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Large quantity of observational data from ground and sounding meteorological observation data, ocean station observation data, ERA reanalysis data are used to count the sea fog process that occurred during April to July in Rizhao City from 1988 to 2019 altogether 32 years in this paper. The synoptic characteristics of the sea fog and their formation mechanism are statistically analyzed, and furthermore the main meteorological elements affecting the formation of sea fog in Rizhao area and their combined effects are put forward. The results indicate that sea fog occur in Rizhao area is most likely under the control of several weather conditions, such as the west of transition high to sea, the Pacific subtropical high ridge or the front of low or trough of the eastern China in spring and summer. Under the control of these synoptic characteristics, the direction and speed of wind, dew-point temperature, atmospheric temperature, SST, stratification stability of these hydrometeorological elements play an important role in the formation of sea fog. On these basis, the method of discriminance analysis is used to establish of discriminant equation for sea fog forecast. Through the forecast testing, the statistical forecasting method of sea fog has a great application, the forecast accuracy is between 88% and 94%. It can be used for short-term forecast of sea fog characteristics in Rizhao City, providing services for industry, agriculture, fishery production, sea and land transportation.

The knowledge of variability in climatic attributes is necessary for the evaluation of their distributions within regions. This research investigated the distributions and how rainfall and temperature (R&T) varied across the Delta State region of Nigeria. The research design employed is the ex post facto. The study employed yearly and monthly (R&T) data gotten from the Climate Research Unit (CRU) through Google earth, and Time-Series (TS) monthly climate variation data from January, 1901 to December, 2019. Statistical analysis involved the mean, climographs, Analysis of variance (ANOVA), Fisher Test, Post Hoc test, and Minitab 18 macro tool. The study observed increase of 0.4oC in temperature and 320.3mm in rainfall. Significant variations in both monthly and annual (R&T) were observed. On the trends results, increasing trend in temperature within the 8 stations were observed, while no increasing trend in rainfall within the 8 stations were observed. Decreasing trend in seasonal temperature from January to June, and August to December was observed. Furthermore, decreasing rainfall trend was observed in February, May, June, July and November, with August and September having increasing trends while the January, March, April and December having no trends. [ABSTRACT FROM AUTHOR]

Understanding the degree to which weather factors influence fire occurrence is crucial for managing wildfires because China's recent rigorous fire prevention measures have made fire occurrence drivers distinct from those in other nations. This study first examined the features of mainland China wildfires in order to provide an explanation. We used 38,158 confirmed wildfires from 2005 to 2021 and used a partial least squares (PLS) regression model and partial correlation analysis (PCA) to investigate which weather element is more important in driving wildfire occurrence in each of the twelve ecoregions in mainland China. According to the findings, there were less wildfires in mainland China reduced significantly from 2005 to 2021 (R2 = 0.78, P

This study assessed the relationship between rainfall and traffic accidents in Lokoja, Nigeria from 2011 - 2020. Rainfall data was obtained from Nigerian Meteorological Agency, while data on traffic accidents and casualties were obtained from the Federal Road Safety Corps (FRSC), both for a period of ten years (2011-2020). Data obtained were summarised using simple percentages, charts, and frequency distribution tables; while Pearson Product Moment Correlation, One-Way Analysis of Variance (ANOVA), and Simple Linear Regression were used for testing of hypotheses. Results revealed that, the amount of rainfall recorded in the study area vary over months and years. The month of September has the highest rainfall amount with an average of 279.35mm, while the month of December is the driest month with an average of 0.78mm. A high positive correlation was found between the monthly traffic crashes and casualties during the period of analysis which was statistically significant (r = .898, n = 12, P < .001). Analysis indicates that 5.4% of the variation in traffic accidents can be explained by rainfall. However, rainfall significantly predict traffic accidents, F (1, 119) = 6.763, P =.01. Analysis also revealed that for every 1mm increase in rainfall, traffic accident cases decrease by 0.018 in the study area. In order to consolidate the efforts of FRSC in effectively reducing the rates of road traffic accidents in the study area and in the country at large, there is need to also obtain data on weather elements of accident prone areas. [ABSTRACT FROM AUTHOR]

The study discusses the climate of Balochistan with a special focus on the variation of weather condition taking into account the mean monthly precipitation, temperature, humidity, atmospheric circulation, air pressure, evapotranspiration and solar energy covering the time duration of 1931-2020 (normal data). The physical barriers that bring variations in the climate of Balochistan contain geographical location, ocean, geomorphology, land use, natural vegetation, and continental extent. Based on precipitation, the province has been divided into two main regions that are arid and semi-arid, while the temperature zones are hot, warm, mild and cool. The southern and eastern part of the province receives heavy rain in the summers (monsoon), whereas it is from the western depressions during the winter season. Balochistan experiences four rainy seasons in winter (cold), pre-monsoon season (warm), monsoon season (hot), and post-monsoon season (mild). Owing to tropical (coastal) and sub-tropical continental characteristics, the area familiarizes two foremost seasons namely winter and summer. The summers of the area long for 5 months in hilly areas while 7 months in continental plains and coastal regions; whereas, winters cover five months in the plains and seven months in the mountains. Based on the appropriationand fluctuation in climate constituents, Balochistan has been classified into two main, 6 meso, and 9 microclimate and weather zones. According to Global Climate Risk Index, 2021, Pakistan has been ranked at 8th in the list of top ten global climate high-risk countries, which are exposed to the ongoing climate change and requires attention to resolve the issue.

The present study focuses on the climate conditions of Gilgit Baltistan. It includes the distribution of different weather elements like precipitation, temperature, number of rainy days, humidity, wind speed, air pressure, evapotranspiration, sunshine, factors affecting climate, and climate classification. Due to glaciation, the area is badly eroded and consists of deep valleys and moraines. There is a marked variability between the climates of the eastern and western observatories of the area. Mostly, terrace agriculture is practiced in the area with deciduous and alpine forests. Based on temperature, the area has been divided into three distinct climate regions, namely, mild, cool, and cold. Whereas, based on precipitation, it classified into arid, semi-arid and undifferentiated highlands. Chilas is the hottest area of the province, while Astor and Skardu are the coldest. The study area receives heavy precipitation from local thunderstorms, particularly during April and May. There are four rainy seasons, namely winter, pre-monsoon, monsoon, and post monsoon precipitation. The highest precipitation in the area is observed at Astore, while the lowest at Gupis. The relative humidity is below 55% throughout the year and causes chill with low temperature and cracking in human skins. Based on the variability of weather elements, the province has been divided into three macro, three meso and six micro climate regions

Electric load variation results from a variety of factors. This study discusses the inner mechanism of load variation and presents a load forecasting method based on multi‐source data and day‐to‐day topological network. Multi‐source data including weather data, seasonal attributes, calendar attributes, holiday attributes and load growth rate are considered to be the dominant factors that cause the load variation. Initially, a day‐to‐day topological network is generated that reflects the similarity between any 2 days. Additionally, the random walk with restart (RWR) algorithm is applied to the topological network to construct the training set. The support vector regression model is then adopted. With case studies using the data of Guangzhou, the authors obtain a decrease of the mean absolute percentage errors of 19.5 and 25.7% over the SV machine method and a neural network ensemble model. Their research reveals three points: (i) weather elements, especially temperature, air pressure and water vapour pressure, have a dominant influence on load variation besides historical load; (ii) multi‐source data improves the accuracy, meanwhile the applying of RWR algorithm maximises its effect; and (iii) since some of the factors that cause a constant error remains unknown, they can use the feedforward correction to decrease the affects. [ABSTRACT FROM AUTHOR]

The teaching of Geography, and mostly of Climatology, must start in the site of experiencing of the student, because the majority of atmospheric and/or climatic phenomena manisfest and are perceived by the society in your immediate area, in other words, in the neighboorhood and the city. This research concerns in to include practical educational resources to the study of climatology with the Ecology Club of Guardiões das Nascentes in the Alfredo Lenhardt Municipal Elementary School and the Itaara State High School in the city of Itaara - RS. That way it was established a methodology with educational relevance with proposed tasks to be accomplished: aplication and confection of clouds from a weather diary; a cotton observation workshop; a cistern installation to collect rain water; construction/installation of meteorological mini station at the schools. With the evolution of the activity, goals to be achieved are created like the appreciation of educational resources for the climatology study, creation of dynamics for the atmospheric phenomena comprehension improving the educational practices of Climatology. Lastly, with quantitative evaluation of applied resources to the teaching of climatic geography, it can be verified it's validity while educational practice, observing progress with the proposed methodology. [ABSTRACT FROM AUTHOR]

Introduction: Understanding how the weather affects physical activity (PA) may help in the design, analysis, and interpretation of future studies, especially when investigating PA across diverse meteorological settings and with long follow-up periods. The present longitudinal study first aims to examine the influence of daily weather elements on intraindividual PA patterns among primary school children across four seasons, reflecting day-to-day variation within each season. Second, we investigate whether the influence of weather elements differs by day of the week (weekdays vs weekends), gender, age, and bodymass index. Method: PA data were collected by ActiGraph accelerometers for 1 wk in each of four school terms that reflect each season in southeast Australia. PA data from 307 children (age range 8.7-12.8 yr) were matched to daily meteorological variables obtained from the Australian Government's Bureau of Meteorology (maximum temperature, relative humidity, solar radiation, day length, and rainfall). Daily PA patterns and their association with weather elements were analyzed using multilevel linear mixed models. Results: Temperature was the strongest predictor of moderate and vigorous PA, followed by solar radiation and humidity. The relation with temperature was curvilinear, showing optimum PA levels at temperatures between 20-C and 22-C. Associations between weather elements on PA did not differ by gender, child's age, or body mass index. Conclusions: This novel study focused on the influence of weather elements on intraindividual PA patterns in children. As weather influences cannot be controlled, knowledge of its effect on individual PA patterns may help in the design of future studies, interpretation of their results, and translation into PA promotion. [ABSTRACT FROM AUTHOR]

• Specify the fire occurrence characteristics in mainland China. • Address the significance of weather elements causing fire occurrence. • Recognize the impact of fire prevention on fire occurrence. Understanding the degree to which weather factors influence fire occurrence is crucial for managing wildfires because China's recent rigorous fire prevention measures have made fire occurrence drivers distinct from those in other nations. This study first examined the features of mainland China wildfires in order to provide an explanation. We used 38,158 confirmed wildfires from 2005 to 2021 and used a partial least squares (PLS) regression model and partial correlation analysis (PCA) to investigate which weather element is more important in driving wildfire occurrence in each of the twelve ecoregions in mainland China. According to the findings, there were less wildfires in mainland China reduced significantly from 2005 to 2021 (R2 = 0.78, P < 0.01), and the fire seasons were varied according to the ecoregions. We discovered using Pearson correlation that weather factors had a significant correlation with the occurrence of wildfires in the majority of locations of mainland China, but at a lower decision level. From the Variable Importance in Projection (VIP) scores of each weather element, we found that humidity and diurnal temperature range (DTR) were more significant than other weather factors in influencing the occurrence of wildfires, especially in southern China. The partial correlation coefficients (P cor) between weather elements and fire occurrence draw the same conclusion that the driver strength of weather elements is different in different ecoregions. The findings also demonstrated that, on a monthly time frame, weather elements had no delay effect in driving fire occurrence. The impact of forest management laws and fire suppression measures on the frequency of wildfires in mainland China was examined. Instead of focusing on the forecast of fire occurrence, this study investigated which weather factors were more relevant in fire occurrence against the backdrop of tight forest fire prevention and control in China and presented a new perspective on wildfire research and management. This study also will be use as a reference for the nations interested in managing wildfires and emphasizes the significance of human actions in causing wildfires, particularly China's fire suppression practices. [ABSTRACT FROM AUTHOR]

This study presents the influence of major weather factors during the blackcurrant blossoming period on the yields of the ‘Ojebyn’, ‘Titania’ and ‘Roodknop’ cultivars, cultivated in Warmia (Olsztyn). The study was conducted for five years (2003-2007). Among the factors analysed were the currant yield, the duration of the blossoming period, average daily air temperature, the number of days with frost at the height of 2 m and at the ground level, total rainfall during the blossoming period and the number of days with rainfall during the blossoming period.

Previous research shows that various weather elements have significant effects on crash occurrence and risk; however, little is known about how these elements affect different crash types. Consequently, this study investigates the impact of weather elements and sudden extreme snow or rain weather changes on crash type. Multivariate models were used for seven crash types using five years of daily weather and crash data collected for the entire City of Edmonton. In addition, the yearly trend and random variation of parameters across the years were analyzed by using four different modeling formulations. The proposed models were estimated in a full Bayesian context via Markov Chain Monte Carlo simulation. The multivariate Poisson lognormal model with yearly varying coefficients provided the best fit for the data according to Deviance Information Criteria. Overall, results showed that temperature and snowfall were statistically significant with intuitive signs (crashes decrease with increasing temperature; crashes increase as snowfall intensity increases) for all crash types, while rainfall was mostly insignificant. Previous snow showed mixed results, being statistically significant and positively related to certain crash types, while negatively related or insignificant in other cases. Maximum wind gust speed was found mostly insignificant with a few exceptions that were positively related to crash type. Major snow or rain events following a dry weather condition were highly significant and positively related to three crash types: Follow-Too-Close, Stop-Sign-Violation, and Ran-Off-Road crashes. The day-of-the-week dummy variables were statistically significant, indicating a possible weekly variation in exposure. Transportation authorities might use the above results to improve road safety by providing drivers with information regarding the risk of certain crash types for a particular weather condition. [ABSTRACT FROM AUTHOR]

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Toaldos 1774 preisgekröntes Buch behandelt mit Folgerichtigkeit die Wirkung der verschiedenen Witterungselemente auf den Boden, die Pflanzen und auch auf die Feldarbeiten. Dann folgen Betrachtungen zu den Abschnitten des Feldarbeitsjahres, hinsichtlich der Wirkung der Witterung, besonders die Ertragsbildung des Getreides betrachtend, Viele Kenntnisse sind begründet, nicht nur aufgeführt. Im Bemühen, Ursachen für die Witterungsänderungen zu finden, folgte Toaldo zum Teil astrologischen Annahmen, verwendete dabei aber eine 50jährige Reihe von zum Teil instrumentellen Wetterbeobachtungen, wobei er mittels Häufigkeitsstatistiken zu bemerkenswerten witterungsklimatischen Ergebnissen gekommen ist.

PM2.5 pollution in China is getting worse. The relationship between PM2.5 concentration and weather elements exhibits complex non-linear feature. Therefore, it is very important to study the sophisticated mathematical and nonlinear method about the relationship between PM2.5 concentration and weather elements. Coupling detrended fluctuation analysis (CDFA) method can reveal the multifractal properties of more than two series. The coupled correlations between PM2.5 concentration and weather elements in Hangzhou and Guangzhou are explored by CDFA. We find that coupled correlations are not only multifractal but also anti-persistent. Compared with Guangzhou, Hangzhou has stronger correlations. Both long-range correlation and fat-tailed distribution can cause the coupled multifractality. and the long-range correlation has a greater influence on the coupled multifractality. In addition, the coupling sensitivity degree (CSD(Y)) is used to study which weather element plays a key role in the change of PM2.5 concentration. • The coupling correlations between PM2.5 concentration and weather elements are discussed by CDFA and CSD. • The coupling correlations between PM2.5 concentration and weather elements are multifractal and anti-persistent. • The coupling multifractality is caused not only by long-range correlation but also by fat-tailed distribution. • The coupling sensitivity degree (CSD(Y)) is used to explore which weather element plays a key role (more sensitive) in the change of PM2.5 concentration. [ABSTRACT FROM AUTHOR]