Severe Weather Research Articles

Energy Service Security for Public Health Resilience: Perception and Concerns in Western Upper Peninsula of Michigan☆

AbstractThe Western Upper Peninsula of Michigan includes six rural counties and one Tribal Nation. The region is characterized by long winters, legacies of the extractive mining economy, and the infrastructural features of extreme rurality, including aging housing and low health service density. The region also faces exceptionally high electricity prices. There is limited research on the public health implications of energy service disruption in rural regions resulting from the increasing intensity and frequency of weather events caused by climate change. This article presents research findings examining the readiness of health facilities in this area to manage the rising intensity, severity, and frequency of severe weather that could disrupt energy services. The study also considers how this knowledge can guide decision‐making to improve energy service access and maintain resilient public health services in the region. This exploratory study utilized a qualitative approach that combines semi‐structured interviews with public health stakeholders and a short survey to triangulate the findings from health facilities. Given the pivotal role of dependable energy services in community health, these findings underscore the community's perception of self‐reliance as both an asset and a hurdle. This perception aligns with the realities of rural communities at the “end of the line” regarding critical infrastructure, which also serves as a formidable barrier to social organization and infrastructure access during energy service disruptions that can severely impact public health.

Vertical Distribution of Water Vapor During Haze Processes in Northeast China Based on Raman Lidar Measurements

Haze refers to an atmospheric phenomenon with extremely low visibility, which has significant impacts on human health and safety. Water vapor alters the scattering properties of atmospheric particulate matter, thus affecting visibility. A comprehensive analysis of the role of water vapor in haze formation is of great scientific significance for forecasting severe pollution weather events. This study investigates the distribution characteristics and variations of water vapor during haze weather in Changchun City (44°N, 125.5°E) in autumn and winter seasons, aiming to reveal the relationship between haze and atmospheric water vapor content. Analysis of observational results for a period of two months (October to November 2023) from a three-wavelength Raman lidar deployed at the site reveals that atmospheric water vapor content is mainly concentrated below 5 km, accounting for 64% to 99% of the total water vapor below 10 km. Furthermore, water vapor content in air pollution exhibits distinct stratification characteristics with altitude, especially within the height range of 1–3 km, where significant water vapor variation layers exist, showing spatial consistency with inversion layers. Statistical analysis of haze events at the site indicates a high correlation between the concentration variations of PM2.5 and PM10 and the variations in average water vapor mixing ratio (WVMR). During haze episodes, the average WVMR within 3 km altitude is 3–4 times higher than that during clear weather. Analysis of spatiotemporal height maps of aerosols and water vapor during a typical haze event suggests that the relative stability of the atmospheric boundary layer may hinder the vertical transport and diffusion of aerosols. This, in turn, could lead to a sharp increase in aerosol extinction coefficients through hygroscopic growth, thereby possibly exacerbating haze processes. These observational findings indicate that water vapor might play a significant role in haze formation, emphasizing the potential importance of observing the vertical distribution of water vapor for better simulation and prediction of haze events.

Time-evolving traffic resilience performance forecasting during hazardous weather toward proactive intervention

Transportation systems experience significant disruptions and loss during hazardous weather events, exhibiting great needs of timely intervention to effectively improve the resilience of the affected traffic systems. An informed and science-based proactive intervention strategy depends on accurate forecasting of the resilience performance of traffic systems with essential lead time during hazards. A new resilience performance forecasting methodology at both global and local scales is proposed for traffic networks under natural hazards by addressing unique challenges such as scarcity and time-evolving nature of hazard-specific data. The proposed methodology consists of two modules: the local traffic resilience performance short-term forecasting module based on the modified diffusion convolutional recurrent neural network (DCRNN) and transfer learning techniques, and the global traffic resilience performance forecasting module integrating percolation-based robustness assessment and SIR-based congestion propagation modeling. A case study of an urban traffic network during a major snowstorm hazard is conducted as a demonstration, followed by the feasibility investigation to guide proactive intervention during hazards. It is found the proposed methodology can forecast the time-evolving traffic resilience performance with good accuracy at both global and local scales. With sufficient lead time for the forecast, it bears promising potential to assist the stakeholders to make informed and timely decision about possible proactive intervention by providing key information to help identify the optimal moments and individual strategic links for possible intervention.

Climatological Tracking and Lifecycle Characteristics of Mesoscale Convective Systems in Northwestern South America

AbstractMesoscale convective systems (MCSs) are crucial in shaping large‐scale tropical circulation and the hydrological cycle, particularly in Northwestern South America (NwSA), a region marked by complex terrain and significant MCS activity. Understanding MCSs in NwSA is vital due to their impact on precipitation patterns and potential for severe weather events. To enhance this understanding, the ATRACKCS algorithm was developed for tracking convective systems, utilizing precipitation and brightness temperature data sets. This research focuses on documenting the spatiotemporal variability of MCS occurrence, life cycle, and movement. Notably, MCS hotspots were identified to the west of the major orographic features in the region, with maximum occurrences at night, contrasting with the region's typical afternoon peak in land convection. MCS movement is also heavily influenced by topography, with higher velocities on the eastern (windward) side of the Andes compared to velocities on the western (leeward) side. MCSs generally move westward, driven by easterly winds, but this pattern is not consistent throughout the year or region. Northward movement is predominant to the west of the Andes, while southward movement is observed to the east. These seasonal and regional movement variations are linked to factors such as the intertropical convergence zone position, moisture availability, topography, and low‐level jets. This research underscores the complexity of MCSs in NwSA and emphasizes the need for detailed studies on the atmospheric environment shaping these systems. Additionally, it provides a robust 21‐year MCS database for NwSA and an advanced tracking tool for research in various geographic contexts and impact areas.

The cost of operational complexity: A causal assessment of pre-fire mitigation and wildfire suppression

Pre-fire mitigation efforts that include the installation and maintenance of fuel breaks are integral to wildfire suppression in Southern California. Fuel breaks alter fire behavior and assist in fire suppression at strategic locations on the landscape. However, the combined effectiveness of fuel breaks and wildfire suppression is not well studied. Using daily firefighting personnel to proxy the quantity and diversity of potential fire suppression operations (i.e., operational complexity), we examined 15 wildfires from 2017 to 2020 in the Los Padres, Angeles, San Bernardino, and Cleveland National Forests to assess how weather and site-specific fuel break characteristics influenced wildfire containment when leveraged during suppression operations. After removing effects of fuel treatments, wildfire and aerial firefighting, we estimated that expanding fuel break width in grass-dominant systems from 10 to 100 m increased the average success rate against a heading fire from 31 % to 41 %. Likewise, recently cleared fuel breaks had higher success rates compared to poorly maintained fuel breaks in both grass (25 % to 45 %) and shrub systems (20 % to 45 %). Combined, grass and shrub systems exhibited an estimated success rate of 80 % under mild weather conditions (20th percentile) and 19 % under severe weather (80th percentile). Other significant determinants included forb and grass production, adjacent tree canopy cover and terrain. Consistent with complexity theory and previous suppression effectiveness research, our analysis showed signs of suppression effectiveness declining as firefighter personnel increased. Future work could better account for the role of suppression with improved data on firefighting resource types, actions, locations, and timing.

Challenges and initiatives regarding the extension of operating hours at Osaka International Airport

Osaka International Airport is an urban airport located near the city center, and its operating hours are set at 14 hours, from 7:00 a.m. to 9:00 p.m. The airport extends its hours under unavoidable circumstances and allows takeoffs and landings after 9:00 p.m. However, the airport has long conflicted with local communities over aircraft noise at night, and now, the airport receives harsh opinions especially regarding noise at night and takeoffs and landings after 9:00 p.m. And the number of flights taking-off and landing outside of the operating hours has been on the rise, reaching a record high in FY2022, despite all the countermeasures described later. This is caused not only by the congestion of air traffic flow seen for the last several years, but also by severe weather events around the airport, such as frequent typhoons and thunderclouds triggered by the recent intensifying climate changes. It is becoming even more critical to ensure on-time performance and to gain the understanding of local communities about the circumstances above. The presentation will introduce many initiatives to assess the situation accurately, ensure punctuality in cooperation with airlines, and engage in regular dialogues with residents in the surrounding area.

Anomalous Arctic warming linked with severe winter weather in Northern Hemisphere continents

We have extended a recently developed metric that ingests United States station data—the accumulated winter season severity index—to a global indicator based on temperature and snowfall from reanalysis output. The expanded index is analyzed to reveal relationships between Arctic air temperatures/pressures and the probability of severe winter weather across the Northern Hemisphere midlatitudes. Here we find a direct and quasilinear relationship between anomalously high Arctic temperatures/pressures and increased severe winter weather, especially in northeastern continental regions downstream of maximum regional Arctic warming. Positive temperature trends in the Arctic are associated with positive trends in severe winter weather across the continents in mid- to late-winter, coinciding with an increase in stratospheric polar vortex disruptions. During the era of rapid Arctic warming, variability has decreased over the Arctic Ocean and Europe, but has increased in Canada, the Northern US and northeast Asia, indicating more pronounced shifts in weather conditions.

The Evolution of the 2021 Seacor Power Tragedy in Coastal Louisiana

Abstract On 13 April 2021, a mesoscale convective system (MCS) swept across the southeastern Louisiana coast, capsizing the 39-m Seacor Power roughly 7 km from shore and leaving 13 mariners drowned or missing. In addition to the severe straight-line winds that sank the vessel, sustained surface winds > 20 m s−1 behind the leading convection persisted well after the main convective band, inhibiting search and rescue efforts. Though complete historical fatality statistics are unavailable, the 13 deaths associated with this event likely represent one of the deadliest severe convective weather events in modern U.S. maritime history. This analysis integrates in situ, remotely sensed, and reanalysis datasets to reconstruct the 2021 Seacor Power accident as well as ascertain its depiction in day-of operational convection-allowing model (CAM) guidance. Results suggest that the MCS formed along an unanalyzed coastal boundary and developed a strong mesohigh to the east of the wreck as it moved offshore. The resulting zonally oriented pressure gradient directed stiff easterly winds over the wreck for several hours, even as the squall line had propagated well away from the coast. This multihour period of severe weather along the Louisiana coast was relatively well resolved by morning-of CAM guidance, providing optimism that future such events may be anticipated with the lead times required by vulnerable seacraft to reach safe harbor. Future severe convective weather watches containing marine zones might include a “marine” section detailing the potential sea conditions, analogous to the “aviation” section in current severe weather watches.

The Use of Probability and Edge Analysis to Solve the Multi-Period Degree Constrained Minimum Spanning Tree Problem

The goal of the Multiperiod Degree Constrained Minimum Spanning Tree (MPDCMST) problem is to determine the smallest weight-spanning tree that satisfies the vertex installation criterion for each period and maintains the degree requirement in each vertex. This issue emerges as a network connection problem. The degree requirement indicates the reliability of each vertex, and the vertex connection/installation requirement denotes the priority vertices that must be inserted in the network within a specific time frame. The installation is split up into multiple phases/stages. This is because of various considerations such as severe weather, budgetary limitations, etc. In this research, two algorithms for solving the MPDCMST using probability hybridized with Prim’s modification, and edge analysis are proposed. The algorithms are implemented on the undirected complete graph of orders 10 to 100. The solutions are compared with some heuristics which are already in the literature. The results show that the proposed algorithms perform better.

Shifting risks back to the state? Flood insurance and responsibility in the face of climate change in Australia

In Australia, with its neoliberal policy tradition, responsibility for dealing with severe and extreme weather events such as floods and bushfires has mainly been left to individual households and insurance markets. With the growing number of extreme weather events, existing institutional arrangements and behavioral patterns are challenged. Individuals have difficulties to reliably assess and manage knowledge about such climate change related hazards. In response to the growing uncertainties of rising costs due to increasing flooding and bushfire events, insurers raise their premiums for house and contents insurance or even withdraw from insuring high-risk areas altogether. Based on semi-structured interviews with 26 (re)insurance, legal, financial, and urban planning experts conducted in 2022, the study provides empirical insights in the still under-researched question of how responsibilities are understood and attributed amongst different stakeholders in the context of changing climate. The findings show that extreme weather events and the individualization of risk lead to new, complex patterns of sharing responsibilities amongst banks, insurers and the different governmental levels with a stronger emphasis on state regulation.

Evaluating Machine Learning–Based Probabilistic Convective Hazard Forecasts Using The HRRR: Quantifying Hazard Predictability and Sensitivity to Training Choices

Abstract The High-Resolution Rapid Refresh (HRRR) model provides hourly updating forecasts of convective-scale phenomena, which can be used to infer the potential for convective hazards (e.g., tornadoes, hail, and wind gusts), across the United States. We used deterministic 2019–20 HRRR, version 4 (HRRRv4), forecasts to train neural networks (NNs) to generate 4-hourly probabilistic convective hazard forecasts [neural network probability forecasts (NNPFs)] for HRRRv4 initializations in 2021, using storm reports as ground truth. The NNPFs were compared to the skill of a smoothed updraft helicity (UH) baseline to quantify the benefit of the NNs. NNPF skill varied by initialization time and time of day but was all superior to the UH forecast. NNPFs valid at hours between 1800 and 0000 UTC were most skillful in aggregate, significantly exceeding the baseline forecast skill. Overnight NNPFs (i.e., valid 0600–1200 UTC) were least skillful, indicating a diurnal cycle in hazard predictability that was present across all HRRRv4 initializations. We explored the sensitivity of HRRRv4 NNPF skill to NN training choices. Including an additional year of 2021 HRRRv4 forecasts for training slightly improved skill for 2022 HRRRv4 NNPFs, while reducing the training dataset size by 40% using only forecasts with storm reports was not detrimental to forecast skill. Finally, NNs trained with 2018–20 HRRRv3 forecasts led to a reduction in NNPF skill when applied to 2021 HRRRv4 forecasts. In addition to documenting practical predictability challenges with convective hazard prediction, these findings reinforce the need for a consistent model configuration for optimal results when training NNs and provide best practices when constructing a training dataset with operational convection-allowing model forecasts. Significance Statement Convective hazards, such as hail and tornadoes, are often challenging to predict. To improve hazard predictions, we used machine learning (ML) to generate forecasts of convective hazards across the United States leveraging forecasts of prior events. The ML hazard forecasts were consistently better than a non-ML approach and varied in skill based on the time of day, with nighttime forecasts being particularly challenging. ML forecasts of wind gusts and hail were more skillful than tornadoes. Different strategies of constructing the dataset of prior events led to differences in forecast performance; thus, this work provides recommendations for how to assemble these datasets and train ML models to generate improved forecasts of severe weather events.

Hurricane disasters and radiation treatment delays among patients diagnosed with non-small cell lung cancer.

98 Background: Hurricanes can disrupt cancer care and adversely affect health outcomes, especially for patients needing daily radiation therapy, which depends on stable electricity and safe facility access. Among patients diagnosed with locally advanced non-small cell lung cancer (NSCLC), disruptions in radiation can decrease survival. This study evaluates the associations of exposure to a hurricane disaster and radiation delays and total dose received among patients diagnosed with NSCLC. Methods: Patients aged ≥18 years who initiated radiation therapy for non-operable stage III NSCLC between 2004-2021 were identified from the National Cancer Database. Exposure was defined as a Federal Emergency Management Agency (FEMA) hurricane disaster declaration at the county of the treating facility during radiation treatment. Patients treated at the same facility, but at a time when no hurricane happened (i.e. unexposed) were propensity score matched in a 1:1 ratio with exposed patients on age at diagnosis, sex, lymph node involvement, comorbidity score, season, and era (radiation start year 2004 – 2009, 2010 – 2015, 2016 – 2021), to account for more active hurricane seasons in recent years. Delays in radiation treatment completion were defined as > 8 weeks between date when radiation started and ended. A two-way t-test and Mantel-Haenszel test were used to examine associations of hurricane exposure and radiation dose and treatment delays respectively. Results: 6,260 matched patients were included, of whom 3,130 were treated at facilities exposed to hurricanes during radiation and 3,130 completed treatment at the same facilities but at a time when no hurricane exposure happened. Patients treated during hurricanes had 1.95 higher odds (95% confidence interval (CI): 1.71 – 2.24) of experiencing treatment delays compared to unexposed patients (23% of exposed patients experienced delays compared to 13% of unexposed patients). Exposed patients were more likely to have received higher radiation doses compared to unexposed patients (mean 5221.81 cGy vs. 4962.75 cGy; p < 0.0001). Conclusions: Patients with locally advanced NSCLC who received radiation during a hurricane disaster were more likely to experience treatment completion delays. Patients treated during a hurricane were also more likely to receive higher total doses of radiation, possibly suggesting compensation to mitigate the detrimental biological impact of radiation delays. As severe weather events become more common, comprehensive emergency preparedness plans that incorporate evidence-based strategies for mitigating adverse effects of treatment delays are essential.

Time-Varying effects of extreme weather shocks on output growth of the United States

This study investigates the impact of a structural shock to a metric of extreme weather, identified using sign restrictions, on output growth (and inflation) in the United States (US) from 1961 to 2022, using a new class of time-varying parameter vector autoregressive (endogenous TVP-VAR) model, whereby the identified structural innovation is allowed to influence the dynamics of the coefficients in the model unlike in traditional TVP-VARs. Our results provided evidence that severe weather shocks adversely affect output growth (and inflation) over the forecast horizon of one- to twelve-quarter-ahead. More importantly, we find that the effect of extreme weather on the US macroeconomic variables is indeed time-varying, with the impacts becoming smaller in recent times, possibly due to improved adaptation to climate change.

Responses of 'Flordaguard' and 'MP-29' Prunus spp. rootstocks to hypoxia and high root zone temperature.

Selecting the right rootstock is crucial for successful fruit production and managing both biotic and abiotic stresses in commercial fruit orchards. To enhance the resilience of peach orchards, this study evaluated the physiological and biochemical responses of Prunus spp. rootstocks , 'Flordaguard' and 'MP-29,' under normoxia (sufficient oxygen content) or short-term hypoxia (low-oxygen content) and ambient or high temperature (40°C) in the root zone. Physiological responses measured were net photosynthesis, stomatal conductance, transpiration, intercellular CO2 concentration, water use efficiency, the leaf chlorophyll index, and the maximum potential quantum efficiency of photosystem II. The leaf concentrations of nitrogen, phosphorus, potassium, magnesium, calcium, sulfur, boron, zinc, manganese, iron, and copper were also analyzed. Reactive oxygen species (ROS) and antioxidants analyzed were superoxide dismutase (SOD) activity, peroxidase (POD) activity, catalase (CAT) activity, ascorbate peroxidase (APX) activity, glutathione peroxidase (GPX) activity, proline content, glycine betaine content (GB), lipid peroxidation (LPO), superoxide (O2 -) concentration, and hydrogen peroxide (H2O2) concentration. When subjected to root zone hypoxia or high temperature individually, 'MP-29' performed better physiologically than 'Flordaguard'. However, when root zone hypoxia and high temperature were combined, 'MP-29' performed better biochemically with enhanced antioxidant activity, osmolyte content, and nutrient absorption. Nutrient analysis of leaves revealed that 'MP-29' had higher N, P, K, Ca, and B concentrations than 'Flordaguard'. Consequently, 'MP-29' demonstrated greater tolerance to short-term exposure to the combined effects of high root zone temperature and hypoxia. This research contributes to identifying a suitable rootstock within the Prunus genus able to withstand root zone conditions that often result from severe weather events commonly experienced in Florida and other parts of the world.

Spanish Speakers in the United States Report Differing Levels of Weather Salience Based on Their Ethnocultural Background

Abstract Weather profoundly shapes our daily lives, yet its psychological impact varies distinctly among different cultural and ethnic groups. In a comprehensive analysis of 974 Spanish-speaking individuals in the United States, our research delves into three dimensions of weather salience—a measure of the psychological importance attributed to weather. Contrary to expectations, geographic location within the United States does not significantly influence weather salience dimensions among Spanish-speaking populations. Rather, differences in weather salience emerge based on the nativity and origin country of a Spanish speaker. Foreign-born Spanish speakers exhibit higher weather salience compared to their U.S.-born counterparts, with notable differences in daily attention to weather and severe weather events. Additionally, the degree of psychological attunement to weather distinctly varies among different Hispanic and Latino ethnic groups within Spanish-speaking communities. These findings help explain why weather salience may be lower among Spanish-speaking groups and highlight the variability across different cultural groups. Tailoring messages to the unique experiences and backgrounds of diverse Spanish-speaking communities is essential, moving beyond simple language translation to embrace the rich cultural tapestry of these groups. Significance Statement Our research explored how different Spanish-speaking groups in the United States conceptualize weather in their daily lives. Surprisingly, where they live in the United States does not change their views much. Instead, whether they were born in the United States or elsewhere, and their specific cultural background, really matters. People born outside the United States generally apply more significance to the weather and its changes. This shows us that when communicating about weather to these groups, it is important to understand and respect the diverse cultures of Spanish-speaking communities, going beyond just translating messages into Spanish.

Unlocking Potential: Analyzing the Content, Style, Structure, and Interactivity of Mesonets as Operational Dashboards

Abstract Emergency managers need data and information to make life-saving decisions on behalf of the public. Operational dashboards, if designed appropriately, can provide this information in a central location and reduce cognitive demands during decision-making. Mesonet websites can serve as a type of operational dashboard that has the potential to provide the meteorological data necessary for emergency managers to make decisions. In this study, we use quantitative content analysis to examine the content, style, structure, and interactivity of 18 Mesonet websites from across the contiguous United States. We find that Mesonet websites vary in the type and amount of content they include. For website style, we find that Mesonet websites primarily present their content with maps and data filters. We find that the structure of the website content was consistent across websites. Finally, we find that most Mesonet websites lacked interactivity, or visual feedback, which inhibits ease of use. We discuss extensions for future work. Significance Statement This study captures the content, style, structure, and interactivity of Mesonet websites when they are used as operational dashboards to support weather-related decision-making. Weather dashboards have the potential to support decision-makers of all types during severe weather events by providing all critical information in one place. With this streamlined approach, decision-making is made more efficient, as gathering information from multiple sources is no longer necessary. Mesonet dashboards provide a valuable context to analyze the content, style, and structure of webpages that can be useful for users. Through this work, we hope to identify the design principles and trends present in these dashboards, providing a basis for future research and efforts to improve design, user experience, and accessibility.

Structural and hydrodynamic modelling of the probability of breakage of branching and plate coral colonies

Climate change is amplifying the intensity of severe weather events, with coastal regions such as coral reefs facing heightened vulnerability to cyclonic wave forces. Structural models to predict bending stress and breakage of corals have been developed for coral colonies to enhance comprehension and prediction of the effects of hydrodynamic disturbances on coral reefs. However, there is scope for improving these predictions by evolving the methodology for quantifying complicated and variable coral morphologies. This study aims to predict breakage thresholds for two of the most prevalent coral morphologies: branching and plate corals (using Acropora muricata and Acropora hyacinthus as study species). Laboratory and field measurements were taken to assess coral morphologies and material characteristics. Morphological features of 47 branching colonies and 100 plate colonies were surveyed at the study site (Heron Reef, southern GBR) and the tensile strength of 80 coral samples was obtained by in situ and laboratory testing. Three-dimensional structural models of branching and plate coral colonies were developed, encompassing multiple coral colonies with varying morphological patterns from relatively shallow (5-7 m) to deep (9-12 m) zones. Model results were calibrated and verified with existing data, revealing that velocity thresholds of 1.7 m/s and 5.0 m/s would destroy 90% of the simulated branching coral structures growing in the deep and shallow parts of the forereef zone, respectively. In contrast, the plate corals have sufficient margins of safety even in extreme flow conditions (7 m/s). Additionally, skeletal strength and structural performance were adjusted based on varying degrees of bioerosion inside the coral skeleton. A higher probability of breakage was observed as the extent of bioerosion increased. The laboratory experiments of hydrodynamic loads on coral colony show that the sheltering effect due to one or two neighbouring colonies in the upwave direction is negligible. These models can be easily adjusted to provide predictions for other coral species, shapes, levels of bioerosion, and locations (e.g., sheltered or exposed areas). Comprehensive predictions about the level of expected damage and rubble generation in different areas can be used in reef management planning and restoration prioritization.

Research on dangerous flight weather prediction based on machine learning

Abstract With the continuous expansion of the scale of air transport, the demand for aviation meteorological support also continues to grow. The impact of hazardous weather on flight safety is critical. How to effectively use meteorological data to improve the early warning capability of flight dangerous weather and ensure the safe flight of aircraft is the primary task of aviation meteorological services. In this work, support vector machine (SVM) models are used to predict hazardous flight weather, especially for meteorological conditions with high uncertainty such as storms and turbulence. SVM is a supervised learning method that distinguishes between different classes of data by finding optimal decision boundaries in a high-dimensional space. To meet the needs of this study, we chose the radial basis function (RBF) as the kernel function, which helps to deal with nonlinear problems and enables the model to better capture complex meteorological data structures. During the model training phase, we used historical meteorological observations from multiple weather stations, including temperature, humidity, wind speed, wind direction, and other meteorological indicators closely related to flight safety. From this data, the SVM model learns how to distinguish between normal and dangerous flight weather conditions.

Relationships between freezing resistance and biochemicals in grapevine buds and canes: Different soluble carbohydrates accumulate in several cultivars during cold acclimation

Owing to recent warming trends during the growing season, wine grapevine (Vitis vinifera) production has increased in subarctic areas such as Hokkaido. However, high freezing resistance remains essential for grapevines due to the severe winter temperatures in these regions. Here, we investigated the seasonal variation in freezing resistance, as well as the water, soluble carbohydrate, proline, and total phenolic content (TPC) in the buds and canes of two V. vinifera cultivars and a Vitis riparia hybrid, Maeve. We found that freezing resistance in the buds and canes increased from fall to winter, with Maeve exhibiting higher freezing resistance than the V. vinifera cultivars during both seasons. Maeve experienced a faster rate of deacclimation, leading to comparable spring freezing resistance among all cultivars. The water content in the buds and canes of all three cultivars decreased during the fall, with Maeve showing an earlier reduction compared to the others. Most soluble carbohydrates increased in winter and decreased in spring. Notably, Maeve, which had higher winter freezing resistance, accumulated more sucrose in its buds and a higher concentration of soluble carbohydrates, particularly monosaccharides, in its canes compared to the other two cultivars. Proline and TPC levels were not linked to freezing resistance. Grapevine buds and canes appeared to accumulate soluble carbohydrates, especially monosaccharides, during winter, while reducing water content in fall to enhance freezing resistance. This study suggests that grapevine cultivars in Hokkaido may utilize different types of soluble carbohydrates to improve the freezing resistance of their buds and canes.

Integrating Micro-Hydro and Solar PV in Rainwater Harvesting: A Hybrid Generation Approach

As the scarcity of water worsens and the frequency of severe weather increases, the efficient harvesting of rainwater becomes more critical. Despite its potential to conserve water and mitigate flooding, the integration of rainwater harvesting with solar PV and micro-hydro technologies remains underutilized in renewable energy production. A significant barrier to widespread adoption is the challenge of accurately quantifying energy generation and optimizing management practices. This project aims to address these issues by proposing a method to develop a rainwater harvesting system that integrates solar PV and micro-hydro technologies. The primary objective is to evaluate the practical performance of this hybrid system in driving applications such as irrigation and sanitation. The findings demonstrate that the integrated system outperforms conventional methods, suggesting its effectiveness in rainwater utilization as a renewable energy resource.