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One of temperature estates is hot day occurrence. In order to recognize the mechanism of this estate، surveying its spatial behavior is crucially important. In current paper it has been attempted to recognized widespread hot days and their spatial distribution by using statistical methods. Accordingly maximum temperature database of Iran during 1962-2008 has been used. The analyses result has illustrated 1537 widespread hot days. Most of these days have been occurred during winter and spring seasons. In order to recognize distribution patterns and spatial autocorrelation، Global and local Moran statistic and also Getis- Ord G* statistic has been used. Using Global Moran has been revealed that in 99% confidence interval، cluster pattern governs the hot days frequency as well as hot days temperature. Frequency clusters has been raised in centre of Iran while clusters of the mean of temperature has been occurred in southern (from west to east) and northwest margin of the country. However the regions that experienced maximum frequencies of hot days do not happened in regions with hottest day's temperature mean. Generally hot days regions are determined by humidity، latitude and elevation. The hot spots are located in south، southeast and cold spots are founded in high lands and northwest of Iran. [ABSTRACT FROM AUTHOR]

Spatial distribution of thermal regions is dependent on local factors and circulation patterns in long terms. Recognition of spatial distribution of temperature in geographical regions could be help on planning and environmental policies. The aim of this paper is recognition and detachment of thermal regions in Iran. For this object, maximum daily temperature data have been provided using 620 synoptic and climatology stations. Maximum temperature converted for any station, base on solar calendar, and maps of maximum daily temperature have been interpolated using kriging method. Spatial resolution of these maps is 15×15 km. So 7187 pixels cover the country and temporal and spatial behavior of maximum temperature could be represented by a 7187×366 matrix. For diagnostic thermal regions in Iran, an agglomerative Hierarchical cluster analysis with ward's linkage applied on this matrix and five different thermal regions which consist: high mountainous, mountainous and Caspian's coasts, central plane, south eastern plane, southern coasts. In creating thermal regions topography spatial configuration and latitude have been involved. Recognition of thermal regions is important for energy consumption and tourism timing management. [ABSTRACT FROM AUTHOR]

To do this research daily interpolated temperature data from Asfezari data base have been used. In order to recognition extreme cold days, the Normalized Temperature Deviation Index (NTDI) applied. Then the index sorted according to extensive and severity and first 500 days selected for analysis. The geopotential height data extracted from www.esrl.noaa.gov data base. The thickness anomaly calculated for each day and classified by cluster analysis and Ward method. The thickness mean and temperature anomaly maps has been showed during each pattern. The results showed that 5 thickness anomaly pattern observed during extreme cold day's occurrence in Iran. The response of Iran absolute temperature anomaly to thickness anomaly are different. The coldest extreme low temperature on Iran occurred when located negative anomaly in thickness over Siberian and northeastern parts of Iran and observed positive anomaly over Barents Sea, Groland and Europe. The mean air temperature anomaly of Iran accompany to this circumstance of thickness anomaly is -5.9° centigrade. [ABSTRACT FROM AUTHOR]