitrate contribution to HB/SX rose notably in heavily polluted winter conditions and during light–moderate pollution episodes in the autumn. Sulfate (SO42−) was dominant among PM2.5 components during summer in the study regions (29.9% in HB/SX, 36.1% in HN/SD, and 49.7% in AH/JX). Additionally, pollution incidents in Chuzhou, Anhui Province, and Baoding, Hebei Province, underscored nitrates and organic matter, respectively, as the primary causes of sharp PM2.5 increases. These incidents highlighted the influence of large emissions of primary aerosols, gaseous precursors, and stagnant meteorological conditions as pivotal factors driving haze pollution in the HB/SX region.

Comparison of COSMO and ICON-LAM high-resolution numerical forecast for Romanian territory: Case studies and evaluation

This paper aims to offer the first detailed inter-comparison of the performance from the numerical weather prediction models Consortium for Small-scale Modeling (COSMO) and Icosahedral Nonhydrostatic General Circulation Model, Limited Area Mode (ICON-LAM), integrated for the Romanian territory at the horizontal resolution of 2.8 km. As the ICON numerical model is set to replace COSMO, a comparison is carried out between the operational configurations of the two models. First, two cases with severe weather (strong atmospheric instability, observed heavy precipitation, and strong wind) are analyzed: February 3-6, 2020 (heavy precipitation [snow] and very strong wind) and May 3-5, 2020 (heavy precipitation). This is followed by a statistical inter-comparison between the two models for the summer of 2020 (June, July, August). A statistical evaluation of the forecast quality from the two models is performed objectively, through statistical scores computed using surface observations from all available meteorological stations on the Romanian territory. Stratifications depending on station altitude and location are carried out, with emphasis on stations of particular interest, depending on the evolution of the synoptic situation. Following the statistical evaluation, results for the precipitation forecast do not show significant improvement in favor of either model. However, ICON-2.8 km mostly outperforms COSMO-2.8 km for surface parameters.

A Review of Remote Sensing of Atmospheric Profiles and Cloud Properties by Ground-Based Microwave Radiometers in Central China

Thermodynamic and liquid water profiles can be retrieved by a ground-based microwave radiometer (MWR) in nearly all weather conditions, which is useful for detecting mesoscale phenomena. This paper reviews the advances in remote sensing of atmospheric profiles and cloud properties by MWR in central China. Comparative studies indicate that MWR retrieval accuracy is different under various skies, especially those that decay under precipitation. The off-zenith method is proven to be capable of reducing the impact of precipitation and snow on MWR retrieval accuracy. Application studies demonstrate that MWR retrievals are helpful for early warning of rainstorms, hailstorms, and thunderstorms. Moreover, MWR retrievals provide a way to study cloud properties. The temporal variations of cloud occurrence frequency (COF) and liquid water path (LWP) are different for low, middle, and high clouds, and the vertical distribution of COF is also different in autumn and other seasons. Note that MWR can infer valid retrievals over the eastern Tibetan Plateau due to the weak precipitation over there. Also, cloud properties over the eastern Tibetan Plateau present differences from those over central China, and this is related to the different characteristics of atmospheric water vapor between these two regions. To bring more benefits for mechanism study and early warning of severe weather and numerical weather prediction, the decayed accuracy of MWR zenith retrievals under precipitation should be resolved. And combining MWR with other instruments is necessary for MWR application in detecting multi-layer clouds and ice clouds.

Assessing the household burdens of infrastructure disruptions in Texas during Winter Storm Uri

In February 2021, severe winter weather conditions associated with Winter Storm Uri triggered an energy infrastructure failure in Texas. Most Texans lost electricity, some lost potable water, natural gas, and suffered other infrastructure-related service disruptions that had profound household consequences. Although several studies have documented the disparities in terms of the types and extent of disruptions Texans experienced during Uri, there is little information about the impact of these disruptions in terms of their burdens on households. Guided by the Capabilities Approach framework, a survey of Texan households was conducted to identify the outage characteristics and capability losses for different household types, as well as the burdens that those disruptions posed to households using both objective and subjective measures. Our results indicate that households that endured longer and constant electricity outages suffered more severe impacts across all measures, including more disrupted household capabilities, increased time, and financial costs to cope during the outages, as well as greater declines in reported life satisfaction. We found that low-income households, households with children, and households with disability challenges suffered more severe objective burdens during the storm, although subjective results for these households were mixed. Households with members over the age of 65 fared better in terms of objective measures, yet suffered more in terms of subjective well-being. Moreover, households that had prior experience with a prolonged outage reported significantly smaller reductions in well-being during the storm than other households, despite not showing any difference in objective measures. Ultimately, our results offer context-specific, post-event information about the impacts and needs of different types of households during Uri that are important for informing emergency management and community resilience planning.

An Experimental Investigation of the Material Properties of the A356 Aluminum Alloy Power Fittings in the Vacuum Die-Casting Process.

To enhance the performance of ultra-high voltage power fittings in severe weather conditions without altering their current structure, the high-strength and toughness aluminum alloys were rationally selected to study the optimization of the die-casting process. This approach aims to improve the overall longevity and function of the power fittings in extreme climates. First of all, the propose of this study is to use the material's strength-toughness product (STP) concept to evaluate the material stability of the power fitting impact resistance and fatigue toughness in order to determine the appropriate material selection. Secondly, the location of the mold's sprue and gate was optimized through finite element simulation to prevent gas volume and flow defects during the casting process. This improves the material's toughness and anti-fatigue failure characteristics of the product. Then, vacuum equipment and a vacuum valve auxiliary system were added based on the existing die-casting machine, and the mold structure was optimized to enable the vacuum die-casting process. Finally, a water-based boron nitride environmentally friendly mold release agent was used to solve demolding difficulties with an A356 aluminum alloy and improve mold lubrication and surface quality. The production of quad-bundled spacers using A356 and vacuum die casting has resulted in parts with a tensile strength of at least 250 MPa and an elongation of no less than 7%. This improvement has laid a foundation for enhancing the operational reliability of existing overhead transmission line fittings.