HiMIC-Monthly: A 1 km high-resolution atmospheric moisture index collection over China, 2003-2020.

Near-surface atmospheric moisture is a key environmental and hydro-climatic variable that has significant implications for the natural and human systems. However, high-resolution moisture data are severely lacking for fine-scale studies. Here, we develop the first 1 km high spatial resolution dataset of monthly moisture index collection in China (HiMIC-Monthly) over a long period of 2003~2020. HiMIC-Monthly is generated by the light gradient boosting machine algorithm (LightGBM) based on observations at 2,419 weather stations and multiple covariates, including land surface temperature, vapor pressure, land cover, impervious surface proportion, population density, and topography. This collection includes six commonly used moisture indices, enabling fine-scale assessment of moisture conditions from different perspectives. Results show that the HiMIC-Monthly dataset has a good performance, with R2 values for all six moisture indices exceeding 0.96 and root mean square error and mean absolute error values within a reasonable range. The dataset exhibits high consistency with in situ observations over various spatial and temporal regimes, demonstrating broad applicability and strong reliability.

Highly efficient photonic radar by incorporating MDM-WDM and machine learning classifiers under adverse weather conditions.

Photonic radar, a cornerstone in the innovative applications of microwave photonics, emerges as a pivotal technology for future Intelligent Transportation Systems (ITS). Offering enhanced accuracy and reliability, it stands at the forefront of target detection and recognition across varying weather conditions. Recent advancements have concentrated on augmenting radar performance through high-speed, wide-band signal processing-a direct benefit of modern photonics' attributes such as EMI immunity, minimal transmission loss, and wide bandwidth. Our work introduces a cutting-edge photonic radar system that employs Frequency Modulated Continuous Wave (FMCW) signals, synergized with Mode Division and Wavelength Division Multiplexing (MDM-WDM). This fusion not only enhances target detection and recognition capabilities across diverse weather scenarios, including various intensities of fog and solar scintillations, but also demonstrates substantial resilience against solar noise. Furthermore, we have integrated machine learning techniques, including Decision Tree, Extremely Randomized Trees (ERT), and Random Forest classifiers, to substantially enhance target recognition accuracy. The results are telling: an accuracy of 91.51%, high sensitivity (91.47%), specificity (97.17%), and an F1 Score of 91.46%. These metrics underscore the efficacy of our approach in refining ITS radar systems, illustrating how advancements in microwave photonics can revolutionize traditional methodologies and systems.

The performance of partially substituted composite ester materials with weathered red-bed soil in ecological restoration.

Ester materials have become a significant topic in ecological restoration because of their degradability and lack of pollution. However, these artificial materials have issues such as high resource consumption and high cost. Therefore, finding a scientific substitute for ester materials is crucial to reduce costs. This study proposes the use of weathered red-bed soil to partially replace ester materials. Orthogonal coupled compounding and ecological effect tests were performed to analyze the soil improvement mechanism based on the mineral composition, soil structure, and electrical conductivity properties of the weathered red-bed soil. The experimental findings indicated that the soil modified using ester materials exhibited improved strength, water retention, and aeration owing to changes in the soil structure. Plant germination and height increased by 55% and 37 mm, respectively, when using a ratio of 15 g/m2 absorbent ester material, 2.5 g/m2 adhesive ester material, and 5% weathered red-bed soil. Through this approach, the amount of ester material to be used could be further reduced by 75%. The weathered red-bed soil offers improved ecological effects by altering the physical, mechanical, and hydraulic properties of the soil structure. This study presents a theoretical foundation for ecological conservation using weathered red-bed soil as a substitute for certain ester materials.

Insights into cork weathering regarding colour, chemical and cellular changes in view of outdoor applications.

A comprehensive analysis of outdoor weathering and soil burial of cork during 1-year experiments was carried out with measurements of CIELAB color parameters, cellular observations by scanning electron microscopy, and surface chemical features analysed by ATR-FTIR and wet chemical analysis. Cork applied in outdoor conditions above and below ground retained its physical structure and integrity without signs of deterioration or fracturing. The cellular structure was maintained with some small changes at the one-cell layer at the surface, featuring cellular expansion and minute cell wall fractures. Surface color and chemistry showed distinct results for outdoor exposure and soil burial. The weathered cork surfaces acquired a lighter color while the soil buried cork surfaces became darker. With outdoor weathering, the cork polar solubles increased (13.0% vs. 7.6% o.d. mass) while a substantial decrease of lignin occurred (about 28% of the original lignin was removed) leading to a suberin-enriched cork surface. The chemical impact on lignin is therefore responsible for the surface change towards lighter colors. Soil-burial induced hydrolysis of ester bonds of suberin and xylan, and the lignin-enriched cork surface displayed a dark brown color. FTIR and wet chemical results were consistent. Overall cork showed a considerable structural and physical stability that allows its application in outdoor conditions, namely for building façades or other surfacing applications. Architects and designers should take into account the color dynamics of the cork surfaces.

Severe weather events and cryptosporidiosis in Aotearoa New Zealand: A case series of space-time clusters.

Occurrence of cryptosporidiosis has been associated with weather conditions in many settings internationally. We explored statistical clusters of human cryptosporidiosis and their relationship with severe weather events in New Zealand (NZ). Notified cases of cryptosporidiosis from 1997 to 2015 were obtained from the national surveillance system. Retrospective space-time permutation was used to identify statistical clusters. Cluster data were compared to severe weather events in a national database. SaTScan analysis detected 38 statistically significant cryptosporidiosis clusters. Around a third (34.2%, 13/38) of these clusters showed temporal and spatial alignment with severe weather events. Of these, nearly half (46.2%, 6/13) occurred in the spring. Only five (38%, 5/13) of these clusters corresponded to a previously reported cryptosporidiosis outbreak. This study provides additional evidence that severe weather events may contribute to the development of some cryptosporidiosis clusters. Further research on this association is needed as rainfall intensity is projected to rise in NZ due to climate change. The findings also provide further arguments for upgrading the quality of drinking water sources to minimize contamination with pathogens from runoff from livestock agriculture.

Personalized analysis of pain-weather associations: a pilot study.

Background and purpose:

It is a wellknown belief that weather can influence human health, including pain sensation. However, the current data are controversial, which might be due to the wide range of interindividual differences. The present study aimed to characterize the individual pain–weather associations during chronic pain by utilizing several data analytical methods.

The study included 3-3 patients with (P1, P3, and P4) or without (P2, P5, P6) diabetes mellitus and signs of trigeminal neuralgia or low back pain. Subjective pain scores (0–10) and 12 weather parameters (terrestrial, geomagnetic, and solar) were recorded for one month repeated three times daily. Nonparametric Spearman’s correlation (Sp), multiple regression (Mx), and principal component (PCA) analyses were performed to evaluate associations between pain and meteorological factors obtained at the day of recorded pain value, 2 days before and 2 days after the recorded pain, and the changes in these parameters (5 × 12 parameters). Complex scores were calculated based on the results of these analyses.

While the temperature had the highest effects on the pain levels in most of the participants, huge interindividual dif­ferences in the degree and the direction of the associations between pain and weather parameters could be obtained. The analytic methods also revealed subjectspecific results, and the synthesis of different statistical methods as total scores provided a personalized map for each patient, which showed disparate patterns across the study participants. Thus, Participants 2 and 5 had higher scores for Mx compared to Sp; furthermore, certain factors showed opposite direction in their associations with the pain level depending on the type of analysis (Sp vs Mx). In contrast, P3 had a lower score for Mx compared to Sp, which might suggest a low level of weather sensitivity on the association between the different weather parameters in this subject. Furthermore, participants P4 and P6 had a very high level of weather sensitivity, while P1 had an opposite pattern. Regarding the time point-related effects on the pain level, most patients were sensitive to parameters obtained at the same day or two days before, except the P1 subject, who had the highest sensitivity to weather parameters detected two days after.

The present study highlights the importance of integrating different data analysis approaches to elucidate the individual connections between pain and most of the weather parameters. In conclusion, complex personalized profiling should be considered for the characterization of pain–weather associations by applying different data analytical approaches, which may provide feedback to physicians and patients. 

Environmental plastics in the context of UV radiation, climate change, and the Montreal Protocol.

There are close links between solar UV radiation, climate change, and plastic pollution. UV-driven weathering is a key process leading to the degradation of plastics in the environment but also the formation of potentially harmful plastic fragments such as micro- and nanoplastic particles. Estimates of the environmental persistence of plastic pollution, and the formation of fragments, will need to take in account plastic dispersal around the globe, as well as projected UV radiation levels and climate change factors.

Using weather data to predict the presence of Lucilia spp. on sheep farms in New Zealand.

Flystrike remains an important animal health issue on New Zealand sheep farms. To date no useful predictive tool to assist farmers to develop control options has been available. The aim of this study was to use National Institute of Water and Atmospheric Research (NIWA) virtual climate station data in New Zealand to develop a weather-based model to accurately predict the presence of Lucilia spp. on sheep farms throughout New Zealand. Three LuciTrap® baited fly traps were positioned on each of eight sheep farms throughout New Zealand (5 in the North Island and 3 in the South Island). The traps were put out for both the 2018-2019 and 2019-2020 seasons. They were emptied each week and the flies morphologically identified; with the counts of Lucilia cuprina and L. sericata combined as Lucilia spp. The count data for Lucilia spp. for each week of trapping was transformed into a binary outcome and a generalised linear mixed effects models fitted to the data, with farm as a random effect. The dependent variable was Lucilia spp. flies caught, yes or no, and the independent variables were mean weekly climate variables from the nearest NIWA virtual climate station to that farm. The model was trained on the 2018-2019 catch data and tested on the 2019-2020 catch data. A cut point was identified which maximised the model's ability to correctly predict whether Lucilia spp. were present or not for the 2019-2020 catch data, and the sensitivity, specificity, accuracy, and area under the curve (AUC) of the model calculated. The final model included just 3 significant variables, mean weekly 10 cm soil temperature, mean weekly soil moisture index, and mean weekly wind speed at 10 m. Mean weekly 10 cm soil temperature accounted for 64.7% of the variance explained by the model, mean weekly soil moisture index 34.7% and mean weekly wind speed at 10 m only 0.6%. The results showed that the predictive model had a sensitivity of 0.93 (95% CI = 0.80-0.98) and a specificity of 0.75 (95% CI = 0.62-0.85), using a cut point for the probability of Lucilia spp. being present on farm = 0.383. This model provides New Zealand farmers with a tool which will allow them to know when Lucilia spp. flies will likely be present and thus more accurately plan their interventions to prevent flystrike.

Weather or not-Global climate databases: Reliable on tropical mountains?

Global, spatially interpolated climate datasets such as WorldClim and CHELSA, widely used in research, are based on station data, which are rare in tropical mountains. However, such biodiversity hotspots are of high ecological interest and require accurate data. Therefore, the quality of such gridded datasets needs to be assessed. This poses a kind of dilemma, as proving the reliability of these potentially weakly modelled data is usually not possible due to the lack of stations. Using a unique climate dataset with 170 stations, mainly from the montane and alpine zones of sixteen mountains in Tanzania including Kilimanjaro, we show that the accuracy of such datasets is very poor. Not only is the maximum amount of mean annual precipitation drastically underestimated (partly more than 50%), but also the elevation of the precipitation maximum deviates up to 850m. Our results show that, at least in tropical regions, they should be used with greater caution than before.

Weather and the City: Machine Learning for Predicting and Attributing Fine Scale Air Quality to Meteorological and Urban Determinants.

Urban air quality persists as a global concern, with critical health implications. This study employs a combination of machine learning (gradient boosting regression, GBR) and spatial analysis to better understand the key drivers behind air pollution and its prediction and mitigation strategies. Focusing on New York City as a representative urban area, we investigate the interplay between urban characteristics and weather factors, showing that urban features, including traffic-related parameters and urban morphology, emerge as crucial predictors for pollutants closely associated with vehicular emissions, such as elemental carbon (EC) and nitrogen oxides (NOx). Conversely, pollutants with secondary formation pathways (e.g., PM2.5) or stemming from nontraffic sources (e.g., sulfur dioxide, SO2) are predominantly influenced by meteorological conditions, particularly wind speed and maximum daily temperature. Urban characteristics are shown to act over spatial scales of 500 × 500 m2, which is thus the footprint needed to effectively capture the impact of urban form, fabric, and function. Our spatial predictive model, needing only meteorological and urban inputs, achieves promising results with mean absolute errors ranging from 8 to 32% when using full-year data. Our approach also yields good performance when applied to the temporal mapping of spatial pollutant variability. Our findings highlight the interacting roles of urban characteristics and weather conditions and can inform urban planning, design, and policy.

Lateral safety evaluation of autonomous trucks in overtaking maneuvers and adverse weather: Exploratory comparison of different lateral control modes.

As the detrimental impact of the commonly recommended centered driving mode for autonomous trucks on road longevity is gaining attention, more lateral control modes are being proposed to enhance road sustainability. However, there is currently a lack of research on the lateral safety analysis of autonomous trucks with different lateral control modes, especially in complex driving scenarios (such as overtaking) and adverse weather conditions. Therefore, this study developed a safety assessment framework to comparatively analyze the risk probability differences in lateral accidents during overtaking maneuvers by autonomous trucks with different lateral control modes under adverse weather conditions. Based on aerodynamics and vehicle dynamics simulations to capture the multifactorial influences on truck lateral deviation, the results are used for model validation and training. In the reliability approach, Support Vector Machine Regression (SVR) is introduced to establish the SVR response surface model with optimal predictive performance, and combined with Monte Carlo simulations for safety assessment, quantifying safety indices. The results indicate that trucks being overtaken during overtaking maneuvers are more prone to lateral accidents under crosswind influences. The overall impact of lateral control modes on the lateral safety trends is minor. Compared to other lateral control modes, following the centered zero-drift mode is generally safer. However, in conditions of low wind speeds (below 20 km/h) or on highly slippery road surfaces (road friction coefficient below 0.1), autonomous trucks following a uniform distribution mode can better maintain a low-risk level. This study provides crucial insights for future considerations integrating road longevity and truck safety in a collaborative manner, and the proposed methodology has broad applications.

Fatal interactions of albatrosses with weather radiosondes/balloons on the Southern and Southeastern coasts of Brazil.

Weather radiosondes play a crucial role in gathering atmospheric data for weather modeling and forecasting. However, their impact on marine wildlife, particularly seabirds, has raised concerns regarding the potential threats posed by these instruments. This study aims to assess the adverse effects of weather balloons on albatrosses, with a focus on the Southwest Atlantic region. The research reveals seven cases of entanglement of radiosonde equipment, leading to severe injuries and mortality along the Southern and Southeastern coasts of Brazil. Recommendations for mitigating the environmental impact of weather balloons include the adoption of biodegradable materials in their design and the implementation of improved retrieval protocols. Furthermore, the study stresses the importance of continued monitoring and research to address the interaction of weather radiosondes with marine animals. This approach is vital for ensuring the sustainable collection of scientific data while minimizing harm to marine life and ecosystems.

Summer solstice orchestrates the subcontinental-scale synchrony of mast seeding.

High interannual variation in seed production in perennial plants can be synchronized at subcontinental scales with wide consequences for ecosystem functioning, but how such synchrony is generated is unclear1-3. We investigated the factors contributing to masting synchrony in European beech (Fagus sylvatica), which extends to a geographic range of 2,000 km. Maximizing masting synchrony via spatial weather coordination, known as the Moran effect, requires a simultaneous response to weather conditions across distant populations. A celestial cue that occurs simultaneously across the entire hemisphere is the longest day (the summer solstice). We show that European beech abruptly opens its temperature-sensing window on the solstice, and hence widely separated populations all start responding to weather signals in the same week. This celestial 'starting gun' generates ecological events with high spatial synchrony across the continent.

Short-Term Changes in Weather Conditions and the Risk of Acute Coronary Syndrome Hospitalization with and without ST-Segment Elevation: A Focus on Vulnerable Subgroups.

Background and Objectives: Acute coronary syndrome (ACS), a prevalent global cardiovascular disease and leading cause of mortality, is significantly correlated with meteorological factors. This study aims to analyze the impact of short-term changes in meteorological factors on the risk of ACS, both with and without ST-segment elevation, and to identify vulnerable subgroups. Materials and Methods: Daily ACS admissions and meteorological variables were collected from October 2016 to December 2021. A generalized linear model (GLM) with a Poisson distribution was employed to examine how short-term fluctuations in meteorological parameters influence ACS hospitalizations. Subgroup analyses were conducted to identify the populations most vulnerable to climate change. Results: Multiple regression analyses showed that short-term fluctuations in atmospheric pressure (≥10 mbar) and air temperature (≥5 °C) seven days prior increased the number of ACS hospitalizations by 58.7% (RR: 1.587; 95% CI: 1.501-1.679) and 55.2% (RR: 1.552; 95% CI: 1.465-1.644), respectively, notably impacting ST-segment elevation myocardial infarctions (STEMIs). The least pronounced association was observed between the daily count of ACS and the variation in relative air humidity (≥20%), resulting in an 18.4% (RR: 1.184; 95% CI: 1.091-1.286) increase in the risk of hospitalization. Subgroup analysis revealed an increased susceptibility among men and older adults to short-term variations in weather parameters. Conclusions: The findings indicate that short-term changes in weather conditions are associated with an increased risk of ACS hospitalizations, particularly STEMIs. Male and older adult patients exhibit heightened susceptibility to variations in climatic factors. Developing effective preventive strategies is imperative to alleviate the adverse consequences of these environmental risk factors.

'They Talk about the Weather, but No One Does Anything about It': A Mixed-Methods Study of Everyday Climate Change Conversations.

Understanding everyday conversations about climate change may provide insights into framing the issue to promote climate change action. As part of a longitudinal online study in the US launched in June 2021, 805 respondents were asked if they had discussed climate change with a friend or family member in the prior month; if not, why not, and if yes, they were asked to delineate the conversation topic. Concurrent mixed methods were used to analyze the data. The majority (62.6%) of respondents reported not having a conversation about climate change in the prior month. Among those who indicated that they had discussed climate change, five themes were identified from the conversation topics, with many having reported discussing the impact of climate change on weather patterns. Very few discussed actions to address climate change, and most of these discussions focused on individual-level behaviors rather than collective actions. Among participants who had not recently discussed climate change, the most prevalent theme was that it was not a priority or an issue they cared about. Results suggest that conversations may not lead to collective actions and that policymakers and environmental organizations should provide guidance on effectively channeling climate change concerns into action.

Synergistic advantages of volcanic ash weathering in saline soils: CO2 sequestration and enhancement of plant growth.

Carbon dioxide (CO2) is a primary greenhouse gas that has experienced a surge in atmospheric concentration due to human activities and lifestyles. It is imperative to curtail atmospheric CO2 levels promptly to alleviate the multifaceted impacts of climate warming. The soil serves as a natural reservoir for CO2 sequestration. The scientific premise of this study is that CO2 sequestration in agriculturally relevant, organically-deficient saline soil can be achieved by incorporating alkaline earth silicates. Volcanic ash (VA) was used as a soil amendment for CO2 removal from saline soil by leveraging enhanced silicate rock weathering (ERW). The study pursued two primary objectives: first, we aimed to evaluate the impact of various doses of VA, employed as an amendment for organically-deficient soil, on the growth performance of key cultivated crops (sorghum and mung bean) in inland saline-alkaline agricultural regions of northeastern China. Second, we aimed to assess alterations in the physical properties of the amended soil through mineralogical examinations, utilizing X-ray diffraction (XRD) and scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM-EDS) analyses, quantifying the increase in inorganic carbon content within the soil. In the potting tests, mung bean plant height exhibited a noteworthy increase of approximately 41 % with the addition of 10 % VA. Sorghum plant height and aboveground and belowground biomass dry weights increased with VA application across all tested doses. At the optimal VA application rate (20 %), the sorghum achieved a CO2 sequestration rate of 0.14 kg CO2·m-2·month-1. XRD and SEM-EDS analyses confirmed that the augmented inorganic carbon in the VA-amended soils stemmed primarily from calcite accumulation. These findings contribute to elucidating the mechanism underlying VA as an amendment for organically-deficient soils and provide an effective approach for enhancing the carbon sink capacity of saline soils.

Responses in the breeding parameters of the collared flycatcher to the changing climate.

Global climate change involves various aspects of climate, including precipitation changes and declining surface wind speeds, but studies investigating biological responses have often focused on the impacts of rising temperatures. Additionally, related long-term studies on bird reproduction tend to concentrate on breeding onset, even though other aspects of breeding could also be sensitive to the diverse weather aspects. This study aimed to explore how multiple aspects of breeding (breeding onset, hatching delay, breeding season length, clutch size, fledgling number) were associated with different weather components. We used an almost four-decade-long dataset to investigate the various aspects of breeding parameters of a collared flycatcher (Ficedula albicollis) population in the Carpathian Basin. Analyses revealed some considerable associations, for example, breeding seasons lengthened with the amount of daily precipitation, and clutch size increased with the number of cool days. Parallel and opposing changes in the correlated pairs of breeding and weather parameters were also observed. The phenological mismatch between prey availability and breeding time slightly increased, and fledgling number strongly decreased with increasing mistiming. Our results highlighted the intricate interplay between climate change and the reproductive patterns of migratory birds, emphasizing the need for a holistic approach. The results also underscored the potential threats posed by climate change to bird populations and the importance of adaptive responses to changing environmental conditions.

Hurricane season hindsight 2020: Applying the IDEA model toward local tropical cyclone forecasts.

Hurricane Laura began as a disorganized tropical depression in August 2020. Early forecast guidance showed that the tropical cyclone could either completely dissipate or strengthen to a major hurricane as it approached the United States Gulf Coast. While this uncertainty was known by meteorologists, it was not necessarily communicated to the public in a direct manner. As it turned out, the worst-case scenario was the correct one. The tropical depression rapidly intensified and made landfall near Cameron, Louisiana, with sustained winds of 150 mph, making Laura a Category 4 hurricane on the Saffir-Simpson scale. Laura's rapid intensification caught some people off guard. Ideally, weather forecasts would have begun warning Louisiana residents to prepare for the possibility of a devastating hurricane in the early stages of tropical cyclone development. No one is suggesting that meteorologists did anything wrong. However, with the benefit of hindsight and decades of scholarly research in risk communication, we can speculate how an ideal forecast would have been written. This paper demonstrates that there are some simple considerations that could be made that might better alert the public to future hurricane worst-case scenarios, even in uncertain situations.

Changes in Cabernet Sauvignon yield and berry quality as affected by variability in weather conditions in the last two decades in Lebanon.

Cabernet Sauvignon is the most planted cultivar in Lebanese vineyards. This study investigated the variation of its production at two vineyards 'Kanafar' (West Bekaa at 1020 m.a.s.l) and 'Taanayel' (Central Bekaa at 800 m.a.s.l) and their interactions with weather conditions from 2006 till 2018. Evaluation of climate records denoted interannual variability in weather conditions occurring in 2015 in Kanafar and in 2008 in Taanayel. Average yield peaked in 2009 in Kanafar (19,187.0 kg ha-1) and in 2011 in Taanayel (14,279.0 kg ha-1), both years marked a turning point after which values of average yield shifted downwards (by 31-67% in Kanafar, and 14-82% in Taanayel). At Kanafar, after 2015, averages of yield, weight of 200 berries (W200B), potential alcohol (PA), and total polyphenolic richness (TPR) decreased by 35%, 1.5%, 36.2 g, and 50%, respectively. In Taanayel, only TPR content in berries was significantly affected by varying weather conditions (decrease by 20%). Also, TPR values followed a progressive decreasing pattern starting from 2006 at both vineyards with minor exceptions. Multiple regression analysis assessed the relationship between various indicators and weather variables at each vineyard. It showed that the decrease in yield at Kanafar correlated with higher temperature during the growing season (by 0.6 °C), higher solar radiation from early-spring to early-summer (by 13.9-27.1 W m-2), and lower values of maximum wind speed during mid to late summer (by 0.4 m s-1), occurring during 2016, 2017, and 2018 at Kanafar. The model explained 60% of yield variations at this vineyard. Further, weather variables accounted for 61% (R2 = 0.61) of changes in PA and for 58% (R2 = 0.58) of TPR of berries at Kanafar. Conclusively, interannual variability in weather conditions had more serious negative influence on Cabernet Sauvignon production at Kanafar than at Taanayel, but had a similar negative influence on polyphenols accumulation in berries, and thus on potential wine quality produced at both vineyards.