scholarly journals To Study the Temporal Variation of Capacity Utilization Factor (CUF) of PV Based Solar Power Plant with Respect to Climatic Condition

2016 ◽  
Vol 11 (2) ◽  
pp. 654-661 ◽  
Author(s):  
Ramesh Chaudhari ◽  
Bharat Chaudhari ◽  
Pratiksinh Dilipsinh ◽  
Vijya Leela bhai
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Solar Radiation ◽  
Power Output ◽  
Climatic Factors ◽  
Capacity Utilization ◽  
Ambient Air ◽  
Daily Basis ◽  
Utilization Factor ◽  
Performance Quality

Performance, quality and reliability of technology are becoming more and more important for the emerging photovoltaic markets worldwide. In this experiment the monitoring of climatic factors like, solar radiation, Ambient Temperature, Module Temperature, Relative Humidity and Wind Speed was carried out on daily basis for six months, between 7:00 A.M to 6:00 P.M. Data was measured with SCADA system. This analysis was carried out by monitoring the fluctuation in power output of the system with climatic factors. From the results, there is direct proportionality between the power output of the system and the climatic factors. The correlation between ambient air temperature, PV module temperature and CUF is strongly positive. The other climatic factor like wind speed is does not have much significant effect on CUF. The Relative humidity is negatively correlated with CUF. The correlation between solar radiation and the CUF is strongly positive.

scholarly journals Examine the impact of weather and ambient air pollutant parameters on daily case of COVID-19 in India

2020 ◽  
Author(s):  
Kousik Das ◽  
Nilanjana Das Chatterjee
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Solar Radiation ◽  
Ambient Air ◽  
List Type ◽  
Positive Relation ◽  
Weather Variables ◽  
Sensitive Analysis ◽  
Negative Relation ◽  
The Impact

AbstractThe present study presents a view on exploring the relationship pattern between COVID 19 daily cases with weather parameters and air pollutants in mainland India. We consider mean temperature, relative humidity, solar radiation, rainfall, wind speed, PM2.5, PM10, SO2, NO2 and CO as independent variable and daily COVID 19 cases as dependent variable for 18 states during 18th march to 30th April, 2020.After dividing the dataset for 0 to 10 day, 10 to 25 days and 0 to 44 days, the current study applied Akaike s Information Criteria (AIC) and Generalized Additive Model (GAM) to examine the kind of relationship between independent variables with COVID 19 cases. Initially GAM model result shows variables like temperature and solar radiation has positive relation (p<0.05) in 0 to 10 days study with daily cases. In 25 days dataset it significantly shows that temperature has positive relation above 23 degree centigrade, SO2 has a negative relationship and relative humidity has negative (between 30% to 45% and > 60%) and a positive relationship (45% to 60%) with COVID 19 cases (p=0.05). 44 days dataset has six parameters includes temperature as positive, relative humidity as negative (between 0 to 45%) and then positive (after >45%), NO2 as Positive (0 to 35 microgram/m3) followed by negative trend (after > 40 microgram/m3), SO2 and rainfall as negative relation. After sensitive analysis, it is found that weather variables like relative humidity, solar radiation and rainfall are more sensitive than temperature and wind speed. Whereas pollutants like NO2, PM2.5, PM10 and CO are more sensitive variables than SO2 in this study. In summary this study finds temperature, relative humidity, solar radiation, wind speed, SO2, PM2.5, and CO may be important factors associated with COVID 19 pandemic.Graphical AbstractHighlights➢There was a significant relationship between daily positive COVID-19 case with weather and pollution factors➢We found PM2.5 and CO positively associated with transmission of positive cases where as NO2 and SO2 have a negative relation after sensitive analysis.➢We have found temperature and wind speed have positive relation whereas, relative humidity and solar radiation have negative relation after sensitive analysis.➢Weather variables like relative humidity and solar radiation and rainfall are more sensitive than temperature and wind speed. Pollutants like NO2, PM2.5, PM10 and CO are more sensitive variables than SO2 in this study.

scholarly journals ASSESSING THE IMPACTS OF CLIMATIC FACTORS ON COTTON YIELD AND ITS ECONOMIC INDICATORS

2021 ◽  
Vol 186 (2) ◽  
pp. 166-182
Author(s):  
Assem MOHAMED ◽  
Mona MAZE ◽  
Mohamed ABDELAZIZ ◽  
Alaa KHALIL
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Solar Radiation ◽  
Climatic Factors ◽  
Economic Benefits ◽  
Economic Indicators ◽  
Growth Stages ◽  
Cotton Yield ◽  
Maximum Minimum ◽  
Different Growth Stages

Cotton is one of the strategic crops in Egypt. This article investigates the impacts of climatic factors and their variations on the cotton yield and its economic benefits during the period from 1998 to 2019. We chose the Kafr El-Sheikh Governorate, where cotton is one of the major planted crops, was chosen for the analysis. The climatic factors utilized were the maximum, minimum and average temperatures; relative humidity; solar radiation and wind speed. Precipitation was excluded, as Egypt depends mainly on irrigation. The climatic factors utilized influenced yield during different growth stages: wind speed showed an influence only on the germination stage, whereas temperature had a major impact before and at the maturity stages. The latter correlation was positive in July and negative in August and September. Relative humidity and solar radiation impacted on yield at different growth stages, with an almost positive correlation with solar radiation and both a positive and a negative correlation with relative humidity. For the study of the economic indicators of cotton, cotton data were taken for the whole Egyptian Governorate during the period 2005-2019. The study showed a decrease in the net return during the period from 2005 to 2015 that reached a loss (minus value) of 195 Egyptian pounds (LE) in 2015, followed by an increase during the period from 2016 to 2019 due to the increase in farm gate prices.

The Concentration Characteristics of PM10 and PM2.5 in Eight Cities of China in March, 2013

2013 ◽  
Vol 807-809 ◽  
pp. 20-23 ◽  
Author(s):  
Tao Sheng ◽  
Jian Wu Shi ◽  
Sen Lin Tian ◽  
Li Mei Bi ◽  
Hao Deng ◽  
...  
Keyword(s):  
Air Quality ◽  
Wind Speed ◽  
Relative Humidity ◽  
Negative Correlation ◽  
Ambient Air ◽  
Quality Standard ◽  
Meteorological Factor ◽  
Urban Air ◽  
Ambient Air Quality ◽  
Ambient Air Quality Standard

According to the information of air quality which published by the urban air quality real-time publishing platform, the concentration characteristics of PM10 and PM2.5 were studied in Kunming (KM), Changsha (CS), Hangzhou (HZ), Shanghai (SH), Harbin (HEB), Beijing (BJ), Wuhan (WH) and Guangzhou (GZ). The results show that the concentrations of PM10 and PM2.5 exceeded the Ambient Air Quality Standard (GB3095-2012) in varying degrees in March, 2013. The concentrations of PM10 in Wuhan is the highest, reached 164μg/m3, exceeded the standard by 9.3%; the concentrations of PM2.5 is much higher in Wuhan, Changsha and Beijing, the average concentrations were 96μg/m3, 103μg/m3 and 110μg/m3, exceeded the standard by 28.0%, 37.3% and 46.7% respectively. The correlation of PM10 with PM2.5 in most of these cities was good in March. The correlation analysis of pollutant with meteorological factor in Hangzhou, Shanghai, Beijing and Guangzhou was also studied, the results show that the concentrations of PM10 and PM2.5 are weakly positive correlation with temperature in the four cities, negative correlation with relative humidity without Beijing, and negative correlation with wind speed.

A Comparative Study of ASOS and USCRN Temperature Measurements

2005 ◽  
Vol 22 (6) ◽  
pp. 679-686 ◽  
Author(s):  
Bomin Sun ◽  
C. Bruce Baker ◽  
Thomas R. Karl ◽  
Malcolm D. Gifford
Keyword(s):  
Wind Speed ◽  
Solar Radiation ◽  
Local Heating ◽  
Ambient Air ◽  
Local Effect ◽  
Temperature Measurements ◽  
Reference Network ◽  
Shield Effectiveness ◽  
Average Difference ◽  
Bias Analysis

Abstract Temperature measurements from the U.S. Climate Reference Network (USCRN) instrument system were compared to the Automated Surface Observing System (ASOS) ambient air temperature measurements and were examined under different regimes of wind speed and solar radiation. Influences due to observing practice differences and the effects of siting differences were discussed. This analysis indicated that the average difference between the ASOS and USCRN temperatures is on the order of 0.1°C. However, problems were noticed that were possibly related to the ASOS shield effectiveness, including a solar radiation warm effect under calm conditions and the dependence of ASOS minus USCRN temperature on wind speed. The ASOS and USCRN time of observation difference was on the order of ∼0.05°C, with a warmer ASOS daily Tmax and a cooler ASOS daily Tmin. The local effect complicates the bias analysis because it depends not only on local heating/cooling, but it can be strongly modified by cloudiness, wind, and solar radiation.

scholarly journals Seasonal and Temporal Variations of Criteria Air Pollutants and the Influence of Meteorological Parameters on the Concentration of Pollutants in Ambient Air in Lahore, Pakistan

2016 ◽  
Vol 59 (1) ◽  
pp. 34-42
Author(s):  
Amtul Bari Tabinda ◽  
Saleha Munir ◽  
Abdullah Yasar ◽  
Asad Ilyas
Keyword(s):  
Wind Speed ◽  
Solar Radiation ◽  
Negative Correlation ◽  
Air Pollutants ◽  
Meteorological Parameters ◽  
Ambient Air ◽  
Temporal Variations ◽  
Criteria Air Pollutants ◽  
Summer Minimum ◽  
One Year

Criteria air pollutants have their significance for causing health threats and damage to theenvironment. The study was conducted to assess the seasonal and temporal variations of criteria air pollutantsand evaluating the correlations of criteria air pollutants with meteorological parameters in the city ofLahore, Pakistan for a period of one year from April 2010 to March 2011. The concentrations of criteriaair pollutants were determined at fixed monitoring stations equipped with HORIBA analyzers. The annualaverage concentrations (µg/m3) of PM2.5, O3, SO2, CO and NOx (NO+NO2) for this study period were118.94±57.46, 46.0±24.2, 39.9±8.9, 1940±1300 and 130.9±81.0 (61.8±46.2+57.3±22.19), respectively.PM2.5, SO2, CO and NOx had maximum concentrations during winter whereas O3 had maximum concentrationduring summer. Minimum concentrations of PM2.5, SO2 and NOx were found during monsoon as comparedto other seasons due to rainfall which scavenged these pollutants. The O3 showed positive correlation withtemperature and solar radiation but negative correlation with wind speed. All other criteria air pollutantsshowed negative correlation with wind speed, temperature and solar radiation. A significant (P<0.01)correlation was found between NOx and CO (r = 0.779) which showed that NOx and CO arise from commonsource that could be the vehicular emission. PM2.5 was significantly correlated (P<0.01) with NOx (r = 0.524)and CO (r = 0.519), respectively. High traffic intensity and traffic jams were responsible for increased airpollutants level especially the PM2.5, NOx and CO.

scholarly journals Assessment of Spatiotemporal Variability and Trend Analysis of Reference Crop Evapotranspiration for the southern region of Peninsular India

2021 ◽  
Author(s):  
Jayashree Tenkila Ramachandra ◽  
Subba Reddy Nandanavana Veerappa ◽  
Dinesh Acharya Udupi
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Solar Radiation ◽  
Water Resource Management ◽  
Spatiotemporal Variability ◽  
Trend Test ◽  
Accurate Estimation ◽  
Peninsular India ◽  
Climatic Zones ◽  
Statistical Measures

Abstract Accurate estimation of reference evapotranspiration (ET0) is an essential requirement for water resource management and scheduling agricultural activities. Several empirical methods have been employed in estimating ET0 across diverse climate regimes over the past decades. The Python implementation for estimation of daily and monthly ET0 values of representative stations of ten agro-climatic zones of Karnataka from 1979 through 2014 using the standard FAO Penman-Monteith method was carried out. The assessment of temporal and spatial variability of monthly ET0 values across the various agro-climatic zones done by the various statistical measures revealed that the variation in spatial ET0 values was higher than temporal indicating major differentiation of ET0 values was with respect to the stations rather than years under study. The non-parametric Mann-Kendall test conducted at 1% significance level on the annual ET0 values revealed that statistically significant increasing trend was observed for all the ten stations during the study period. The trend test conducted on the climate variables like mean air temperature, wind speed, relative humidity and solar radiation signify their influence the annual ET0 values. The magnitude changes in the trends detected by the Theil Sen’s slope indicated that increasing values of mean temperature, solar radiation and decreasing values of relative humidity predominantly contributed to the annual upward trend in ET0 values for the 10 stations. A trivial impact of wind speed on annual ET0 values was observed for the stations. Kalburgi and Udupi stations exhibited positive ET0 trend with the highest and lowest annual values among ten stations.

scholarly journals Using ERA-Interim Reanalysis output for creating datasets of energy-relevant climate variables

2017 ◽  
Author(s):  
Philip D. Jones ◽  
Colin Harpham ◽  
Alberto Troccoli ◽  
Benoit Gschwind ◽  
Thierry Ranchin ◽  
...  
Keyword(s):  
Climate Change ◽  
Wind Speed ◽  
Relative Humidity ◽  
Solar Radiation ◽  
Climate Variables ◽  
Climate Data ◽  
Near Surface ◽  
Bias Adjustment ◽  
Dewpoint Temperature ◽  
Data Store

Abstract. The construction of a bias-adjusted dataset of climate variables at the near surface using ERA-Interim Reanalysis is presented. A number of different bias-adjustment approaches have been proposed. Here we modify the parameters of different distributions (depending on the variable), adjusting those calculated from ERA-Interim to those based on gridded station or direct station observations. The variables are air temperature, dewpoint temperature, precipitation (daily only), solar radiation, wind speed and relative humidity, available at either 3 or 6 h timescales over the period 1979-2014. This dataset is available to anyone through the Climate Data Store (CDS) of the Copernicus Climate Change Data Store (C3S), and can be accessed at present from (ftp://ecem.climate.copernicus.eu). The benefit of performing bias-adjustment is demonstrated by comparing initial and bias-adjusted ERA-Interim data against observations.

scholarly journals Future climate uncertainty and spatial variability over Tamilnadu State, India

2015 ◽  
Vol 17 (1) ◽  
pp. 175-185
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Solar Radiation ◽  
Minimum Temperature ◽  
Climate Models ◽  
Regional Climate ◽  
Future Climate ◽  
Maximum Temperature ◽  
Projection Data ◽  
Climate Uncertainty

<div> <p>The present study analyses future climate uncertainty for the 21st century over Tamilnadu state for six weather parameters: solar radiation, maximum temperature, minimum temperature, relative humidity, wind speed and rainfall. The climate projection data was dynamically downscaled using high resolution regional climate models, PRECIS and RegCM4 at 0.22&deg;x0.22&deg; resolution. PRECIS RCM was driven by HadCM3Q ensembles (HQ0, HQ1, HQ3, HQ16) lateral boundary conditions (LBCs) and RegCM4 driven by ECHAM5 LBCs for 130 years (1971-2100). The deviations in weather variables between 2091-2100 decade and the base years (1971-2000) were calculated for all grids of Tamilnadu for ascertaining the uncertainty. These deviations indicated that all model members projected no appreciable difference in relative humidity, wind speed and solar radiation. The temperature (maximum and minimum) however showed a definite increasing trend with 1.8 to 4.0&deg;C and 2.0 to 4.8&deg;C, respectively. The model members for rainfall exhibited a high uncertainty as they projected high negative and positive deviations (-379 to 854 mm). The spatial representation of maximum and minimum temperature indicated a definite rhythm of increment from coastal area to inland. However, variability in projected rainfall was noticed.</p> </div> <p>&nbsp;</p>

scholarly journals Using ERA-Interim reanalysis for creating datasets of energy-relevant climate variables

2017 ◽  
Vol 9 (2) ◽  
pp. 471-495 ◽  
Author(s):  
Philip D. Jones ◽  
Colin Harpham ◽  
Alberto Troccoli ◽  
Benoit Gschwind ◽  
Thierry Ranchin ◽  
...  
Keyword(s):  
Climate Change ◽  
Wind Speed ◽  
Relative Humidity ◽  
Solar Radiation ◽  
Climate Variables ◽  
Climate Data ◽  
Near Surface ◽  
Bias Adjustment ◽  
Dewpoint Temperature ◽  
Data Store

Abstract. The construction of a bias-adjusted dataset of climate variables at the near surface using ERA-Interim reanalysis is presented. A number of different, variable-dependent, bias-adjustment approaches have been proposed. Here we modify the parameters of different distributions (depending on the variable), adjusting ERA-Interim based on gridded station or direct station observations. The variables are air temperature, dewpoint temperature, precipitation (daily only), solar radiation, wind speed, and relative humidity. These are available on either 3 or 6 h timescales over the period 1979–2016. The resulting bias-adjusted dataset is available through the Climate Data Store (CDS) of the Copernicus Climate Change Data Store (C3S) and can be accessed at present from ftp://ecem.climate.copernicus.eu. The benefit of performing bias adjustment is demonstrated by comparing initial and bias-adjusted ERA-Interim data against gridded observational fields.

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