scholarly journals Meteorological factors affecting the risk of transmission of HPAI in Miyazaki, Japan

2019 ◽  
Vol 6 (1) ◽  
pp. e000341 ◽  
Author(s):  
Genki Arikawa ◽  
Yoshinori Fujii ◽  
Maiku Abe ◽  
Ngan Thi Mai ◽  
Shuya Mitoma ◽  
...  
Keyword(s):  
Bird Migration ◽  
Meteorological Factors ◽  
Meteorological Data ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Migratory Waterfowl ◽  
Generalised Linear Mixed Model ◽  
Fulica Atra ◽  
The Relationship

Highly pathogenic avian influenza (HPAI) outbreaks engender a severe economic impact on the poultry industry and public health. Migratory waterfowl are considered the natural hosts of HPAI virus, and HPAI viruses are known to be transmitted over long distances during seasonal bird migration. Bird migration is greatly affected by the weather. Many studies have shown the relationship between either autumn or spring bird migration and climate. However, few studies have shown the relationship between annual bird migration and annual weather. This study aimed to establish a model for the number of migratory waterfowl involved in HPAI virus transmission based on meteorological data. From 136 species of waterfowl that were observed at Futatsudate in Miyazaki, Japan, from 2008 to 2016, we selected potential high-risk species that could introduce the HPAI virus into Miyazaki and defined them as ‘risky birds’. We also performed cluster analysis to select meteorological factors. We then analysed the meteorological data and the total number of risky birds using a generalised linear mixed model. We selected 10 species as risky birds: Mallard (Anas platyrhynchos), Northern pintail (Anas acuta), Eurasian wigeon (Anas penelope), Eurasian teal (Anas crecca), Common pochard (Aythya ferina), Eurasian coot (Fulica atra), Northern shoveler (Anas clypeata), Common shelduck (Tadorna tadorna), Tufted duck (Aythya fuligula) and Herring gull (Larus argentatus). We succeeded in clustering 35 meteorological factors into four clusters and identified three meteorological factors associated with their migration: (1) the average daily maximum temperature; (2) the mean value of global solar radiation and (3) the maximum daily precipitation. We thus demonstrated the relationship between the number of risky birds and meteorological data. The dynamics of migratory waterfowl was relevant to the risk of an HPAI outbreak, and our data could contribute to cost and time savings in strengthening preventive measures against epidemics.

scholarly journals Identification of meteorological factors affecting migration of wild birds into miyazaki and its relation to circulation of highly pathogenic avian influenza virus

2018 ◽  
Author(s):  
Genki Arikawa ◽  
Maiku Abe ◽  
Mai Thi Ngan ◽  
Shuya Mitoma ◽  
Kosuke Notsu ◽  
...  
Keyword(s):  
Avian Influenza ◽  
Highly Pathogenic Avian Influenza ◽  
Meteorological Factors ◽  
Meteorological Data ◽  
Wild Birds ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Highly Pathogenic ◽  
Pathogenic Avian Influenza ◽  
Fulica Atra

AbstractAim of our study is to establish models for predicting the number of migratory wild birds based on the meteorological data. From 136 species of wild birds, which have been observed at Futatsudate in Miyazaki, Japan, from 2008 to 2016, we selected the potential high-risk species, which can introduce highly pathogenic avian influenza (HPAI) virus into Miyazaki; we defined them as “risky birds”. We then performed regression analysis to model the relationship between the number of risky birds and meteorological data. We selected 10 wild bird species as risky birds: Mallard (Anas platyrhynchos), Northern pintail (Anas acuta), Eurasian wigeon (Anas penelope), Eurasian teal (Anas crecca), Common pochard (Aythya ferina), Eurasian coot (Fulica atra), Northern shoveler (Anas clypeata), Common shelduck (Tadorna tadorna), Tufted duck (Aythya fuligula), and Herring gull (Larus argentatus). We succeeded in identifying five meteorological factors associated with their migration: station pressure, mean value of global solar radiation, minimum of daily maximum temperature, days with thundering, and days with daily hours of daylight under 0.1 h. We could establish some models for predicting the number of risky birds based only on the published meteorological data, without manual counting. Dynamics of migratory wild birds has relevance to the risk of HPAI outbreak, so our data could contribute to save the cost and time in strengthening preventive measures against the epidemics.

scholarly journals Relationship between several meteorological factors and phenological features of apple cultivars

2008 ◽  
Vol 14 (1-2.) ◽  
Author(s):  
L. Lakatos ◽  
T. Szabó ◽  
S. Zhongfu ◽  
Y. Wang ◽  
J. Racskó ◽  
...  
Keyword(s):  
Daily Precipitation ◽  
Meteorological Factors ◽  
Meteorological Data ◽  
Gene Bank ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Night Time ◽  
Apple Cultivars

The trees observed are grown at Ofeherto, Eastern Hungary in the plantation of an assortment (gene bank) with 586 apple cultivars. Each of the cultivars were observed as for their dates of subsequent phenophases, the beginning of bloom, main bloom and the end of bloom over a period between 1984 and 2001. during this period the meteorological data-base keeps the following variables: daily means of temperature (°C), daily maximum temperature (°C), daily minimum temperature (°C), daily precipitation sums (mm), daily sums of sunny hours, daily means of the differences between the day-time and night-time temperatures (°C), average differences between temperatures of successive daily means (°C). Between the 90th and 147th day of the year over the 18 years of observation. The early blooming cultivars start blooming at 10-21April. The cultivars of intermediate bloom start at the interval 20 April to 3 May, whereas the late blooming group start at 2-10 May. Among the meteorological variables of the former autumnal and hibernal periods, the hibernal maxima were the most active factor influencing the start of bloom in the subsequent spring.

scholarly journals Relationship between several meteorological factors and phenological features of pear cultivars

2016 ◽  
Vol 18 (2) ◽  
Author(s):  
L. Lakatos ◽  
T. Szabó ◽  
G. Kocsisné Molnár ◽  
J. Racskó ◽  
M. Soltész ◽  
...  
Keyword(s):  
Meteorological Parameters ◽  
Daily Precipitation ◽  
Meteorological Factors ◽  
Meteorological Data ◽  
Gene Bank ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Night Time

The aim of this paper was to investigate the fl owering characteristic of apples and their relationship to meteorological parameters. The trees observed are grown at Újfehértó, Eastern Hungary in the plantation of an assortment (gene bank) with 586 apple varieties. Each of the varieties were observed as for their dates of subsequent phenophases, the beginning of bloom, main bloom and the end of bloom over a period between 1984 and 2001 during this period the meteorological data-base keeps the following variables: daily means of temperature (°C), daily maximum temperature (°C), daily minimum temperature (°C), daily precipitation sums (mm), daily sums of sunny hours, daily means of the differences between the day-time and night-time temperatures (°C), average differences between temperatures of successive daily means (°C). Between the 90th and 147th day of the year over the 18 years of observation. The early blooming varieties start blooming at 10–21April. The varieties of intermediate bloom start at the interval 20 April to 3 May, whereas the late blooming group start at 2–10 May. Among the meteorological variables of the former autumnal and hibernal periods, the hibernal maxima were the most active factor infl uencing the start of bloom in the subsequent spring.

scholarly journals The effects of ambient temperature and heatwaves on dailyCampylobactercases in Adelaide, Australia, 1990–2012

2017 ◽  
Vol 145 (12) ◽  
pp. 2603-2610 ◽  
Author(s):  
A. MILAZZO ◽  
L. C. GILES ◽  
Y. ZHANG ◽  
A. P. KOEHLER ◽  
J. E. HILLER ◽  
...  
Keyword(s):  
Regression Models ◽  
South Australia ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Infectious Gastroenteritis ◽  
Food Borne ◽  
Relationship Of ◽  
The Relationship

SUMMARYCampylobacterspp. is a commonly reported food-borne disease with major consequences for morbidity. In conjunction with predicted increases in temperature, proliferation in the survival of microorganisms in hotter environments is expected. This is likely to lead, in turn, to an increase in contamination of food and water and a rise in numbers of cases of infectious gastroenteritis. This study assessed the relationship ofCampylobacterspp. with temperature and heatwaves, in Adelaide, South Australia.We estimated the effect of (i) maximum temperature and (ii) heatwaves on dailyCampylobactercases during the warm seasons (1 October to 31 March) from 1990 to 2012 using Poisson regression models.There was no evidence of a substantive effect of maximum temperature per 1 °C rise (incidence rate ratio (IRR) 0·995, 95% confidence interval (95% CI) 0·993–0·997) nor heatwaves (IRR 0·906, 95% CI 0·800–1·026) onCampylobactercases. In relation to heatwave intensity, which is the daily maximum temperature during a heatwave, notifications decreased by 19% within a temperature range of 39–40·9 °C (IRR 0·811, 95% CI 0·692–0·952). We found little evidence of an increase in risk and lack of association betweenCampylobactercases and temperature or heatwaves in the warm seasons. Heatwave intensity may play a role in that notifications decreased with higher temperatures. Further examination of the role of behavioural and environmental factors in an effort to reduce the risk of increasedCampylobactercases is warranted.

scholarly journals Relationship beetwen the phenological features of pear cultivars and the main meteorological parameters in a gene bank with 555 pear

2009 ◽  
Vol 15 (1-2) ◽  
Author(s):  
L. Lakatos ◽  
S. Musacchi ◽  
T. Szabó ◽  
G. Kocsisné Molnár ◽  
Z. Szabó ◽  
...  
Keyword(s):  
Gene Bank ◽  
Meteorological Variables ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Night Time ◽  
Correlation And Regression Analysis ◽  
Fruit Growing ◽  
Autumn And Winter ◽  
The Relationship

The trees observed are grown at Ujfehert6, Eastern Hungary in a gene bank with 555 pear cultivars. Each of the cultivars was monitored for its dates of: the beginning of bloom, main bloom and the end of bloom and ripe phenophasis separately between I 984 and 2002. We analyzed the statistical features, frequency, distribution of these phenophasis and its' correlation the meteorological variables bet ween the interval. During this period the meteorological database recorded the following variables: daily mean temperature (°C), daily maximum temperature (0C), daily mini m um temperature (0C), daily precipitation (mm), daily hours of bright sunshine, daily means or the differences between the day-time and night-time temperatures (0C). For the analysis of data the cultivars have been grouped according to dates of maturity, blooming period as well as types of the seasons. Groups of maturity dates: summer ripe, autumnal ripening, winter ripe cultivars. Groups of blooming dates: early blooming, intermediate blooming, late blooming cultivars. At all the separated groups we analyzed the relationship between phenophasis and meteorological variables. During the 18 years of observation , the early blooming cultivars started blooming on 10-21 April, those of intermediate bloom date started flowering bet ween 20 April and 3 May, whereas the late blooming group started on 2- 10 May. Among the meteorological variables of the former autumn and winter periods, the winter maxima were the most active factor influencing the start dates of bloom in the subsequent spring. For the research of fruit growing-weather relationships we used simple, well known statistical methods, correlation and regression analysis. We used the SPSS 1 1.0 software for the linear regression fitting and for calculation of dispersions as well. The 1ables made by Excel programme.

Prediction of temperatures in cattle dung for estimating development times of coprophilous organisms

1988 ◽  
Vol 78 (2) ◽  
pp. 235-240 ◽  
Author(s):  
J. N. Matthiessen ◽  
M. J. Palmer
Keyword(s):  
Air Temperature ◽  
Meteorological Data ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Threshold Temperature ◽  
Cattle Dung ◽  
Daily Maximum ◽  
Daily Minimum ◽  
Maximum Temperatures ◽  
Minimum Air Temperature

AbstractIn studies in Western Australia, temperatures in air and one- and two-litre pads of cattle dung set out weekly and ranging from one to 20 days old were measured hourly for 438 days over all seasons, producing 1437 day x dung-pad observations. Daily maximum temperatures (and hence thermal accumulation) in cattle dung pads could not be accurately predicted using meteorological data alone. An accurate predictor of daily maximum dung temperature, using multiple regression analysis, required measurement of the following factors: maximum air temperature, hours of sunshine, rainfall, a seasonal factor (the day number derived from a linear interpolation of day number from day 0 at the winter solstice to day 182 at the preceding and following summer solstices) and a dung-pad age-specific intercept term, giving an equation that explained a 91·4% of the variation in maximum dung temperature. Daily maximum temperature in two-litre dung pads was 0·6°C cooler than in one-litre pads. Daily minimum dung temperature equalled minimum air temperature, and daily minimum dung temperatures occurred at 05.00 h and maximum temperatures at 14.00 h for one-litre and 14.30 h for two-litre pads. Thus, thermal summation in a dung pad above any threshold temperature can be computed using a skewed sine curve fitted to daily minimum air temperature and the calculated maximum dung temperature.

scholarly journals Applicability of Heat Index for Effective Heatwave Response

2021 ◽  
Vol 21 (4) ◽  
pp. 23-30
Author(s):  
Ji Yoon Kang ◽  
Bong-Chur Park ◽  
Jongbae Heo ◽  
Keewook Kim
Keyword(s):  
Heat Index ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Maximum Heat ◽  
Temperature And Humidity ◽  
Summer Temperatures ◽  
The Difference ◽  
The Relationship

Damage caused by heatwaves has been increasing recently worldwide. As climate change led by global warming progresses, heatwaves are projected to cause the most damage. Thus, it is very important to issue an appropriate heatwave advisory so that one can be prepared for it. Considering that the degree of heat experienced by people differs depending on the difference in humidity between regions with similar summer temperatures, it is necessary to evaluate whether the issuance of a heatwave warning using only the daily maximum temperature is appropriate. This study intends to examine the applicability of the heat index considering both temperature and humidity for effective heatwave response. First, the agreement between the occurrences of heatwaves and heat-related illness, where the occurrence is decided by the daily maximum temperature and daily maximum heat index, was evaluated. The results show that when the daily maximum heat index was applied as a criterion for issuing a heatwave warning, the coincidence with the occurrence of heat-related illness was more than two times higher than when the daily maximum temperature was applied. Next, on evaluating the prediction accuracy of the heat index according to the prediction-related leading time, the accuracy of the heat index was noted to be higher than that of the temperature for all the leading times; the highest accuracy was shown at the leading time of 10 hours (NSE = 0.7196; CORR = 0.8698). Based on the results of this study, it is necessary to consider using a heat index that contains both temperature and humidity elements to issue a heatwave warning. Furthermore, to establish regional standards for heatwave warnings, the relationship between heatwave characteristics and meteorological factors should be first analyzed using long-term data from various observation points.

scholarly journals Observational Evidences of Double Cropping Impacts on the Climate in the Northern China Plains

2012 ◽  
Vol 25 (13) ◽  
pp. 4721-4728 ◽  
Author(s):  
C.-H. Ho ◽  
S.-J. Park ◽  
S.-J. Jeong ◽  
J. Kim ◽  
J.-G. Jhun
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Meteorological Data ◽  
Regional Climate ◽  
Northern China ◽  
Specific Humidity ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Double Cropping

Abstract The impacts of harvested cropland in the double cropping region (DCR) of the northern China plains (NCP) on the regional climate are examined using surface meteorological data and the satellite-derived normalized difference vegetation index (NDVI) and land surface temperature (LST). The NDVI data are used to distinguish the DCR from the single cropping region (SCR) in the NCP. Notable increases in LST in the period May–June are found in the area identified as the DCR on the basis of the NDVI data. The difference between the mean daily maximum temperature averaged over the DCR and SCR stations peaks at 1.27°C in June. The specific humidity in the DCR is significantly smaller than in the SCR. These results suggest that the enhanced agricultural production by multiple cropping may amplify regional warming and aridity to further modify the regional climate in addition to the global climate change. Results in this study may also be used as a quantitative observed reference state of the crop/vegetation effects for future climate modeling studies.

Probabilistic Forecasts from the National Digital Forecast Database

2008 ◽  
Vol 23 (2) ◽  
pp. 270-289 ◽  
Author(s):  
Roman Krzysztofowicz ◽  
W. Britt Evans
Keyword(s):  
Meteorological Data ◽  
Gaussian Model ◽  
Distribution Functions ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Point Estimate ◽  
Daily Maximum ◽  
Climatic Data ◽  
Estimation Of Parameters ◽  
Probabilistic Forecasts

Abstract The Bayesian processor of forecast (BPF) is developed for a continuous predictand. Its purpose is to process a deterministic forecast (a point estimate of the predictand) into a probabilistic forecast (a distribution function, a density function, and a quantile function). The quantification of uncertainty is accomplished via Bayes theorem by extracting and fusing two kinds of information from two different sources: (i) a long sample of the predictand from the National Climatic Data Center, and (ii) a short sample of the official National Weather Service forecast from the National Digital Forecast Database. The official forecast is deterministic and hence deficient: it contains no information about uncertainty. The BPF remedies this deficiency by outputting the complete and well-calibrated characterization of uncertainty needed by decision makers and information providers. The BPF comes furnished with (i) the meta-Gaussian model, which fits meteorological data well as it allows all forms of marginal distribution functions, and nonlinear and heteroscedastic dependence structures, and (ii) the statistical procedures for estimation of parameters from asymmetric samples and for coping with nonstationarities in the predictand and the forecast due to the annual cycle and the lead time. A comprehensive illustration of the BPF is reported for forecasts of the daily maximum temperature issued with lead times of 1, 4, and 7 days for three stations in two seasons (cool and warm).

scholarly journals Influence of Meteorological Factors on the COVID-19 Transmission with Season and Geographic Location

2021 ◽  
Vol 18 (2) ◽  
pp. 484
Author(s):  
Xiao-Dong Yang ◽  
Hong-Li Li ◽  
Yue-E Cao
Keyword(s):  
Relative Humidity ◽  
Lognormal Distribution ◽  
Prevention And Control ◽  
Meteorological Factors ◽  
Geographical Location ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Community Based ◽  
And Control ◽  
The Relationship

The purpose of this study is to investigate whether the relationship between meteorological factors (i.e., daily maximum temperature, minimum temperature, average temperature, temperature range, relative humidity, average wind speed and total precipitation) and COVID-19 transmission is affected by season and geographical location during the period of community-based pandemic prevention and control. COVID-19 infected case records and meteorological data in four cities (Wuhan, Beijing, Urumqi and Dalian) in China were collected. Then, the best-fitting model of COVID-19 infected cases was selected from four statistic models (Gaussian, logistic, lognormal distribution and allometric models), and the relationship between meteorological factors and COVID-19 infected cases was analyzed using multiple stepwise regression and Pearson correlation. The results showed that the lognormal distribution model was well adapted to describing the change of COVID-19 infected cases compared with other models (R2 > 0.78; p-values < 0.001). Under the condition of implementing community-based pandemic prevention and control, relationship between COVID-19 infected cases and meteorological factors differed among the four cities. Temperature and relative humidity were mainly the driving factors on COVID-19 transmission, but their relations obviously varied with season and geographical location. In summer, the increase in relative humidity and the decrease in maximum temperature facilitate COVID-19 transmission in arid inland cities, while at this point the decrease in relative humidity is good for the spread of COVID-19 in coastal cities. For the humid cities, the reduction of relative humidity and the lowest temperature in the winter promote COVID-19 transmission.

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