Weather explains high annual variation in butterfly dispersal

Weather conditions fundamentally affect the activity of short-lived insects. Annual variation in weather is therefore likely to be an important determinant of their between-year variation in dispersal, but conclusive empirical studies are lacking. We studied whether the annual variation of dispersal can be explained by the flight season's weather conditions in a Clouded Apollo ( Parnassius mnemosyne ) metapopulation. This metapopulation was monitored using the mark–release–recapture method for 12 years. Dispersal was quantified for each monitoring year using three complementary measures: emigration rate (fraction of individuals moving between habitat patches), average residence time in the natal patch, and average distance moved. There was much variation both in dispersal and average weather conditions among the years. Weather variables significantly affected the three measures of dispersal and together with adjusting variables explained 79–91% of the variation observed in dispersal. Different weather variables became selected in the models explaining variation in three dispersal measures apparently because of the notable intercorrelations. In general, dispersal rate increased with increasing temperature, solar radiation, proportion of especially warm days, and butterfly density, and decreased with increasing cloudiness, rainfall, and wind speed. These results help to understand and model annually varying dispersal dynamics of species affected by global warming.

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Combining Weather Stations for Electric Load Forecasting

Energies ◽

10.3390/en12081510 ◽

2019 ◽

Vol 12 (8) ◽

pp. 1510 ◽

Cited By ~ 10

Author(s):

Masoud Sobhani ◽

Allison Campbell ◽

Saurabh Sangamwar ◽

Changlin Li ◽

Tao Hong

Keyword(s):

Load Forecasting ◽

Weather Conditions ◽

Vital Role ◽

Alternative Methods ◽

Electric Load ◽

Weather Variables ◽

Energy Forecasting ◽

Key Factor ◽

Weather Stations ◽

Vast Area

Weather is a key factor affecting electricity demand. Many load forecasting models rely on weather variables. Weather stations provide point measurements of weather conditions in a service area. Since the load is spread geographically, a single weather station may not sufficiently explain the variations of the load over a vast area. Therefore, a proper combination of multiple weather stations plays a vital role in load forecasting. This paper answers the question: given a number of weather stations, how should they be combined for load forecasting? Simple averaging has been a commonly used and effective method in the literature. In this paper, we compared the performance of seven alternative methods with simple averaging as the benchmark using the data of the Global Energy Forecasting Competition 2012. The results demonstrate that some of the methods outperform the benchmark in combining weather stations. In addition, averaging the forecasts from these methods outperforms most individual methods.

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Perceived Influence of Weather Conditions on Rheumatic Pain in Romania

Advances in Meteorology ◽

10.1155/2019/9187105 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9 ◽

Cited By ~ 2

Author(s):

Adina-Eliza Croitoru ◽

Gabriela Dogaru ◽

Titus Cristian Man ◽

Simona Mălăescu ◽

Marieta Motricală ◽

...

Keyword(s):

Relative Humidity ◽

Pain Intensity ◽

Air Temperature ◽

Correlation Matrix ◽

Great Majority ◽

Weather Conditions ◽

Weather Variables ◽

Rheumatic Pain ◽

Atmospheric Fronts ◽

The Relationship

The main objective of this study was to analyze the perception of the influence of various weather conditions on patients with rheumatic pathology. A group of 394 patients, aged between 39 and 87 years and diagnosed with degenerative rheumatic diseases, were interviewed individually by using a questionnaire created specifically for this study. Further on, to assess the relationship between pain intensity and weather conditions, a frequency analysis based on Pearson’s correlation matrix was employed. The most important results are as follows: the great majority of the participants (more than 75%) believe that their rheumatic pain is definitely or to a great extent influenced by different weather conditions; most of the patients reported intensification of their pain with weather worsening, especially when cloudiness and humidity suddenly increase (83.8% and 82.0%, respectively), air temperature suddenly decreases (81.5%), and in fog or rain conditions (81.2%). In our research, alongside simple meteorological variables, we established that complex weather variables such as atmospheric fronts, in particular, the cold ones and winter anticyclonic conditions, greatly intensify the rheumatic pain, whereas summer anticyclonic conditions usually lead to a decrease in pain severity. In terms of relationships between pain intensity and weather conditions, we found the strongest correlations (ranging between 0.725 and 0.830) when temperature, relative humidity, and cloudiness are constantly high.

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Investigating the Impact of Rainfall on Travel Speed

Jurnal Teknologi ◽

10.11113/jt.v71.3756 ◽

2014 ◽

Vol 71 (3) ◽

Cited By ~ 1

Author(s):

Nordiana Mashros ◽

Johnnie Ben-Edigbe ◽

Hashim Mohammed Alhassan ◽

Sitti Asmah Hassan

Keyword(s):

Empirical Studies ◽

Heavy Rain ◽

Weather Conditions ◽

Travel Speed ◽

Operating Conditions ◽

Speed Reduction ◽

Light Rain ◽

Rainfall Condition ◽

Adverse Weather ◽

The Impact

The road network is particularly susceptible to adverse weather with a range of impacts when different weather conditions are experienced. Adverse weather often leads to decreases in traffic speed and subsequently affects the service levels. The paper is aimed at investigating the impact of rainfall on travel speed and quantifying the extent to which travel speed reduction occurs. Empirical studies were conducted on principle road in Terengganu and Johor, respectively for three months. Traffic data were collected by way of automatic traffic counter and rainfall data from the nearest raingauge station were supplied by the Department of Irrigation and Drainage supplemented by local survey data. These data were filtered to obtain traffic flow information for both dry and wet operating conditions and then were analyzed to see the effect of rainfall on percentile speeds. The results indicated that travel speed at 15th, 50th and 85th percentiles decrease with increasing rainfall intensities. It was observed that allpercentile speeds decreased from a minimum of 1% during light rain to a maximum of 14% during heavy rain. Based on the hypothesis that travel speed differ significantly between dry and rainfall condition; the study found substantial changes in percentile speeds and concluded that rainfalls irrespective of their intensities have significant impact on the travel speed.

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Statistical Modelling of the Effects of Weather Factors on Malaria Occurrence in Abuja, Nigeria

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17103474 ◽

2020 ◽

Vol 17 (10) ◽

pp. 3474 ◽

Cited By ~ 1

Author(s):

Oguntade Emmanuel Segun ◽

Shamarina Shohaimi ◽

Meenakshii Nallapan ◽

Alaba Ajibola Lamidi-Sarumoh ◽

Nader Salari

Keyword(s):

Linear Models ◽

Negative Binomial ◽

Malaria Incidence ◽

Pearson Correlation ◽

Ecological Factors ◽

Weather Conditions ◽

Statistical Modelling ◽

Weather Variables ◽

Weather Factors ◽

Unit Increase

Background: despite the increase in malaria control and elimination efforts, weather patterns and ecological factors continue to serve as important drivers of malaria transmission dynamics. This study examined the statistical relationship between weather variables and malaria incidence in Abuja, Nigeria. Methodology/Principal Findings: monthly data on malaria incidence and weather variables were collected in Abuja from the year 2000 to 2013. The analysis of count outcomes was based on generalized linear models, while Pearson correlation analysis was undertaken at the bivariate level. The results showed more malaria incidence in the months with the highest rainfall recorded (June–August). Based on the negative binomial model, every unit increase in humidity corresponds to about 1.010 (95% confidence interval (CI), 1.005–1.015) times increase in malaria cases while the odds of having malaria decreases by 5.8% for every extra unit increase in temperature: 0.942 (95% CI, 0.928–0.956). At lag 1 month, there was a significant positive effect of rainfall on malaria incidence while at lag 4, temperature and humidity had significant influences. Conclusions: malaria remains a widespread infectious disease among the local subjects in the study area. Relative humidity was identified as one of the factors that influence a malaria epidemic at lag 0 while the biggest significant influence of temperature was observed at lag 4. Therefore, emphasis should be given to vector control activities and to create public health awareness on the proper usage of intervention measures such as indoor residual sprays to reduce the epidemic especially during peak periods with suitable weather conditions.

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Liquidity Risk and the Credit Crunch of 2007–2008: Evidence from Micro-Level Data on Mortgage Loan Applications

Journal of Financial and Quantitative Analysis ◽

10.1017/s0022109016000740 ◽

2016 ◽

Vol 51 (6) ◽

pp. 1795-1822 ◽

Cited By ~ 7

Author(s):

Adonis Antoniades

Keyword(s):

Important Determinant ◽

Empirical Studies ◽

Liquidity Risk ◽

Mortgage Market ◽

Mortgage Loan ◽

Asset Risk ◽

Level Data ◽

Micro Level ◽

Credit Demand ◽

Loan Officers

Recent empirical studies have shown that during the financial crisis of 2007–2008, banks that were more heavily exposed to liquidity risk contracted their supply of credit more sharply. I contribute to the identification of this effect by relying on the use of micro-level data on U.S. mortgage loan applications, which allows me to identify liquidity risk as an important determinant of the contraction of credit in the mortgage market but as separate from the precipitous fall in credit demand, disruptions in the securitization and subprime markets, shifts in asset risk, and changing risk aversion among loan officers.

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Modeling Infection of Strawberry Flowers by Botrytis cinerea Using Field Data

Phytopathology ◽

10.1094/phyto.2000.90.12.1367 ◽

2000 ◽

Vol 90 (12) ◽

pp. 1367-1374 ◽

Cited By ~ 33

Author(s):

Xiangming Xu ◽

David C. Harris ◽

Angela M. Berrie

Keyword(s):

Vapor Pressure ◽

Botrytis Cinerea ◽

Vapor Pressure Deficit ◽

Meteorological Data ◽

Weather Conditions ◽

Gray Mold ◽

Weather Variables ◽

Infection Models ◽

The Relationship ◽

Field Plots

The incidence of strawberry flower infection by Botrytis cinerea was monitored in unsprayed field plots in three successive years together with meteorological data and numbers of conidia in the air. There were large differences in conidia numbers and weather conditions in the 3 years. Three sets of models were derived to relate inoculum and weather conditions to the incidence of flower infection; by inoculum only, by weather variables only, and by both inoculum and weather variables. All the models fitted the observed incidence satisfactorily. High inoculum led to more infection. Models using weather variables only gave more accurate predictions than models using inoculum only. Models using both weather variables and inoculum gave the best predictions, but the improvement over the models based on weather variables only was small. The relationship between incidence of flower infection and inoculum and weather variables was generally consistent between years. Of the weather variables examined, daytime vapor pressure deficit and nighttime temperature had the greatest effect in determining daily incidence of flower infection. Infection was favored by low day vapor pressure deficit and high night temperature. The accuracy and consistency of the weather-based models suggest they could be explored to assist in management of gray mold.

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Factors affecting colony attendance by Ancient Murrelets (Synthliboramphus antiquus)

Canadian Journal of Zoology ◽

10.1139/z90-064 ◽

1990 ◽

Vol 68 (3) ◽

pp. 433-441 ◽

Cited By ~ 22

Author(s):

Ian L. Jones ◽

Anthony J. Gaston ◽

J. Bruce Falls

Keyword(s):

Weather Conditions ◽

Weather Variables ◽

Short Term ◽

Weather Effects ◽

Factors Affecting ◽

Cyclical Pattern ◽

The Hours ◽

Nesting Season ◽

Colony Attendance

We studied factors influencing variation in nightly levels of activity (birds arriving and vocalizing) and numbers of birds staging offshore at a colony of Ancient Murrelets at Reef Island, British Columbia, during 1984, 1985, and 1986. Activity was restricted to the hours of darkness and extremely variable in magnitude from night to night. The rate of entry into burrows tended to decrease, and the amount of vocalization and numbers of birds at the staging area increased during the nesting season. We detected an underlying 4-day cyclical pattern of attendance. Nightly variability of activity at the colony was affected by moonlight and weather conditions. Since activity, particularly vocalization, was reduced on moonlit nights, we suggest that nocturnal colony attendance is a strategy to avoid diurnal predators in this species. The largest numbers of birds were present and vocalizing at the colony on calm moonless nights. Weather conditions explained a substantial proportion of the night to night variability in murrelet activity. Among weather variables, wind speed had the most consistent effect and was particularly important in 1985. Both short-term, i.e., of a particular night, and long-term, i.e., over the previous 3 days, conditions influenced activity. Our observations suggest that direct weather effects at the colony may be more important than weather effects related to foraging conditions. Interyear differences in activity may have resulted from the interaction of weather and general foraging conditions.

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On the Role of Grain Boundaries During Sintering

10.20944/preprints202101.0422.v1 ◽

2021 ◽

Author(s):

Torsten Staab ◽

Ricardo Helm ◽

Andreas Diegeler

Keyword(s):

Mass Transport ◽

Grain Boundaries ◽

Average Distance ◽

Intermediate Stage ◽

Sintering Process ◽

Positron Annihilation Lifetime ◽

Boundary Signal ◽

Increasing Temperature ◽

Good Agreement

We present new results in positron annihilation lifetime spectroscopy (PALS), thermo-optical dilatometry and microscopy, which are indicating a strong correlation between grain-boundaries and mass transport during the sintering process of carbonyl iron powder. In this particular system we were able to show that the change in particle shape and size with increasing temperature yields an anisotropy in shrinkage, which manifests itself in a higher shrinkage perpendicular to the compaction axis. In the intermediate stage of sintering, where the major mass transport occurs, the average distance between two grain boundaries could be determined to (3,73 ± 0,18) μm at T = 744°C. This is in good agreement with previous calculations of positron pathways in defect free particles. Furthermore, due to sintering temperatures far above the annealing temperature of dislocations in iron, the existence of dislocations in the bulk of the particles is very unlikely. These claims are reflected by the collected positron data, which exhibit a clear grain boundary signal of &sim; 250ps while no vacancy or dislocation signal (typically &sim; 160 ps) is evident in the intermediate stage of sintering.

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Effects of Variable Weather Conditions on Baled Proportion of Varied Amounts of Harvestable Cereal Straw, Based on Simulations

Sustainability ◽

10.3390/su13169449 ◽

2021 ◽

Vol 13 (16) ◽

pp. 9449

Author(s):

Alfredo de Toro ◽

Carina Gunnarsson ◽

Nils Jonsson ◽

Martin Sundberg

Keyword(s):

Standard Deviation ◽

Moisture Content ◽

Annual Variation ◽

Weather Conditions ◽

Weather Data ◽

Cereal Straw ◽

Grain Moisture ◽

Machine Systems ◽

Variable Weather

All harvestable cereal straw cannot be collected every year in regions where wet periods are probable during the baling season, so some Swedish studies have used 'recovery coefficients’ to estimate potential harvestable amounts. Current Swedish recovery coefficients were first formulated by researchers in the early 1990s, after discussions with crop advisors, but there are no recent Swedish publications on available baling times and recovery proportions. Therefore, this study evaluated baling operations over a series of years for representative virtual farms and machine systems in four Swedish regions, to determine the available time for baling, baled straw ratio and annual variation in both. The hourly grain moisture content of pre-harvested cereals and swathed straw was estimated using moisture models and real weather data for 22/23 years, and the results were used as input to a model for simulating harvesting and baling operations. Expected available baling time during August and September was estimated to be 39–49%, depending on region, with large annual variation (standard deviation 22%). The average baling coefficient was estimated to be 80–86%, with 1400 t·year−1 harvestable straw and 15 t·h−1 baling capacity, and the annual variation was also considerable (s.d. 20%).

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Estimating non-additive within-season temperature effects on maize yields using Bayesian approaches

Scientific Reports ◽

10.1038/s41598-019-55037-6 ◽

2019 ◽

Vol 9 (1) ◽

Author(s):

Jisang Yu ◽

Gyuhyeong Goh

Keyword(s):

Temperature Effects ◽

Crop Yields ◽

Empirical Studies ◽

Growing Season ◽

Weather Variables ◽

Temperature And Precipitation ◽

Maize Yields ◽

Yield Responses ◽

The Impact ◽

The U.S

AbstractDetrimental impacts of extreme heats on the U.S. crop yields have been well-documented by a number of empirical studies. However, less have focused on within-growing season weather variation and the interaction between temperature and precipitation. The objective of this study is to emphasize the importance of disaggregating temperature exposures within growing season. To achieve our objective, we estimate the impact of within-season monthly temperature and precipitation variations on maize yields in the U.S. corn belt region. We provide a discussion on variable selection methods in the context of estimating crop yield responses to climate variables. We find that the models that utilize within-growing season monthly variations performs better compared to the models with growing season aggregated weather variables and show the strength of Bayesian estimations. We also find that the warming impacts predicted by the models that utilize within-growing season variations are smaller than the predicted impacts of the models with aggregated weather variables. The findings indicate that the temperature effects are not additive across months within growing season.

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