Showing 5 results for Maximum 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.
Phd Student Farahnaz Khoramabadi, Master Seyyed Abolfazl Masoudian, Assistant Professor Mohammad Sadeq Keykhosravi Kiani,
Volume 0, Issue 0 (3-1921)
Abstract
Global warming is one of the most challenging climatic phenomena of the current era, accompanied by a rise in the average temperature of the Earth's oceans and land over the past few decades. This study provides a comprehensive analysis of the temporal and spatial changes in Iran's maximum temperature over the last four decades, using ERA5 reanalysis data.
Daily maximum temperature data was extracted from ERA5 products in HDF5 file format and processed using the Python programming language. For data analysis, Principal Component Analysis (PCA) was used for dimensionality reduction, and Ward's clustering method was employed to determine homogeneous climatic regions based on the similarity of weather elements. The long-term mean maximum temperature for the country during this period was estimated at 24.2∘C, with a range of variation from −0.4∘C to 53.7∘C. The results from smoothing the data's time series showed a significant temperature jump around the year 1998, which led to an increase in the mean maximum temperature from 23∘C to 25∘C. In the spatial dimension, the maximum temperature was directly influenced by topography, altitude, and latitude. The southern and southeastern regions were identified as the warmest, while the high-altitude areas of the west, north, and northeast were the coolest. The results of the PCA showed that the first two components explained more than 78% of the spatial variance and 93% of the temporal variance, respectively. This indicates the existence of consistent and interpretable climatic patterns. Additionally, the Ward's clustering analysis, which divided the data into five distinct clusters, reflected the diversity of temporal behavior of the maximum temperature across the country.
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 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.
Hossein Jahantigh, Esmail Rashidi, Abdolhossein Adel Zadeh,
Volume 24, Issue 73 (6-2024)
Abstract
Objectives: The purpose of this article, the relationship between maximum temperature of Kerman province geopotential height at 500 hPa to avoid risks and losses are extreme temperatures.
Method: In this paper, the approach has been used in the circulation to peripheral circulation patterns will be assessed based approach to environmental data. Therefore, we used two databases. First Base event database environment (surface). In this regard, the surface temperature is selected stations Kerman province. The maximum temperature of the stations in the period 01.01.1368 to 01.01.1398 for 30 years to 10957 the number of days were obtained from the meteorological province. Another database contains data that the data of geopotential height at 500 hPa
