Climate change driven by anthropogenic greenhouse gas (GHG) emissions represents an immediate and grave threat to human health and survival. Sea level rise, altered weather patterns and increasingly frequent and severe extreme weather events can damage health directly (eg, injury, heat stress, altered aeroallergen and particulate exposure). They also bring indirect health impacts through altered patterns of zoonotic and vectorborne diseases, disruption of food systems and downstream social consequences (economic collapse, mass migration and conflict).Healthcare providers and healthcare workers all need to take immediate action to drive and deliver reductions in GHG emissions, and to help patients in better managing the health impacts brought about by climate change. Here, we propose the '4Ps framework' (Personal, Professional, Pathway-specific and Policy) to empower and facilitate such action.

Heat is now the deadliest weather hazard in the United States. California faces this hazard in two forms, acute heat waves affecting millions and chronic exposure, which affects its large agricultural workforce. The state directs climate resilience resources using tools such as composite vulnerability indices and competitive grant programs. Whether these instruments reach the communities actually experiencing heat-related illness has not been systematically tested against health outcomes. We map heat-related Medicaid claims across California ZIP codes from 2011 to 2019 to evaluate who bears the health burden of heat, how well existing vulnerability indices capture it, and whether state grant funding reaches the highest-burden communities. We show that ZIP codes with the highest claim rates have lower median incomes, more farmworkers, and more mobile homes than the state average. Heat-related claim rates rise 24.4% per 1 °C in majority-cropland ZIP codes, compared with 20.6% per 1 °C in majority built-up areas. Of three vulnerability indices tested, only the CDC heat and health index, which itself incorporates emergency room data, correlates strongly with observed claim rates. Our analysis suggests that State Extreme Heat and Community Resilience Program funding broadly tracks county-level claim counts, but several high-burden counties, including Kern, Fresno, and Imperial, are substantially underfunded. We conclude that using medical claims data in conjunction with indices could lead to a more effective allocation of funding to communities experiencing heat risk in California than considering indices alone.

ABSTRACT Weather is a critical factor in tourism, yet most climate–tourism research uses top-down approaches that do not fully capture how weather impacts tourists’ situated experiences. By analysing online reviews of urban attractions, this paper shows that, while weather is rarely a strong standalone determinant of tourist satisfaction, it exerts influence through interactions with other attraction attributes: modulating the visual appeal of scenery, interacting with infrastructure to protect or compromise physical comfort and limiting activities by imposing operational constraints in severe conditions. This study also uncovers counterintuitive pathways often ignored in conventional climate assessments, such as the aesthetic appeal of severe weather. Weather is thus not merely a physiological stressor, and these findings contribute nuance to theories of tourism climatology and a better understanding of the impacts of weather.

Abstract Machine learning (ML) algorithms have emerged in many meteorological applications. However, these algorithms struggle to extrapolate beyond the data they were trained on, i.e., they may adopt faulty strategies that lead to catastrophic failures. These failures are difficult to predict due to the opaque nature of ML algorithms. In high-stakes applications, such as severe weather forecasting, it is crucial to avoid such failures. One approach to address this issue is to develop more interpretable ML algorithms. The primary goal of this work is to illustrate the use of a specific interpretable ML algorithm that has not yet found much use in meteorology, Explainable Boosting Machines (EBMs). We demonstrate that EBMs are particularly suitable to implement human-guided strategies in an ML algorithm. As a guiding example, we show how to develop an EBM to detect overshooting tops (OTs) in satellite imagery. EBMs require input features to be scalar. We use techniques from Knowledge-Guided Machine Learning to first extract scalar features from meteorological imagery. For the application of identifying OTs this includes extracting cloud texture from satellite imagery using Gray-Level Co-occurrence Matrices. Once trained, the EBM was examined and minimally altered to more closely match strategies used by domain scientists to identify OTs. The result of our efforts is a fully interpretable ML algorithm developed in a human-machine collaboration that uses human-guided strategies. While the final model does not reach the accuracy of more complex approaches, it performs reasonably well and we hope paves the way for building more interpretable ML algorithms for this and other meteorological applications.

Climate refugia endemics have persisted through past environmental shifts, but their responses to modern severe weather remain poorly understood. Torreya taxifolia (Taxaceae), a critically endangered conifer endemic to steephead ravines of Florida and Georgia, has declined for decades due to fungal canker diseases. Hurricane Michael, a Category 5 storm in 2018, caused extensive forest damage in its refugial habitat. We investigated how natural variation in disturbance was linked to tree health and microclimatic conditions relevant to disease. We monitored the health and habitat of 40 wild T. taxifolia individuals for 4 years after the hurricane by measuring branch growth and mortality and temperature and light around each T. taxifolia. We assessed the abundance and area of fungal cankers. Hurricane disturbance was characterized using complementary metrics of forest structural change (basal area) and the size-weighted abundance of fallen and standing trees. Tree fall was associated with increased branch growth when quantified with summed diameter at breast height (DBH) metrics, but not with basal area metrics. Neither metric of tree fall was significantly associated with branch mortality. Local temperatures were positively associated with canker abundance, though not with total canker area, and were not directly linked to measures of forest change. Hurricane disturbance was associated with short-term after-storm growth responses among surviving T. taxifolia individuals, while warmer local conditions were linked to increased canker disease symptoms. Together, these results suggest that torreya growth was more closely associated with neighborhood-scale disturbance intensity than with broad reductions in stand-level biomass.

Insulator defect detection under foggy conditions suffers from complex backgrounds, small targets, weak features, and severe weather interference, remaining a challenging task for UAV-based inspection. To address these issues, this paper proposes Fog-Adaptive-YOLO, a lightweight fog-adaptive detection network. The FogEnhance module suppresses fog noise and enhances weak defect features; the C3MSGR and C2fMSGR modules optimize lightweight multi-scale feature extraction and aggregation. Experimental results show that on the self-constructed InsDef-Fog dataset, the proposed model achieves 65.4% mAP50 with only 2.74M parameters. It obtains 60.3% mAP50 on the public IDID_FOG dataset and 80.2% mAP50 on the real-world WM-FOG dataset. The model also maintains stable precision on the cross-scene RTTS foggy dataset. These results demonstrate that Fog-Adaptive-YOLO achieves a favorable balance between detection accuracy and lightweight efficiency, well-suited for practical foggy insulator defect detection tasks.

Corner cases, such as severe weather and abnormal lighting, present significant challenges in autonomous driving. The main obstacles involve large-scale data collection and costly annotations. Leveraging generative models to expand corner-case data based on existing annotations offers a promising solution. Unlike monocular videos, multi-view videos introduce an additional "view" dimension, increasing the consistency requirements and making precise control of annotations more challenging. Existing methods decouple multi-view videos along the temporal and view-spatial axes, using separate attention mechanisms, which causes motion discrepancies and limits consistency. Additionally, current approaches employ an independent adapter or ControlNet to encode different 3D annotations, leading to high computational costs and suboptimal alignment between annotations and video latents. These issues arise from neglecting the temporal-spatial relationship and insufficient alignment between 3D annotations and video latents. To address these challenges, we propose DriveGen, which uses 4D position embeddings to encode the positional information of multi-view videos. DriveGen also designs Dual-Scale Full Attention to ensure both global and local spatiotemporal consistency. Furthermore, our Shared Video-Condition Encoding (SVCE) Mechanism converts 3D annotations into 2D masks and encodes both video and annotation sequences using a 3D VAE, requiring only 0.37M learnable parameters to achieve pixel-level alignment and improving generation quality. Numerous experiments have proven that DriveGen has reached the state-of-the-art, capable of generating high-quality controlled autonomous driving videos.

This study assesses the performance of a regional WRF configuration used operationally by SIMEPAR (SIWRF) and a set of MPAS experiments at 5-km grid spacing over southern Brazil to predict four recent severe weather events. MPAS is tested using a 200-5 km global variable-resolution (VR) mesh with two physics suites—Mesoscale Reference (MR) and Convection-Permitting (CP)—and two sources of initial conditions — NCEP-GFS and ECMWF-IFS forecasts. Skill is evaluated with respect to 48-h accumulated precipitation (correlation, bias, RMSE), hourly precipitation (fractions skill score; FSS, and QPF measures), radar reflectivity (FSS), near-surface variables (MAE, Taylor diagrams), and vertical soundings (bias, RMSE). Across events, MPAS generally outperforms SIWRF for precipitation, with CP physics suite often reducing positive bias relative to MR, while SIWRF tends to underestimate totals during the heaviest events. Notably, MR-IFS achieves the highest correlation for the event dominated by a long-lived, organized convective system, whereas CP-IFS provides the most balanced metrics in two other cases, including the coastal event, consistent with improved placement and intensity of convection. SIWRF exhibits the lowest skill for radar reflectivity, while MPAS shows event-dependent gains, particularly for MR-IFS and CP-GFS. Analysis of convective parameters reveal that MR configurations produce higher median CAPE and 0–3-km SRH than CP and SIWRF, implying more favorable storm environments. These results highlight event-dependent sensitivity to physics and initial conditions and suggest that tailoring MPAS configuration to event features (e.g., favoring CP physics for coastal/diurnally forced convection and MR physics for strongly forced organized systems) may improve forecasts, but this inference is preliminary and requires broader testing (larger case set, data assimilation, ensemble and operational-runtime evaluation) before operational adoption. • First comparison of MPAS and operational WRF for severe weather in southern Brazil. • MPAS with 200-5 km global VR mesh often outperformed 5-km regional WRF. • MPAS precipitation skill and biases strongly depend on physical options and IC source. • MPAS forecasts improved when initialized with ECMWF-IFS ICs rather than NCEP-GFS.

Forecast errors of severe weather events aggravate economic damage and degrade public mental health. Whether forecast errors are underestimated or overestimated can shape public emotional responses differently, which remains unknown. In this study, we investigate the socio-psychological impacts of forecast errors during the landfall of Typhoon Khanun over the Korean Peninsula. We evaluate the predictive performance of multiple lead-hour precipitation forecasts against observational data and conduct a sentimental analysis of over 43,000 online discourses from the NAVER Weather Report Talk platform. Multiple lead-hour precipitation forecasts demonstrate underestimation in the eastern and southeastern regions of the Korean Peninsula and overestimation in the western and southwestern regions. The spatial discrepancies of precipitation forecasts are associated with distinct emotional responses: overestimation (underestimation) makes anxiety and worry (stress and confusion) the dominant emotion types in the discourses from the NAVER Report Talk platform. The findings of this study suggest that expectation-reality mismatch is a key mechanism in risk communication, and the direction of this mismatch differentiates public response. This study provides insights into the potential value of improved forecast accuracy on reducing emotional distress and strengthening public resilience during extreme weather events.

Navigating Extreme Weather Events: Experiences of Nursing Leaders in a Rural, Acute Care Hospital in Atlantic Canada.

Nocturnal evolution of physicochemical characteristics of water-soluble and insoluble organic aerosols in a polluted environment: New insights from a combined online and offline study.

Evaluating dietary starch concentrations in reduced-forage rations fed to lactating dairy cattle.

Gas-to-particle transformation of organic compounds is a key formation pathway for secondary organic aerosol (SOA), especially during haze periods in China. However, the drivers of this process remain poorly understood. On the basis of simultaneous hourly measurements of gas- and particle-phase water-soluble organic compounds (WSOC) at a rural site in the North China Plain (NCP) during winter 2023, we show at a bulk level that the partitioning of WSOC played an important role in SOA formation. Random forest analysis identified temperature, aerosol liquid water content (ALWC), and gas-particle conversion of NH3 as the dominant factors governing WSOC partitioning. Notably, high concentrations of ammonium nitrate (NH4NO3) under winter haze conditions increased ALWC, which in turn enhanced the partitioning of organics into the particle phase. Thermodynamic model analysis of formic acid confirmed the critical roles of low temperature and high ALWC in amplifying its gas-particle partitioning in the NCP winter. We found that the molar ratio of nitrate-to-sulfate in PM2.5 in China has continuously increased in the past few years due to strict SO2 emission control, which has resulted in atmospheric particles in the country becoming more hygroscopic and frequently dominated by liquid water. This aqueous-rich environment favors the gas-to-particle phase transformation of WSOC and thus promotes SOA formation, especially in winter haze periods. Our study highlights the synergistic effect of low temperature and aerosol water in driving organic partitioning to the aerosol phase, providing mechanistic insight into severe winter haze formation.

This paper addresses the urgent need for organisations to strengthen operational resilience in response to the escalating effects of climate change, highlighted by the record-breaking temperatures and severe weather events of recent years. It presents a comprehensive, practical framework designed to help businesses anticipate, adapt to, and recover from climate-related disruptions. The paper covers critical topics including advanced climate risk assessment - distinguishing physical, transition, and liability risks - adaptation of organisational structures, workforce empowerment, robust stakeholder engagement, supply chain resilience, and integration of modern technology such as artificial intelligence (AI), blockchain, and the Internet of Things (IoT). Readers will gain a thorough understanding of the multifaceted threats posed by climate change, as well as strategies for turning these challenges into opportunities for innovation, efficiency, and market differentiation. The paper explores how organisations can nurture a climate-aware culture, develop adaptive skills, support mental well-being, implement flexible work policies, and harmonise incentives with climate objectives. Case studies and recent data illustrate the tangible benefits of comprehensive resilience planning, including reduced operational risk, cost savings, improved reputation, and enhanced competitiveness. By engaging with this paper, readers will acquire actionable knowledge on assessing climate risks, designing resilient organisations and supply chains, leveraging technology for climate adaptation, and empowering their workforce. The paper equips business leaders and professionals with the skills and insights needed to build a proactive, resilient organisation capable of thriving amid the challenges of a changing climate. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.

Climate change-related severe weather events are heavily impacting regions with the highest prevalence of HIV, creating additional vulnerabilities for already vulnerable populations. Little is understood about how people perceive or experience the mechanisms by which extreme weather events affect the health of people living with HIV (PLHIV). We conducted a qualitative study using in-depth, semi-structured interviews with 40 PLHIV enrolled in a cluster randomized clinical trial that included 8 pairs of health facilities in rural Western Kenya, who were 18 years or older, receiving ART for > 6 months; had moderate to severe food insecurity; and practiced smallholder farming with access to surface water or aquifers. Intervention participants received a loan to purchase an irrigation pump and farming inputs and were provided climate-responsive, sustainable agriculture and financial literacy training. This study did not evaluate the impacts of the clinical trial. We aimed to understand participant perceptions of how climate change impacted their HIV health, and associated pathways for these impacts. Interviews were transcribed, translated, and double coded using an inductive-deductive-abductive thematic content approach. Almost all participants noted that droughts, flooding, and elevated temperatures had serious negative impacts on their health and wellbeing. Extreme weather negatively impacted their health via five key pathways, with the first being most prominent: (1) decreases in agricultural yields and income; (2) increased food insecurity and undernutrition; (3) medication non-adherence, missed clinic visits, and infrastructure erosion; (4) increased infections, and (5) displacement and forced migration. Several pathways were interrelated, with decreased agricultural yields and income being proximal to most other pathways. Participants perceived pathways by which severe weather negatively impacted their HIV health, and these pathways were bi-directional and reinforcing. Food and livelihood sources were devastated, and housing and infrastructure were negatively affected, causing a cascade of nutrition and health vulnerabilities. Understanding the contexts in which PLHIV are vulnerable to impacts of climate change will be essential to establish climate-responsive policies that can interrupt the pathways identified here. Cross-sector collaboration between the Kenyan Ministries of Agriculture and Health to develop climate-responsive policies to support PLHIV should be prioritized. The clinical trial start date was June 23, 2016. Trial Registration: Registered with ClinicalTrials.gov Identifier: NCT02815579.

Abstract Convection-allowing models (CAMs) provide valuable information on convective storm timing, mode, intensity, and evolution compared to coarser-resolution models with parameterized convection. This information is useful for severe weather forecasting at 1–2-day lead times. At longer lead times, there is not currently an operational CAM prediction system available for the contiguous United States, so forecasters rely on synoptic patterns and environmental information from global, convection-parameterized deterministic models and ensembles to assess severe convection potential. Little research has explored the value and predictability of CAMs at extended-range lead times outside of testbed experiments. In this study, two exploratory sets of random forest (RF) probabilistic severe convection forecasts, one trained on a convection-parameterized ensemble and one trained on a CAM ensemble, are generated to evaluate whether storm attribute output from the CAMs provides additional value at extended lead times. The forecast model data are provided by the NOAA Geophysical Fluid Dynamics Laboratory (GFDL) and use their System for High-Resolution Prediction on Earth-to-Local Domains for the Contiguous United States (C-SHiELD). Daily RF severe convection forecasts are generated from ensemble runs initialized once per week out to day 15 from October through early June from 2016 to 2020 and evaluated using both objective and subjective metrics against severe storm reports. Results show that using the nested CAM ensemble and its simulated updraft helicity improves severe weather forecast skill through day 7, after which neither model demonstrates much skill at predicting severe weather on a daily scale. Significance Statement Higher-resolution convection-allowing forecast models are an important source of guidance for severe thunderstorm forecasting. Due to computational constraints, these forecasts typically extend out to only 1–2 days. Experimental convection-allowing forecast models which simulate convection out to 15 days have been developed, and in this study, we employ a machine learning model to compare daily severe thunderstorm forecasts generated using output from both the experimental forecast model and traditional forecast models which parameterize convection. We find that using the experimental, convection-simulating forecast model improves our machine learning daily severe thunderstorm forecasts through day 7.

A dual-weighted loss function for lightning nowcasting

Telomeres have emerged as important indicators of organismal longevity and population health; however, our understanding of their dynamics in ectotherms remains incomplete. Here, we investigated variables influencing relative telomere length (rTL) in the Western-Canaries Lizard (Gallotia galloti) across diverse environments over 10 years. Using mixed-effect model-averaging and hierarchical partitioning, we assessed the effects of intrinsic morphological (sex and body length) and extrinsic environmental (elevation, radiant sky temperature, wind speed and relative humidity) factors while controlling for temporal (year sampled) effects on rTL variation. In addition, we investigated temporal signals corresponding to extreme weather events over the sampling period. Intrinsic factors had the strongest influence, with males exhibiting shorter rTL than females, and females showing shorter rTL with increasing size. Temporal patterns revealed a negative correlation with dry years, indicating that even though environmental drivers may be secondary predictors compared to individual determinants, severe weather conditions may represent cumulative burdens. Multiple intrinsic and extrinsic variables, including climate, should be considered when investigating telomere dynamics in ectotherms.

The relationship between climate change and human health has become increasingly clear as global temperatures rises. The cardiovascular and pulmonary consequences of extreme weather events have been well researched and documented in the literature. However, the effects of climate change on MSK health are not well understood. This mini- review explores the complex relationship between MSK health and climate change particularly global warming. It highlights the emerging challenges for rehabilitation medicine due to the climate change and suggests adaptive approaches to clinical practice. The mechanisms linking environmental factors and MSK health are multifactorial and intricate. Temperature extremes can disrupt tissue physiology, while severe weather events may result in trauma and limit access to healthcare services. In addition, exposure to poor air quality has been associated with the exacerbation of inflammatory MSK conditions. Vulnerable populations including elderly adults, outdoor workers, and those with pre-existing MSK disorders face increased risks from climate changes. Climate-resilient rehabilitation services using telemedicine, mobile units, and environmental monitoring can be important considerations. Further research is suggested to establish evidence-based guidelines for climate-adaptive rehabilitation protocols.

Abstract Complex terrain substantially influences weather systems, with prior work documenting impacts to the intensity, longevity, and severe weather production of supercell thunderstorms. However, the extent to which these impacts are sensitive to the maturity of the supercell and its angle of approach to the terrain is not well understood. These sensitivities were systematically tested through idealized simulations of supercell thunderstorms traversing the complex terrain of the central and southern Appalachian Mountains. Three maturity levels were imposed by varying the initiation location of the supercell, while the approach angle was varied by rotating the wind profile of the background environment. Supercell evolution was also compared to a simulation with flat terrain. The results demonstrate that supercell intensity, longevity, and motion metrics vary considerably and are sensitive to supercell maturity and approach angle. Supercells initiating closer to the terrain experienced the largest impacts to intensity and longevity, while impacts were more varied among approach angles and highly dependent on localized terrain features traversed. As the approach angle became more perpendicular to the principal crest, low-level rotation was more frequently enhanced due to channeling flow. Significance Statement The evolution of thunderstorms with rotating updrafts as they traverse complex terrain can be difficult to predict and is thus not well understood. This study sought to understand the range of supercell thunderstorm behavior through identifying the sensitivities to modifications in the approach angle of the storm as well as variations in the life cycle stage when encountering terrain. Idealized numerical model simulations revealed substantial impacts to supercell intensity and longevity. Supercells that were still growing when traversing terrain had the greatest impacts to intensity and longevity, while changing approach angle led to more varied impacts on the supercell with enhancing or suppressing effects dependent on local terrain features.