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.

Relationship between meteorological conditions and respiratory syncytial virus in a tropical country

2015 ◽  
Vol 143 (12) ◽  
pp. 2679-2686 ◽  
Author(s):  
C. E. RODRIGUEZ-MARTINEZ ◽  
M. P. SOSSA-BRICEÑO ◽  
R. ACUÑA-CORDERO
Keyword(s):  
Respiratory Syncytial Virus ◽  
Wind Speed ◽  
Relative Humidity ◽  
Solar Radiation ◽  
Latin American ◽  
Meteorological Conditions ◽  
Acute Lower Respiratory Infection ◽  
Average Temperature ◽  
Monthly Average ◽  
Syncytial Virus

SUMMARYThis study aimed to determine which meteorological conditions are associated with respiratory syncytial virus (RSV) isolates in a population of children hospitalized with acute lower respiratory infection (ALRI) in Bogota, Colombia. In an analytical cross-sectional study, links were examined between the number of monthly RSV infections and monthly average climatic variation (temperature, relative humidity, rainfall, wind speed, solar radiation) between 1 January 2010 and 30 April 2011 in a population of hospitalized children aged <3 years with ALRI caused by RSV. Out of a total of 1548 children included in the study (mean age 9·2 ± 8·5 months), 1194 (77·1%) presented RSV infection during the 3-month period from March to May. In the multivariate analysis, after controlling for wind speed, relative humidity, and solar radiation, monthly average temperature [incident rate ratio (IRR) 3·14, 95% confidence interval (CI) 1·56–6·30,P= 0·001] and rainfall (IRR 1·008, 95% CI 1·00–1·01,P= 0·048) were independently associated with the monthly number of RSV infections. In conclusion, in Bogota, a tropical Latin American city, average temperature and rainfall are the meteorological variables most strongly associated with RSV isolation in children hospitalized with ALRI in the city.

scholarly journals Spatio-Temporal Evolution and Prediction of Tourism Comprehensive Climate Comfort in Henan Province, China

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 823
Author(s):  
Junyuan Zhao ◽  
Shengjie Wang
Keyword(s):  
Large Scale ◽  
Hierarchical Classification ◽  
Development Trend ◽  
Scientific Basis ◽  
Henan Province ◽  
Comfort Level ◽  
Tourist Destinations ◽  
Change Trend ◽  
Tourism Climate ◽  
Climate Comfort

The tourism comprehensive climate comfort index (TCCI) was used to evaluate the tourism climate comfort in Henan Province in the last 61 years, and its future development trend is predicted. The results showed that the temporal variation of the TCCI had a “double peak” type (monthly variation), and an overall comfort improvement trend (interannual variation). The change of tourism climate comfort days was similar to the change of the index, especially in the months with a low comfort level. In space, the distribution of the TCCI gradually increased from northeast to southwest, and the area with a high comfort level also increased over time. Meanwhile, it also showed the spatial distribution of months with a low comfort level, which provides reliable information for tourists to use when choosing tourist destinations across all periods of the year. The TCCI was classified by hierarchical classification, and principal components were extracted to explore the main climate factors controlling different types of TCCIs and the relationship between them, and large-scale atmospheric–oceanic variability. According to the temporal change trend and correlation, the long-term change trend of tourism climate comfort was predicted, which will provide a scientific basis for tourism planners to choose tourist destinations.

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 Meteorological Factors Affecting Pan Evaporation in the Haihe River Basin, China

Water ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 317 ◽  
Author(s):  
Zhihong Yan ◽  
Shuqian Wang ◽  
Ding Ma ◽  
Bin Liu ◽  
Hong Lin ◽  
...  
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Minimum Temperature ◽  
Sunshine Duration ◽  
Maximum Temperature ◽  
Haihe River Basin ◽  
Pan Evaporation ◽  
Haihe River ◽  
Average Temperature ◽  
The Haihe River Basin

Pan evaporation (Epan) is an important indicator of regional evaporation intensity and degree of drought. However, although more evaporation is expected under rising temperatures, the reverse trend has been observed in many parts of the world, known as the “pan evaporation paradox”. In this paper, the Haihe River Basin (HRB) is divided into six sub-regions using the Canopy and k-means (The process for partitioning an N-dimensional population into k sets on the basis of a sample is called “k-means”) to cluster 44 meteorological stations in the area. The interannual and seasonal trends and the significance of eight meteorological indicators, including average temperature, maximum temperature, minimum temperature, precipitation, relative humidity, sunshine duration, wind speed, and Epan, were analyzed for 1961 to 2010 using the trend-free pre-whitening Mann-Kendall (TFPW-MK) test. Then, the correlation between meteorological elements and Epan was analyzed using the Spearman correlation coefficient. Results show that the average temperature, maximum temperature, and minimum temperature of the HRB increased, while precipitation, relative humidity, sunshine duration, wind speed and Epan exhibited a downward trend. The minimum temperature rose 2 and 1.5 times faster than the maximum temperature and average temperature, respectively. A significant reduction in sunshine duration was found to be the primary factor in the Epan decrease, while declining wind speed was the secondary factor.

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.

The impact of COVID-19 pandemic on the psychological needs of tourists: implications for the travel and tourism industry

2021 ◽  
Vol 38 (2) ◽  
pp. 155-166
Author(s):  
Catherine Cheung ◽  
Miki Takashima ◽  
Hyunjung (Helen) Choi ◽  
Huijun Yang ◽  
Vincent Tung
Keyword(s):  
Psychological Needs ◽  
Tourism Industry ◽  
Travel And Tourism ◽  
The Impact

scholarly journals Potential Evapotranspiration Reduction and Its Influence on Crop Yield in the North China Plain in 1961–2014

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Wanlin Dong ◽  
Chao Li ◽  
Qi Hu ◽  
Feifei Pan ◽  
Jyoti Bhandari ◽  
...  
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Crop Yield ◽  
North China Plain ◽  
North China ◽  
Potential Evapotranspiration ◽  
The North ◽  
The North China Plain ◽  
Sunshine Hours ◽  
Humidity Index

Climate change has caused uneven changes in hydrological processes (precipitation and evapotranspiration) on a space-temporal scale, which would influence climate types, eventually impact agricultural production. Based on data from 61 meteorological stations from 1961 to 2014 in the North China Plain (NCP), the spatiotemporal characteristics of climate variables, such as humidity index, precipitation, and potential evapotranspiration (ET0), were analyzed. The sensitivity coefficients and contribution rates were applied to ET0. The NCP has experienced a semiarid to humid climate from north to south due to the significant decline of ET0 (−13.8 mm decade−1). In the study region, 71.0% of the sites showed a “pan evaporation paradox” phenomenon. Relative humidity had the most negative influence on ET0, while wind speed, sunshine hours, and air temperature had a positive effect on ET0. Wind speed and sunshine hours contributed the most to the spatiotemporal variation of ET0, followed by relative humidity and air temperature. Overall, the key climate factor impacting ET0 was wind speed decline in the NCP, particularly in Beijing and Tianjin. The crop yield in Shandong and Henan provinces was higher than that in the other regions with a higher humidity index. The lower the humidity index in Hebei province, the lower the crop yield. Therefore, potential water shortages and water conflict should be considered in the future because of spatiotemporal humidity variations in the NCP.

scholarly journals Evaluation of the AMPS Boundary Layer Simulations on the Ross Ice Shelf, Antarctica, with Unmanned Aircraft Observations

2017 ◽  
Vol 56 (8) ◽  
pp. 2239-2258 ◽  
Author(s):  
Jonathan D. Wille ◽  
David H. Bromwich ◽  
John J. Cassano ◽  
Melissa A. Nigro ◽  
Marian E. Mateling ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Wind Speed ◽  
Relative Humidity ◽  
High Wind ◽  
Ice Shelf ◽  
High Wind Speed ◽  
Near Surface ◽  
Ross Ice Shelf ◽  
Low Cloud ◽  
The Antarctic

AbstractAccurately predicting moisture and stability in the Antarctic planetary boundary layer (PBL) is essential for low-cloud forecasts, especially when Antarctic forecasters often use relative humidity as a proxy for cloud cover. These forecasters typically rely on the Antarctic Mesoscale Prediction System (AMPS) Polar Weather Research and Forecasting (Polar WRF) Model for high-resolution forecasts. To complement the PBL observations from the 30-m Alexander Tall Tower! (ATT) on the Ross Ice Shelf as discussed in a recent paper by Wille and coworkers, a field campaign was conducted at the ATT site from 13 to 26 January 2014 using Small Unmanned Meteorological Observer (SUMO) aerial systems to collect PBL data. The 3-km-resolution AMPS forecast output is combined with the global European Centre for Medium-Range Weather Forecasts interim reanalysis (ERAI), SUMO flights, and ATT data to describe atmospheric conditions on the Ross Ice Shelf. The SUMO comparison showed that AMPS had an average 2–3 m s−1 high wind speed bias from the near surface to 600 m, which led to excessive mechanical mixing and reduced stability in the PBL. As discussed in previous Polar WRF studies, the Mellor–Yamada–Janjić PBL scheme is likely responsible for the high wind speed bias. The SUMO comparison also showed a near-surface 10–15-percentage-point dry relative humidity bias in AMPS that increased to a 25–30-percentage-point deficit from 200 to 400 m above the surface. A large dry bias at these critical heights for aircraft operations implies poor AMPS low-cloud forecasts. The ERAI showed that the katabatic flow from the Transantarctic Mountains is unrealistically dry in AMPS.

Statistical Analysis of Weather Effects on PM2.5

2012 ◽  
Vol 610-613 ◽  
pp. 1033-1040
Author(s):  
Wei Dai ◽  
Jia Qi Gao ◽  
Bo Wang ◽  
Feng Ouyang
Keyword(s):  
Statistical Analysis ◽  
Wind Speed ◽  
Relative Humidity ◽  
Statistical Methods ◽  
Predictive Analytics ◽  
Hypothesis Test ◽  
Meteorological Data ◽  
Weather Conditions ◽  
Statistical Distribution ◽  
Weather Effects

Effects of weather conditions including temperature, relative humidity, wind speed, wind and direction on PM2.5 were studied using statistical methods. PM2.5 samples were collected during the summer and the winter in a suburb of Shenzhen. Then, correlations, hypothesis test and statistical distribution of PM2.5 and meteorological data were analyzed with IBM SPSS predictive analytics software. Seasonal and daily variations of PM2.5 have been found and these mainly resulted from the weather effects.

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