Journal articles: 'Weather indicators'

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Shacham, Mordechai, and Neima Brauner. "Danger by the Numbers:Meaningful Cold Weather Indicators." Weatherwise 48, no. 5 (November 1995): 27–28. http://dx.doi.org/10.1080/00431672.1995.9925360.

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NITU, C., and A. S. DOBRESCU. "The Role of Weather Indicators in Energy Consumption." Advances in Electrical and Computer Engineering 8, no. 1 (2008): 17–20. http://dx.doi.org/10.4316/aece.2008.01003.

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Tsai, Stella, Teresa Hamby, Alvin Chu, Jessie A. Gleason, Gabrielle M. Goodrow, Hui Gu, Edward Lifshitz, and Jerald A. Fagliano. "Development and Application of Syndromic Surveillance for Severe Weather Events Following Hurricane Sandy." Disaster Medicine and Public Health Preparedness 10, no. 3 (May 5, 2016): 463–71. http://dx.doi.org/10.1017/dmp.2016.74.

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AbstractObjectiveFollowing Hurricane Superstorm Sandy, the New Jersey Department of Health (NJDOH) developed indicators to enhance syndromic surveillance for extreme weather events in EpiCenter, an online system that collects and analyzes real-time chief complaint emergency department (ED) data and classifies each visit by indicator or syndrome.MethodsThese severe weather indicators were finalized by using 2 steps: (1) key word inclusion by review of chief complaints from cases where diagnostic codes met selection criteria and (2) key word exclusion by evaluating cases with key words of interest that lacked selected diagnostic codes.ResultsGraphs compared 1-month, 3-month, and 1-year periods of 8 Hurricane Sandy-related severe weather event indicators against the same period in the following year. Spikes in overall ED visits were observed immediately after the hurricane for carbon monoxide (CO) poisoning, the 3 disrupted outpatient medical care indicators, asthma, and methadone-related substance use. Zip code level scan statistics indicated clusters of CO poisoning and increased medicine refill needs during the 2 weeks after Hurricane Sandy. CO poisoning clusters were identified in areas with power outages of 4 days or longer.ConclusionsThis endeavor gave the NJDOH a clearer picture of the effects of Hurricane Sandy and yielded valuable state preparation information to monitor the effects of future severe weather events. (Disaster Med Public Health Preparedness. 2016;10:463–471)

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Park, Yujin, Sang-Woo Lee, and Junga Lee. "Comparison of Fuzzy AHP and AHP in Multicriteria Inventory Classification While Planning Green Infrastructure for Resilient Stream Ecosystems." Sustainability 12, no. 21 (October 30, 2020): 9035. http://dx.doi.org/10.3390/su12219035.

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As climate change-induced weather variability increases, various green infrastructure plans, such as stream ecosystems, have been studied to overcome ecological and environmental problems arising from extreme weather events; however, our understanding of them and their resilience to extreme weather events is considerably limited. This study proposes a multicriteria inventory classification while planning green infrastructure for resilient stream ecosystems under extreme weather events. Literature reviews, expert surveys, and reliability/validity analyses were used to enlist indicators for this classification. The analytic hierarchy process (AHP) and fuzzy analytic hierarchy process (fuzzy AHP) were used to compute the weights and ranks of indicators for identifying critical indices while planning green infrastructure. The AHP and fuzzy AHP analysis suggested that meteorological phenomena and disasters, hydraulic characteristic of streams, land use/geographic characteristics, and experience/damage restoration were important factors. High weights were attributed to aquatic ecology, potentially vulnerable areas, population, topography, and heat waves. The weights and ranks attributed by AHP and fuzzy AHP varied slightly, but the indicator groups with high and low weights were the same; hence, primary indicators to be considered while planning green infrastructure for resilient stream ecosystems could be suggested. These results could be used as a preliminary analysis in establishing countermeasures against climate change or in distributing budgets for green infrastructure plans.

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Evseev, Vadim, Reza Barkhi, Aleksandr Pleshivtsev, and Anton Scrynnik. "Modeling the Influence of Weather and Climatic Conditions on the Safety Characteristics of the Construction Process." E3S Web of Conferences 97 (2019): 03035. http://dx.doi.org/10.1051/e3sconf/20199703035.

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This study investigates the influence of climatic conditions on the performance of the construction process and the characteristics of the elements of the construction process: construction machines, workers, the estimated cost of construction, and the reliability of construction deadlines. As an integral indicator of weather and climatic conditions, the indicator of “weather hardness” was chosen. The method of simulation modeling with its ability to generate random numbers with the given parameters is chosen as a mathematical model on a computer. The aim of the study is to identify the regression and correlation relationships between the indicators of weather hardness and the corresponding indicators of the elements of the construction process: the Fund for working time of workers, the coefficient of technical readiness of construction equipment, increasing the estimated cost of construction, increasing labor costs for the re-installation of construction equipment, identification of additional costs by types of work, for all the stated dependencies, regression equations are obtained. The results are presented in graphical form, and the results show that the proposed simulation model of the impact of weather and climatic conditions on the characteristics of the construction process allows to optimize management decisions, and reduce construction risks, reduce the estimated cost of construction.

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ZOUNON, Hermione Noumawudo, Nasser Mohamed BACO, Bienvenu Akowedaho DAGOUDO, and Saddam Kadjogbé SAHAGUI. "Traditional Ecological knowledge of predicting rain for climate adapting in North Benin." International Journal for Innovation Education and Research 8, no. 1 (January 31, 2020): 141–55. http://dx.doi.org/10.31686/ijier.vol8.iss1.2132.

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Traditional knowledge is base of decisions taking by local population affecting their livelihood. This traditional knowledge, focusing on practices and experiences highlighted the weather and climate information which is important for rain fed agriculture in Kandi commune. This research focuses on traditional knowledge of predicting rain through the climate indicators. It was carried in four districts 4 districts (Sam, Donwari, Kassakou and Sonsoro) of Kandi commune. Through 75 interviews (resource persons at least 40 years of experience) and 7 focus groups in the community, information was gathered about traditional climate and weather indicators and prediction tools. The snowball sampling technique was used to choose the respondents. Knowledge about climate indicator is exchanged, passed on from generation to generation and concerned plant species, animal species and astronomical elements. These climate indicators revealed onset of rain season, intensity of rain in full season and the end rain season. Multiple correspondence analyses with statistical software R Version 3.02 show three categories group. One shows the indicators such as wind, thunder, Cloud, Bird. The second group combines the factors transmit to member of family and acquire by initiation. The third group concerns bird indicator.

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Kotelnikov, Roman, and Alexander Martynyuk. "An Indicator for Assessing the Effectiveness of Organizing Forest Fire Protection." Lesnoy Zhurnal (Forestry Journal), no. 2 (April 4, 2021): 213–22. http://dx.doi.org/10.37482/0536-1036-2021-2-213-222.

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The article briefly analyzes the main indicators used to assess the effectiveness of the organization of forest fire protection in the Russian Federation. The need to improve approaches to such an assessment has been substantiated. Based on the expert analysis of various situations and existing operational indicators of aviation forest protection units and regional dispatching services, it can be concluded that all possible factors that affect the effectiveness of work should be divided into two groups: organizational and weather-related. At the same time, all organizational factors ultimately affect the area covered by the fire. Weather factors cannot be controlled, and their influence must be excluded when calculating performance indicators. Thus, all indicators that characterize the weather factor are directly or indirectly related. The article proves the expediency of using the indicator of fire season intensity to account for weather factors. It is proposed to calculate the relative deviation of the values of forest fire frequency and intensity from the average long-term values for assessing the effectiveness. The article provides a formula, an algorithm, and a number of recommendations for automating the calculation. A verbal-numerical scale of conditional assessment of the effectiveness of forest firefighting units is proposed. The interpretation of the values of the conditional indicator of the effectiveness of forest firefighting units for the selected scale is given. On the basis of the proposed method, an analysis of the effectiveness assessment of organizing forest fire protection in 2019 for all constituent entities of the Russian Federation is carried out. In the framework of the existing accounting system of forest fires, the cost of their suppression, as well as the features of existing system of account of funding of activities related to the protection of forests from fires, the proposed approach is optimal for assessing the forest fire service as it considers previous work experience in a variety of weather conditions. The proposed approach can be used in systems to support management decisions in the field of forest fire protection, which will significantly increase the adequacy of management decisions in the forest sector.

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Wanic, Maria, Magdalena Jastrzębska, Marta K. Kostrzewska, and Janusz Nowicki. "Analysis of weeds communities using selected biological indicators." Acta Agrobotanica 58, no. 1 (2012): 227–42. http://dx.doi.org/10.5586/aa.2005.026.

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The paper analyses the weeds community in oats cultivated at different position in two crop rotations: after potato (in the rotation system with 25% share of oats) and twice after oats (with its 75% share) during the years 1990-2000, using the diversity of species indicators (by Simpson and by Shannon-Wiener), homogeneity of species by Simpson and communities similarity ratios. The dependence of biological indicators on the weather conditions and the dependence of oats grain yield on the number and diversity of species were assessed. Diversity and homogeneity of species in communities of weeds in the field of oats showed high differences from year to year of studies and dates of measurements (stage of oats tillering, end of its vegetation). To a lesser degree they changed under the influence of the position in the rotation system. With the passage of years the number of weeds during the spring period gradually increased. The analyzed parameters showed the differentiated dependence on the weather conditions. The communities form years with similar weather conditions did not show analogy in individual density and species. No significant dependence between the yield and the diversity of weeds was confirmed.

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Koloskov, A. V., V. G. Sinitsin, N. N. Gerasimova, and Yu M. Yampolski. "ELF resonant cavities in the geospace as space weather indicators." Kosmìčna nauka ì tehnologìâ 14, no. 5 (September 30, 2008): 49–64. http://dx.doi.org/10.15407/knit2008.05.049.

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Morishita, Claudia, Julien Berger, and Nathan Mendes. "Weather-based indicators for analysis of moisture risks in buildings." Science of The Total Environment 709 (March 2020): 134850. http://dx.doi.org/10.1016/j.scitotenv.2019.134850.

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Williams, Travis M., and William R. Travis. "Evaluating Alternative Drought Indicators in a Weather Index Insurance Instrument." Weather, Climate, and Society 11, no. 3 (July 1, 2019): 629–49. http://dx.doi.org/10.1175/wcas-d-18-0107.1.

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Abstract Weather index insurance is a popular means of mitigating agricultural risks. Drought is a significant cause of lost agricultural production, and so precipitation index–based plans are common. Simple “percent of normal” indices are often used because they are easy to calculate and communicate to policyholders. However, the ability of such indices to reflect production losses is limited, reducing the ability of insurance to efficiently mitigate risk. This is especially true in rangeland livestock production given the cumulative effects of rainfall and other factors on range production and the complex relationships between range and livestock weight gain, which is the rancher’s main product and source of income. More sophisticated drought indices incorporate the complexities of drought into their design and would, in theory, serve as more appropriate payment triggers. This study uses a suite of drought indices to test correlation with production and the behavior of insurance based on those indices. Payout patterns based on each index were simulated within the actuarial framework of a precipitation-based insurance program aimed at livestock producers. Results were compared with the program’s precipitation index, showing that drought indices have higher correlations with range production, a tendency to incentivize growing-season protection, more even geographic distributions of risk, reduced policyholder ability to seek higher payments through strategic coverage choices, and increased provider ability to adjust payment patterns to reduce the risk of nonpayment given loss.

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Heaton, Matthew J., Matthias Katzfuss, Shahla Ramachandar, Kathryn Pedings, Eric Gilleland, Elizabeth Mannshardt-Shamseldin, and Richard L. Smith. "Spatio-temporal models for large-scale indicators of extreme weather." Environmetrics 22, no. 3 (November 18, 2010): 294–303. http://dx.doi.org/10.1002/env.1050.

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Ahmar, Ansari Saleh. "Correlation between Covid-19 and weather/climate indicators: A Response." JINAV: Journal of Information and Visualization 1, no. 1 (October 1, 2020): 34–35. http://dx.doi.org/10.35877/454ri.jinav158.

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This is a response to article by Dr. Ramadhan Tosepu and his colleagues entitled: “Correlation between the weather and the Covid-19 pandemic in Jakarta, Indonesia” in Science of The Total Environment, Vol. 725; and Dr. Muhammad Farhan Bashir and his colleagues entitled: “Correlation between the climate indicators and the Covid-19 pandemics in New York, USA.” in Science of The Total Environment, Vol. 728. We agree with the results of their research, but there are things that need to be clarified in relation to the selection of the Spearman-rank correlation as an analytical tool, because the selection of this analysis is without the normality test of the data. Spearman-rank correlation is useful for non-normally distributed continuous data; can be used for ordinal data; and is relatively robust for outliers.

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R. ANJU and BINOO P. BONNY. "Biotic indicators as weather predictors in Wayanad district of Kerala." Journal of Agrometeorology 21, no. 1 (November 10, 2021): 114–18. http://dx.doi.org/10.54386/jam.v21i1.219.

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Rijal, G., C. Petropoulou, J. K. Tolson, M. DeFlaun, C. Gerba, R. Gore, T. Glymph, et al. "Dry and wet weather microbial characterization of the Chicago area waterway system." Water Science and Technology 60, no. 7 (October 1, 2009): 1847–55. http://dx.doi.org/10.2166/wst.2009.598.

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The Chicago Area Waterway System (CAWS) is a man-made channel, which serves the Chicago area for the drainage of urban storm water and the conveyance of secondary treated effluent from the Metropolitan Water Reclamation District of Greater Chicago's (District) North Side, Stickney and Calumet water reclamation plants (WRPs). A microbial characterization of the CAWS upstream and downstream of the WRPs and from the WRP outfall was initiated by collecting dry and wet weather samples and analyzing for indicators and pathogens. During dry weather, indicator bacteria (fecal coliform [FC], E. coli [EC], enterococci [EN]) were the most abundant microbial species detected in the CAWS compared to pathogens (Salmonella spp [SA], enteric viruses [EV], adenovirus [AV], norovirus [NV] and Giardia and Cryptosporidium). Pseudomonas aeruginosa [PA] levels in the outfall samples were either lower or equivalent to the CAWS. The wet weather samples had a higher frequency of detection of indicator bacteria and pathogens compared to dry weather samples. Overall, the concentrations of pathogens in the CAWS, representing the weather conditions experienced in a recreational year, were relatively low. The study concluded that the presence of pathogens in the CAWS downstream of the WRPs were due to secondary loading of the waterway under wet weather conditions from combined sewer overflows (CSOs) and other discharges.

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Lubich, V., and I. Polyanetska. "Evaluation of durum winter wheat varieties by growth and development indicators." Agrobìologìâ, no. 1(163) (May 25, 2021): 85–92. http://dx.doi.org/10.33245/2310-9270-2021-163-1-85-92.

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The article presents the study results of the formation of growth and development indicators (growing season length of plants, plant height, density dynamics, ear parameters and grain yield) of domestic varieties of durum winter wheat. It is analyzed that the level of the signs manifestation varies signifcantly depending on the weather conditions of the growing season. However, the growing season duration hardly depend on weather conditions and is 268–269 days. In the full ripeness stage, the height of plants increased 1.4–1.8 times in comparison with earing with insignifcant variation (V=1–5 %) depending on the variety. The lowest plants were Athena, Continent and Laguna – 71–76 cm. Uneven precipitation distribution and high air temperature in 2013 contributed to the formation of fewer stems of durum winter wheat plants compared to more favourable 2014. In the full ripeness stage of durum winter wheat grain in 2013, the coefcient of total tillering was 1.01–1.26 depending on the variety. In 2014, the stem density was the highest. In the full ripeness stage of grain, the number of stems ranged from 650 to 812 pcs/m2 depending on the variety with a total tillering rate of 1.54–1.91. It should be noted that Athena and Linkor varieties retained a higher ability to tillering and stem survival in different weather conditions. The ear length of winter durum wheat varied from 5.9 to 6.7 cm depending on the variety with slight and small variation (V=3–16 %). The number of spikelets in the ear was from 16 to 20 pcs with a small variation coefcient. The ear length of durum winter wheat varied most. Thus, of the nine varieties, the variation coefcient of this indicator in six varieties was average, and in the rest of them - small. However, there was no signifcant difference between the varieties. The highest yields were formed by Argonaut, Gardemaryn and Linkor varieties – 6.00–6.31 t/ha or 13–19 % more than the standard. Grain yield varied signifcantly depending on the weather conditions of the growing season, which is also evidenced by the stability index and was below 1 (0.48–0.64). Less favourable weather conditions in 2013 provided the yield of 3.74–4.63 t/ha depending on the variety. More favourable weather conditions in 2014 provided the grain yield of 5.46–8.25 t/ha depending on durum winter wheat variety. It is obvious that the positive effect of weather conditions in 2014 on tillering and stem survival of winter durum wheat determined the formation of higher grain yield. Key words: durum winter wheat, variety, growth and development indicators, yield.

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Friedrich, Maik, and Christoph Möhlenbrink. "How to Evaluate Remote Tower Metrics in Connection With Weather Observations." Aviation Psychology and Applied Human Factors 8, no. 2 (September 2018): 100–111. http://dx.doi.org/10.1027/2192-0923/a000142.

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Abstract. Owing to the different approaches for remote tower operation, a standardized set of indicators is needed to evaluate the technical implementations at a task performance level. One of the most influential factors for air traffic control is weather. This article describes the influence of weather metrics on remote tower operations and how to validate them against each other. Weather metrics are essential to the evaluation of different remote controller working positions. Therefore, weather metrics were identified as part of a validation at the Erfurt-Weimar Airport. Air traffic control officers observed weather events at the tower control working position and the remote control working position. The eight participating air traffic control officers answered time-synchronized questionnaires at both workplaces. The questionnaires addressed operationally relevant weather events in the aerodrome. The validation experiment targeted the air traffic control officer’s ability to categorize and judge the same weather event at different workplaces. The results show the potential of standardized indicators for the evaluation of performance and the importance of weather metrics in relation to other evaluation metrics.

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Chen, Chen, Xiaohua Zhao, Hao Liu, Guichao Ren, and Xiaoming Liu. "Influence of adverse weather on drivers’ perceived risk during car following based on driving simulations." Journal of Modern Transportation 27, no. 4 (September 19, 2019): 282–92. http://dx.doi.org/10.1007/s40534-019-00197-4.

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Abstract Adverse weather has a considerable impact on the behavior of drivers, which puts vehicles and drivers in hazardous situations that can easily cause traffic accidents. This research examines how drivers’ perceived risk changes during car following under different adverse weather conditions by using driving simulation experiment. An expressway road scenario was built in a driving simulator. Eleven types of weather conditions, including clear sky, four levels of fog, four levels of rain and two levels of snow, were designed. Furthermore, to simulate the car-following behavior, three car-following situations were designed according to the motion of the lead car. Seven car-following indicators were extracted based on risk homeostasis theory. Then, the entropy weight method was used to integrate the selected indicators into an index to represent the drivers’ perceived risk. Multiple linear regression was applied to measure the influence of adverse weather conditions on perceived risk, and the coefficients were considered as indicators. The results demonstrate that both the weather conditions and road type have significant effects on car-following behavior. Drivers’ perceived risk tends to increase with the worsening weather conditions. Under conditions of extremely poor visibility, such as heavy dense fog, the measured drivers’ perceived risk is low due to the difficulties in vehicle operation and limited visibility.

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Zheng, Yukun, Ying Wang, Yun Sun, Yao Qi, and Ning Wang. "Multi-aircraft Rerouting Method Under Dangerous Weather." MATEC Web of Conferences 198 (2018): 05003. http://dx.doi.org/10.1051/matecconf/201819805003.

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In view of the problems that single-aircraft rerouting method existing in civil aviation considered little about the associated effects of multi-aircraft and route network characteristics. A multi-aircraft rerouting method under dangerous weather was proposed. Based on the characteristics of network failures during the occurrence of dangerous weather, the performance indicators and related constraints of temporary routes are analyzed to establish a singleaircraft rerouting model. Then, a multi-aircraft rerouting model based on layout of diversion points was proposed in combination with the performance indicators of the rerouted route network, and solved by NSGA-III algorithm. Finally, a simulation analysis was conducted using the regional flight route network in part of the Beijing Flight Information District as an example and the results show that multi-aircraft rerouting method can effectively avoid flight conflicts and improve the comprehensive characteristics of the network.

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Putri, Retno Wahyuni, and Miftahuddin Miftahuddin. "Perbandingan Data Harian dan Data Bulanan pada Suhu Permukaan Laut di Samudera Hindia Menggunakan Partial Least Square-Structural Equation Modelling." Jambura Journal of Mathematics 4, no. 1 (January 1, 2022): 1–16. http://dx.doi.org/10.34312/jjom.v4i1.11191.

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Sea surface temperature (SST) is one of the features of climate variability that has a significant role in human activities. This study aims to predict and determine whether weather and climate variables with their measuring indicators can predict changes in SST by comparing daily and monthly data. This study uses a partial least square-structural equation modeling (PLS-SEM) approach which can predict the causality relationship between exogenous latent variables and endogenous latent variables. The results obtained from this study are, from the nine indicators used there are only 6 significant indicators with a loading factor value 0.7, namely sea surface temperature (oC) as a measure of latent variables SST changes, wind speed (m/s) and humidity relative (%) as a measure of the latent variable of weather, and air temperature (oC), short-wave solar radiation (w/m2) for daily data, and long-wave solar radiation (w/m2) for monthly data as a measure of climate latent variable. Inner model obtained on daily data: SST change (η) = -0.285 weather + 0.650 climate + and on monthly data SST change (η) = -0.330 weather + 0.793 climate +. In monthly data, weather and climate latent variables and their measuring indicators have a greater influence on changes in SST with the coefficient values in the model obtained being greater than in daily data. Latent variables that have a significant effect on changes in SST are weather and climate. This shows that if there is an increase or decrease in weather and climate it can cause significant changes to the SST. The value of the criteria on the outer model and inner model on daily and monthly data obtained better results on monthly data. The presence of more missing data in daily data can be one of the causes of this happening.

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Leeming, R., N. Bate, R. Hewlett, and P. D. Nichols. "Discriminating faecal pollution: a case study of stormwater entering Port Phillip Bay, Australia." Water Science and Technology 38, no. 10 (November 1, 1998): 15–22. http://dx.doi.org/10.2166/wst.1998.0369.

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This investigation was designed to provide preliminary information to the Environment Protection Authority concerning the input of faecal matter to stormwater drains in the Rippleside area of Geelong, Victoria. Results derived from the combined use of sterol biomarkers (e.g. coprostanol and 24-ethylcoprostanol) and four sub-groups of bacterial indicators (e.g. thermotolerant coliforms, E. coli, faecal streptococci and enterococci) indicated that during wet weather, all sites sampled were affected by significant human faecal contamination. Ratios of coprostanol to bacterial indicators were similar to those for samples collected from nearby sewer mains. During dry weather, there were still severely elevated levels of faecal contamination based on bacterial indicators, but correspondingly low concentrations of faecal sterols suggesting minimal human or herbivore faecal contamination. The origin of the majority of the faecal pollution in dry weather therefore remains to be fully explained. It is clear from this and related studies that the combined measurement of faecal sterols and bacterial indicators can greatly assist distinguishing sources of faecal pollution. It is also shown for aquatic environments that the measurement of coprostanol or other single indicators alone, is inadequate to fully discern faecal contamination from human sources.

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Toeglhofer, Christoph, Roland Mestel, and Franz Prettenthaler. "Weather Value at Risk: On the Measurement of Noncatastrophic Weather Risk." Weather, Climate, and Society 4, no. 3 (July 1, 2012): 190–99. http://dx.doi.org/10.1175/wcas-d-11-00062.1.

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Abstract An evaluation of weather risk entails two important objectives: indicating the economic impact of weather variability and climate change, and evaluating the use of weather derivatives in weather risk reduction and climate change adaptation strategy. This paper illustrates a straightforward approach for measuring weather risk, which captures both the exposure and the sensitivity of business and economic indicators to weather variability. Using the example of the accommodation industry in Kitzbuehel (Austria), it is demonstrated that the risk measure Weather-VaR can be used to serve both ends. It is found that compared to a normal climatological season, adverse snow conditions (at the level of a 1 in 20-yr event) result in a substantial financial loss of approximately 4 million euros. However, results show that quantification of weather risk is substantially affected by the choice of the weather variable or index, the time period under consideration, the model specification for estimating the sensitivity parameter, and whether time trends are considered when estimating the probability of unfavorable weather conditions.

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PIRCALABU, Liliana, Elena BRINDUSE, and Marian ION. "Prediction of Botrytis cinerea Risk in Vineyards Based on Weather Indicators." Bulletin of University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca. Horticulture 77, no. 2 (November 18, 2020): 58. http://dx.doi.org/10.15835/buasvmcn-hort:2020.0038.

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Botrytis cinerea causes grey mould, a major disease occurring in vineyards worldwide, resulting in loss of grape production and wine quality. Predictive models of favorability of Botrytis cinerea were used. Therefore, a series of meteorological data from 2010 to 2019 was used. The results showed that the frequency of years with low risk of Botrytis cinerea was 10%, medium risk 10%, high risk 80%. The disease can drastically reduce both yield and wine quality (Ribereau Gayon et al., 1980). The harvest years 2010, 2011, 2012, 2013 and 2014, favored the manifestation of an attack degree of 62.9% (2012) and 34.2% (2013). Positive correlations were observed in the case of Broome index and Bacchus index with the duration of sunlight (r2 = 0.935), respectively (r2 = 0.944) and the sum of the hours of moisture on the leaves (r2 = 0.833, r2 = 0.848). Based on the results a model for prediction of Botrytis cinerea risk will be developed.

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Gommes, René, Bingfang Wu, Ning Zhang, Xueliang Feng, Hongwei Zeng, Zhongyuan Li, and Bo Chen. "CropWatch agroclimatic indicators (CWAIs) for weather impact assessment on global agriculture." International Journal of Biometeorology 61, no. 2 (July 8, 2016): 199–215. http://dx.doi.org/10.1007/s00484-016-1199-7.

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Molarius, R., V. Könönen, P. Leviäkangas, Zulkarnain, J. Rönty, A. M. Hietajärvi, and K. Oiva. "The extreme weather risk indicators (EWRI) for the European transport system." Natural Hazards 72, no. 1 (April 3, 2013): 189–210. http://dx.doi.org/10.1007/s11069-013-0650-x.

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WARD, MICHELLE, RADHIKA DHINGRA, JUSTIN V. REMAIS, HOWARD H. CHANG, LYNETTE M. JOHNSTON, LEE-ANN JAYKUS, and JUAN LEON. "Associations between Weather and Microbial Load on Fresh Produce Prior to Harvest." Journal of Food Protection 78, no. 4 (April 1, 2015): 849–54. http://dx.doi.org/10.4315/0362-028x.jfp-14-381.

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Contaminated produce causes approximately 1 million cases of foodborne illness and 1 billion dollars in damages to the U.S. economy annually. The environmental conditions, especially weather, that influence the inoculation, proliferation, and dispersal of microbial load on produce are not well understood. Using a mixed models approach, we examined the relationship of temperature and precipitation to microbial indicators of contamination on fresh produce on the farm over a week-long period prior to harvest. Between 2000 and 2002, we assayed for four microbial indicators of contamination (aerobic plate count, Enterococcus, total coliforms, and Escherichia coli) on 10 produce types in 15 fields in the southern United States. The sample collection times varied, with most occurring between January and May. We collected hourly weather data for the corresponding time period and location. Our results indicated that there was a significant association between the average daily temperature (20°C) and both log aerobic plate count (e.g., an increase of 0.074 log CFU/g [standard error {SE}, 0.023] per °C increase in weekly average temperature) and log Enterococcus (e.g., an increase of 0.15 log CFU/g [SE, 0.031] per °C increase in weekly average temperature) for approximately 5 days prior to sample collection. Daily total precipitation was significantly associated with log coliforms on 2 days (~0.11 log CFU/g [SE, 0.06] per mm of precipitation) during the week-long lag period prior to harvest. Our results suggest that microbial indicator concentrations may increase as the temperature increases. Precipitation may have a positive but complex relationship with microbial indicators, as precipitation may create moist conditions conducive to bacterial growth, spread contamination onto the field, or wash contamination off of the plant.

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Torbicki, Mateusz. "System Safety Model Related to Climate-Weather Change Process Application to Port and Maritime Transport." Journal of KONBiN 46, no. 1 (June 1, 2018): 21–34. http://dx.doi.org/10.2478/jok-2018-0021.

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Abstract The conditional safety functions at the climate-weather particular states and the unconditional safety functions of the port oil piping transportation system area and the maritime ferry, the mean values and the variances of those systems unconditional lifetimes and other safety indicators are determined. Those safety indicators, considering impact of the climate-weather change process, are evaluated for the piping system operating at under water Baltic sea area and for the maritime ferry operating at Gdynia Port area.

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Kim, Keumji, Jeonggeun Hwang, Gahui Kim, and Dojoon Jung. "A Review of the Possibility of Assessment Indicators for Natural Disaster Risk Analysis through Expert Survey." Journal of the Korean Society of Hazard Mitigation 21, no. 1 (February 28, 2021): 47–55. http://dx.doi.org/10.9798/kosham.2021.21.1.47.

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In recent years, the frequent occurrence of extreme weather due to climate change has increased the possibility of potential danger across the world. Additionally, South Korea has been consistently exposed to disaster risk. Studies on the prediction of damage from natural disasters are critical for the government and local governments to formulate disaster recovery policies. However, the scope of risk analysis evaluation indicators currently utilized by the relevant organizations comprises limitations, as there are no consistent standards for indicator selection. From this viewpoint, this study selected 118 surrogate indicators, including indicators frequently used in previous studies, and conducted a survey with 52 experts in the disaster field to examine the possibility of use of these indicators. As a result of the expert survey, 37 indicators were analyzed as “very appropriate” and 54 indicators were analyzed as “appropriate” regarding their possibility of use. Finally, we suggested four natural disaster risk assessment methods, which can be used in policymaking by the government and local governments in the future.

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Lindquist, John L., and Martin J. Kropff. "Applications of an Ecophysiological Model for Irrigated Rice (Oryza sativa)-EchinochloaCompetition." Weed Science 44, no. 1 (March 1996): 52–56. http://dx.doi.org/10.1017/s0043174500093541.

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A simulation model of rice-barnyardgrass competition for light was used for two management applications. First, simulations using 47 weather data sets from four locations in Asia were conducted to evaluate the influence of weather variation on single year economic threshold densities of barnyardgrass. Second, rapid leaf area expansion and leaf area index were evaluated as potential indicators of improved rice competitiveness and tolerance to barnyardgrass. Influence of weather variation on single year economic thresholds was small under the assumption that competition was for light only. Increasing early leaf area expansion rate reduced simulated barnyardgrass seed production and increased single year economic thresholds, suggesting that the use of competitive rice cultivars may reduce the need for chemical weed control. The model predicted that rice leaf area index 70 to 75 d after planting was a good indicator of early leaf area expansion rate.

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Ibn Musah, Abdul-Aziz, Jianguo Du, Thomas Bilaliib Udimal, and Mohammed Abubakari Sadick. "The Nexus of Weather Extremes to Agriculture Production Indexes and the Future Risk in Ghana." Climate 6, no. 4 (October 31, 2018): 86. http://dx.doi.org/10.3390/cli6040086.

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The agricultural industry employs a large workforce in Ghana and remains the primary source of food security and income. The consequences of extreme weather in this sector can be catastrophic. A consistent picture of meteorological risk and adaptation patterns can lead to useful information, which can help local farmers make informed decisions to advance their livelihoods. We modelled historical data using extreme value theory and structural equation modelling. Subsequently, we studied extreme weather variability and its relationship to composite indicators of agricultural production and the long-term trend of weather risk. Minimum and maximum annual temperatures have negligible heterogeneity in their trends, while the annual maximum rainfall is homogenous in trend. Severe rainfall affects cereals and cocoa production, resulting in reduced yields. Cereals and cocoa grow well when there is even distribution of rainfall. The return levels for the next 20–100 years are gradually increasing with the long-term prediction of extreme weather. Also, heavy rains affect cereals and cocoa production negatively. All indicators of agriculture had a positive relationship with maximum extreme weather.

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Ma, Bingbing, Zhiguo Zhang, Gang Guo, and Zhuo Hu. "Study on influence factors of Intelligent Connected Vehicle (ICV) performanc under Complex Weather Conditions." E3S Web of Conferences 213 (2020): 02024. http://dx.doi.org/10.1051/e3sconf/202021302024.

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At the moment, ICV performance test method doesn’t exist yet under the complex weather conditions, the revolution of ICV is greatly limited. By studying the weather environment that influence ICV, the meteorological indicators which influence ICV performance will be acquired. Then by analyzing the main complex meteorological factors, their scene characteristics and main control indexes are given. It provides a basis for the construction of the complex weather scene and the research of the test method of ICV under the complex weather condition.

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Utama, I. Gusti Bagus Rai, Christimulia Purnama Trimurti, Ni Made Diana Erfiani, Ni Putu Dyah Krismawintari, and Dermawan Waruwu. "The Tourism Destination Determinant Quality Factor from Stakeholders Perspective." Indonesian Journal of Tourism and Leisure 2, no. 2 (October 31, 2021): 96–106. http://dx.doi.org/10.36256/ijtl.v2i2.164.

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High-quality tourism destinations nowadays become the expectations of tourism stakeholders. However, these quality tourism destinations do not have a definition and key performance indicator that can be used as a measurement indicator, so this research is necessary. This study tries to measure the quality of tourism destinations based on the perspective of Bali tourism respondents. In this survey, the attitude of the respondents was measured using a choice of 5 Likert scales that directly asked their attitude towards various indicators on two elements, namely the tourist quality variable and the destination quality measurement variable. The results of a survey of 200 informants illustrate that the respondents' views on all indicators that are considered as indicators of measuring the quality of tourists and indicators of tourism destinations. They can be used as indicators to measure other destinations with adjustments according to their conditions. The novelty in this studies is three factors that have been formed key factors of tourism destination quality. The first is named the quality beautiful destination and unique attraction. The second factor is named quality tourist spending and respect local culture minded. The third factor is named the quality climate and weather condition and infrastructure.

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Gbangou, Talardia, Erik Van Slobbe, Fulco Ludwig, Gordana Kranjac-Berisavljevic, and Spyridon Paparrizos. "Harnessing Local Forecasting Knowledge on Weather and Climate in Ghana: Documentation, Skills, and Integration with Scientific Forecasting Knowledge." Weather, Climate, and Society 13, no. 1 (January 2021): 23–37. http://dx.doi.org/10.1175/wcas-d-20-0012.1.

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AbstractImproved weather and climate forecast information services are important to sustain small-scale crop production in many developing countries. Previous studies recognized the value of integrating local forecasting knowledge (LFK) with scientific forecasting knowledge (SFK) to support farmers’ decision-making. Yet, little work has focused on proper documentation, quality verification, and integration techniques. The skills of local and scientific forecasts were compared, and new integration approaches were derived over the coastal zone of Ghana. LFK indicators were documented, and farmers were trained to collect indicators’ observations and record rainfall in real time using digital tools and rain gauges, respectively, in 2019. Dichotomous forecasts verification metrics were then used to verify the skills of both local and scientific forecasts against rainfall records. Farmers use a diverse set of LKF indicators for both weather and seasonal climate time-scale predictions. LFK indicators are mainly used to predict rainfall occurrence, amount of seasonal rainfall, dry spell occurrence, and onset and cessation of the rainy season. The average skill of a set of LFK indicators in predicting one-day rainfall is higher than individual LFK indicators. Also, the skills of a set of LFK indicators can potentially be higher than the forecasts given by the Ghana Meteorological Agency for the Ada District. The results of the documentation and skills indicate that approaches and methods developed for integrating LFK and SFK can contribute to increasing forecast resolution and skills and reducing recurring tensions between the two knowledge systems. Future research and application of these methods can help improve weather and climate information services in Ghana.

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USHKALENKO, Irina. "PECULIARITIES OF MODELING IN AGRICULTURAL PRODUCTION WITH RISK ACCOUNTING." "EСONOMY. FINANСES. MANAGEMENT: Topical issues of science and practical activity", no. 1 (50) (April 28, 2020): 119–34. http://dx.doi.org/10.37128/2411-4413-2020-1-8.

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The causes of the risk inherent in any field of human activity, in particular for agricultural production are considered. AIC, in contrast to other branches of production, has a very important specific reason for the appearance of risk - weather conditions. Weather conditions are one of the main factors of the degree of uncertainty for agricultural production. When developing the organization of production of the agricultural sector, the natural and climatic conditions must be taken into account. In the agroindustrial complex, weather risk is most important and the main task of modeling and optimizing agricultural production is the development of ways of taking into account and reducing the weather risk. Economic result, which is an indicator effectiveness production in agriculture economy, depends independent characteristics of weather conditions. The problem of weather economic sustainability and risk far exceeds the problem of productive stability, because not only all agricultural industries, but also the national economy of the country as a whole are closely related to agricultural production. The instability of agricultural production significantly affects all the integral indicators of the national economy, including the volume of the national product. In the agroindustrial complex, weather risk is most important and the main task of modeling and optimizing agricultural production is the development of ways of taking into account and reducing the weather risk. Economic result, which is an indicator effectiveness production in agriculture economy, depends independent characteristics of weather conditions, that is why it is a random variable. This applies to all regions, all forms of ownership, all types of agricultural activities. Weather conditions can not be managed, but they can be adapted to achieve maximum integral result. To do this, you must select the other projects, strategic and tactical solutions with multiple permissible ones. In principle, there is the ability to manage the characteristics of weather resistance economic efficiency. In this case, we must try to obtain the maximum economic effect not only in a separate weather situation, but also during the long term, so that the effectiveness of the production is as possible as possible in relation to the accidental weather conditions in the given climate zone.

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Makosko, A. A., and L. O. Maximenkov. "New prognostic indicator of adverse and dangerous weather phenomena — gradient of the integral helicity of the atmospheric motion velocity field." Issues of Risk Analysis 16, no. 2 (April 28, 2019): 50–57. http://dx.doi.org/10.32686/1812-5220-2019-16-2-50-57.

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Some new properties of integral helicity are revealed, in particular, the dipole structure of the structure of its field. Taking this circumstance into account, a more informative and illustrative criterion for evaluating integral helicity is a gradient of integral helicity that is promising in analyzing and forecasting adverse and dangerous weather phenomena.Using the example of a convective storm inMoscow05.29.2017. Indicators of predictive quality and clarity, used as diagnostic indicators and dangerous weather phenomena. Considering that such a convective storm is predicted in just a couple of hours, or even several tens of minutes, the proposed criterion, which ensures the forecast lead time of about 12 hours, can become an important link in the technological line for predicting dangerous weather phenomena and a significant reserve for reducing economic damage due to meteorological reasons.

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McEgan, Rachel, Gabriel Mootian, Lawrence D. Goodridge, Donald W. Schaffner, and Michelle D. Danyluk. "Predicting Salmonella Populations from Biological, Chemical, and Physical Indicators in Florida Surface Waters." Applied and Environmental Microbiology 79, no. 13 (April 26, 2013): 4094–105. http://dx.doi.org/10.1128/aem.00777-13.

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ABSTRACTColiforms,Escherichia coli, and various physicochemical water characteristics have been suggested as indicators of microbial water quality or index organisms for pathogen populations. The relationship between the presence and/or concentration ofSalmonellaand biological, physical, or chemical indicators in Central Florida surface water samples over 12 consecutive months was explored. Samples were taken monthly for 12 months from 18 locations throughout Central Florida (n= 202). Air and water temperature, pH, oxidation-reduction potential (ORP), turbidity, and conductivity were measured. Weather data were obtained from nearby weather stations. Aerobic plate counts and most probable numbers (MPN) forSalmonella,E. coli, and coliforms were performed. Weak linear relationships existed between biological indicators (E. coli/coliforms) andSalmonellalevels (R2< 0.1) and between physicochemical indicators andSalmonellalevels (R2< 0.1). The average rainfall (previous day, week, and month) before sampling did not correlate well with bacterial levels. Logistic regression analysis showed thatE. coliconcentration can predict the probability of enumerating selectedSalmonellalevels. The lack of good correlations between biological indicators andSalmonellalevels and between physicochemical indicators andSalmonellalevels shows that the relationship between pathogens and indicators is complex. However,Escherichia coliprovides a reasonable way to predictSalmonellalevels in Central Florida surface water through logistic regression.

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Mofijur, M., I. M. Rizwanul Fattah, A. B. M. Saiful Islam, M. N. Uddin, S. M. Ashrafur Rahman, M. A. Chowdhury, Md Asraful Alam, and Md Alhaz Uddin. "Relationship between Weather Variables and New Daily COVID-19 Cases in Dhaka, Bangladesh." Sustainability 12, no. 20 (October 9, 2020): 8319. http://dx.doi.org/10.3390/su12208319.

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The present study investigated the relationship between the transmission of COVID-19 infections and climate indicators in Dhaka, Bangladesh, using coronavirus infections data available from the Institute of Epidemiology, Disease Control and Research (IEDCR), Bangladesh. The Spearman rank correlation test was carried out to study the association of seven climate indicators, including humidity, air quality, minimum temperature, precipitation, maximum temperature, mean temperature, and wind speed with the COVID-19 outbreak in Dhaka, Bangladesh. The study found that, among the seven indicators, only two indicators (minimum temperature and average temperature) had a significant relationship with new COVID-19 cases. The study also found that air quality index (AQI) had a strong negative correlation with cumulative cases of COVID-19 in Dhaka city. The results of this paper will give health regulators and policymakers valuable information to lessen the COVID-19 spread in Dhaka and other countries around the world.

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Leroyer, Sylvie, Stéphane Bélair, Lubos Spacek, and Ismail Gultepe. "Modelling of radiation-based thermal stress indicators for urban numerical weather prediction." Urban Climate 25 (September 2018): 64–81. http://dx.doi.org/10.1016/j.uclim.2018.05.003.

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Bautista, Francisco, Aristeo Pacheco, and Inna Dubrovina. "Climate change indicators software for computing climate change indices for agriculture." Ecosistemas y Recursos Agropecuarios 6, no. 17 (May 3, 2019): 343. http://dx.doi.org/10.19136/era.a6n17.1770.

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Climatic anomalies affect agricultural production, so the identification of climate change at the local level is a pressing task. The Intergovernmental Panel on Climate Change (IPCC) has generated indices that allow the identification of extreme climate events. Recent studies of weather and climate variation have increasingly used the climate extreme indices defined by the IPCC. This article dwells upon the ICC (climate change indicators) software written in the Java language which is a convenient tool for storing and processing large sets of daily weather data. The software allows the calculation of 27 climate change indices and four indices for growing vines. Built-in analytical tools help identify trends and climate anomalies on different time intervals. The application is additionally supplied with important functionality for statistical data processing and visualization. Tools that help analyzing climate change can foster wiser strategic decision-making in the management of agriculture and ecosystems.

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Noskov, Sergej N., Aleksandr O. Karelin, Elena G. Golovina, Olga M. Stupishina, and Gennadij B. Yeremin. "Assessment of the relationship of the population’s medical care with the factors of earth and space weather." Hygiene and sanitation 100, no. 8 (August 31, 2021): 775–81. http://dx.doi.org/10.47470/0016-9900-2021-100-8-775-781.

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Introduction. In recent years, the influence of climatic factors on population health has become particularly relevant. With significant fluctuations in meteorological conditions, there is an overstrain and failure of the adaptation. This leads to disorders of the functioning of the cardiovascular and central nervous systems. The purpose of the study is to assess the relationship of the population’s medical care with climatic factors, based on the available databases of indicators of Earth and space weather and data on the frequency of the population’s medical care. Material and Methods. For the analysis, we used a database of the population’s access to medical care and variations of climatic factors from 19.12.2005 to 31.12.2009 in the Kalininsky district of St. Petersburg. The total number of environmental parameters included in the study was 237, and the number of requests for medical care was 2.444. Results. In this paper, we tested a model for assessing the relationship between the population’s access to medical care and Earth and space weather factors. Gender and seasonal analysis were carried out. Time dependencies were studied. The obtained data allowed us to form a list of climate indicators that affect the health of the population. Conclusion. The most significant climatic indicators associated with coronary heart disease were identified. The most dangerous season of the year is autumn. Women have a higher sensitivity to changes in Earth and space weather than men. Accounting for variations in space weather allows predicting changes in medical treatment requests in 2-5 days, accounting for variations in the Earth’s weather - in 1 day.

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Ubisi, Nomcebo R., Unathi Kolanisi, and Obert Jiri. "The Role of Indigenous Knowledge Systems in Rural Smallholder Farmers’ Response to Climate Change: Case Study of Nkomazi Local Municipality, Mpumalanga, South Africa." Journal of Asian and African Studies 55, no. 2 (September 28, 2019): 273–84. http://dx.doi.org/10.1177/0021909619874824.

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Climate change and variability have direct negative impacts on rural smallholder farmers. These impacts involve extreme climatic events such as excessive temperatures, prolonged droughts and floods which affect people’s livelihoods. This study was conducted in Nkomazi Local Municipality, Mpumalanga, South Africa. The main objective of the study was to investigate indigenous weather and climate indicators used by smallholder farmers and the role of indigenous knowledge in their farming systems. The research used qualitative methods, including focus group discussions and key informant interviews. The findings indicated that farmers observed animal behaviour, plants, atmospheric indicators and human ailments to predict weather.

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Jajere, Ahmed Abubakar, Joshua, Jonah Kunda, Umar Muhammed Bibi, and Yusuf Maina-Bukar. "Accuracy of Local Knowledge in Prediction Seasonal Weather: Empirical Evidence from North eastern Nigeria." Environment and Pollution 10, no. 2 (June 4, 2021): 33. http://dx.doi.org/10.5539/ep.v10n2p33.

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Over the years, West African Sahel’s people developed some strategies for predicting the seasonal weather using meteorological indicators to plan for extreme weather events. This study used information on local indicators of seasonal weather prediction and mean monthly rainfall and temperature record (1981-2017) from Nguru weather station located at Latitude 14°N in achieving the aim of the study. Both qualitative and quantitate (descriptive and inferential) statistical tools were employed in analysing the collected data. The study found that the local population of the study area used meteorological indicators in predicting the seasonal weather. The results of the analysis revealed that the variability of the annual rainfall during the study period was large. An increasing trend of 3.1mm annually was observed. While decreasing trend in the cold, dry and hot dry season temperature and an increasing trend in warm moist temperature by 0.025°C, 0.05°C and 0.0004°C respectively, was observed. Annual rainfall amount accounts for 31% and 2% variability in cold dry and warm moist season temperature, respectively. Cold, dry season and warm moist season temperature respond to any 1mm increase in annual rainfall by decreasing by 0.012°C and 0.002°C, respectively. The Hot, dry season temperature also accounts for 4% of the variability in annual rainfall. The model’s result revealed anyone 1°C increase in hot dry season temperature lowers the annual rainfall by 10mm. This study confirmed that the observed relationship between seasons weather conditions by local population exist. Therefore annual rainfall is the major determinant of cold dry seasonal temperature in the study area.

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Chernikov, Evgeniy, Anna Marmorshtein, Valentina Popova, and Galina Aleynikova. "Similarities and differences in trends of changes of the agroclimatic conditions of the main viticulture zones of the Krasnodar region." BIO Web of Conferences 39 (2021): 01005. http://dx.doi.org/10.1051/bioconf/20213901005.

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In this article are presented the results of studying the similarities and differences in the trends of changes of the agroclimatic conditions of the main viticulture zones of the Krasnodar region according to the data of the Temryuk, Anapa and Taman weather stations. The average values of heat and water supply indicators for two thirty-year (1961-1990, 1991-2020) and three twenty-year averaging periods (1961-1980, 1981-2000, 2001-2020) were obtained. Statistically significant changes in the average agroclimatic indicators were noted. The connection of the course of indicators between weather stations is determined, especially a close connection is noted in the indicators of Anapa and Temryuk. The values of linear trends in 1991-2020 for Anapa and Temryuk are calculated. Statistically significant trends are the growth of the accumulated air temperature above +10 °C in Anapa and Temryuk, a decrease in the moisture coefficient and an increase in the dryness index in Temryuk.

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Kołowrocki, Krzysztof, Joanna Soszyńska-Budny, and Mateusz Torbicki. "Model of System Safety Related to Climate-Weather Change Process." Journal of KONBiN 43, no. 1 (October 1, 2017): 241–48. http://dx.doi.org/10.1515/jok-2017-0048.

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Abstract The article is devoted to the climate-weather change impact on the critical infrastructure safety. A general safety analytical model of the technical system related to the climate-weather change process in its operating area is proposed. It is the integrated model of the complex technical system safety, linking its multistate safety model and the model of the climate-weather change process at its operating area. The conditional safety functions at the climate-weather particular states and the unconditional safety function of the complex system at changing in time climate-weather conditions, the mean value and the variance of the system unconditional lifetime and other safety indicators are defined in general for any critical infrastructure.

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Cin, Beom Cheol, Sung Woo Kim, and Byoung Joon Kim. "Weather Effects on Industrial Stock Index Returns and Volatilities: Evidence from Korean Stock Market." Perspectives on Global Development and Technology 18, no. 5-6 (February 17, 2020): 602–18. http://dx.doi.org/10.1163/15691497-12341535.

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Abstract This study empirically investigates weather effects on Korean stock index returns and market volatility based on the GJR-GARCH-X model. We focus on the issue about whether the weather effect is associated with financial deregulation and foreign investors’ transactions, which should not be affected by the weather conditions in Seoul City. To explore the weather effects by controlling for foreign investors’ trading, we employ daily stock index data and weather indicators (cloud cover, precipitation, sunshine hours, snow, and humidity) during the period from 1981 to 2016. Our empirical results suggest that there is little evidence for weather effects on KOSPI and stock returns by industry listed in the Korea Stock Exchange even if Korean financial market deregulations in 1997 increases foreign investors’ trading, but there is some evidence for weather effects on market volatilities.

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Bączkiewicz, Aleksandra, Jarosław Wątróbski, Wojciech Sałabun, and Joanna Kołodziejczyk. "An ANN Model Trained on Regional Data in the Prediction of Particular Weather Conditions." Applied Sciences 11, no. 11 (May 22, 2021): 4757. http://dx.doi.org/10.3390/app11114757.

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Artificial Neural Networks (ANNs) have proven to be a powerful tool for solving a wide variety of real-life problems. The possibility of using them for forecasting phenomena occurring in nature, especially weather indicators, has been widely discussed. However, the various areas of the world differ in terms of their difficulty and ability in preparing accurate weather forecasts. Poland lies in a zone with a moderate transition climate, which is characterized by seasonality and the inflow of many types of air masses from different directions, which, combined with the compound terrain, causes climate variability and makes it difficult to accurately predict the weather. For this reason, it is necessary to adapt the model to the prediction of weather conditions and verify its effectiveness on real data. The principal aim of this study is to present the use of a regressive model based on a unidirectional multilayer neural network, also called a Multilayer Perceptron (MLP), to predict selected weather indicators for the city of Szczecin in Poland. The forecast of the model we implemented was effective in determining the daily parameters at 96% compliance with the actual measurements for the prediction of the minimum and maximum temperature for the next day and 83.27% for the prediction of atmospheric pressure.

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van der Fels-Klerx, H., M. Kandhai, and C. Booij. "A conceptual model for identification of emerging risks, applied to mycotoxins in wheat-based supply chains." World Mycotoxin Journal 1, no. 1 (February 1, 2008): 13–22. http://dx.doi.org/10.3920/wmj2008.x002.

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The research described in this paper focuses on identification of the most important indicators for emerging mycotoxins, starting from those produced by Fusarium fungi, in wheat-based feed and food supply chains, as well as the development of a conceptual model to predict the occurrence of these emerging toxins, based on the selected indicators. The selection of the most important indicators was based on a literature review and evaluation of the resulting indicators for their relevance. Each indicator selected was appointed to relevant stage(s) of the supply chain to which it is related and, for each indicator, a suggestion for a potential information source is given. The selected indicators cover various influential sectors, amongst others, weather conditions, agronomical practices, trade and legislation, as well as a variety of information sources, e.g. on-farm records and statistical organisations. The conceptual model developed is aimed at predicting the occurrence of emerging mycotoxins – based on the selected indicators – in a particular unit of wheat. The model takes a supply chain approach and can handle various types of indicators and various levels of detail of information on origin of the unit of wheat. The proposed model could be useful in the development of an identification system for emerging risks related to mycotoxins in wheat-based supply chains. Ultimately, such a system will help industry and policy makers in their decision-making process with regard to prevention and control of upcoming risks.

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Dudych, V., and I. Gnatyak. "Climatic factors and their impact on the development of exogenous processes in the basin of the Upper Prut River." Visnyk of the Lviv University. Series Geography 1, no. 40 (December 12, 2012): 214–24. http://dx.doi.org/10.30970/vgg.2012.40.2046.

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In this article are analyzed some climatic indicators which were taken from three meteorological stations in the basin of the upper Prut River and influence this climatic indicators on the development of modern relief-forming processes. Meteorological data taken from weather station Pozhyzhevska (altitude 1433 m), Chornohora Geographical Station (altitude 978 m) and weather station in Yaremcha (altitude 530 m). Altitudinal magnitude between these stations is 903 meters. The main task of the study, was to clarify the changes of certain climatic characteristics and course of exogenous processes on different hypsometric levels in the basin of the upper Prut River. Key words: air temperature, frost-free period, snow cover, precipitation layer.

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Fan, Jingfeng, Hongxia Ming, Lili Li, and Jie Su. "Evaluating spatial-temporal variations and correlation between fecal indicator bacteria (FIB) in marine bathing beaches." Journal of Water and Health 13, no. 4 (June 1, 2015): 1029–38. http://dx.doi.org/10.2166/wh.2015.031.

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The horizontal distribution and temporal variation of bacterial indicators (total coliforms (TC), fecal coliforms (FC), enterococcus (EC) and Escherichia coli (E. coli)) were investigated to identify the proper bacterial indicators for a marine bathing beach in China. Two different sampling efforts were conducted during dry weather and two large rain events at Xinghai Bathing Beach in Dalian, China. Samples were collected from three different water depths and analyzed for the four indicator bacteria. The results indicated that all four bacterial indicators exceeded the single sample standards at different levels. Specifically, the water quality exceeded the standard for TC, FC, EC and E. coli in 7%, 28%, 38% and 10% of the samples, respectively. Comparison of the rate of the indicators before and after rainfall revealed a significant increasing post-rainfall. The concentrations of bacteria differed significantly with distance from the shoreline, with knee-depth near the shore exceeding the standard most frequently. This was primarily due to contamination by excessive sewage discharge and rainfall. Based upon the concentration of indicators and exceedance rates, as well as the correlation between indicators, both EC and FC should be evaluated at the same time as fecal pollution bacterial indicators in marine bathing beaches in China.

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Becsi, Benedikt, Herbert Formayer, and Robert Brodschneider. "A biophysical approach to assess weather impacts on honey bee colony winter mortality." Royal Society Open Science 8, no. 9 (September 2021): 210618. http://dx.doi.org/10.1098/rsos.210618.

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The western honey bee ( Apis mellifera ) is one of the most important insects kept by humans, but high colony losses are reported around the world. While the effects of general climatic conditions on colony winter mortality were already demonstrated, no study has investigated specific weather conditions linked to biophysical processes governing colony vitality. Here, we quantify the comparative relevance of four such processes that co-determine the colonies' fitness for wintering during the annual hive management cycle, using a 10-year dataset of winter colony mortality in Austria that includes 266 378 bee colonies. We formulate four process-based hypotheses for wintering success and operationalize them with weather indicators. The empirical data is used to fit simple and multiple linear regression models on different geographical scales. The results show that approximately 20% of winter mortality variability can be explained by the analysed weather conditions, and that it is most sensitive to the duration of extreme cold spells in mid and late winter. Our approach shows the potential of developing weather indicators based on biophysical processes and discusses the way forward for applying them in climate change studies.

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Journal articles on the topic 'Weather indicators'

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