Air pollution is one of the most important natural hazards in cities that is one of the priorities of climate research.  In this research, synoptic situation of days polluted by ozone in Tehran have been studied and by environmental to circulation approach and cluster analysis. At first, was formed a matrix in 2417*41. Rows are indicated days and columns represent the number of stations. by cluster analysis and Ward method eight different clusters were identified. The results showed that the frequency of the ozone days have a seasonal trend and more can be seen in the first half of year in these cases the establishment subtropical high pressure in Iran. Therefore, cause the persistency of pollution in Tehran.
 

In this research, coding the rainfalls, prepares daily 45 stations with the statistical period of 20 years to zero and one codes to realize the daily dry periods in west and north west of the country and then, by establishing the main condition of occurrence of code one for 30 stations, we extracted the dry 4 to 10-day frequencies. And the results gained of considering the atmospheric weathering, shows that the most clear rotational pattern in sea level is related to Siberia-Europe high-pressure panels and sometimes both of them that increase the rotation on the region and also, the local high-pressure reinforcement and there is a high altitude in atmospheric middle level which is derived from sample patterns and is placed on the studied region and these sample patterns are from omega, bi-polar and rex models that they are on Russia and Scandinavian countries with some changes. The 500 level TAVA and omega shows well that in most regions, the air course, has decreasing case and so, we can result that placement of a high altitude in atmospheric middle and upper level on the high-pressures of the earth, causes the weather stability and lack of rainfall and as a result, the stability and durability of these conditions for several days, is related to sample patterns.         

In this research, we are trying to determine the “beginning time” as well as the “end” of the climatic seasons; and we will focus on identifying the displacement of these dates, which is influenced by the “climate changes” and “descriptionAbstract
The purpose of this study is to investigate possible changes and displacements in Iran's climatic seasons due to climate change. To do this, temperature, relative humidity, water vapor, wind and cloud data for 36 stations were received from the Meteorological Agency over 40 years. The data were divided into two 20-year series to allow comparison. Daily temperature data for each clustering time series were determined, then by considering 7-day sequences, the beginning and end of the seasons. The designated times were tested using the Rayman model. The results of comparing the seasons in the two time series indicated that in all stations, changes in climatic seasons occurred from Insignificant to significant. Climatic seasons in Iran do not correspond to calendar seasons, and climate change, especially temperature changes in recent decades, has caused the seasons to shift and shorten and lengthen. Although the beginning and end of the seasons do not generally correspond to their calendar dates, most of the days of these seasons occur in its calendar periods. The changes that have taken place have not only affected the length of the seasons, and these shifts have also changed the quality of the natural seasons.
Keywords: Climate change, natural seasons, cluster analysis, Rayman model of the qualitative conditions” created in them, compared to the past climatic periods. “Meteorological Organization” data has been used in this research. Forty years of received data, was divided into two groups of 20. Applying SPSS, each group was divided into four stages representing each seasons. From these stages, the beginning time and the end of seasons were determined and the accuracy of the obtained dates was controlled with the comfort indicators of the Rayman model. The results of the comparison of seasons in two time series indicated that, the changes occurred in natural seasons from an almost non-existent one in all stations. Climatic seasons in Iran are not compatible with the summer season and climate change, especially the change in temperature in recent decades, has caused changes and shortening of seasons. Most of the days in these seasons occur during its monthly periods, although the beginning and end of the seasons generally do not match their calendar dates. Changes have not only affected the duration of the season, and these changes have also led to a change in the natural quality of the season.

The aim of this study is to synoptic analyze of the number of frost days in a year of Khuzestan province. For this purpose, using the minimum daily temperature data of 12 stations during the statistical period of 1992 to 2017, the Meteorological Organization of the country, 54 days of frost was identified. Sea level pressure, Geopotential Height, Zonal and meridian wind and temperature of 500 hPa data with size of 2/5 * 2/5 degree arc from the National Oceanic and Atmospheric United States of America were extracted. On the matrix of the variance of sea level pressure data in 54 days, the analysis of the basic components was performed and 10 components which identified 83% variance of the sea level pressure. Then, by applying the hierarchical cluster analysis method, the integration method was applied to the scores of the 10 components and 5 patterns of sea level pressure were identified. The results showed that frost phenomenon in Khuzestan province occurs from November to march and its trend is decreasing during the statistical period. Also northern and western parts of the province have allocated the most frequency of frost. Also the synoptic condition analysis of troposphere showed that 5 sea level pressure pattern with different make ups lead to pervasive frosts of Khuzestan province. Weak and moderate frosts formed by the influence of Siberian and European cold high pressure systems. But severe frosts occur with spread of Iceland low pressure to Iran, along with strong cold pressures. Meanwhile, the powerful Siberian high pressure is present in most of the patterns, which its interaction with sub polar and Icelandic low pressure, plays the most role in the most severe frost in the province of Khuzestan. Also in the middle level of troposphere penetration of deep troughs from northern latitudes and east European huge blockings has the most role, which has advection of cold air from the side west of troughs on the country and during the intensity of the frost added to its continuity.
 

In the history of humanity, human always has suffered all difficulties with effort to reach to comfort and well-being until the human provides a way to achieve the comfort. In the viewpoint of climate four elements have significant role in formation of human comfort and discomfort conditions that according to the climatic conditions in different areas, the type and effect of these elements on individuals are also different. The aim of this research is to determine the areas of climatic comfort. For this purpose, temperature, precipitation and humidity data were derived from database of Esfazari for Khuzestan province during statistical period 1965 to 2014. In this process, at first discomfort climate has been defined using temperature, precipitation and humidity based on distribution probability conditional. This research is to determine the areas of climatic comfort in Khuzestan province using multivariate analysis (Cluster analysis and Discriminant analysis) and spatial autocorrelation pattern (Hot Spot index and Moran index) with emphasis on architecture. The results showed that the areas with climatic comfort are included in north and east parts of Khuzestan province. However, the areas of climatic comfort by spatial method have been limited somewhat. Results further indicated that the areas of climatic comfort have decreased significantly towards recent periods especially in cluster analysis and discriminant analysis that a trend of reduction has been remarkable in cluster analysis (from 23.60% in the first period to 17.60% in the fifth period) and discriminant analysis (from 26.97% in the first period to 14.98% in the fifth period).