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Showing 33 results for Temperature

Ms Asieh Asgari Dastnaei, Dr Amir Gandomkar, Dr Morteza Khodagholi,
Volume 0, Issue 0 (3-1921)
Abstract

Teleconnection patterns represent large changes that occur in the pattern of atmospheric waves and tornadoes and affect temperature patterns in large areas and are also used to predict average weather conditions over time periods, usually several months or annually. In this study, the effects of 26 Teleconnection patterns with the average monthly maximum temperature on a quarterly and annual basis were investigated. In this study, 4 synoptic stations of Borujen, Shahrekord, Lordegan and Koohrang in Chaharmahal and Bakhtiari province were analyzed. Data were analyzed using descriptive statistics, correlation and Mann-Kendall test. The results showed that the patterns of PNA, WP, NAO, SOI, TNA, TSA, WHWP, Niño 4, NP, Trend, AO, AAO, AMO, AMM, NTA, CAR and GMLO have a positive relationship with all stations studied and The patterns of EA WR, Niño 3, ONI, MEI V2, Niño 1 + 2, Niño 3.4 and TNI had a negative relationship with all studied stations.
 
Dr Hassan Kharajpour, Dr Zahra Hejazizadeh, Dr Bohloul Alijani, Dr Mohammad Hossein Nasserzadeh,
Volume 0, Issue 0 (3-1921)
Abstract

      Considering the undeniable impact of agricultural plants on climatic and regional changes, it seems necessary to conduct regional research to understand the reaction of each agricultural plant in different stages of growth in relation to weather elements. If the temperature of the air along with the warm cloud is lower or higher than a certain threshold, its development will stop. Between the two limits, there is an optimal temperature where the plant grows the fastest. Temperature and clouds are both the most important climatic elements in agriculture. Both climatic parameters together cause stress in wheat and lower the productivity of the product. Considering the strategic nature of wheat, in order to increase the level of production, in the present research, while taking advantage of the experiences and methods and models used in foreign and domestic researches, it was practical in Kermanshah province due to the extent of the land under wheat cultivation and The significant amount of production, which has a special place in this field at the level of the country, the determination of the statistical threshold and the synoptic analysis of warem cloud temperatures on the performance of the wheat crop are investigated. According to the investigations and consultations with agricultural engineers, the maximum temperature along with cloudy days causes the phenomenon of greenhouse and excessive heat, which causes the fall of flowers, rot, sterility of pollen grains, fruit reduction, Premature aging and poverty become seeds, and this phenomenon occurs mostly in the months of May and June.

Ali Hashemi, Hojjatollah Yazdanpanah, Mehdi Momeni,
Volume 0, Issue 0 (3-1921)
Abstract

Studying the effect of climatic variables on vegetation indices (Case study: Orange orchards in Hassan Abad, Darab County)
 
Abstract
Climatic variables are the most significant factors affecting vegetation changes. Nowadays, the satellite imagery is widely used to investigate the effect of fluctuations in climatic variables on vegetation changes. This research aims to investigate the effect of climatic variables of precipitation, temperature, and humidity on changes in vegetation indices of orange orchards in Hassan Abad, Darab County using satellite data. Hence, observational data, including orange tree phenology data and meteorological data on the agricultural weather station have been collected for over 10 years (2006 to 2016). MODIS images from 2006 to 2016 were referenced based on the territorial data and 1:25000 maps of the Iran National Cartographic Center. These images were used to calculate the remote sensing vegetation indices including normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). Results demonstrated that the variables of maximum humidity, minimum temperature, and precipitation have a significant positive effect on the NDVI variable. Additionally, the variables of maximum temperature and minimum humidity have a significant negative effect on the NDVI and EVI dependent variables. To determine the significance of each of the independent variables in predicting the dependent variables, the artificial neural network method was used. Findings showed that the climatic elements of precipitation, minimum temperature, maximum temperature, minimum humidity and maximum humidity with values (0.39, 0.3, 0.13, 0.1 and 0.06) had the greatest effect on EVI, respectively. Moreover, the effect of these variables on the NDVI index is equal to their coefficients (0.2, 0.28, 0.22, 0.11 and 0.17), respectively. Finally, ARMAX regression method was used to increase the explanatory power of the model. Results showed that this method could increase the explanatory power of the model and reduce the forecasting error.
Hamed Heidari, Darush Yarahmadi, Hamid Mirhashemi,
Volume 0, Issue 0 (3-1921)
Abstract

Human interventions in natural areas as a change in land use have led to a domino effect of anomalies and then environmental hazards. These extensive and cumulative changes in land cover and land use have manifested themselves in the form of anomalies such as the formation of severe runoff, soil erosion, the spread of desertification, and salinization of the soil. The main purpose of this study is to reveal the temperature inductions of the land cover structure of Lorestan province and to analyze the effect of land use changes on the temperature structure of the province. In this regard, the data of land cover classes of MCD12Q2 composite product and ground temperature of MOD11A2 product of MODIS sensor were used. Also, in order to detect the temperature inductions of each land cover during the hot and cold seasons, cross-analysis matrix (CTM) technique was used. The results showed that in general in Lorestan province 5 cover classes including: forest lands, pastures, agricultural lands, constructed lands and barren lands could be detected. The results of cross-matrix analysis showed that in hot and cold seasons, forest cover (IGBP code 5) with a temperature of 48 ° C and urban and residential land cover (IGBP code 13) with a temperature of 16 ° C as the hottest land use, respectively. They count. In addition, it was observed that the thermal inductions of land cover in the warm season are minimized and there is no significant difference between the temperature structure of land cover classes; But in the cold season, the thermal impulses of land cover are more pronounced. The results of analysis of variance test showed that in the cold period of the year, unlike the warm period of the year, different land cover classes; Significantly (Sig = 0.026) has created different thermal impressions in the province. Scheffe's post hoc analysis indicated that this was the difference between rangeland cover classes and billet up cover.
Zohreh Maryanji, Fatemeh Sotoudeh, Meysam Toulabi Nejad, Ziba Zarrin,
Volume 0, Issue 0 (3-1921)
Abstract

Understanding and predicting future climatic conditions and characteristics is essential because of their importance in all aspects of life. This study seeks to examine the process of modifying temperatures in the Hamedan region by using Downscaling data to predict the public circulation data and its changes. The Lars Explore Downscaling Model has been used to fine-tune the data of the General Transport Model (HADGEM2-ES) and the paired model (CMIP5) and under the three release scenarios RCP2.5, RCP4.5, and RCP8.5). Estimates of the correlation of simulated data and actual data show values of more than 0.95 for all months. P_value also showed the statistical tests of model output, acceptable values in model performance in production and simulation. As a result, the data were extracted from 2011 to 2050. Data were examined in three intervals to detect trend changes. The results show that in the optimistic scenario (RCP2.5) there is no tangible trend in the mean and minimum temperature, while in the RCP4.5 and RCP8.5 scenario there are significant trends in temperature data and accordingly increase the minimum temperature, according to the increase in the minimum temperature, according to the increase in the minimum temperature, according to the increase 1 degree in the average temperature. It shows severe climate change that, especially in the cold season, changes the type of precipitation. Also, based on the data process, the significant increase in the average annual and monthly scale temperature in all three scenarios under study will indicate the environmental crisis ahead.

Yosouf Ghavidel Rahimi, Manochehr Farajzadeh, Mehdi Alijahan,
Volume 15, Issue 36 (6-2015)
Abstract

Global warming and the meaningful relationship between temperature and precipitation changes over different areas of the earth with temperature increment of the earth, are considered as the most important patterns of this century’s climate changes. Today, there is debate over climate change and global temperatures increasing. Damaging effects of this phenomenon on the planet is one of the most challenging issues in global scale. Because of this, the research ahead is done for the detection of global warming on maximum temperatures, monthly and periodic (hot and cold) as well. For this study, two groups of data, temperature data of 17 synoptic stations and corresponding amounts of data in global temperature anomalies were figured out over 60 years period of time (1951 to 2010). Goals, the Pearson correlation method for detecting relationships between data's, linear and polynomial regression for trend analysis time series data , To illustrate the correlation between the spatial distribution of temperature data with global warming stations nationwide Geostatistical model Finally, non-parametric test for detecting significant temperature change Man - Kendall were used. According to the results impact of global warming on the maximum temperature in the cold months like January, December and November should be much lower, and the highest in spring and summer season in the southern stations such as Abadan, Ahwaz and Shiraz seen. The above process is also evident in periods of hot and cold temperatures and the influence of the stations temperature of the warm period of global warming were higher than cold period and represent an increase in the temperature of the warm period of years. In between, the number of stations as well as Anzali, Urmia and Khorramabad stations in some months had the opposite influence of global warming and seen drop in the maximum temperatures of them. It is also observed in the results obtained from the analysis period. Station's maximum temperature trend change is represents significant in the summer month. Changes trend in the months of July, August and September, is significant that the process is more pronounced in the southern stations. Significant analysis trend changes have been taken in periods (cold and hot) in studied stations indicative of its significance in warm period.
Ahmad Mazidi, Mahdi Narangifard,
Volume 16, Issue 40 (6-2016)
Abstract

Expansion of urban areas has impacted on climate in local and global scale. As a result, residents’ life would be influenced accordingly. In this study, the effects of changes in land cover and land use change on climate of Shiraz and Fasa were studied in a long-term period. To ensure the normal data, Kolmogrov-Smirnoff test was employed. The minimum and maximum temperature parameters and relative humidity were evaluated using non-parametric Mann-Kendall and regression method in a 45-year period (1966-2010). The results showed a decreasing trend for the minimum temperature and humidity for Fasa and minimum and maximum relative humidity for Shiraz whereas other quantities showed an increasing trend. The study examined the effects of land use changes and land cover types in the 23-year period (1987-2010) using images of LANDSAT satellite of TM sensor on temperature pattern in two cities. During these 23 years, a reduction of 113 square kilometers for barren land of Shiraz and 5/7 square kilometers for barren land of Fasa and extending 110 kilometers residential users and 3/5 square kilometers in Shiraz and Fasa were reported.


Abasali Arvin, Abdolazim Ghangherme, Davar Hajipour, Mehran Hidari,
Volume 16, Issue 41 (9-2016)
Abstract

In this study, by using the Mann-Kendall nonparametric method and Sen' s Estimator slope test, the trend of some elements including  precipitation, average of maximum and minimum temperature and the  number of snowy days Chaharmahal and Bakhtiari Province covers part of Zagros and Zardkohe-Bakhtiari highlands, from which three major rivers including Zayandehrud, Dez and Karun originate. in an annual and monthly scale, was evaluated in  the stations of the province during a period of 30 years (1986-2015). The output was presented in the form of tables, graphs and iso-trend maps as drawn in the Arc_GIS. The results showed that although changes in rainfall did not follow any specific trend in most months of the year, the amount of precipitation in the stations of Koohrang as the rainiest station in the Province, Lordegan and Yan-Cheshme had a decreasing trend at the significance level of 99%; also, the the number of snowy days during March showed a decreasing trend in Koohrang station. However, the average minimum and maximum temperature in most areas of the province, in both monthly and yearly scales, except for the months of November and December, had a significantly increasing trend.


, ,
Volume 16, Issue 42 (12-2016)
Abstract

  Temperature alteration plays special role as one of the most basic climate elements. So inspection of temperature alteration and anticipation has scientific- applied magnitude. In this study inspection of several cases of statistical characteristics of monthly­ average, maximum and minimum temperature and illumination of their alteration method­, temperatures predictability by ANFIS is evaluated­. Applied data is over 288 months during 24 years of statistical period since January of 1987 until December of 2010 through synoptic stations of Pars Abad, Ardebil and Khalkhal. According to equations of data lineal process­, lineal process of temperatures through all of the stations is positive and­ additive­. Lineal process gradient in minimum temperature is more than other­ maximum and average temperature. Less amplitude more variance and standard aviation and­­ data ­predictability is more. According to present article adaptive Neuro – fuzzy inference system mostly has acceptable function through anticipation of monthly minimum, maximum and average temperature in the stations of Ardebil province.


, , ,
Volume 16, Issue 42 (12-2016)
Abstract

An AO is an example of teleconnection pattern in the northern hemisphere’s winter. In this study, the effect of AO on the monthly minimum temperatures in the North-East region of Iran is investigated. The required statistics, including statistics monthly minimum temperatures for 17 synoptic stations of the under study area was provided from Iran Meteorological Organization’s (IRMO). Pearson correlation analysis as the main method used in this study Show an inverse relationship between minimum temperatures and most selected stations and AO index.The results of this study shows that there is a significant relationship between the AO teleconnection pattern and minimum temperatures of north-east of Iran during the cold season of the year. This shows decrease of temperature in positive phase of the AO and rise of temperature is negative phase. In terms of time correlation coefficients were calculated showed a significant negative correlation between the temperature of stations and the AO Among the studied stations, the relationship between the minimum and average monthly temperatures in Birjand and Bojnoord station more than other stations, affected by the AO pattern, has been fluctuated and significant correlation coefficients between the minimum and average temperature of Birjand and Bojnoord Stations with AO has been calculated. The effect of Arctic oscillation on mean temperature of the studied area, north-east of Iran, during the three months, January to march, and ,two months, January to February, is very distinctive against any other periods of time.


Ahmad Reza Ghasemi,
Volume 16, Issue 43 (12-2016)
Abstract

Air temperature is one of the most frequently used parameters in the assessment of climate change at global and regional scale. So researchers have tried to modeling and predicting it with different models. This study also aims to model and predict the country's monthly minimum and maximum temperature. Investigates of temporal temperature changes is done by Sen’s estimator and Pettit method and predicting made by Holt-Winters model. The results indicated that the minimum temperature during 1961 to 2010 increased by 2.9º C . This rate is more in stations located in the warm and dry regions (3.1°C) than any other stations (1.8°C). While the maximum temperature gradient changes are lower and is about 2.1°C. The results also confirmed the performance of Holt-Winter's forecasting model. Beside a few exceptions, the minimum and maximum temperature will be increased until 2020. The highest increase of temperature will occur in Khoy, so that the minimum and maximum temperature will be increased about 0.6°C and 0.28°C, respectively.


Kamal Omidvar, Reza Ebrahimi, Mohammad Kykhsrvy Kayani, Ghasem Lkzashkoor,
Volume 16, Issue 43 (12-2016)
Abstract

The aim of this study was to investigate the effects of global warming on where the slope changes when the monthly temperature in Iranian territory over the coming decades (2050-2015). The simulated temperature dynamic model EH5OM subset Hybrid Models atmospheric circulations (GCM) selection and data model of the Center for Theoretical Physics Salam (Italy) were derived from emission scenarios A1B scenario was chosen given the scenario of 2100 -2001 found that from 2050 to 2015 were used in this study data is then output the data in the fourth edition of the regional climate model (RegCM4) Linux environment was fine scale output data Downscaling model with dimensions of 27/0 * 27 / Degrees latitude is where the dimensions of 30 x 30 km area of ​​approximately cover the average temperature of the matrix deals 13140 2140 * was extracted. Finally, the slope of the average monthly temperature during the period under study by Mann-Kendall slope age and matrix computation in MATLAB software 13140 * 12 respectively. Results show rising temperatures in March and April to June, more than 90% of the country, that it will be spring's warmer. Increasing the temperature in the winter months and spring mountainous parts of the western half of the country is warming the cold regions of Iran. Temperature negative trend in October and November in the northern part of the eastern half of the region's countries could be indicative of colder temperatures in the northern West.


Ghasem Keikhosravi,
Volume 17, Issue 47 (12-2017)
Abstract

In this study, precipitation simulated annual and seasonal in East and North-East of Iran ,in 1987-2011, by using RegCM4 dynamic model in two case; with and without using post-processing technique. The required data for RegCM4 model with NetCDf format, received from ICTP center. For the implementation of the main dynamic model, Convective precipitation test scheme and the horizontal resolution, performed for 2007. According to the test, Kuo Schema had less error than Emmanuel and Gurl schemes in Precipitation and region temperature modeling. Horizontal resolution selected 30 Km. After model implementation with Gurl schema and 30 Km horizontal resolution, Precipitation and temperature output post- processed using MA model. According to results, in the study area, during 2006-2011 verification period, average annual rainfall raw bias of RegCM4 model was calculated and post-processed equal to 8.3 millimeter and 61.04 respectively. Briefly in the annual time scale, in 75% of studied stations, post-processing is effective and MA model is more efficient. In seasonal scale, bias error of average precipitation is equal to 54.99 millimeter in the winter, 27011 millimeters in the spring, -3.6 millimeter in the summer and 7.21 millimeter in the fall. Simulation of the temperature data in the stations using RegCM4 and MA model in north-east of Iran, revealed high performance. Bias error of average temperature is equal to -2.78 for RegCM4 model and post-processed equal to -0.05. In all stations, modeled Annual temperature and observational data has difference less than 0/1 ° C. In seasonal scale, the mean bias error range according ° C is equal to -4.1 in the winter, -4.09 in the spring, -1.8 in the summer and -1.5 in the fall.
 


Dr. Mostafa Karimi, Mis Fatemeh Sotoudeh, Dr. Somayeh Rafati,
Volume 18, Issue 48 (4-2018)
Abstract

Increasing CO2 emissions and consequently, air temperature causes climate anomalies which affects all the aspects of human life. The purpose of this study was to assess the temperature changes and also to predict the extreme temperatures in Gilan and Mazandaran Provinces. To do this, the SDSM statistical and dynamical model was used. As well, it was applied the Mann-Kendal graphical and statistical technique to analyze the temperature changes and its trend. In this regard, the daily temperature was obtained from Rasht, Ramsar and Babolsar synoptic stations during 1961 – 2010, and also the general circulation models data of HadCM3 and NCEP were collected from related databases. The results revealed a significant positive trend in monthly and annual minimum and maximum temperature in all three stations in the first (1961-2010) and third (1961-2040) periods.  There is not a significant trend in extreme temperatures in Ramsar and maximum temperature in Rasht in the second period (2011-2040). The Mann-Kendal graphical test was used for the yearly extreme temperatures in all periods. The results showed that it was occurred both increasing trend and suddenly changes or shifts at the 95% confidence level in all stations. It is occurred the highest of changes in monthly and annual of the minimum temperature at forecasted period (2011-2040). It was predicted extreme temperature to increase about 0.1 to 1.7° C. The short time oscillations and significant positive trend occurred in both the maximum and minimum temperature shows the temperature increase and climate changes in the future. Thus it is obvious the decrease in temperature difference in warm and cold seasons.

 


Chenoor Mohammadi, Manouchehr Farajzadeh, Yousef Ghavdel Rahimi, Abbas Ali Aliakbar Bidokhti,
Volume 18, Issue 48 (4-2018)
Abstract

 This study is aimed at estimating monthly mean air temperature (Ta) using the MODIS Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), latitude, altitude, slope gradient and land use data during 2001-2015. The results showed that despite some spatial similarities between annual spatial patterns of Ta and LST, their variations are significantly different, so that the Ta variation coefficient is four times the one of the LST. Our analysis indicated that while in winter latitude is the key factor in explaining the distribution of the differences LST-Ta, in other seasons the role of slope and vegetation become more prominent. After obtaining the spatial patterns of LST and Ta, we estimated Ta using regression models in spatial resolution of 0.125˚. The lowest estimation error was found in the months of November and December with a high explanatory coefficient (R2) of 70% and a standard error of 1 ° C.  On the other hand, the maximum error was obtained from May to August with R2 between 59 to 63% and a standard error of 1.6 ° C which is significant at the 0.05 level. In addition, result of evaluation of individual months showed that estimation of Ta is more accurate at the cold months of the year (November, December, January, February, and March). With considering different land uses, the highest R2 was related to waters and urban areas (96 to 99%) in warm months, and the lowest R2 was for mixed forest and grassland (between 15 and 36%) in cold months.

Sayyed Mohamad Hosseini, Abdolhossein Adelzadeh,
Volume 19, Issue 52 (3-2019)
Abstract

In this research, applied synoptic model for determining the average daily temperature and its relationship with the Geopotential Height in middle level (500 HPa). Therefore, two database were used: database of atmospheric circulations, includes the data of geopotential height at 500 HPa and its data were extracted from the NCEP/DOE(US National Oceanic and Atmospheric Administration) in hours 00:00; 03:00; 06:00; 09:00; 12:00; 15:00; 18:00; and 21:00 in Zulu and other, database of environmental (surface) events. Contain of average daily temperature in the Mashhad, Torbat-Heydarieh and Sabzevar stations in Khorasan Razavi Province. The maximum and minimum of these stations in the time interval from 01/01/1987 to 01/01/2014 equal as 9862 days from the meteorological organization of Iran. Then, was calculated the correlation of the average daily temperature of selected stations with high atmospheric data (500 HPa level) with the northern hemisphere in Surfer Software. The result shown, four regions in the northern hemisphere which had high correlations with selected stations. The correlation results suggest that the United States has 25 pixels, Northern China 25 pixels, Africa 45 pixels and Japan with 65 pixels. Then, weighted average of pixels in heights by multiple regression equation station. The results of diagnostic models indicate that, per geopotential height increase in the profile, the average daily temperatures of selected stations in the Sabzevar 1.4, Torbat-Heydarieh 1.3 and Mashhad 1.3 degrees Celsius will increase.
 


Dr. Ali Bayat, Mr. Saeed Mashhadizadeh Maleki,
Volume 19, Issue 53 (7-2019)
Abstract

Precipitable Water Vapor (PWV) is one of the most important quantities in meteorology and climate studies. PWV in Earth's atmosphere can be measured by Sun-photometer, the Atmospheric Infrared Sounder (AIRS), and radiosonde from surface, atmosphere and space-based systems, respectively. In this paper, we use PWV measured by Sun-photometer located in Institute for Advanced Studies in Basic Sciences (IASBS), AIRS and 29 Iranian synoptic stations data include temperature, dew-point temperature, pressure and relative humidity. For validation of AIRS data, the correlation coefficient between AIRS and Sun-photometer data calculated. The correlation is 90%. Average of PWV measured with sun-photometer and AIRS are 9.8 and 10.8 mm, respectively. Pearson's correlation coefficients between PWV of AIRS  data set and temperature, dew-point temperature, pressure and relative humidity for synoptic stations are calculated. Correlation between PWV and temperature, dew-point temperature, pressure, and humidity are 73%, 74%, -40% and -30%, respectively. PWV and temperature correlation coefficient map shows a positive trend between latitude and correlation coefficient. Rising a degree in latitude lead to increasing 2.8 percent in the correlation coefficient.

Dr Sayyad Asghari, Hadi Emami,
Volume 19, Issue 53 (7-2019)
Abstract

Earth surface temperature is an important indicator in the study of energy equilibrium models at the ground level on a regional and global scale. Due to the limitation of meteorological stations, remote sensing can be an appropriate alternative to the Earth's surface temperature. The main objective of this study is to monitor the surface temperature and its relationship with land use, which is monitored using satellite imagery. For this purpose, the images were first obtained and the necessary pre-processing was applied to each one. Then it was compared to modeling and classification of images.  Firstly, in order to investigate the changes in user-orientation, a user-defined classification map for each object was extracted using the object-oriented method. Then, to investigate the land use change, a map of user-landing changes map was extracted in an 18-year time period (2000-2017). Finally, in order to monitor the surface temperature, the surface temperature map of Ardebil was extracted.  The results showed that there is a strong relationship between land use and surface temperature. As a user, urban users have a temperature of about 41 ° C (2017), which is also due to heat-absorbing urban temperatures.  This is despite the fact that the use of hydrocarbons is due to a lower heat absorption of 34 ° C (2017). This shows the role of different uses in determining surface temperatures.  Also, the relationship between surface temperature and vegetation cover was investigated in this study. The results showed that areas such as soil and urban areas with a lower coverage than areas such as agriculture and pasture, have a higher temperature.  Because the coating is always an obstacle to the entry of heat, it has an inverse relationship with superficial heat.


Dr. Ebrahim Fattahi, Shookat Moghimi,
Volume 19, Issue 54 (12-2019)
Abstract

 In this study in order to monitor snow cover, the Moderate Resolution Imaging Spectroradiometer (MODIS) optical images were used, while for detection of snow covered areas, the  snow index-NDSI, was applied. The results showed - according to the climatic conditions of the region- during the following months: December, January, February and March, most of the area is covered by snow and the maximum extent of snow cover occurred in January. In West Azerbaijan province there is found a negative trend of snow cover with a drastically reduction in January, as well as the provinces East Azerbaijan and Ardebil showed the decreasing of snow cover in this month. The results of this study show that, changes in snow cover imply a rise in temperature in this region leading to the reduction of snow cover in January. This trend represents global warming and climate change impacts on snow cover in the study area. Investigation of extreme indices  confirms the assumption that by taking temperature increase into consideration, regional winter precipitation pattern has been changed from snow to rain, causing the reduction of snow storage in the catchment of study area. In addition ,the extreme temperature index study  in the period of 2011- 2040 and the baseline by considering climate change approach in North West of Iran by using outputs of general circulation models under A2 scenario and downscaling models LARS-WG indicates the number of frost days or the number of  icy days decreased compared to the baseline which is not unexpected according to reports by the Intergovernmental Panel on Climate Change (IPCC) as well as several studies confirmed  global warming. Moreover, indices such as growth period increased, while diurnal temperature variation decreased compared to the baseline confirming   snow cover reduction in the region as a threat of snow storage in the region. 

Mokhtar Karami,, Rahman Zandi,, Jalal Taheri,
Volume 20, Issue 56 (3-2020)
Abstract

In recent years with the development of cities coatings of the Earth's has changed surface.  These changes have caused some urban areas to have a few degrees higher than the surrounding temperature. This phenomenon is known as thermal islands. Mashhad is one of the major metropolises in Iran with the problem of thermal islands. Various parameters affect the formation of thermal islands in this city that should be considered. In this study TM, ETM+ and OLI images were used to obtain surface temperature over the period 1987-2016. The study of temporal variations in surface temperature showed that in the studied period, thermal islands were transferred from outside the city to the city. The model for describing the temperature of the surface of the earth has changed and has diminished from the temperature of the city's moderate and cool temperatures, and in contrast, the amount of high temperatures (thermal islands) has increased significantly. The TOPSIS method was also used to obtain the thermal forming factors. 13 natural and human factors affecting the formation of thermal islands were identified. Each expert opinion factor was used to determine the degree of importance. According to experts, the distance from the sanctuary with a weight of %234 and traffic of %155 is the most important and the height with a weight of %022 is least important in the formation of thermal islands. The final results obtained from this model showed that the factors affecting the formation of thermal islands are well recognized and the temperature decreases with these factors.
 


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