scholarly journals Predicting the local COVID-19 outbreak around the world with meteorological conditions: a model-based qualitative study

BMJ Open ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. e041397
Author(s):  
Biqing Chen ◽  
Hao Liang ◽  
Xiaomin Yuan ◽  
Yingying Hu ◽  
Miao Xu ◽  
...  
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Qualitative Study ◽  
Meteorological Factors ◽  
Weather Data ◽  
Epidemic Control ◽  
Average Temperature ◽  
Term Model ◽  
The Future ◽  
Future Outbreak

ObjectivesThis study aims to investigate the relationship between daily weather and transmission rate of SARS-CoV-2, and to develop a generalised model for future prediction of the COVID-19 spreading rate for a certain area with meteorological factors.DesignA retrospective, qualitative study.Methods and analysisWe collected 382 596 records of weather data with four meteorological factors, namely, average temperature, relative humidity, wind speed, and air visibility, and 15 192 records of epidemic data with daily new confirmed case counts (1 587 209 confirmed cases in total) in nearly 500 areas worldwide from 20 January 2020 to 9 April 2020. Epidemic data were modelled against weather data to find a model that could best predict the future outbreak.ResultsSignificant correlation of the daily new confirmed case count with the weather 3 to 7 days ago were found. SARS-CoV-2 is easy to spread under weather conditions of average temperature at 5 to 15°C, relative humidity at 70% to 80%, wind speed at 1.5 to 4.5 m/s and air visibility less than 10 statute miles. A short-term model with these four meteorological variables was derived to predict the daily increase in COVID-19 cases; and a long-term model using temperature to predict the pandemic in the next week to month was derived. Taken China as a discovery dataset, it was well validated with worldwide data. According to this model, there are five viral transmission patterns, ‘restricted’, ‘controlled’, ‘natural’, ‘tropical’ and ‘southern’. This model’s prediction performance correlates with actual observations best (over 0.9 correlation coefficient) under natural spread mode of SARS-CoV-2 when there is not much human interference such as epidemic control.ConclusionsThis model can be used for prediction of the future outbreak, and illustrating the effect of epidemic control for a certain area.

scholarly journals The effects of weather on the spread of COVID-19: evidence from Ghana

2021 ◽  
Vol 45 (1) ◽  
Author(s):  
Eric N. Aidoo ◽  
Atinuke O. Adebanji ◽  
Gaston E. Awashie ◽  
Simon K. Appiah
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Linear Relationship ◽  
Atmospheric Pressure ◽  
Climatic Factors ◽  
Communicable Disease ◽  
Weather Data ◽  
Positive Linear Relationship ◽  
Average Temperature ◽  
Short Period

Abstract Background Climatic factors have been shown to influence communicable disease dynamics especially in tropical regions where temperature could swing from extreme heat and dryness to wet and cold within a short period of time. This is more pronounced in the spread of airborne diseases. In this study, the effect of some local weather variables (average temperature, average relative humidity, average wind speed and average atmospheric pressure) on the risk of Severe Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in Ghana is investigated. The daily confirmed new COVID-19 cases were compiled from the Ghana Health Service and the weather data extracted from Weatherbase. The type of relationship between the climatic variable and risk of spread were explored using the Generalized Additive Model (GAM). Results Results obtained showed that wind speed and atmospheric pressure have positive linear relationship with the spread of infection an increase in the risk of COVID-19 spread. In addition, the risk of spread fluctuates for temperature between 24 and 29 °C but sharply decreases when average temperature exceeds 29 °C. The risk of spread of COVID-19 significantly decrease for relative humidity between 72 and 76% and leveled afterwards. Conclusion The results indicate that wind speed and pressure have a positive linear relationship with the risk of spread of COVID-19 whilst temperature and humidity have a non-linear relationship with the spread of COVID-19. These findings highlight the need for policy makers to design effective countermeasures for controlling the spread as we are still within the low temperature season.

Prediction of Wind Speed, Potential Wind Power, and the Associated Uncertainties for Offshore Wind Farm Using Deep Learning

2020 ◽  
Author(s):  
Do-Eun Choe ◽  
Gary Talor ◽  
Changkyu Kim
Keyword(s):  
Power Generation ◽  
Deep Learning ◽  
Wind Speed ◽  
Wind Power ◽  
Wind Power Generation ◽  
Offshore Wind ◽  
Weather Data ◽  
Local Wind ◽  
Offshore Wind Power ◽  
The Future

Abstract Floating offshore wind turbines hold great potential for future solutions to the growing demand for renewable energy production. Thereafter, the prediction of the offshore wind power generation became critical in locating and designing wind farms and turbines. The purpose of this research is to improve the prediction of the offshore wind power generation by the prediction of local wind speed using a Deep Learning technique. In this paper, the future local wind speed is predicted based on the historical weather data collected from National Oceanic and Atmospheric Administration. Then, the prediction of the wind power generation is performed using the traditional methods using the future wind speed data predicted using Deep Learning. The network layers are designed using both Long Short-Term Memory (LSTM) and Bi-directional LSTM (BLSTM), known to be effective on capturing long-term time-dependency. The selected networks are fine-tuned, trained using a part of the weather data, and tested using the other part of the data. To evaluate the performance of the networks, a parameter study has been performed to find the relationships among: length of the training data, prediction accuracy, and length of the future prediction that is reliable given desired prediction accuracy and the training size.

Concentration Features and Elemental Characteristics of PM10 in Brown Haze Episode

2010 ◽  
Vol 113-116 ◽  
pp. 1661-1664
Author(s):  
Li Kun Huang ◽  
Chung Shin Yuan ◽  
Guang Zhi Wang ◽  
Kun Wang
Keyword(s):  
Particulate Matter ◽  
Wind Speed ◽  
Relative Humidity ◽  
Human Activities ◽  
Meteorological Factors ◽  
Dew Point ◽  
Metallic Elements ◽  
Haze Episode ◽  
Respirable Particulate ◽  
Haze Days

The correlation between PM10 and meteorological factors were investigated, such as wind speed, atmospheric visibility, dew point, relative humidity, and ambient temperature during the brown haze episode. In order to identify the elemental characteristics and concentration features of PM10 during brown haze episode, respirable particulate matter (PM10) was collected during non-haze days and haze days and further analyzed for 20 elements. Among the metallic elements, S, K, Si, and Ca contributed major composition of PM10. S came mainly from coal burning and K was mainly attributed from incinerators and abandoned biomass burning. Furthermore, As was not detectable in non-haze days, while its concentration was 0.15~0.17 μg/m3 in haze days, which would be very much harmful to human health. However, the variation of Sr, Ti, Cr, and Cd was insignificantly, mainly due to low relevance with human activities and/or cross-boundary transportation.

Analysis and Evaluation of Tourism Climate Resources in Turpan Region

2013 ◽  
Vol 869-870 ◽  
pp. 80-83
Author(s):  
Zhang Ying
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Development Planning ◽  
Scientific Basis ◽  
Tourism Industry ◽  
Temporal And Spatial Distribution ◽  
Travel And Tourism ◽  
Average Temperature ◽  
Tourism Climate ◽  
Climate Comfort

The article analyzes Turpan average temperature, relative humidity and wind speed the change characteristics of the climate elements and their travel to the comfort of climate influence mechanism and gets the conclusion: temperature, relative humidity, wind speed of Xinjiang tourism is the effect of climate comfort of main elements of sunshine. So from the average temperature, relative humidity, wind speed on average three climate elements start, select the suitable for Turpan tourism climate comfort evaluation index of the grain, and puts forward some concrete methods of tourism scenic area as part of the site, focus on the discussion were in Xinjiang tourism climate comfort of the temporal and spatial distribution of feature, make pleasant climate degrees in time and space more comparability. For the development of tourism resources, tourist season choice more objective and scientific guidance and practical. For the comfort of the climate in Turpan, it can arrange for the tour operator for tourism activities, visitors to choose the proper place and time travel and tourism destination development planning to provide the necessary guidance. It can also for the further development of the tourism industry in Turpan development space and offer scientific basis.

Spatiotemporal Variability of Potential Evaporation in Heihe River Basin Influenced by Irrigation

2020 ◽  
Author(s):  
Congying Han
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Spatial Pattern ◽  
River Basin ◽  
Meteorological Factors ◽  
Heihe River Basin ◽  
Spatiotemporal Variability ◽  
Maximum Temperature ◽  
Heihe River ◽  
Potential Evaporation

<p><strong>Spatiotemporal Variability of Potential Evaporation in Heihe River Basin Influenced by Irrigation </strong></p><p>Congying Han<sup>1,2</sup>, Baozhong Zhang<sup>1,2</sup>, Songjun Han<sup>1,2</sup></p><p><sup>1</sup> State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China.</p><p><sup>2</sup> National Center of Efficient Irrigation Engineering and Technology Research-Beijing, Beijing 100048, China.</p><p>Corresponding author: Baozhong Zhang ([email protected])</p><p><strong>Abstract: </strong>Potential evaporation is a key factor in crop water requirement estimation and agricultural water resource planning. The spatial pattern and temporal changes of potential evaporation calculated by Penman equation (E<sub>Pen</sub>) (1970-2017) in Heihe River Basin (HRB), Northwest China were evaluated by using data from 10 meteorological stations, with a serious consideration of the influences of irrigation development. Results indicated that the spatial pattern of annual E<sub>Pen</sub> in HRB was significantly different, among which the E<sub>Pen</sub> of agricultural sites (average between 1154 mm and 1333 mm) was significantly higher than that of natural sites (average between 794 mm and 899 mm). Besides, the coefficient of spatial variation of the aerodynamic term (E<sub>aero</sub>) was 0.4, while that of the radiation term (E<sub>rad</sub>) was 0.09. The agricultural irrigation water withdrawal increased annually before 2000, but decreased significantly after 2000 which was influenced by the agricultural development and the water policy. Coincidentally, the annual variation of E<sub>pen</sub> in agricultural sites decreased at -40 mm/decade in 1970-2000 but increased at 60 mm/decade in 2001-2017, while that in natural sites with little influence of irrigation, only decreased at -0.5mm/decade in 1970-2000 but increased at 11 mm/decade in 2001-2017. So it was obvious that irrigation influenced E<sub>pen </sub>significantly and the change of E<sub>pen</sub> was mainly caused by the aerodynamic term. The analysis of the main meteorological factors that affect E<sub>pen</sub> showed that wind speed had the greatest impact on E<sub>pen</sub> of agricultural sites, followed by relative humidity and average temperature, while the meteorological factors that had the greatest impact on E<sub>pen</sub> of natural sites were maximum temperature, followed by wind speed and relative humidity.</p>

Assessment of the future climate potential for tourism over Europe using a combination of downscaling approaches and quantitative impact models

2020 ◽  
Author(s):  
Maria Francisca Cardell ◽  
Arnau Amengual ◽  
Romualdo Romero
Keyword(s):  
Climate Change ◽  
Wind Speed ◽  
Relative Humidity ◽  
Future Climate ◽  
Cumulative Distribution ◽  
Maximum Temperature ◽  
Model Errors ◽  
Present Climate ◽  
Ensemble Strategy ◽  
The Future

<p>Europe and particularly, the Mediterranean countries, are among the most visited tourist destinations worldwide, while it is also recognized as one of the most sensitive regions to climate change. Climate is a key resource and even a limiting factor for many types of tourism. Owing to climate change, modified patterns of atmospheric variables such as temperature, rainfall, relative humidity, hours of sunshine and wind speed will likely affect the suitability of the European destinations for certain outdoor leisure activities.</p><p>Perspectives on the future of second-generation climate indices for tourism (CIT) that depend on thermal, aesthetic and physical facets are derived using model projected daily atmospheric data and present climate “observations”. Specifically, daily series of 2-m maximum temperature, accumulated precipitation, 2-m relative humidity, mean cloud cover and 10-m wind speed from ERA-5 reanalysis are used to derive the present climate potential. For projections, the same daily variables have been obtained from a set of regional climate models (RCMs) included in the European CORDEX project, considering the rcp8.5 future emissions scenario. The adoption of a multi-model ensemble strategy allows quantifying the uncertainties arising from the model errors and the GCM-derived boundary conditions. To properly derive CITs at local scale, a quantile–quantile adjustment has been applied to the simulated regional scenarios. The method detects changes in the continuous CIT cumulative distribution functions (CDFs) between the recent past and successive time slices of the simulated climate and applies these changes, once calibrated, to the observed CDFs. </p><p>Assessments on the future climate potential for several types of tourist activities in Europe (i.e., sun, sea and sand (3S) tourism, cycling, cultural, football, golf, nautical and hiking) will be presented by applying suitable quantitative indicators of CIT evolutions adapted to regional contexts. It is expected that such kind of information will ultimately benefit the design of mitigation and adaptation strategies of the tourist sector.</p>

scholarly journals Effect of meteorological factors on the activity of influenza in Chongqing, China, 2012–2019

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246023
Author(s):  
Li Qi ◽  
Tian Liu ◽  
Yuan Gao ◽  
Dechao Tian ◽  
Wenge Tang ◽  
...  
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Meteorological Factors ◽  
Meteorological Data ◽  
Warning System ◽  
Absolute Humidity ◽  
Influenza Surveillance ◽  
Average Wind Speed ◽  
Mean Temperature ◽  
Influenza Activity

Background The effects of multiple meteorological factors on influenza activity remain unclear in Chongqing, the largest municipality in China. We aimed to fix this gap in this study. Methods Weekly meteorological data and influenza surveillance data in Chongqing were collected from 2012 to 2019. Distributed lag nonlinear models (DLNMs) were conducted to estimate the effects of multiple meteorological factors on influenza activity. Results Inverted J-shaped nonlinear associations between mean temperature, absolute humidity, wind speed, sunshine and influenza activity were found. The relative risks (RRs) of influenza activity increased as weekly average mean temperature fell below 18.18°C, average absolute humidity fell below 12.66 g/m3, average wind speed fell below 1.55 m/s and average sunshine fell below 2.36 hours. Taking the median values as the references, lower temperature, lower absolute humidity and windless could significantly increase the risks of influenza activity and last for 4 weeks. A J-shaped nonlinear association was observed between relative humidity and influenza activity; the risk of influenza activity increased with rising relative humidity with 78.26% as the break point. Taking the median value as the reference, high relative humidity could increase the risk of influenza activity and last for 3 weeks. In addition, we found the relationship between aggregate rainfall and influenza activity could be described with a U-shaped curve. Rainfall effect has significantly higher RR than rainless effect. Conclusions Our study shows that multiple meteorological factors have strong associations with influenza activity in Chongqing, providing evidence for developing a meteorology-based early warning system for influenza to facilitate timely response to upsurge of influenza activity.

scholarly journals The spatial-temporal association between mrteorological factors and bacillary dysentery

2019 ◽  
Author(s):  
Xiangxue Zhang ◽  
Li Wang ◽  
Chengdong Xu ◽  
Xinchen Gu ◽  
Yuke Zhou ◽  
...  
Keyword(s):  
Wind Speed ◽  
Bacillary Dysentery ◽  
Meteorological Factors ◽  
Interactive Effect ◽  
Coastal Region ◽  
Public Health Problem ◽  
Temporal Association ◽  
Significant Discrepancy ◽  
Average Temperature ◽  
Temperature And Precipitation

Abstract BackgroundBacillary dysentery remains a worldwide public health problem, which has been found to have spatial–temporal heterogeneity, however most studies have only focused on the disease from either a time or space perspective, the spatial–temporal association between them has been still unclear. MethodIn this study, the Bayesian space–time hierarchy model was used to identify the spatial-temporal patterns of this disease in Shandong province, China. And then GeoDetector was used to quantify the determinant power of meteorological factors and their interactive effect among different regions in Shandong. ResultsThe results indicated that, temporally, the incidence peaked in summer. Geographically, the hot spots were distributed discretely among three regions, among which the effect of meteorological factors on this disease exist significant discrepancy. The most important two dominant factors of eastern coastal region were wind speed and average temperature, with determinant powers of 28% and 25%, respectively. The first two dominant factors of western inland region were average temperature and precipitation, with determinant powers of 47% and 32%, respectively. The first two dominant factors of middle region were average temperature and wind speed, with determinant powers of 66% and 48%, respectively. ConclusionsThese findings suggest that in a hot and humid environment would boost the transmission of bacillary dysentery, which can be served as a suggestion and basis for the surveillance and will be helpful for this disease control and implementing disease-prevention policies.

Grey relation between main meteorological factors and mortality

2019 ◽  
Vol 9 (2) ◽  
pp. 185-196
Author(s):  
Xiaodong Chen ◽  
Desheng Pei ◽  
Liping Li
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Climate Changes ◽  
Meteorological Factors ◽  
Air Pressure ◽  
Content Type ◽  
Relational Analysis ◽  
Curve Approximation ◽  
Grey Relational ◽  
Grey Relation

PurposeThe purpose of this paper is to explore the effects of main meteorological factors on the mortality of urban residents and provide empirical evidence for the prevention of effects of climate changes.Design/methodology/approachGrey relational analysis (GRA) was used to analyse the interrelationships between meteorological factors and mortality among residents in Chaoyang District, Beijing, during the period between 1998 and 2008.FindingsThe changes of annual average mortality had a strong grey relation with temperature and relative humidity. The monthly average mortality (MAM) showed a strong grey relation with air pressure and the MAM in Summer season had a strong grey relation with air pressure, relative humidity and wind speed.Originality/valueMeteorological factors including temperature, relative humidity, air pressure and wind speed are all related with mortality changes. GRA can well reveal the trend of the curve approximation between meteorological factors and mortality and can quantify the different approximation.

scholarly journals Prediction model for deoxynivalenol in wheat grain based on weather conditions

2010 ◽  
Vol 45 (Special Issue) ◽  
pp. S33-S37 ◽  
Author(s):  
M. Váňová ◽  
K. Klem ◽  
P. Matušinský ◽  
M. Trnka
Keyword(s):  
Relative Humidity ◽  
Environmental Factors ◽  
Winter Wheat ◽  
Correlation Coefficients ◽  
Weather Conditions ◽  
Weather Data ◽  
Wheat Grain ◽  
Average Temperature ◽  
Preceding Crop ◽  
Beginning Of Flowering

Environmental factors influence the growth, survival, dissemination and hence the incidence of <i>Fusarium</i> fungi and the disease severity. The knowledge of the quantitative and qualitative effects of environmental factors and growing practices on initial infection, disease development and mycotoxin production is important for prediction of disease severity, yield impact and grain contamination with mycotoxins. The objective of this study was to design a model for prediction of deoxynivalenol (DON) content in winter wheat grain based on weather conditions, preceding crop and soil cultivation. The grain samples from winter wheat field experiments conducted in 2002–2005 to determine the effect of preceding crop in combination with soil cultivation on Fusarium head blight infection were analysed for the DON content. Average daily weather data (temperature, rainfall, relative humidity) were collected using an automated meteorological station and analysed separately for April, May and a 5 days period prior to the beginning of flowering and 5 days after the beginning of flowering. The correlation coefficients of DON content to weather data were calculated for monthly data prior to heading and 5 days data prior to and after the beginning of anthesis. Highest positive correlation coefficients were found for sum of precipitation in April, average temperature in April, and sum of precipitation 5 days prior to anthesis. Significant negative correlation was found for average temperature in May and average relative humidity 5 days prior to anthesis. Using the data from this experiment, we trained neural networks for prediction of deoxynivalenol content on the basis of weather data and preceding crop. The most appropriate neural network model was then coupled with AgriClim model to simulate spatial and temporal variation of DON content in wheat samples for south Moravia and north-east Austria area.

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