Meteorological Disasters Research Articles

Response of winter wheat to different drought levels based on Google Earth Engine in the Huang-Huai-Hai Region, China

Remote sensing technologies are frequently employed in monitoring and evaluating agricultural meteorological disasters because they can provide high throughput and cost-effective accesses to obtain a variety of real-time spatial data related to crop growth information. Accurately and timely estimating the drought dynamics using remote sensing is valuable for improving crop management level and promoting agricultural sustainable development. In this study, the responses of winter wheat during the critical growth stages to different drought levels from 2010 to 2020 in the Huang-Huai-Hai (HHH) region of China were evaluated based on Google Earth Engine. Four critical developmental stages of winter wheat were highlighted: greening－jointing, jointing－heading, heading－milking maturity, and milking maturity－maturity stages. The temperature vegetation dryness index (TVDI) was constructed for evaluating the drought of winter wheat. Our results indicated that the winter wheat in the HHH region during 2010－2020 was clearly impacted by drought disasters, mainly mild to moderate droughts. In particular, the TVDI index during the critical development stages of winter wheat from 2010 to 2020 wavered in the scope of 0.48 and 0.61, and the soil moisture content at a depth of 10 cm tended to obviously reducing with a rate of − 0.1615/a. For critical growth stages, the winter wheat in the HHH Region was the most prone to threatened by drought during the stage of jointing－heading, following the stage of milking maturity－maturity. The high probability incidence of drought in winter wheat gradually shifted from the nutritional growth to the reproductive growth stages, which increased the risk of drought affecting yield. Furthermore, the notable spatial variations of drought in winter wheat in the HHH region were found. The winter wheat was evidently threatened by moderate and severe droughts in certain areas such as southern Hebei province, northern Jiangsu province, Anhui province, etc. Our results provide new insights into the studies on monitoring and evaluating agricultural meteorological disasters based on remote sensing technology.

An early warning approach for the rapid identification of extreme weather disasters based on phased array dual polarization radar cooperative network data.

In recent years, X-band phase array dual polarization weather radar technology has matured. The cooperative networking data from X-band phase array dual polarization weather radar have many advantages compared with traditional methods, namely, high spatial and temporal resolution (approximately 70 seconds in one scan, 30 m in radial distance resolution), wide coverages that can compensate for the observation blind spots, and data fusion technology that is used in the observation overlap area to ensure that the observed precipitation data have spatial continuity. Based on the above radar systems, this study proposes an improved hail and lightning weather disaster rapid identification and early warning algorithm. The improved thunderstorm identification, tracking, analysis, and nowcasting (TITAN) algorithm is used to quickly identify three-dimensional strong convective storm cells. Large sample observation experiment data are used to invert the localized hail index (HDR) to identify the hail position. The fuzzy logic method is used to comprehensively determine the probability of lightning occurrence. The comparative analysis experiment shows that, compared with the live observation data from the ground-based automatic station, the hail and lightning disaster weather warning algorithm developed by this study can increase warning times by approximately 7 minutes over the traditional algorithm, and its critical success index (CSI), false alarm ratio (FAR) and omission alarm ratio (OAR) scores are better than those of the traditional method. The average root mean square error (ARMSE) for identifying hail and lightning locations by this improved method is also significantly better than that of traditional methods. We show that our method can provide probabilistic predictions that improve hail and lightning identification, improve the precision of early warning and support operational utility at higher resolutions and with greater lead times that traditional methods struggle to achieve.

ABCNet: A comprehensive highway visibility prediction model based on attention, Bi-LSTM and CNN.

Meteorological disasters along highways significantly reduce road traffic efficiency. Low visibility caused by heavy fog is a severe meteorological disaster that greatly increases highway traffic accidents. Accurately predicting highway visibility and taking timely response measures can reduce the impact of meteorological disasters and improve traffic safety. We proposed an Attention-based BiLSTM-CNN (ABCNet) model, which synergized attention mechanisms with BiLSTM and CNN technologies to forecast atmospheric visibility more accurately. First, the Bi-LSTM module processed information both forward and backward, capturing intricate temporal dependencies in the model. Second, the multi-head attention mechanism following the Bi-LSTM distilled and prioritized salient features from multiple aspects of the sequence data. Third, the CNN module recognized local spatial features, and a singular attention mechanism refined the feature map after the CNN module, further enhancing the model's accuracy and predictive capability. Experiments showed that the model was accurate, effective, and significantly advanced compared to conventional models. It could fully extract the spatiotemporal characteristics of meteorological elements. The model was integrated into practical systems with positive results. Additionally, this study provides a self-collected meteorological dataset for highways in high-altitude mountainous areas.

Differences in disaster warning and community engagement between families with and without members suffering from chronic Diseases: The mediating role of satisfaction with warning service

Early warning systems in disasters are crucial for life preservation and loss mitigation, endorsed globally by various UN agencies, governments, and disaster researchers. However, the social aspects pose significant challenges to the success of early warning. Using survey data from Sanya, a coastal city in China, our study explores the relationship between early warning services and community engagement. The primary warning channels are social media (SMS and WeChat), the Internet, and TV. These mediums, along with traditional and mass media, foster higher community engagement, like volunteer service and disaster risk reduction efforts. Satisfaction with warning service mediates these connections. Interestingly, families with chronic disease members are more likely to engage in community activities and utilize the Internet and social media for warnings. Notably, different patterns emerge when comparing families with and without chronic disease members, especially regarding Internet and social media usage. This study enhances our understanding of public adaptation to disaster warnings and provides insight for early warning services in the face of increasing climate change impacts and meteorological disasters.

UPM Satellite Ground Station Data for Borneo NDVI Image Analysis

Small satellites have been developed and used for earth observations as the remote sensing data can be used to perform analysis such as weather forecasts, vegetation monitorings, climate changes, and disaster monitorings. The Flying Laptop satellite (Flying Laptop) is the first small satellite developed by the University of Stuttgart (Germany), which is mainly used for research and development projects. The raw data obtained from Flying Laptop is used in the data analysis and data processing, which involve the ground tracks and images analysis. The filtered images are used to formulate Normalized Difference Vegetation Index (NDVI) plots, which are broadly used for the vegetation monitor in recent years. Due to the weather influences and the available filter in the Flying Laptop imaging payload, the generated NDVI plot needs to be improved in terms of its contrast and the difference in an identification of landcover type for better monitoring purposes. The ground track analysis and image comparison with respect to the fixed ground were used to identify the location of the image being captured and filtered out for unusable images. The formulation of NDVI is repeated with cloud maskings using Normalized Difference Water Index (NDWI) as references and accordingly normalized the plot in a logarithmic scale. The proposed method of using the cloud masking with references to NDWI improves the contrast of the overall NDVI plots. The usage of automation also helps to improve the efficiency in generating NDVI plots.

The Spatial-Temporal Characteristics of Meteorological Disasters in the Southwest Region of Zhejiang Province during 1953-2022

The Spatial-Temporal Characteristics of Meteorological Disasters in the Southwest Region of Zhejiang Province during 1953-2022

Impact Assessment of Natural Meteorological Disasters on Food Security—A Case Study of 11 Provinces along the Yangtze River

Impact Assessment of Natural Meteorological Disasters on Food Security—A Case Study of 11 Provinces along the Yangtze River

Weather Disaster Attention and High Frequency REIT Returns: A Near Continuous Approach

Weather Disaster Attention and High Frequency REIT Returns: A Near Continuous Approach

Annals of Education: Teaching Climate Change and Global Public Health.

The climate crisis is a health emergency: breaking temperature records every successive month, increasing mortality from hurricanes/cyclones resulting in >USD150 billion/year in damages, and mounting global loss of life from floods, droughts, and food insecurity. An entire course on climate change and global public health was envisioned, designed for students in public health, and delivered to Masters level students. The course content included the physical science behind global heating, heat waves, extreme weather disasters, arthropod-related diseases, allergies, air pollution epidemiology, melting ice and sea level rise, climate denialism, renewable energy and economics, social cost of carbon, and public policy. The methods included student engagement in presenting two air pollution epidemiological or experimental papers on fossil fuel air pollution. Second, they authored a mid-term paper on a specific topic in the climate crisis facing their locale, e.g., New York City. Third, they focused on a State, evaluating their climate change laws and their plans to harness renewable wind, solar, storage, nuclear, and geothermal energy. Students elsewhere covered regional entities' approach to renewable energy. Fourth, the global impact was presented by student teams presenting a country's nationally determined contribution to the Paris Climate Agreement. Over 200 Master's students completed the course; the participation and feedback demonstrated markedly improved knowledge and evaluation of the course over time.

High ductility cementitious composites incorporating seawater and coral sand (SCS-HDCC) for offshore engineering: Microstructure, mechanical performance and sustainability

In support of sustainable development strategies, engineering construction on remote islands is conductive to promoting the development of marine resources and mitigating increasingly severe resource conflicts. However, marine engineering, facing extreme weather and natural disasters, necessitates concrete with high ductility, durability and crack control capacity. This study evaluates the feasibility of using seawater and coral sand instead of fresh water and river sand to prepare the high ductility cementitious composites (SCS-HDCC). The results indicate that increased coral sand substitution introduces defects and expands the aggregate-matrix interfacial zone due to its rough surface, high specific area, and low strength. Coral sand's internal curing enhances compactness and reduces harmful pores. The dual action of seawater and coral sand, SCS-HDCC matrix is more tenacious, and the damage and blockage during the fiber pull-out process becomes obvious, and the slip hardening coefficient of fiber-matrix interfacial zone increases. SCS-HDCC achieves satisfactory ductility (up to 1.18 %) with initial cracking strength of 4.18 MPa, ultimate tensile strength of 5.66 MPa, and crack width of 71.2 ± 6.74 μm, average crack spacing of 6.02 mm. The SCS-HDCCs prepared in this study offers economic and environmental benefits by reducing economic costs and carbon emissions through transporting raw materials from the continent. The research results have guiding significance for the construction of islands and reefs by SCS-HDCC.

L’Œil du cyclone

This article probes the convergence of meteorological disaster and memory in L’Île et une nuit by Daniel Maximin and La Case du commandeur by Édouard Glissant, reading fragments of these novels through reflections lifted from spiritual autobiographies by M. Jacqui Alexander and Mimerose Beaubrun. While anglophone Black studies scholar Christina Sharpe employs weather systems and climate as figures to apprehend the pervasiveness of antiblackness in today’s world, hurricane disaster takes another form as a device used by francophone Caribbean authors to observe and disrupt the ontological negation of Black people. Tracing the multiple resonances between ‘the wake’ (Sharpe, 2016) as a prismatic theoretical tool and meanings of water, passages, and wakefulness generated by hurricanes in Glissant and Maximin, this article explores the relation between this meteorological disaster event and an expansive consciousness of the human disasters of deportation and rupture. It pays attention to homonymic language tethering the eye of the hurricane and other manifestations of suspended wind and saltwater to the ‘vision’ or modes of sensorial knowing this phenomenon confers, addressing the ways in which African diaspora authors and spiritual practitioners understand the disaster of the Middle Passage through the disaster of the hurricane as temporally amplified, immanent, and beyond human.

VA’S EXPERIENCE IN BUILDING CAPABILITY TO CONDUCT OUTREACH TO VULNERABLE PATIENTS DURING EMERGENCIES

Abstract The United States (US) has sustained more than 340 weather and climate disasters since 1980. Older adults with complex medical conditions, increasing in numbers, are typically most severely impacted by such events. A growing population of older, medically complex, individuals residing at home, and an increased frequency of severe natural disasters creates a compounded burden on local communities to reduce potential harm to these community-dwelling members during disaster response and recovery. In response, the Veterans Health Administration (VHA) established the Vulnerable Patient Care, Access, and Response in Emergencies (VP CARE) program to provide standardized data tools and guidance to assist VA medical facilities in conducting outreach to and care coordination of medically frail and older Veterans during major emergencies. VP CARE utilizes geographic information system (GIS) tools to allow outreach prioritization based upon geographic proximity to an event and patient clinical needs. In response to Hurricanes Ian and Fiona, the VP CARE team, with the support of the national VEText team, successfully deployed a new 2-way patient texting communication capability. During an emergency, patients receive a text from VEText, are asked whether they require assistance, and the specific help they need. The new VEText capability helped VA staff reduce outreach call volume by 20% and allowed for triage and prioritization of those indicated as needing assistance to the top of the outreach list. This presentation provides an overview of VP CARE approaches and what VHA has learned through its implementation to assist other healthcare systems with establishing similar capabilities.

Using geographic rescue time contours, point-of-care strategies, and spatial care paths to prepare island communities for global warming, rising oceans, and weather disasters

ObjectivesTo perform geographic contour analysis of sea and land ambulance rescue times in an archipelago subject to super typhoons; to design point-of-care testing strategies for medical emergencies and weather disasters made more intense by global warming and rising oceans; and to assess needs for prehospital testing on spatial care paths that accelerate decision making, increase efficiency, improve outcomes, and enhance standards of care in island nations.MethodsWe performed needs assessments, inspected healthcare facilities, and collected ambulance rescue times from professionals in the Bantayan Archipelago, Philippines. We mapped sea/land ambulance rescue routes and time contours. To reveal gaps, we statistically compared the fastest and slowest patient rescue times from islands/islets and barangays to the District Hospital on Bantayan Island. We developed spatial care paths (the fastest routes to care) for acute myocardial infarction, community care, and infectious diseases. We generated a compendium of prehospital diagnostic testing and integrated outcomes evidence, diagnostic needs, and public health goals to recommend point-of-care strategies that build geographic health resilience.ResultsWe observed limited access to COVID-19 assays, absence of blood gas/pH testing for critical care support, and spatial gaps in land and airborne rescues that worsened during inclement weather and sea swells. Mean paired differences (slowest-fastest) in ambulance rescue times to the District Hospital for both islands and barangays were significant (P < 0.0001). Spatial care path analysis showed where point-of-care cardiac troponin testing should be implemented for expedited care of acute myocardial infarction. Geospatial strengths comprised distributed primary care that can be facilitated by point-of-care testing, logical interisland transfers for which decision making and triage could be accelerated with onboard diagnostics, and healthcare networks amenable to medical advances in prehospital testing that accelerate treatment.ConclusionsPoint-of-care testing should be positioned upstream close to homes and island populations that have prolonged rescue time contours. Geospatially optimized point-of-need diagnostics and distributed prehospital testing have high potential to improve outcomes. These improvements will potentially decrease disparities in mortality among archipelago versus urban dwellers, help improve island public health, and enhance resilience for increasingly adverse and frequent climate change weather disasters that impact vulnerable coastal areas. [350 words].

Quantitative Assessment of the Impacts of Climate Change and Human Activity on the Net Primary Productivity of Subtropical Vegetation: The Case of Shaoguan, Guangdong, China

Vegetation net primary productivity (NPP) is critical to maintaining and enhancing the carbon sink of vegetation. Shaoguan is a characteristic forest city in the subtropical region of South China and an ecological barrier in the Guangdong–Hong Kong–Macau Greater Bay Area (GBA), playing an instrumental role in protecting water resources, purifying air, and maintaining ecological balance. However, studies that quantify subtropical vegetation NPP dynamics in Shaoguan under the influence of climate and human drivers are still incomplete. In this research, vegetation NPP at 30 m resolution was estimated from 2001 to 2020 using the enhanced CASA model based on the GF-SG algorithm in Shaoguan. The RESTREND method was then utilized to quantify climatic and human effects on NPP. The results indicated that the vegetation NPP in Shaoguan increased rapidly (4.09 g C/m2/yr, p &lt; 0.001) over the past 20 years. Climate and human drivers contributed 0.948 g C/m2/yr and 3.137 g C/m2/yr to vegetation NPP, respectively. Human activity plays a major role in vegetation restoration through ecological projects, whereas vegetation deterioration is primarily attributable to the combined action of climate change and human activity, such as urban expansion, deforestation, and meteorological disasters. The results emphasize the importance of ecological projects for the restoration of vegetated ecosystems and ecological construction in Shaoguan.

Research on Hail Mechanism Features Based on Dual-Polarization Radar Data

Hail is a type of severe convective weather disaster characterized by abundant water vapor and strong updrafts, resulting in intense and high reflectivity echoes in hail clouds, often accompanied by an overhanging form. Although hail research has made great progress, it is still challenging to achieve accurate identification of hail. Compared with traditional radar, dual-polarization radar can output a variety of polarization parameters and provide information about the shape and phase of precipitation particles, which is conducive to the identification of hail particles. In this study, dual-polarization radar data are used to explore more hail features from various perspectives, starting with the morphological characteristics of hail clouds and using common feature extraction methods in the field of image processing. A comprehensive approach to high-dimensional features is developed. Using machine learning methods, hail identification models are constructed in both the traditional mechanism feature space and the new feature space constructed in this study. Experimental results strongly confirm the significant effectiveness of the five-dimensional new mechanism features developed in this paper for hail identification.

Tropical cyclones over the South China Sea suppress the monsoonal rainfall in southern China

The tropical cyclones (TCs) often cause intense rain and destructive winds. While these catastrophic weather conditions capture our attention, the less-known impact of TCs remains overlooked. This study reveals that TCs have a notable suppressive effect on monsoonal rainfall in southern China when they traverse the South China Sea. This phenomenon can be attributed to the influence of these mesoscale disturbances on the quasi-stationary, large-scale monsoonal circulation, which alters the moisture pathway. Furthermore, the case-by-case analysis indicates that ~76% of TCs correspond to rainfall reduction, whereas the other 24% correspond to rainfall increase. The latter is due to the concurrent occurrence of another independent influencing factor: extratropical cyclonic anomalies that promote enhanced rainfall through favoring lower-tropospheric moisture convergence. This study suggests that TCs, as mesoscale disturbances, can change the moisture channel that is usually controlled by quasi-stationary and large-scale monsoonal airflows, and suppress the adjacent land rainfall over China.

Elucidation of the Mechanism of Rapid Growth Recovery in Rice Seedlings after Exposure to Low-Temperature Low-Light Stress: Analysis of Rice Root Transcriptome, Metabolome, and Physiology.

Late spring cold is a disastrous weather condition that often affects early rice seedlings in southern China, limiting the promotion of direct seeding cultivation. However, there are few reports on the effect of these events and on the growth recovery mechanism of rice root systems after rice seedlings are exposed to this stress. This study selected the strong-growth-recovery variety B116 (R310/R974, F17) and the slow-recovery variety B811 (Zhonghui 286) for direct seeding cultivation and exposed them to low temperature and low-light stress to simulate a late spring cold event in an artificial climate chamber. The treatment consisted of 4 days of exposure to a day/night temperature of 14/10 °C and a light intensity of 266 µmol m-2s-1 while the control group was kept at a day/night temperature of 27/25 °C and light intensity of 533 µmol m-2s-1. The results showed that 6 days after stress, the total length, surface area, and volume of B116 roots increased by 335.5%, 290.1%, and 298.5%, respectively, while those of B811 increased by 228.8%, 262.0%, and 289.1%, respectively. In B116, the increase in root fresh weight was 223.1%, and that in B811 was 165.6%, demonstrating rapid root recovery after stress and significant differences among genotypes. The content of H2O2 and MDA in the B116 roots decreased faster than that in the B811 roots after normal light intensity and temperature conditions were restored, and the activity of ROS metabolism enzymes was stronger in B116 roots than in B811 roots. The correlation analysis between the transcriptome and metabolome showed that endogenous signal transduction and starch and sucrose metabolism were the main metabolic pathways affecting the rapid growth of rice seedling roots after exposure to combined stress from low temperature and low light intensities. The levels of auxin and sucrose in the roots of the strong-recovery variety B116 were higher, and this variety's metabolism was downregulated significantly faster than that of B811. The auxin response factor and sucrose synthesis-related genes SPS1 and SUS4 were significantly upregulated. This study contributes to an understanding of the rapid growth recovery mechanism in rice after exposure to combined stress from low-temperature and low-light conditions.

Climatic Characteristics of Heavy Snowfall and the Water Vapor Transport Characteristics in Typical Snowfall Events in Hunan Province of China

Due to the unique topography and geographical location, severe snowfall is the main disastrous weather in winter in the Hunan Province of China. Based on the daily precipitation data in Hunan Province from 1961 to 2021, the regional heavy snowfall processes are classified by using the synoptic diagnostic method. In addition, the water vapor transport characteristics of typical heavy snowfall processes are analyzed by the hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) air mass backward trajectory model. Then, the responses of the differences in water vapor transport to heavy snowfall under different weather situations are discussed. The results show that the spatial distribution of climatic mean heavy snowfall days in Hunan Province is extremely uneven, and the heavy snowfall days decrease from north to south, with the most in the Dongting Lake area and the least in the Nanling Mountains. In the past decades, snowstorms mainly occur in local areas, and there are fewer widespread snowstorms. The frequency of heavy snowfall days generally shows a decreasing trend, with three peaks all appearing before 1990. After the 2010s, the number of days and stations of heavy snowfall decreased noticeably, and so did the number of regional heavy snowfall processes. This result indicates that global warming has remarkable effects on the snowstorm events in Hunan Province. Heavy snowfall mainly occurs from December to February, and peaks from mid-January to early February. Over the past 61 years, more than 50% of heavy snowstorm events occurred after 2000. According to the main weather systems affecting regional heavy snowfall processes, these weather processes in Hunan Province can be classified into three categories: southern branch trough (SBT) type, blocking high collapse (BHC) type, and stepped trough type. Among them, the SBT type accounts for more than 60% of the heavy snowfall events in Hunan. In terms of the SBT type and the stepped trough type, the water vapor from the high-latitude inland and low-latitude sea surface accounts for a comparable proportion, each accounting for nearly 50%. For the SBT type, the proportion of the water vapor from warm-humid airflows is slightly higher than that from cold-humid airflows. However, in terms of the stepped trough type, the water vapor transported by cold-humid airflows from the north contributes more than that by warm-humid airflows. For the BHC type, the specific humidity and the water vapor from the high-latitude inland contribute 70% of heavy snowfall processes. In addition, the contribution of the two southwesterly water vapor channels to heavy snowfall processes is small. The water vapor sources differ remarkably for different heavy snowfall types, but all of them are dominated by the water vapor transport in the middle and lower troposphere, which is the main reason why the formation of snowfall areas under different weather types is obviously different.

Identification of Surface Deformation-Sensitive Features under Extreme Rainfall Conditions in Zhengzhou City Based on Multi-Source Remote Sensing Data

Extreme precipitation is one of the most prevalent meteorological disasters occurring today. Its occurrence not only causes significant social and economic losses but also indirectly affects surface deformation, creating safety hazards for diverse ground features. Although there are presently high-precision, comprehensive tools such as continuous scattering interferometry to observe surface deformation, it takes a long time to locate potentially vulnerable objects. A monitoring scheme for surface deformation anomalies was devised to address the timeliness issue of identifying sensitive surface features under extreme rainfall conditions. An SAR image of Sentinel-1A is used to derive the surface deformation in three years before and after a rainstorm in the main urban area of Zhengzhou, and the anomaly surface deformation objects after extreme precipitation are screened to determine the surface deformation-sensitive objects. The results indicate that, in the past three years, a 22.14 km2 area in Zhengzhou City has experienced a settlement speed greater than 10 mm/yr. Under the influence of the “7–20” rainstorm in the main urban area of Zhengzhou City, among them, the area of highly sensitive agricultural land for deformation is 2,581,215 m2, and there are 955 highly sensitive houses for deformation, with an excellent recognition effect. This method is effective in rapidly locating surface deformation-sensitive or potentially damaged features; it can provide a reference for the vulnerability and risk assessment of buildings.

Advancements in Encryption Techniques for Enhancing Meteorological Data Security During Catastrophic Weather Events

With the widespread utilization of meteorological data across diverse fields, the imperative for ensuring its security and safeguarding privacy has escalated. Meteorological data encompasses a wealth of sensitive information, including frequency bands and the precise locations of sensing devices. Inappropriate handling of this data may engender profound ramifications for multiple stakeholders. Particularly in the face of severe catastrophic weather conditions, the conundrum of optimizing the analysis and transmission of critical meteorological data within the confines of finite resources and time constraints looms large, demanding immediate attention from researchers. This paper centralizes its focus on the analysis and storage of information derived from common meteorological devices, proposing a priority-weighted encryption strategy. Within this strategy, meteorological data attains the utmost degree of privacy attainment while concurrently improving encryption efficiency, even when faced with the stringent limitations of limited resources and time constraints during severe catastrophic weather events.