Wave Disturbances of Atmospheric Pressure and Wind Speed in the Troposphere Associated with the Solar Terminator

2021 ◽  
Vol 57 (6) ◽  
pp. 581-593
Author(s):  
I. P. Chunchuzov ◽  
S. N. Kulichkov ◽  
O. E. Popov ◽  
V. G. Perepelkin ◽  
D. V. Zaitseva ◽  
...  
Keyword(s):  
Wind Speed ◽  
Atmospheric Pressure ◽  
Wave Disturbances ◽  
Solar Terminator

scholarly journals SARS-CoV-2 and the Weather: Correlation between COVID-19 and Meteorological Variables in 3 Cities in Mexico

2021 ◽  
pp. 74-91
Author(s):  
Hermes Ulises Ramirez-Sanchez ◽  
Alma Delia Ortiz-Bañuelos ◽  
Aida Lucia Fajardo-Montiel
Keyword(s):  
Wind Speed ◽  
Atmospheric Pressure ◽  
Ecological Study ◽  
Metropolitan Areas ◽  
Meteorological Factors ◽  
Correlation Coefficients ◽  
Weather Conditions ◽  
Dew Point ◽  
Meteorological Variables ◽  
Infected Person

Meteorological factors such as temperature, humidity, atmospheric pressure, wind speed and direction are associated with the dispersion of the SARS-CoV-2 virus through aerosols, particles <5μm are suspended in the air being infective at least three hours and dispersing from eight to ten meters. It has been shown that a 10-minute conversation, an infected person produces up to 6000 aerosol particles, which remain in the air from minutes to hours, depending on the prevailing weather conditions. Objective: To establish the correlation between meteorological variables, confirmed cases and deaths from COVID-19 in the 3 most important cities of Mexico. Methodology: A retrospective ecological study was conducted to evaluate the correlation of meteorological factors with COVID-19 cases and deaths in three Mexican cities. Results: The correlations between health and meteorological variables show that in the CDMX the meteorological variables that best correlate with the health variables are Temperature (T), Dew Point (DP), Wind speed (WS), Atmospheric Pressure (AP) and Relative Humidity (RH) in that order. In the ZMG are T, WS, RH, DP and AP; and in the ZMM are RH, WS, DP, T and AP. Conclusions In the 3 Metropolitan Areas showed that the meteorological factors that best correlate with the confirmed cases and deaths from COVID-19 are the T, RH; however, the correlation coefficients are low, so their association with health variables is less than other factors such as social distancing, hand washing, use of antibacterial gel and use of masks.

scholarly journals Time Series Analysis on Incidence of Pulmonary Tuberculosis With Weather Factors During 2004-2017 in Guangdong Province, China

2021 ◽  
Author(s):  
Tianyu Qin ◽  
Yu Hao ◽  
Juan He
Keyword(s):  
Wind Speed ◽  
Atmospheric Pressure ◽  
Guangdong Province ◽  
Maximum Temperature ◽  
Maximum Pressure ◽  
Weather Factors ◽  
Sarima Model ◽  
Extreme Wind ◽  
Wind Speed Maximum ◽  
Extreme Wind Speed

Abstract Background: Although the occurrence of some infectious diseases including TB was found to be associated with specific weather factors, few studies have incorporated weather factors into the model to predict the incidence of tuberculosis (TB). We aimed to establish an accurate forecasting model using TB data in Guangdong Province, incorporating local weather factors.Methods: Data of sixteen meteorological variables (2003-2016) and the TB incidence data (2004-2016) of Guangdong were collected. Seasonal autoregressive integrated moving average (SARIMA) model was constructed based on the data. SARIMA model with weather factors as explanatory variables (SARIMAX) was performed to fit and predict TB incidence in 2017. Results: Maximum temperature, maximum daily rainfall, minimum relative humidity, mean vapor pressure, extreme wind speed, maximum atmospheric pressure, mean atmospheric pressure and illumination duration were significantly associated with log(TB incidence). After fitting the SARIMAX model, maximum pressure at lag 6 (β= -0.007, P < 0.05, 95% confidence interval (CI): -0.011, -0.002, mean square error (MSE): 0.279) was negatively associated with log(TB incidence), while extreme wind speed at lag 5 (β=0.009, P < 0.05, 95% CI: 0.005, 0.013, MSE: 0.143) was positively associated. SARIMAX (1, 1, 1) (0, 1, 1)12 with extreme wind speed at lag 5 was the best predictive model with lower Akaike information criterion (AIC) and MSE. The predicted monthly TB incidence all fall within the confidence intervals using this model. Conclusions: Weather factors have different effects on TB incidence in Guangdong. Incorporating meteorological factors into the model increased the accuracy of prediction.

Analysis on mass concentration of dust aerosols in rough terrains of the Taklimakan Desert Hinterland

2020 ◽  
Author(s):  
Qing He ◽  
Quanwei Zhao
Keyword(s):  
Wind Speed ◽  
Atmospheric Pressure ◽  
Mass Concentration ◽  
Weather Conditions ◽  
Dust Aerosol ◽  
Taklimakan Desert ◽  
Aerosol Mass ◽  
Aerosol Mass Concentration ◽  
High Site ◽  
Mass Concentrations

&lt;p&gt;A three-month experiment (June-August 2019) had been&amp;#160;carried out on the undulating terrain of the Taklimakan Desert. The mass concentration characteristics of PM&lt;sub&gt;2.5&lt;/sub&gt;&amp;#160;and PM&lt;sub&gt;10&lt;/sub&gt;&amp;#160;at different locations of the sand ridge were obtained, studying&amp;#160;the correlation between dust aerosol mass concentration and meteorological factors under different weather conditions. The results show that: (1) There are differences about&amp;#160;the concentration of PM&lt;sub&gt;2.5&lt;/sub&gt;&amp;#160;and PM&lt;sub&gt;10&lt;/sub&gt;&amp;#160;in&amp;#160;different locations of sand ridges under different typical weather conditions. The average mass concentration of PM&lt;sub&gt;2.5&lt;/sub&gt;&amp;#160;on sunny days meets: West Low Site&amp;#160;&gt; East Low Site&amp;#160;&gt; High Site, According to the dynamic &amp;#160;characteristic of PM&lt;sub&gt;10&lt;/sub&gt;, peak-valley value&amp;#160;of the three stations fluctuated sharply, and the daily average value of mass concentration shows: High Site&amp;#160;&gt; East Low Site&amp;#160;&gt; West Low Site. When the sand blowing and floating&amp;#160;weather occurred, the variation of PM&lt;sub&gt;2.5&lt;/sub&gt;&amp;#160;mass concentration meet the following rule: East Low Site&amp;#160;&gt; High Site, PM&lt;sub&gt;10&lt;/sub&gt;&amp;#160;shows the opposite law. When the first sandstorm occurs, the PM&lt;sub&gt;2.5&lt;/sub&gt;&amp;#160;mass concentration satisfies the following Law: West Low Site&amp;#160;10&amp;#160;mass concentration change is generally expressed as: West Low Site&amp;#160;2.5&amp;#160;and PM&lt;sub&gt;10&lt;/sub&gt;&amp;#160;meets: West Low Site&gt; High Site&gt; East Low Site&amp;#160;(2) Sunny Temperature&amp;#12289;&amp;#160;Atmospheric Pressure, Relative Humidity&amp;#160;of&amp;#160;east low site, high site&amp;#160;have a close correlation with PM&lt;sub&gt;2.5&lt;/sub&gt;, PM&lt;sub&gt;10&lt;/sub&gt;&lt;sub&gt;&amp;#160;&lt;/sub&gt;Mass Concentrations, the wind speed of&amp;#160;the west low site&amp;#160;and the high site&amp;#160;was significantly correlated with the PM&lt;sub&gt;2.5&lt;/sub&gt;&amp;#160;and PM&lt;sub&gt;10&lt;/sub&gt;&amp;#160;mass concentrations. When the dusty weather occurs, the wind speed has a significant effect on the mass concentration of dust aerosol in&amp;#160;the high site, and there is a significant positive correlation between the atmospheric pressure and the aerosol mass concentration in&amp;#160;the east low site&amp;#160;or high site. During the sand-dust weather&amp;#160;, the PM&lt;sub&gt;2.5&lt;/sub&gt;&amp;#160;and PM&lt;sub&gt;10&lt;/sub&gt;&amp;#160;mass concentrations were significantly negatively correlated with the atmospheric pressure&amp;#160;in the high sand dunes,&amp;#160;the correlation between wind speed and the PM&lt;sub&gt;2.5&lt;/sub&gt;&amp;#160;and PM&lt;sub&gt;10&lt;/sub&gt;&amp;#160;mass concentrations was greater than the East low Site. During the sandstorm, atmospheric pressure and temperature have a significant effect on the mass concentration of PM&lt;sub&gt;2.5&lt;/sub&gt;&amp;#160;and PM&lt;sub&gt;10.&lt;/sub&gt;&lt;/p&gt;

scholarly journals Wind Power Cogeneration to Reduce Peak Electricity Demand in Mexican States Along the Gulf of Mexico

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2330 ◽  
Author(s):  
Quetzalcoatl Hernandez-Escobedo ◽  
Javier Garrido ◽  
Fernando Rueda-Martinez ◽  
Gerardo Alcalá ◽  
Alberto-Jesus Perea-Moreno
Keyword(s):  
Wind Speed ◽  
Gulf Of Mexico ◽  
Atmospheric Pressure ◽  
Power Plants ◽  
Thermal Power ◽  
Wind Farms ◽  
Electricity Demand ◽  
Eastern Region ◽  
Thermal Power Plants ◽  
Northeast Region

The Energetic Transition Law in Mexico has established that in the next years, the country has to produce at least 35% of its energy from clean sources in 2024. Based on this, a proposal in this study is the cogeneration between the principal thermal power plants along the Mexican states of the Gulf of Mexico with modeled wind farms near to these thermal plants with the objective to reduce peak electricity demand. These microscale models were done with hourly MERRA-2 data that included wind speed, wind direction, temperature, and atmospheric pressure with records from 1980–2018 and taking into account roughness, orography, and climatology of the site. Wind speed daily profile for each model was compared to electricity demand trajectory, and it was seen that wind speed has a peak at the same time. The amount of power delivered to the electric grid with this cogeneration in Rio Bravo and Altamira (Northeast region) is 2657.02 MW and for Tuxpan and Dos Bocas from the Eastern region is 3196.18 MW. This implies a reduction at the peak demand. In the Northeast region, the power demand at the peak is 8000 MW, and for Eastern region 7200 MW. If wind farms and thermal power plants work at the same time in Northeast and Eastern regions, the amount of power delivered by other sources of energy at this moment will be 5342.98 MW and 4003.82 MW, respectively.

scholarly journals Designing a better weather display

2012 ◽  
Vol 12 (3-4) ◽  
pp. 221-239 ◽  
Author(s):  
Colin Ware ◽  
Matthew D Plumlee
Keyword(s):  
Wind Speed ◽  
Atmospheric Pressure ◽  
Surface Wind ◽  
Scalar Fields ◽  
Surface Wind Speed ◽  
Color Channel ◽  
Pressure Surface ◽  
Visual Interference ◽  
Conventional Solution ◽  
Wind Patterns

Weather maps commonly display several variables at once, usually a subset of the following: atmospheric pressure, surface wind speed and direction, surface temperature, cloud cover, and precipitation. Most often, a single variable is mapped separately and occasionally two are shown together. But sometimes there is an attempt to show three or four variables with a result that is difficult to interpret because of visual interference between the graphical elements. As a design exercise, we set the goal of finding out if it is possible to show three variables (two 2D scalar fields and one 2D vector field) simultaneously so that values can be accurately read using keys for all variables, a reasonable level of detail is shown, and important meteorological features stand out clearly. Our solution involves employing three perceptual “channels”: a color channel, a texture channel, and a motion channel in order to perceptually separate the variables and make them independently readable. We describe a set of interactive weather displays, which enable users to view two meteorological scalar fields of various kinds and a field showing wind patterns. To evaluate the method, we implemented three alternative representations each simultaneously showing temperature, atmospheric pressure, wind speed, and direction. Both animated and static variants of our new design were compared to a conventional solution and a glyph-based solution. The evaluation tested the abilities of participants both to read values using a key and to see meteorological patterns in the data. Our new scheme was superior, especially in the representation of wind patterns using the motion channel. It also performed well enough in the representation of pressure using the texture channel to suggest it as a viable design alternative.

scholarly journals Analysis on atmospheric pressure, temperature, and wind speed profiles during total solar eclipse 9 March 2016 using time series clustering

2016 ◽  
Vol 771 ◽  
pp. 012009 ◽  
Author(s):  
Lala Septem Riza ◽  
Yaya Wihardi ◽  
Enjang Ali Nurdin ◽  
Nanang Dwi Ardi ◽  
Cahyo Puji Asmoro ◽  
...  
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Solar Eclipse ◽  
Atmospheric Pressure ◽  
Total Solar Eclipse ◽  
Time Series Clustering

scholarly journals Długoterminowe trendy widzialności oraz jej cechy charakterystyczne w wybranych regionach Polski

2019 ◽  
Vol 28 (4) ◽  
pp. 594-609
Author(s):  
Ewa Anioł ◽  
Grzegorz Majewski
Keyword(s):  
Wind Speed ◽  
Atmospheric Pressure ◽  
Precipitation Amount ◽  
Meteorological Conditions ◽  
North Central ◽  
The North ◽  
Number Of Factors ◽  
Negative Effect ◽  
Meteorological Elements ◽  
Pollution Concentrations

Air quality in Poland is determined by a large number of factors. The influence of atmospheric air and meteorological conditions on atmospheric visibility in Poland was examined. The article is based on statistical analysis of meteorological elements parameters (air temperature, relative humidity, precipitation amount, wind speed and direction, and atmospheric pressure) and air pollution concentrations (PM10, SO2, NO2) in 2004–2017. Data was provided from three Polish cities, located in the north, central and south Poland. It was shown that PM10 concentration was the most important parameter affecting visibility in all monitoring stations. Air pollutants NO2 and SO2 have a negative effect on visibility, but to a lesser extent than PM10. The influence of meteorological conditions on the effect of the air humidity on the deterioration of the visibility ratio and the stimulating effect of wind speed on the improvement of visibility conditions has been demonstrated.

scholarly journals Simultaneous Forecasting of Meteorological Data Based on a Self-Organizing Incremental Neural Network

2018 ◽  
Vol 22 (6) ◽  
pp. 900-906 ◽  
Author(s):  
Wonjik Kim ◽  
Osamu Hasegawa ◽  
◽  
◽  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Wind Speed ◽  
Atmospheric Pressure ◽  
Time Series Data ◽  
Meteorological Data ◽  
Series Data ◽  
Total Solar Radiation ◽  
Proposed Model ◽  
Self Organizing

In this study, we propose a simultaneous forecasting model for meteorological time-series data based on a self-organizing incremental neural network (SOINN). Meteorological parameters (i.e., temperature, wet bulb temperature, humidity, wind speed, atmospheric pressure, and total solar radiation on a horizontal surface) are considered as input data for the prediction of meteorological time-series information. Based on a SOINN within normalized-refined-meteorological data, proposed model succeeded forecasting temperature, humidity, wind speed and atmospheric pressure simultaneously. In addition, proposed model does not take more than 2 s in training half-year period and 15 s in testing half-year period. This paper also elucidates the SOINN and the algorithm of the learning process. The effectiveness of our model is established by comparison of our results with experimental results and with results obtained by another model. Three advantages of our model are also described. The obtained information can be effective in applications based on neural networks, and the proposed model for handling meteorological phenomena may be helpful for other studies worldwide including energy management system.

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