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Improved understanding of the effects of meteorological conditions on the transmission of SARS-CoV-2, the causative agent for COVID-19 disease, is needed. Here, we estimate the relationship between air temperature, specific humidity, and ultraviolet radiation and SARS-CoV-2 transmission in 2669 U.S. counties with abundant reported cases from March 15 to December 31, 2020. Specifically, we quantify the associations of daily mean temperature, specific humidity, and ultraviolet radiation with daily estimates of the SARS-CoV-2 reproduction number ( R t ) and calculate the fraction of R t attributable to these meteorological conditions. Lower air temperature (within the 20–40 °C range), lower specific humidity, and lower ultraviolet radiation were significantly associated with increased R t . The fraction of R t attributable to temperature, specific humidity, and ultraviolet radiation were 3.73% (95% empirical confidence interval [eCI]: 3.66–3.76%), 9.35% (95% eCI: 9.27–9.39%), and 4.44% (95% eCI: 4.38–4.47%), respectively. In total, 17.5% of R t was attributable to meteorological factors. The fractions attributable to meteorological factors generally were higher in northern counties than in southern counties. Our findings indicate that cold and dry weather and low levels of ultraviolet radiation are moderately associated with increased SARS-CoV-2 transmissibility, with humidity playing the largest role. The role of meteorological factors in SARS-COV-2 transmission is not well understood. Here, the authors use county-level data from the United States to the end of 2020 and find evidence of a moderate association between increased transmissibility and cold, dry weather and low ultraviolet radiation.

Dengue is a mosquito-borne viral disease affecting tropical and subtropical regions. In Bangladesh, dengue fever remains a rising public health threat driven by meteorological factors. This study aimed to assess the temporal trends and how meteorological factors influence dengue incidence in Bangladesh from 2008 to 2024. Monthly reported dengue cases were analyzed using time series forecasting techniques and multivariate Poisson regression models. Seasonal Autoregressive Integrated Moving Average (SARIMA) and Extreme Gradient Boosting (XGBoost) models were used for forecasting. Correlation analysis and Poisson regression assessed meteorological effects with one- and two-month lags. The result indicates that the highest number of dengue cases was found in September 2023 (79,598 cases). Autocorrelation revealed a strong positive correlation at 1-month and 2-month lags. Forecasts from 2024–2027 predict that dengue cases will fluctuate between 10,000 and 20,000 annually from the predictive models. Spearman’s rank correlation indicated significant positive associations between dengue cases and precipitation, temperature, wind speed, and humidity. Multivariable Poisson regression revealed that temperature (°C) (IRR = 1.02), Humidity (%) (IRR = 1.25), and Wind speed (m/s) (IRR = 1.10) significantly increased dengue incidence. Between multivariate SARIMA, XGBoost, and Poisson regression, the best-performing model was ARIMA (RMSE: 5058.066). In conclusion, the study highlights the substantial influence of climatic factors on dengue dynamics in Bangladesh, emphasizing the need to integrate meteorological data into early warning systems and develop adaptive, climate-informed control and surveillance strategies.

The shock-induced transition from graphite to diamond has been of great scientific and technological interest since the discovery of microscopic diamonds in remnants of explosively driven graphite. Furthermore, shock synthesis of diamond and lonsdaleite, a speculative hexagonal carbon polymorph with unique hardness, is expected to happen during violent meteor impacts. Here, we show unprecedented in situ X-ray diffraction measurements of diamond formation on nanosecond timescales by shock compression of pyrolytic as well as polycrystalline graphite to pressures from 19 GPa up to 228 GPa. While we observe the transition to diamond starting at 50 GPa for both pyrolytic and polycrystalline graphite, we also record the direct formation of lonsdaleite above 170 GPa for pyrolytic samples only. Our experiment provides new insights into the processes of the shock-induced transition from graphite to diamond and uniquely resolves the dynamics that explain the main natural occurrence of the lonsdaleite crystal structure being close to meteor impact sites. Shock synthesis of diamond and even harder carbon polymorphs from graphite is of great interest for science and technology. Here, the authors present unprecedented in situ measurements of the structural changes, showing ultrafast formation of diamond and, at higher pressures, evidence for a pure lonsdaleite structure.

By collecting daily data on measles cases, air pollutants, and meteorological data from 2005 to 2009 in Chengguan District of Lanzhou City, semi-parametric generalized additive model (GAM) was used to quantitatively study the impact of air pollutants and meteorological factors on daily measles cases. The results showed that air pollutants and meteorological factors had effect on the number of daily measles cases, and there was a certain lag effect. Except for SO 2 and relative humidity, other factors showed statistically significant associations with daily measles cases: NO 2 lag 6 days, PM 10 and maximum temperature lag 5 days, minimum temperature and average temperature and average air pressure lag 4 days, visibility, and wind speed lag 3 days had the greatest impact on the number of daily measles cases. Under the optimum lag conditions, the number of daily measles cases increased by 15.1%, 17.6%, 7.0%, 116.6%, 98.6%, 85.7%, and 14.4% with the increase of 1 IQR in SO 2 , NO 2 , PM 10 , maximum temperature, minimum temperature, average temperature, and wind speed; with the increase of 1 IQR in average pressure, relative humidity, visibility, and daily measles cases decreased by 12.8%, 9.7%, and 13.1%, respectively. And different factors showed different seasonal effects. The effects of SO 2 and temperature factors on daily measles cases were greater in spring and winter, but PM 10 in summer.

As climate changes, many regions of the world are projected to experiencemore intense droughts, which can drive changes in plant community composition through a variety ofmechanisms.During drought, communitycomposition can respond directly to resource limitation, but biotic interactions modify the availability of these resources. Here, we develop the Community Response to Extreme Drought framework (CRED),which organizes the temporal progression ofmechanisms and plant– plant interactions that may lead to community changes during and after a drought. The CRED framework applies someprinciples of the stress gradient hypothesis (SGH), which proposes that the balance between competition and facilitation changeswith increasing stress. TheCRED framework suggests that net biotic interactions (NBI), the relative frequency and intensity of facilitative (+) and competitive (−) interactionsbetweenplants,will changetemporally,becomingmorepositiveunder increasing drought stress andmore negative as drought stress decreases. Furthermore,we suggest that rewettingrates affect the rate of resource amelioration, specifically water andnitrogen, altering productivity responses and the intensity and importance ofNBI, all of whichwill influence droughtinduced compositional changes. System-specific variables and the intensity of drought influence the strength of these interactions, and ultimately the system’s resistance and resilience to drought.

Background This scoping review summarized research on (a) seasonal differences in physical activity and sedentary behavior, and (b) specific weather indices associated with those behaviors. Methods PubMed, CINAHL, and SPORTDiscus were searched to identify relevant studies. After identifying and screening 1459 articles, data were extracted from 110 articles with 118,189 participants from 30 countries (almost exclusively high-income countries) on five continents. Results Both physical activity volume and moderate-to-vigorous physical activity (MVPA) were greater in summer than winter. Sedentary behavior was greater in winter than either spring or summer, and insufficient evidence existed to draw conclusions about seasonal differences in light physical activity. Physical activity volume and MVPA duration were positively associated with both the photoperiod and temperature, and negatively associated with precipitation. Sedentary behavior was negatively associated with photoperiod and positively associated with precipitation. Insufficient evidence existed to draw conclusions about light physical activity and specific weather indices. Many weather indices have been neglected in this literature (e.g., air quality, barometric pressure, cloud coverage, humidity, snow, visibility, windchill). Conclusions The natural environment can influence health by facilitating or inhibiting physical activity. Behavioral interventions should be sensitive to potential weather impacts. Extreme weather conditions brought about by climate change may compromise health-enhancing physical activity in the short term and, over longer periods of time, stimulate human migration in search of more suitable environmental niches.

There is conflicting evidence on the influence of weather on COVID-19 transmission. Our aim is to estimate weather-dependent signatures in the early phase of the pandemic, while controlling for socio-economic factors and non-pharmaceutical interventions. We identify a modest non-linear association between mean temperature and the effective reproduction number (R e ) in 409 cities in 26 countries, with a decrease of 0.087 (95% CI: 0.025; 0.148) for a 10 °C increase. Early interventions have a greater effect on R e with a decrease of 0.285 (95% CI 0.223; 0.347) for a 5th - 95th percentile increase in the government response index. The variation in the effective reproduction number explained by government interventions is 6 times greater than for mean temperature. We find little evidence of meteorological conditions having influenced the early stages of local epidemics and conclude that population behaviour and government interventions are more important drivers of transmission. Possible effects of weather conditions on COVID-19 transmission are debated. Here, the authors analyse data from early in the pandemic and show that although temperature and humidity had small effects on transmission, they were far out-weighed by the effects of non-pharmaceutical interventions.

Meteorological sensors deployed on ocean buoys frequently suffer from data loss or outliers due to electromagnetic interference and component failures caused by harsh weather and environmental conditions. Accurate reconstruction of corrupted buoy data remains a significant challenge, as conventional interpolation and imputation methods often fail to capture the inherent spatio-temporal dependencies in marine meteorological variables. To address this issue, this paper proposes a novel deep learning model that integrates Transformer and Graph Attention Network (GAT) architectures—termed the Spatio-Temporal Dual-Attention Network (ST-DAN). The model uses parallel computing to capture two aspects of the data: on one hand, it captures temporal dependencies through a Transformer enhanced by position encoding; on the other, it models inter-variable spatial correlations with a Graph Attention Network (GAT) based on a physically informed adjacency matrix, which dynamically adjusts the influence weights between variables to significantly enhance reconstruction accuracy. To evaluate the ST-DAN model, extensive experiments were conducted leveraging the ERA5 reanalysis dataset and in-situ observations from a Qingdao buoy, focusing on the reconstruction of temperature and wind speed data. The experiment result shows that ST-DAN outperformed baseline models (e.g., ARIMA, RNN, Bi-LSTM, and Transformer) across metrics including MAE, MSE, RMSE, and R². It indicates that the proposed model (ST-DAN) is off high robustness and achieves high-precision interpolation and anomaly correction for meteorological data.

Existing procedures for the assessment of the thermal environment in the fields of public weather services, public health systems, precautionary planning, urban design, tourism and recreation and climate impact research exhibit significant shortcomings. This is most evident for simple (mostly two-parameter) indices, when comparing them to complete heat budget models developed since the 1960s. ISB Commission 6 took up the idea of developing a Universal Thermal Climate Index (UTCI) based on the most advanced multi-node model of thermoregulation representing progress in science within the last three to four decades, both in thermo-physiological and heat exchange theory. Creating the essential research synergies for the development of UTCI required pooling the resources of multidisciplinary experts in the fields of thermal physiology, mathematical modelling, occupational medicine, meteorological data handling (in particular radiation modelling) and application development in a network. It was possible to extend the expertise of ISB Commission 6 substantially by COST (a European programme promoting Cooperation in Science and Technology) Action 730 so that finally over 45 scientists from 23 countries (Australia, Canada, Israel, several Europe countries, New Zealand, and the United States) worked together. The work was performed under the umbrella of the WMO Commission on Climatology (CCl). After extensive evaluations, Fiala’s multi-node human physiology and thermal comfort model (FPC) was adopted for this study. The model was validated extensively, applying as yet unused data from other research groups, and extended for the purposes of the project. This model was coupled with a state-of-the-art clothing model taking into consideration behavioural adaptation of clothing insulation by the general urban population in response to actual environmental temperature. UTCI was then derived conceptually as an equivalent temperature (ET). Thus, for any combination of air temperature, wind, radiation, and humidity (stress), UTCI is defined as the isothermal air temperature of the reference condition that would elicit the same dynamic response (strain) of the physiological model. As UTCI is based on contemporary science its use will standardise applications in the major fields of human biometeorology, thus making research results comparable and physiologically relevant.

Over the past century more than 100 indices have been developed and used to assess bioclimatic conditions for human beings. The majority of these indices are used sporadically or for specific purposes. Some are based on generalized results of measurements (wind chill, cooling power, wet bulb temperature) and some on the empirically observed reactions of the human body to thermal stress (physiological strain, effective temperature). Those indices that are based on human heat balance considerations are referred to as \"rational indices\". Several simple human heat balance models are known and are used in research and practice. This paper presents a comparative analysis of the newly developed Universal Thermal Climate Index (UTCI), and some of the more prevalent thermal indices. The analysis is based on three groups of data: global data-set, synoptic datasets from Europe, and local scale data from special measurement campaigns of COST Action 730. We found the present indices to express bioclimatic conditions reasonably only under specific meteorological situations, while the UTCI represents specific climates, weather, and locations much better. Furthermore, similar to the human body, the UTCI is very sensitive to changes in ambient stimuli: temperature, solar radiation, wind and humidity. UTCI depicts temporal variability of thermal conditions better than other indices. The UTCI scale is able to express even slight differences in the intensity of meteorological stimuli.