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The study aimed to investigate the relationship between the patients who applied to the emergency department with acute urticarial and meteorological factors and determine the associated weather conditions. The relationship between acute urticaria patients who applied to the emergency department of a training and research hospital in a city with high altitude and continental climate characteristics in the eastern part of Turkey between January 2019 and December 2019 and meteorological data was evaluated retrospectively. The necessary data for the weather were obtained from the data of the Erzurum Meteorology Directorate, and the patient data were obtained from the hospital information management system and patient files. The meteorological data of the patients at the date of admission and the characteristics of the patients at that date were compared. The study identified 691 patients who applied to the emergency department diagnosed with urticaria in 2019. According to the seasons, it was observed that the patients applied most frequently in the summer months [n = 239; 34.6%]. In the univariable regression model, which was created by taking the values of weather events as a reference on the days when the urticaria presentation was not observed (Group I), it was determined that every 1-hour increase in the sunshine hour increased the probability of urticaria by 7.4% (p = 0.018). When the meteorological parameters on the days without urticaria (Group I) and the days with urticaria presentation (Group II) were compared, the sunshine hours were 7.9 (4.0-10.6) hours in Group II and 6.65 (3.3-8.85) hours in Group I (p = 0.001). Regarding relative humidity, higher humidity rates were observed in Group I compared to Group II (p = 0.009). In terms of mean temperature, daily maximum, and minimum temperature, higher temperature rates were detected in Group II (p<0.001). A relationship was determined between urticaria attacks and relative humidity and daily maximum and minimum temperature in patients who applied to the emergency department with acute urticaria., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Delice et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Heat stress negatively affects livestock, with undesirable effects on animals' production and reproduction. Temperature and humidity index (THI) is a climatic variable used worldwide to study the effect of heat stress on farm animals. Temperature and humidity data can be obtained in Brazil through the National Institute of Meteorology (INMET), but complete data may not be available due to temporary failures on weather stations. An alternative to obtaining meteorological data is the National Aeronautics and Space Administration Prediction of Worldwide Energy Resources (NASA POWER) satellite-based weather system. We aimed to compare THI estimates obtained from INMET weather stations and NASA POWER meteorological information sources using Pearson correlation and linear regression. After quality check, data from 489 INMET weather stations were used. The hourly, average daily and maximum daily THI were evaluated. We found greater correlations and better regression evaluation metrics when average daily THI values were considered, followed by maximum daily THI, and hourly THI. NASA POWER satellite-based weather system is a suitable tool for obtaining the average and maximum THI values using information collected from Brazil, showing high correlations with THI estimates from INMET and good regression evaluation metrics, and can assist studies that aim to analyze the impact of heat stress on livestock production in Brazil, providing additional data to complement the existing information available in the INMET database., (© 2023. The Author(s) under exclusive licence to International Society of Biometeorology.)

Radiance observations are typically affected by biases that come mainly from instrument error (scanning or calibration) and inaccuracies of the radiative transfer model. These biases need to be removed for successful assimilation, so a bias correction scheme is crucial in the Numerical Weather Prediction (NWP) system. Today, most NWP centres, including the Bureau of Meteorology (hereafter, "the Bureau"), correct the biases through variational bias correction (VarBC) schemes, which were originally developed for global models. However, there are difficulties in estimating the biases in a limited-area model (LAM) domain. As a result, the Bureau's regional NWP system, ACCESS-C (Australian Community Climate and Earth System Simulator-City), uses variational bias coefficients obtained directly from its global NWP system ACCESS-G (Global). This study investigates independent radiance bias correction in the data assimilation system for ACCESS-C. We assessed the impact of using independent bias correction for the LAM compared with the operational bias coefficients derived in ACCESS-G between February and April 2020. The results from our experiment show no significant difference between the control and test, suggesting a neutral impact on the forecast. Our findings point out that the VarBC-LAM strategy should be further explored with different settings of predictors and adaptivity for a more extended period and over additional domains.

The Precipitating Change Project was a 5-year development, implementation, and research study of an innovative 4-week middle school curricular unit in computational weather forecasting that integrates students' learning and use of meteorology and computational thinking (CT) concepts and practices. The project produced a list of CT skills and definitions that students use to predict the weather, CT assessment instruments, and a CT classroom observation protocol. Data was collected from 306 eighth grade (ages 13-14) students in rural indigenous communities in the Arctic and urban and suburban Northeast communities in the USA. The project met its goal of producing an intentional instructional sequence that integrates disciplinary science and CT practices to increase students' science knowledge and their ability to use CT skills and processes. The results indicate that teachers were able to use the curriculum to embed CT practices into the classroom. Students, in turn, had the opportunity to practice using these skills in class discussion as evidenced by the classroom observation data, and students' science knowledge of CT content and practices significantly increased as evidenced by their performance on the weather content and CT skills pre- and post-assessments. While statistically significant gains in science knowledge and CT skills and practices were evident in all settings (urban, suburban, and rural indigenous communities), there were noticeable differences in gains in students' CT skills and practices between the three settings and additional research is needed in a diversity of settings to understand this difference.

This study was grounded in the spatial computational thinking model developed by the "3D Weather" project funded by the NSF STEM+C program. The model reflects a discipline-based perspective towards computational thinking and captures the spatial nature of computational thinking in meteorology and the reliance of computational thinking on spatial thinking for geospatial analysis. The research was conducted among nineteen teachers attending the summer workshop offered by the project in its third project year to prepare them for teaching spatial computational thinking with IDV ("Integrated Data Viewer," downloadable at https://www.unidata.ucar.edu/software/idv/) visualization of weather data. Quantitative survey data were collected measuring these teachers' meteorology content knowledge, spatial computational thinking, self-efficacy for teaching spatial computational thinking, and epistemic cognition of teaching meteorology. The data were analyzed to examine the effects of the workshop in terms of these variables and the correlations among them were also explored.

The leakage current (LC) caused by the surface contamination of insulators, together with environmental variables, is one of the most basic online monitoring parameters for insulator status. However, the impact of weather conditions such as temperature, air humidity, and dew point on the LC has not been deeply studied until now. In this paper, based on meteorological data obtained online and LC obtained with an optical fiber sensor, installed in 500-kV insulator strings of a transmission line, the impact of weather conditions was studied. Results indicate that the LCs follow a specific pattern, according to weather conditions. The system has been continuously monitoring LC, humidity, temperature, and dew point uninterrupted for three years, sending the acquired data to a web page; therefore, it has been demonstrated to be robust, reliable, and repetitive. The sensor features the broadband response and acquisition capabilities of partial discharge pulses in high-voltage insulators, allowing the detection of high-frequency pulses. When comparing the LC measured in this work with those from other works, our measurements are substantially higher; this is due to the type of pollution found in this specific situation, which includes iron oxide powder, producing a conductive layer over the insulator surface that, unlike sea salt, does not depend on humidity to conduct an LC. One of the conclusions reached in this work is that partial discharge surges are caused when the local temperature reaches the dew point and not simply from the presence of high humidity, as stated in many works dealing with LCs. The monitored LC can be used as an indicative parameter of a possible flashover, enabling the proper planning of insulator predictive maintenance, either by jet-washing the surface or even changing the insulators when they are damaged.

Regional weather variability depends on various meteorological variables such as temperature and rainfall. The current research focuses on the variability and trends in annual aerosol optical depth (AOD), temperature (T), and rainfall (RF) in 11 Vidarbha districts. The annual trend analysis of AOD, T, and R is determined using the non-parametric Sen slope and Mann-Kendall (MK) test at a 5% significant level from 1980 to 2019. Annual T and AOD indicate a substantial increase in this study, whereas rainfall shows a non-significant trend (MK, test) over the study period. According to Sen's slope trends, the relatively high rainfall area (Chandrapur = 1.273 and Garchiroli = 4.06) got positive trends, but Gondia and Bhandara districts have negative (Sen's slope =  - 2.79 and - 2.56) trends. The moderate rainfall areas are showing a less negative Sen slope (Wardha =  - 0.21, Washim =  - 1.13 and Yavatmal =  - 2.75), whereas Nagpur districts' Sen's slope shows a positive value (Sens's slope = 0.72). The assured rainfall area districts show Sen's slope trends are positive (Akola = 0.45, Amravati = 1.17 and Buldana = 0.42). Sen's slope trend indicates rising rainfall, whereas negative trends indicate decreasing rainfall in the time series. This study has also looked at the effect of RF, AOD, and T on the last two decades' cash crop production (2000-2019) for Vidarbha districts. The relationship between rainfall departure (DRF) and cash crop yield has also been highlighted. Five cash crops, such as cotton (Ct), total cereals (TCrl), total oilseeds (TOsd), total pulses (TPS), and sugarcane (Sc), are selected for the present study., (© 2022. The Author(s) under exclusive licence to International Society of Biometeorology.)

The association of fainting with specific situations and circumstances, such as the sight of blood, response to pain, prolonged standing position and fatigue, is well recognized and described in medical literature. Clinical experience also indicates that specific, local physical conditions, such as exposure to heat or remaining in a small, stuffy room may also trigger fainting. This paper verifies the hypothesis concerning the association between atmospheric conditions and the incidence of fainting. This is a retrospective cohort study of data relating to fainting collected in the city of Olsztyn (Poland). In total, 10,449 emergency service interventions in the period 2012-2019 that concluded with the R55 (syncope and collapse) diagnosis according to the ICD 10 were analyzed. The obtained data were matched with meteorological data, including basic parameters (temperature, humidity, atmospheric pressure) and complex parameters, with special attention given to the Universal Thermal Climate Index (UTCI). This index is derived from an analysis of human thermal balance and is particularly useful for describing the organism's response to thermal stress. Statistically significant differences in the occurrences of fainting depending on the season were revealed (more in the summer), but only for women. Among the analyzed meteorological and biometeorological parameters, statistical significance was found for parameters relating to temperature, with the greatest usefulness revealed for the UTCI. Periods with heat stress were more conducive to fainting, whereas the result for the general population was influenced by women in two age groups: 25-45 and 46-60. To our best knowledge, this is the first attempt worldwide to utilize the UTCI as a predictor of fainting. Our results confirmed the applicability of the UTCI as a universal biometeorological tool for the assessment of relationships between atmospheric conditions and the incidence of fainting., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Due to the emergence of the coronavirus disease (COVID 19), education systems in most countries have adapted and quickly changed their teaching strategy to online teaching. This paper presents the design and implementation of a novel Internet of Things (IoT) device, called MEIoT weather station, which incorporates an exogenous disturbance input, within the National Digital Observatory of Smart Environments (OBNiSE) architecture. The exogenous disturbance input involves a wind blower based on a DC brushless motor. It can be controlled, via Node-RED platform, manually through a sliding bar, or automatically via different predefined profile functions, modifying the wind speed and the wind vane sensor variables. An application to Engineering Education is presented with a case study that includes the instructional design for the least-squares regression topic for linear, quadratic, and cubic approximations within the Educational Mechatronics Conceptual Framework (EMCF) to show the relevance of this proposal. This work's main contribution to the state-of-the-art is to turn a weather monitoring system into a hybrid hands-on learning approach thanks to the integrated exogenous disturbance input.

We analyzed the climate ecological suitability of Apis cerana cerana in the northern mountain area of Sichuan Basin, based on the meteorological data of two meteorological stations, the production and the ecological characteristics and activity of A. cerana cerana . Taking temperature, moisture, light, wind and weather conditions as impact indices and referring to life meteorological index, we constructed a hierarchical calculation method for activity meteorological index and annual evaluation of A. cerana cerana in northern mountainous area of Sichuan Basin. Results showed that the main meteorological factors affecting A. cerana cearna activity in the study were temperature, relative humidity, sunshine hour, wind power and daytime cumulative duration of precipitation. Among those factors, temperature and daytime cumulative duration of precipitation were the main ones. The meteorological index of A. cerana cerana activity was constructed through the combination of different values of these five index factors, with which the grading evaluation being carried out. When the index was greater than 12, the weather condition was good and suitable for A. cerana cearna activity. When the index was 7-12, the weather condition was general, which was sub-suitable for A. cerana cearna activity. When the index was 1-7, the weather condition was poor, the activity of A. cerana cearna would significantly decrease. When the index was less than or equal to 1, the weather condition was bad and would not be suitable for A. cerana cearna to be active or in the overwintering period. We used this index to evaluate the climate of Zhongshan bee farm. Results showed that the average comprehensive meteorological index was 129.3 (score 60.4) for many years (April to October), the overall meteorological condition was good. The yield of "local honey" was significantly correlated with the annual comprehensive meteorological index. The accuracy rate of annual meteorological evaluation was 90%.

Pests, pathogens and diseases cause some of the most widespread and damaging impacts worldwide - threatening lives and leading to severe disruption to economic, environmental and social systems. The overarching goal of biosecurity is to protect the health and security of plants and animals (including humans) and the wider environment from these threats. As nearly all living organisms and biological systems are sensitive to weather and climate, meteorological, 'met', data are used extensively in biosecurity. Typical applications include, (i) bioclimatic modelling to understand and predict organism distributions and responses, (ii) risk assessment to estimate the probability of events and horizon scan for future potential risks, and (iii) early warning systems to support outbreak management. Given the vast array of available met data types and sources, selecting which data is most effective for each of these applications can be challenging. Here we provide an overview of the different types of met data available and highlight their use in a wide range of biosecurity studies and applications. We argue that there are many synergies between meteorology and biosecurity, and these provide opportunities for more widespread integration and collaboration across the disciplines. To help communicate typical uses of meteorological data in biosecurity to a wide audience we have designed the 'Meteorology for biosecurity' infographic., (© 2020 The Author(s).)

Given the large transformation and fast-growing population that the Greater Toronto Area (GTA) is facing, and the increasing impact of climate change in urbanized areas, it is crucial to investigate strategies that could mitigate the effects of heat waves. In this paper, the effects of greenery enhancements are investigated using mesoscale and microscale simulations performed by the Weather Research and Forecasting model and the ENVI-met model, respectively. In particular, two vulnerable areas located in the GTA are investigated. Comparing the results of simulations with measurements show the differences in how mesoscale and microscale models predict the meteorological processes happening within the urban canopy and the local climate. Then, two mitigation scenarios, a moderate green scenario (MGS) and an intensive green scenario (IGS) are assessed considering different increases in the vegetation area. The results of the mesoscale simulations show that by increasing the greenery canopy, the maximum daily air temperature decreases by 1.6 to 2.3 °C, while the relative humidity increases by 10% to 12%. The microscale simulations show that increasing the tree canopy would cool the air temperature by 0.5 °C to 1.4 °C locally. Overall, depending on wind conditions and the arrangement of buildings and existing green areas, the cooling effect is shown to have an impact on up to 250 m downwind from the new green area locations. Finally, this study demonstrates that both mesoscale (WRF) and microscale (ENVI-met) modeling confirm similar results in how greenery enhancements may improve the human thermal comfort in the continental climate of the GTA., Competing Interests: Declaration of competing interest The authors of this paper certify that they have NO affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; participation in speakers’ bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest in the subject matter or materials discussed in this manuscript. As such the authors declare NO CONFLICT OF INTEREST with the present submission and with the content of this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

With a large population and fast economic growth, the Sichuan Basin (SCB) has been suffering from air pollution in recent years. However, limited studies have estimated air pollution levels in consideration of the feedbacks of aerosols on meteorology in the SCB. In this study, simulation of fine particulate matter (PM 2.5 ) and ozone (O 3 ) over the SCB with a horizontal resolution of 36 km over China and 12 km was conducted for summer (July) and winter (January) in 2015 using the Weather Research and Forecasting model coupled with Chemistry (WRF/Chem). The model well captured the variations of PM 2.5 and daily maximum 8 h average (MDA8) O 3 in the 18 cities, especially O 3 in July and PM 2.5 in January. From rim to center, averaged PM 2.5 increased from 40 μg/m 3 to 100 μg/m 3 in January while averaged O 3 ranged from 60 to 90 ppb in July. Aerosol radiation decreased surface temperature by 1-2 °C, wind speed (WS) by ∼ 0.3 m/s, planetary boundary layer (PBL) height by 10-20%, solar radiation (SR) by ∼30%, and precipitation by 0.02-0.2 mm, while increased relative humidity (RH) by up to 2-4% in January, which resulted in up to 10 μg/m 3 increase of PM 2.5 in January and 2 ppb decrease of O 3 in July. The effect increased as the increase of PM 2.5 concentration and can be up to 18% in January and 25% in July. This study highlights the importance of considering meteorology feedbacks in understanding and controlling air pollution in the SCB., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

The aim of the present investigations was to simulate the annual risk of bunch rot (Botrytis cinerea) on Vitis vinifera L. cv. Riesling grapes based on three long-term (n = 3 × 7 = 21 cases) assessment data sets originating from three Central European grape-growing regions. Periods when meteorological parameters were significantly (p < 0.01) correlated with the cumulative degree day (CDD 7;18;24 ) reaching 5% disease severity were determined by Window Pane analysis. Analyses revealed five critical weather constellations ("events") influencing annual epidemics: relatively low temperatures after bud break, dry conditions during flowering, high temperatures after flowering, and low temperatures and high precipitation sums during/after veraison were all associated with thermal-temporal early epidemics. Meteorological data in each of the five events served as input for the bunch rot risk model "BotRisk." The multiple linear regression model resulted in an adjusted coefficient of determination (R 2 adj. ) of 0.63. BotRisk enables (i) the simulation of the thermal-temporal position of the annual epidemic and, based on this, (ii) the classification of the annual bunch rot risk into three classes: low, medium, or high risk. According to leave-one-out cross-validation, 11 of 21 case studies were correctly classified. No systematic bias caused by location was observed, indicating that the transfer of the model into other locations with comparable climatic conditions could be possible. BotRisk (i) represents a novel viticultural decision support tool for crop cultural and chemical measures against bunch rot and (ii) enables an estimation of the bunch rot risk under changing environmental conditions.

In human biometeorology, the estimation of mean radiant temperature (MRT) is generally considered challenging. This work presents a general framework to compute the MRT at the global scale for a human subject placed in an outdoor environment and irradiated by solar and thermal radiation both directly and diffusely. The proposed framework requires as input radiation fluxes computed by numerical weather prediction (NWP) models and generates as output gridded globe-wide maps of MRT. It also considers changes in the Sun's position affecting radiation components when these are stored by NWP models as an accumulated-over-time quantity. The applicability of the framework was demonstrated using NWP reanalysis radiation data from the European Centre for Medium-Range Weather Forecasts. Mapped distributions of MRT were correspondingly computed at the global scale. Comparison against measurements from radiation monitoring stations showed a good agreement with NWP-based MRT (coefficient of determination greater than 0.88; average bias equal to 0.42 °C) suggesting its potential as a proxy for observations in application studies.

Cases of anthrax in livestock are infrequently and irregularly reported in the state of Victoria, Australia; however, their impact on individual livestock, farming communities and the government agencies tasked with containing these outbreaks is high. This infrequency has been anecdotally associated with differences in annual and local weather patterns. In this study, we used historical anthrax cases and meteorological data from weather stations throughout Victoria to train a generalized linear mixed effects model to predict the daily odds of a case of anthrax occurring in each shire in the coming 30 days. Meteorological variables were transformed to deviations from the mean values for temperature or cumulative values for rainfall in the shire across all years. Shire was incorporated as a random effect to account for meteorological variation between shires. The model incorporated a post hoc weighting for the frequency of historic cases within each shire and the spatial contribution of each shire to the recently redefined Australian Anthrax Belt. Our model reveals that anthrax cases were associated with drier summer conditions (OR 0.96 (95% CI 0.95-0.97) and OR 0.98 (95% CI 0.97-0.99) for every mm increase in rainfall during September and December, respectively) and cooler than average spring (OR 0.20 (95% CI 0.11-0.52) for every °C increase in minimum daily temperature during November and warmer than average summer temperatures (OR 1.45 (95% CI 1.29-1.61) for every °C increase in maximum daily temperature during January. Cases were also preceded by a 40-day period of cooler, drier temperatures (OR 0.5 (95% CI 0.27-0.74) for every °C increase in maximum daily temperature and OR 0.96 (95% CI 0.95-0.97) for every mm increase in rainfall followed by a warmer than average minimum (or nightly) temperature 10 days immediately before the case (OR 1.46 (95% CI 1.35-1.58) for every °C increase in maximum daily temperature). These coefficients of this training model were then applied daily to meteorological data for each shire, and output of these models was presented as a choropleth and timeline plot in a Shiny web application. The application builds on previous spatial modelling and provides Victorian agencies with a tool to engage at-risk farmers and guide discussions towards anthrax control. This application can contribute to the wider rejuvenation of anthrax knowledge and control in Victoria and corroborates the anecdote that increased odds of disease can be linked to meteorological events.

The low availability of high-quality meteorological data resulted in the development of synthetic meteorological data generated by satellite or data interpolation, which are available in grids with varying spatio-temporal resolution. Among these different data sources, NASA/POWER and DailyGridded databases have been applied for crop yield simulations. The objective of this study was to evaluate the performance of these two datasets, in different time scales (daily, 10-day, monthly, and annual), as input data for estimating potential (YP) and attainable (YA) maize yields, using the FAO Agroecological Zone crop simulation model (FAO-AEZ), properly calibrated and validated. For that, daily weather data from ten Brazilian locations were collected and compared to the data extracted from NASA/POWER and DailyGridded systems and later applied to estimate the potential and attainable maize yields. DailyGridded data showed a better performance than NASA/POWER for all weather variables and time scales, with confidence index (C) ranging from 0.52 to 0.99 for the former and from 0.09 and 0.99 for the latter. As a consequence of that, DailyGridded data was better than NASA/POWER to estimate maize yields with estimates close to those obtained with observed data, with a lower mean absolute errors (< 30 kg ha -1 ) and a higher confidence index (C = 0.99).

Wearable devices have the potential to track and monitor a wide range of biometeorological conditions (e.g., temperature, UV, air quality) and health outcomes (e.g., mental stress, physical activity, physiologic strain, and cognitive impairments). These sensors provide the potential for personalized environmental exposure information that can be harnessed for at-risk populations. Personalized environmental exposure information is of particular importance for populations that are continuously exposed to hazardous environmental conditions or with underlying health conditions. Yet, for these devices to be effective, individuals must be willing to monitor their health and, if prompted, adhere to warnings or notifications. To date, no study has examined the perceptions and use of digital devices and wearable sensors among vulnerable outdoor working populations. This study evaluated digital device use and perceptions among a population of groundworkers in three diverse geographic sites in the southeastern USA (Boone, NC, Raleigh, NC, and Starkville, MS). Our results demonstrate that biometeorological health interventions should focus on smartphone technology as a platform for monitoring environmental exposure and associated health outcomes. It was encouraging to find that those study participants were very likely to wear sensors and utilize global positioning system technology despite potential privacy issues. In addition, 3 out of 4 workers indicated that they would change their behavior if given a personalized heat preventive warning. Future development of wearable sensors and smartphone applications should integrate personalized weather warnings and ensure privacy to facilitate the use of these technologies among vulnerable populations.

Human comfort in outdoor spaces (HCOS) is linked to people's psychological responses to environmental variables. Previous studies have established comfort ranges for these variables through interviews and questionnaires, reaching only limited populations. However, larger amounts of data would not only generate more robust results in local studies, but it would also allow for the possibility of creating an approach that could be applied to a wider range of weather conditions and different climates. Therefore, this study describes a new methodology to assess people's perception of weather based on human responses to weather conditions extracted from tweets, with the purpose of establishing comfort ranges for environmental variables. Tweets containing weather-associated keywords were collected, stored, and then linked to real-time meteorological data acquired nearby the locations in which the tweets were posted. Afterwards, people's perception of weather was extracted from the tweets using a classifier trained specifically on weather data that identified irrelevant, neutral, positive, and negative tweets. The obtained tweets and their related atmospheric data were analyzed to establish comfort ranges. The tweets' responses to effective temperature were very similar to those obtained in previous studies, although the peak of comfort is shifted towards the cold stress. Similarly, the tweets' responses to the thermohygrometric index were alike to previous results, but the peak of comfort is shifted towards the heat stress. Regarding the single weather variables under study, the obtained comfort ranges are similar to the ones found in previous research; in particular, the temperature comfort range matches perfectly at 20-22 °C. Therefore, it was concluded that tweets can be used to assess HCOS; not only are the results of this methodology comparable to results obtained in previous studies, but the procedure itself also shows new features and unexpected future applications.

Weather affects physical and mental health through several modalities which are not fully elucidated. The aim of the present study was to investigate the impact of meteorological variables and other indexes in a large sample of hospitalized patients, focusing on subjects who were involuntarily admitted. We hypothesized a direct relation between the amount of involuntary admissions and mean sunshine hours. Furthermore, we supposed that specific meteorological factors may significantly influence hospitalizations of patients affected by severe psychiatric conditions. All subjects were consecutively recruited from the Psychiatric Inpatient Unit of San Luigi Gonzaga Hospital, Orbassano (Turin, Italy) from September 2013 to August 2015. Socio-demographic and clinical characteristics were carefully collected. Meteorological data were derived by the Italian Meteorology's Climate Data Service of Physics Department of the University of Turin (Latitude: 45°03'07,15″ Nord, Longitude: 007°40'53,30″ Est, Altitude: 254 m above the sea level) (http://www.meteo.dfg.unito.it/). Our data indicate significant differences regarding temperature (minimum, maximum, and medium), solar radiation, humidex and windchill index, and hours of sunshine in psychiatric patients who were involuntarily hospitalized. After logistic regression analyses, only maximum and medium temperature, and humidex index remained significantly associated with involuntary admission in an emergency psychiatric ward. The limitations of this study include the cross-sectional study design and the single hospital for patients' recruitment. Furthermore, results and seasonal patterns obtained by patients requiring hospitalization might significantly differ from those who were not hospitalized. Exploring in a more detailed manner those environmental factors associated with involuntary admissions could lead to early intervention and prevention strategies for such distressing hospitalizations., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Cold- and heat-related mortality poses significant public health concerns worldwide. Although there are numerous studies dealing with the association between extreme ambient temperature and mortality, only a small number adopt a synoptic climatological approach in order to understand the nature of weather systems that precipitate increases in cold- or heat-related mortality. In this paper, the Lamb Weather Type synoptic classification is used to examine the relationship between daily mortality and weather patterns across nine regions of England. Analysis results revealed that the population in England is more susceptible to cold weather. Furthermore, it was found that the Easterly weather types are the most hazardous for public health all-year-long; however, during the cold period, the results are more evident and spatially homogenous. Nevertheless, it is noteworthy that the most dangerous weather conditions are not always associated with extreme (high or low) temperatures, a finding which points to the complexity of weather-related health effects and highlights the importance of a synoptic climatological approach in elucidating the relationship between temperature and mortality.

The outdoor radon concentration was monitored together with the meteorological parameters at two contrasting complex topographies: sub-Alpine basin (SA) and sub-Mediterranean valley (SM) in winter (December 2017-February 2018) and summer (June-August 2018). The time series for each site and each season are evaluated in three different ways: (i) clear-sky and cloudy condition together, (ii) clear-sky conditions only (cloud cover <20%) and (iii) cloudy condition only (cloud cover >20%), and compared to the expected atmospheric boundary layer (ABL) 'mixing volume' caused by meteorological changes. The results have confirmed the sensitivity of diurnal and seasonal radon concentration to the expected ABL 'mixing volume' at the two selected sites. The relationship is more pronounced in calm clear-sky conditions. Cloudy conditions are associated with fast weather changes, when the ABL is well mixed and hourly mean radon concentrations do not follow the typical diurnal trend., (© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Outdoor thermal comfort studies have proved that urban design has a great influence on pedestrians' thermal comfort and that its assessment helps one to understand the quality and usage of the pedestrian environment. However, the majority of outdoor thermal comfort studies perceive pedestrian thermal comfort as "static". The dynamic multiple uses of urban spaces and the highly inhomogeneous urban morphology in high-density cities of the tropics are seldom considered, which leads to a lack of understanding about how pedestrians respond to the changes of the outdoor environment. This study contributes to the understanding of the dynamic thermal comfort using a longitudinal survey that was conducted to obtain information about how thermal sensation changes throughout the walking route and how it is affected by micro-meteorological conditions and the urban geometry. The large variations in micro-meteorological conditions throughout the walking routes are predominantly influenced by the urban geometry. Additionally, the spatial pattern of thermal sensation varies based on the weather conditions, emphasizing the need to account for such variations in the assessment of pedestrian thermal comfort. The results also show that thermal sensation was associated with participants' short-term thermal experience (2-3 min) and that the urban geometry plays an important role in the time-lag effect of meteorological variables on thermal sensation. The findings of this study contribute to improving urban geometry design in order to mitigate the thermal discomfort and create a better pedestrian environment in high-density cities.

Several studies have examined the cardiovascular effects of atmospheric parameters as separate factors; however, few have investigated atmospheric parameters' joint effects. We aim to explore the joint effects of atmospheric parameters on acute cardiovascular diseases (ACVDs) and on major cardiovascular risk factors (CRFs). We correlated all ACVD admissions with major CRFs and local atmospheric conditions during a 5-year study period. A seasonal variation was detected in a higher incidence rate during cold atmospheric conditions. There were significant incidence relative ratios, including: 1.140 (95% CI [1.020, 1.283]) for daily temperature change (≥5 °C); 0.991 (95% CI [0.988, 0.994]) for average daily temperature; and 1.290 (95% CI [1.090, 1.599]) for the interaction of daily temperature change (≥5 °C) with humidity change (≥40%). We observed a significant association between the atmospheric parameters' joint effects and hyperlipidaemia, diabetes, and previous ACVDs. Patients with diabetes had the highest significant incidence relative ratio at 2.429 (95% CI [1.088, 5.424]) for humidity-temperature interactions. Thus, the atmospheric parameters' joint effects play an important role as minor CRFs. These unfavourable atmospheric situations are predicted to increase the number of ACVDs mainly. Our study may help to organize prevention strategies more effectively and to reduce cardiovascular risks.

Soil moisture is one of the main factors in agricultural production and hydrological cycles, and its precise prediction is important for the rational use and management of water resources. However, soil moisture involves complex structural characteristics and meteorological factors, and it is difficult to establish an ideal mathematical model for soil moisture prediction. Existing prediction models have problems such as prediction accuracy, generalization, and multi-feature processing capability, and prediction performance must improve. Based on this, taking the Beijing area as the research object, the deep learning regression network (DNNR) with big data fitting capability was proposed to construct a soil moisture prediction model. By integrating the dataset, analyzing the time series of the predictive variables, and clarifying the relationship between features and predictive variables through the Taylor diagram, selected meteorological parameters can provide effective weights for moisture prediction. Test results prove that the deep learning model is feasible and effective for soil moisture prediction. Its' good data fitting and generalization capability can enrich the input characteristics while ensuring high accuracy in predicting the trends and values of soil moisture data and provides an effective theoretical basis for water-saving irrigation and drought control., Competing Interests: The authors have declared that no competing interests exist.

This paper proposes a method based on a local weather type classification approach to facilitate analysis and communication of climate information in local climate studies. Presented herein is an application to urban climatology in Toulouse, France, but the method can be used in other applied fields of climatology as well. To describe the climatic context of this urbanized area, the local weather types that explain the plurality of weather situations Toulouse faces are presented in depth. In order to show the potential for use of this approach, this information is applied to the study of changes in local weather types in terms of frequency and intensity within a series of future climate projections, a classic urban canopy and a series of atmospheric boundary layer analyses, and as a support for communication aimed to initiate urban climate awareness in urban planning practices. The proposed classification method has been coded in an R script and is provided as a supporting information file. The paper concludes that a systematic pre-study using this kind of climatic analysis is a good practice for performing climatic contextualization in local scale applied studies, both for scientific analysis and communication., Competing Interests: The authors have declared that no competing interests exist.

Background: Longitudinal and time series analyses are needed to characterize the associations between hydrometeorological parameters and health outcomes. Earth Observation (EO) climate data products derived from satellites and global model-based reanalysis have the potential to be used as surrogates in situations and locations where weather-station based observations are inadequate or incomplete. However, these products often lack direct evaluation at specific sites of epidemiological interest., Methods: Standard evaluation metrics of correlation, agreement, bias and error were applied to a set of ten hydrometeorological variables extracted from two quasi-global, commonly used climate data products - the Global Land Data Assimilation System (GLDAS) and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) - to evaluate their performance relative to weather-station derived estimates at the specific geographic locations of the eight sites in a multi-site cohort study. These metrics were calculated for both daily estimates and 7-day averages and for a rotavirus-peak-season subset. Then the variables from the two sources were each used as predictors in longitudinal regression models to test their association with rotavirus infection in the cohort after adjusting for covariates., Results: The availability and completeness of station-based validation data varied depending on the variable and study site. The performance of the two gridded climate models varied considerably within the same location and for the same variable across locations, according to different evaluation criteria and for the peak-season compared to the full dataset in ways that showed no obvious pattern. They also differed in the statistical significance of their association with the rotavirus outcome. For some variables, the station-based records showed a strong association while the EO-derived estimates showed none, while for others, the opposite was true., Conclusion: Researchers wishing to utilize publicly available climate data - whether EO-derived or station based - are advised to recognize their specific limitations both in the analysis and the interpretation of the results. Epidemiologists engaged in prospective research into environmentally driven diseases should install their own weather monitoring stations at their study sites whenever possible, in order to circumvent the constraints of choosing between distant or incomplete station data or unverified EO estimates., (Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.)

This paper presents the development of an operational forecasting service focusing on human-biometeorological conditions. The service is based on the coupling of numerical weather prediction models with an advanced human-biometeorological model. Human thermal perception and stress forecasts are issued on a daily basis for Greece, in both point and gridded format. A user-friendly presentation approach is adopted for communicating the forecasts to the public via the worldwide web. The development of the presented service highlights the feasibility of replacing standard meteorological parameters and/or indices used in operational weather forecasting activities for assessing the thermal environment. This is of particular significance for providing effective, human-biometeorology-oriented, warnings for both heat waves and cold outbreaks.

The design and implementation of any climate-related health service, nowadays, imply avoiding the digital divide as it means having access and being able to use complex technological devices, massive meteorological data, user's geographic location and biophysical information. This article presents the co-creation, in detail, of a biometeorological data infrastructure, which is a complex platform formed by multiple components: a mainframe, a biometeorological model called Pronbiomet, a relational database management system, data procedures, communication protocols, different software packages, users, datasets and a mobile application. The system produces four daily world maps of the partial density of the atmospheric oxygen and collects user feedback on their health condition. The infrastructure is shown to be a useful tool to delineate individual vulnerability to meteorological changes as one key factor in the definition of any biometeorological risk. This technological approach to study weather-related health impacts is the initial seed for the definition of biometeorological profiles of persons, and for the future development of customized climate services for users in the near future.

The Turner method is the "benchmark method" for computing the stability class that is used to compute the X/Q (s m). The Turner method should be used to ascertain the validity of X/Q results determined by other methods. This paper used site-specific meteorological data obtained from the National Weather Service. The Turner method described herein is simple, quick, accurate, and transparent because all of the data, calculations, and results are visible for verification and validation with published literature.

An early method of biometeorological forecasts was developed for Cuba during the late 90s. It was based on the relationship between the daily occurrence of massive health crisis and the magnitude of the 24-h differences of partial density of oxygen in the air (PODA index). Ten years later, applying new technological facilities, a new model was developed in order to offer operational biometeorological forecast to Cuban health institutions. After a satisfactory validation process, the official bioforecast service to health institutions in Villa Clara province began on February of 2012. The effectiveness had different success levels: for the bronchial asthma crisis (94%), in the hypertensive crisis (88%), with the cerebrovascular illnesses (85%), as well as migraines (82%) and in case of cardiovascular diseases (75%) were acceptable. Since 2008, the application of the model was extended to other regions of the world, including some national applications. Furthermore, it allowed the beginning of regional monitoring of meteor-tropic effects, following the occurrence and movement of areas with higher weather contrasts, defined according to the normalized scale of PODA index. The paper describes the main regional results already available, with emphasis in the observed meteor-tropic effects increasing in all regions during recent years. It coincides with the general increase of energy imbalance in the whole climate system. Finally, the paper describes the current development of new global biometeorological forecast services.

Nine dust storms in south-central Arizona were simulated with the Weather Research and Forecasting with Chemistry model (WRF-Chem) at 2 km resolution. The windblown dust emission algorithm was the Air Force Weather Agency model. In comparison with ground-based PM 10 observations, the model unevenly reproduces the dust-storm events. The model adequately estimates the location and timing of the events, but it is unable to precisely replicate the magnitude and timing of the elevated hourly concentrations of particles 10 µm and smaller ([PM 10 ]).Furthermore, the model underestimated [PM 10 ] in highly agricultural Pinal County because it underestimated surface wind speeds and because the model's erodible fractions of the land surface data were too coarse to effectively resolve the active and abandoned agricultural lands. In contrast, the model overestimated [PM 10 ] in western Arizona along the Colorado River because it generated daytime sea breezes (from the nearby Gulf of California) for which the surface-layer speeds were too strong. In Phoenix, AZ, the model's performance depended on the event, with both under- and overestimations partly due to incorrect representation of urban features. Sensitivity tests indicate that [PM 10 ] highly relies on meteorological forcing. Increasing the fraction of erodible surfaces in the Pinal County agricultural areas improved the simulation of [PM 10 ] in that region. Both 24-hr and 1-hr measured [PM 10 ] were, for the most part, and especially in Pinal County, extremely elevated, with the former exceeding the health standard by as much as 10-fold and the latter exceeding health-based guidelines by as much as 70-fold. Monsoonal thunderstorms not only produce elevated [PM 10 ], but also cause urban flash floods and disrupt water resource deliveries. Given the severity and frequency of these dust storms, and conceding that the modeling system applied in this work did not produce the desired agreement between simulations and observations, additional research in both the windblown dust emissions model and the weather research/physicochemical model is called for., Implications: While many dust storms can be considered to be natural, in semi-arid climates such storms often have an anthropogenic component in their sources of dust. Applying the natural, exceptional events policy to these storms with strong signatures of anthropogenic sources would appear not only to be misguided but also to stifle genuine regulatory efforts at remediation. Those dust storms that have resulted, in part, from passage over abandoned farm land should no longer be considered "natural"; policymakers and lawmakers need to compel the owners of such land to reduce its potential for windblown dust.

We propose the formal inclusion of the K-12 education system as a critical step to achieving the National Weather Service's (NWS) vision of a Weather Ready Nation (WRN). The WRN is a NWS initiative to build resilience within the U.S. population to extreme weather, water, and climate events, and includes considerable research investments in science and technology and social, behavioral, and economic science (SBES). The SBES research has been focused on weather communications/messaging and development of impacts-based products, with the goal of better-informed decisions by the public during hazardous weather. While there is a long history of educators working with meteorological professionals on educational initiatives within the K-12 system, the majority of these efforts have involved individual educator initiatives. We believe that including K-12 educators as formal stakeholders in the NWS WRN strategy is one of the most effective ways to reach the greatest segment of the population, and our limited literature review reveals evidence of educational efforts leading to better preparedness. We suggest that formally integrating K-12 educators as partners can best be accomplished by creating a committee of NWS meteorologists, emergency managers, school administrators, and faculty in order to ensure that each partner's concerns are addressed. The curriculum itself should be designed to "connect the dots" from meteorological theory to hazardous weather, hazard products and understanding them to make safe, informed decisions during dangerous weather. We recommend testing this curriculum through a pilot study in a single district or school, with a longitudinal component to monitor its effectiveness.

Improvements in global sustainability, health, and equity will largely be determined by the extent to which cities are able to become more efficient, hospitable, and productive places. The development and evolution of urban areas has a significant impact on local and regional weather and climate, which subsequently affect people and other organisms that live in and near cities. Biometeorologists, researchers who study the impact of weather and climate on living creatures, are well positioned to help evaluate and anticipate the consequences of urbanization on the biosphere. Motivated by the 60th anniversary of the International Society of Biometeorology, we reviewed articles published in the Society's International Journal of Biometeorology over the period 1974-2017 to understand if and how biometeorologists have directed attention to urban areas. We found that interest in urban areas has rapidly accelerated; urban-oriented articles accounted for more than 20% of all articles published in the journal in the most recent decade. Urban-focused articles in the journal span five themes: measuring urban climate, theoretical foundations and models, human thermal comfort, human morbidity and mortality, and ecosystem impacts. Within these themes, articles published in the journal represent a sizeable share of the total academic literature. More explicit attention from urban biometeorologists publishing in the journal to low- and middle-income countries, indoor environments, animals, and the impacts of climate change on human health would help ensure that the distinctive perspectives of biometeorology reach the places, people, and processes that are the foci of global sustainability, health, and equity goals.

Background: A lot of papers have been published about the impact on mortality of Sahara dust intrusions in individual cities. However, there is a lack of studies that analyse the impact on a country and scarcer if in addition the analysis takes into account the meteorological conditions that favour these intrusions., Objectives: The main aim is to examine the effect of Saharan dust intrusions on daily mortality in different Spanish regions and to characterize the large-scale atmospheric circulation anomalies associated with such dust intrusions., Methods: For determination of days with Saharan dust intrusions, we used information supplied by the Ministry of Agriculture, Food & Environment, it divides Spain into 9 main areas. In each of these regions, a representative province was selected. A time series analysis has been performed to analyse the relationship between daily mortality and PM 10 levels in the period from 01.01.04 to 31.12.09, using Poisson regression and stratifying the analysis by the presence or absence of Saharan dust advections., Results: The proportion of days on which there are Saharan dust intrusions rises to 30% of days. The synoptic pattern is characterised by an anticyclonic ridge extending from northern Africa to the Iberian Peninsula. Particulate matter (PM) on days with intrusions are associated with daily mortality, something that does not occur on days without intrusions, indicating that Saharan dust may be a risk factor for daily mortality. In other cases, what Saharan dust intrusions do is to change the PM-related mortality behaviour pattern, going from PM 2.5 ., Conclusions: A study such as the one conducted here, in which meteorological analysis of synoptic situations which favour Saharan dust intrusions, is combined with the effect on health at a city level, would seem to be crucial when it comes to analysing the differentiated mortality pattern in situations of Saharan dust intrusions., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Pregnancy and birth outcomes have been found to be sensitive to meteorological variation, yet few studies explore this relationship in sub-Saharan Africa where infant mortality rates are the highest in the world. We address this research gap by examining the association between meteorological factors and birth weight in a rural population in southwestern Uganda. Our study included hospital birth records (n = 3197) from 2012 to 2015, for which we extracted meteorological exposure data for the three trimesters preceding each birth. We used linear regression, controlling for key covariates, to estimate the timing, strength, and direction of meteorological effects on birth weight. Our results indicated that precipitation during the third trimester had a positive association with birth weight, with more frequent days of precipitation associated with higher birth weight: we observed a 3.1g (95% CI: 1.0-5.3g) increase in birth weight per additional day of exposure to rainfall over 5mm. Increases in average daily temperature during the third trimester were also associated with birth weight, with an increase of 41.8g (95% CI: 0.6-82.9g) per additional degree Celsius. When the sample was stratified by season of birth, only infants born between June and November experienced a significant associated between meteorological exposures and birth weight. The association of meteorological variation with foetal growth seemed to differ by ethnicity; effect sizes of meteorological were greater among an Indigenous subset of the population, in particular for variation in temperature. Effects in all populations in this study are higher than estimates of the African continental average, highlighting the heterogeneity in the vulnerability of infant health to meteorological variation in different contexts. Our results indicate that while there is an association between meteorological variation and birth weight, the magnitude of these associations may vary across ethnic groups with differential socioeconomic resources, with implications for interventions to reduce these gradients and offset the health impacts predicted under climate change.