Iranian Journal of Geophysics (20080336)(2)pp. 241-251
Surface albedo is a climatic parameter that is a function of surface type. In this study, to investigate the relationship between albedo and Elevation components in Iran from the combined data (Terra / Aqua) of Modis sensor in the period 3/20/2000 to 3/20/2019 (6940 days) on a daily basis and in a spatial resolution of 500 Meters were utilized. Also, Iran's Digital Elevation Model (DEM) data in spatial resolution of 500 meters and with a sinusoidal projection system coordinated with the spatial resolution and projection system of albedo data were downloaded from the NASA website. In the DEM data used, In data used, in addition to the elevation of the points, the slope and aspect information for each pixel is also in decis. Prior to data usage, some preprocessing was performed on digital data. Based on nearly 60 billion pixels, the albedo and DEM showed a linear fragmentary pattern. The results showed that the amount of albedo has 5 different patterns with increasing elevation. The first pattern shows the whiteness behavior at elevation below sea level. In these zones, albedo increases sharply with increasing elevation due to rising land surface temperature(LST). So that the albedo goes from 4% to about 16%. The second pattern shows the uniform behavior of the albedo at an elevation of 0-800 m. The third pattern of albedo decreasing behavior with elevation is revealed in the 1300-800 m belt. One of the reasons for the decrease in albedo in these elevation belts can be the expansion of cities and consequently the decrease in albedo due to the smooth surface of the asphalt of the streets and the paving of the streets. In the fourth pattern, a direct connection of albedo with elevation is observed in the belt of 3500-1300 meters. One of the main reasons for the increase in albedo in this elevation belt is the increase in snow cover in this elevation belt. The fifth pattern also shows a direct link between albedo and elevation above 3500 meters and is a continuation of the fourth pattern, with the difference that this pattern loses some of its order and shows a scattered pattern. The correlation of albedo and slope also showed that due to more sun exposure, the southern aspect of Iran has about 3% more albedo than the northern aspect. The correlation between the albedo and the slope is a straight line. As the albedo decreases to a slope of 30 °, the albedo will increase on slopes above 30 °.
Theoretical and Applied Climatology (14344483)145(1-2)pp. 245-260
The aim of this study is to investigate the spatiotemporal variations of albedo in Iran. To this aim, the daily albedo datasets of Moderate Resolution Imaging Spectroradiometer (MODIS) from onboard Aqua and Terra (MCD43A3v006) were applied for the period of 2000 to 2019 with a spatial resolution of 500 x 500 m(2). First, the long-term average of Iran's albedo was calculated; the obtained results show that the average albedo of spring, summer, autumn, and winter in Iran is 13.7%, 14.7%, 15.2%, and 19.2%, respectively. Second, the temporal-spatial variations of albedo values in Iran were analyzed using principal component analysis, and the results showed that the three main components are able to explain 97% of the data variation. The first component explains more than 74% of the total changes, the second component more than 20%, and finally, the third component explains more than 3% of the changes. Finally, the linkage between the three main components with aspect, slope, and elevation was examined in Iran. The results showed that the role of solar zenith angle, elevation, and aspect in the first and third components and also the role of slope and elevation in the second component were the most significant. In general, it can be said that snow cover in the first component, salt cover in the second component, and also snow reservoirs in the third component had albedo above average; this issue depends on the roughness and the surface of the ground. The results showed that this technique is very suitable for the analysis of the spatiotemporal variations of Albedo.
Indian Journal of Science and Technology (discontinued) (09746846)9(40)
Objectives: The purpose of this study is to find the relationships of snow-covered days (SCDs) with topographic variables such as elevation, slope and aspect by using MODIS data. Methods/Statistical Analysis: MODIS data were used from 1 January 2003 to 31 December 2015. Due to cloud contamination in the daily data of MODIS snow products, it is not reasonable to use the data without any processing aimed at reducing cloud blockage. To reduce cloud cover effect, the daily data of MOD10A1 and MYD10A1 were combined and a three day filtering technique was applied to further reduce cloud cover effect. Findings: 1) The relationship of SCDs with elevation is not necessarily a linear relation; 2) The slope value of 22° is the critical slope in the Basin above which the snow accumulation decreases; and 3) The most SCDs are on the N and NE facing slopes and the least SCDs are on the SW facing slopes. Application/Improvements: The study showed that the slopes steeper than 22 degree are not suitable for snow accumulation. So in a country like Iran that is 3 degree Celsius warmer than the globe and snowfall frequency is dramatically low and this may be considered as a new restriction to accumulation of snow in high altitudes of the country.
Journal of the Earth and Space Physics (25383906)40(4)pp. 155-168
Temperature is one of the essential elements of forming a climate and plays a crucial role in the lives of flora, fauna and human activities. The extreme temperature is one of the thermal indexes in meteorological and climatological studies. The extreme temperature is divided into two types: the extreme warm and extreme cold. The extreme warm includes the temperatures much above the normal value and the extreme cold includes temperatures much below the normal value. Studying the extreme warm events due to their social and economical effects and their impact on human's health has prominent importance. In order to regionalize the extreme warm of Iran, we used Sphezari dataset. The Sphezari base has been provided from the average temperature based on daily data from 663 synoptic and climatological stations from 1 January 1961 to 31 December 2004. The pixel of this dataset has been calculated in the form of 15 × 15 km2 and by kriging method. Therefore, the matrix dimensions of day to day temperature of Iran is in the form of 15992 × 7187 Sphezari dataset. In this dataset the rows (915992 days) represent the time and the columns (7187 pixel) represent the place. We have used normalized temperature departure index to identify the events of extreme warm events in this survey.The index has been introduced by Fujibi et al. (2007). To obtain this index, the long term average temperature of calendar days must first be calculated. The thermal amounts of 44 years are averaged to calculate the long term mean temperature of the given days. To avoid the existing noise in the daily mean temperature,the nine-day running average was applied three times in order to filter out day-to-day irregularities. After carrying out this phases temperature departure (AT) of each of the 15992 days is investigated in the long term mean of the same day. Thus it is necessary that the amount of the absolute temperature departure becomes standardized by the averages of AT. In this way, the amount of temperature departure in different times of a geographical point and different spatials in a particular time can be compared to each other. As an index of day-to-day variability, the variance of AT in the 31 days centered on each calendar day was calculated as σ2 Then the moving mean of nine days σ2 in three times will be conducted to dimnish the noise. Then normalized temperature departure (NTD) indexed with x∗ symbol was calculated. This index was calculated for 7187 pixels, each pixel for 15992 days. Then, the index of location x∗ was investigated over Iran and the percent area of Iran which had the amount of x∗≥2 was determined. In this way, an index of 15992 × 2 was obtained, indicating the greatness highest temperatures of Iran for the period of 1 Jan 1961 to 31 Dec 2004. This matrix was arranged according to the mean of NTD and area amount. The first 264 days was selected as the sample. Whereas the temperature was in over of Iran, at least, 2 standard deviation more than its long term mean (x∗≥2) and a large area was warmmer of Iran. The NTD of 7187 pixels in the selected 264 days was classified using the cluster analysis technique and agglomeration based on the entered method. Results of this research showed that according to the extreme warm events, Iran can be classified into five distinctive regions.The most important characteristics of the extreme warm events in Iran are as follow: Most of the extreme warm events of Iran have occurred in winter and autumn days. The maximum warm events of Iran has occurred in west and southwest of Iran, specially, in recent years. NTD is one degree above the other areas. The setting of this region with the maximum rate of the NTD index shows that the systems creating the extreme warm events was entered from west and southwest of Iran; thus there are regions was influenced more and prior to the other regions. The highest spatial standard deviation belongs to these regions. It means that these regions have little spatial similarity from the viewpoint of the NTD index. It means that the extreme warm events creating systems donot attack this region equally. Some regions are influenced more and some less than others by these systems. Maximum temporal standard deviation belongs to northern and western regions. This means that events of the extreme warm events happen in these regions in some months. Therefore the systems creating the extreme warm events in these regions are activated in part of the year. The least temporal standard deviation belongs to the northeastern region and the least spatial standard deviation belongs to south and southeast regions.
Journal of the Earth and Space Physics (25383906)38(4)pp. 241-258
In different regions, precipitation takes place with different persistencies and every persistency supplies a share of rainfall days and precipitation. Therefore, the importance of rainfall persistence could be evaluated in all places. Iran is located in Mid-Latitude of an arid region, in which the mean rainfall is 250 mm and it has dramatic tempo-spatial changes. Rainfalls with short persistence are of characteristics of arid regions and it is also tangible in Iran. However, Iran's rainfalls persistence ranges from 1 to 45 days and have dramatic tempo-spatial changes, but the maximum amount and days of rainfalls are supplied by rainfalls with short persistency. So, the phenomenon of rainfalls with long persistency is considered as an extreme event which has extreme variability. As the persistence of precipitations increases, their role in generating Iran's rainfall days decreases severely in such a way that the maximum rainfall days of Iran is supplied by one-day rainfalls. However, the share of one-day rainfalls in the supply of precipitation days of Iran's Western half is more accentuated. In contrast, the increase in the persistence of rainfalls does not have an identical role in decreasing the supply of Iran's precipitation. As the persistence of precipitations increases, the share of precipitation in the Central and Southwestern Iran decrease severely, but in Western and Northern Iran, vice versa is the case. In some heavy precipitation regions of Iran's Western half, the decrease of precipitation persistence is associated with the decrease of the share of precipitation supply and in other regions; the decrease of the share of precipitation supply is gradual. Therefore, in every space, some of the persistent rainfalls supply the great share of precipitation days and precipitation amount and are considered important. However, it is possible that this precipitation persistency do not have such importance in those areas. Every kind of variability and change in the role of precipitation persistence in every space will be considerable. Spatial changes of one-day precipitation's share in the supply of Iran's precipitation days and precipitation amount could be evaluated from this angle. To evaluate the changes in one-day precipitation' share in the supply of precipitation days and precipitation amount, the daily observations of precipitations in 1437 stations of throughout Iran was used. Drawing upon Kriging method, the observations of the stations were generalized in a regular network by 15*15 km dimensions and Iran's isotheral digital maps were developed from 1961/03/21 till 2004/12/30. These digital maps include daily time series (15991 days) of precipitation amount for 7187 cells. Precipitation persistence in the time series of every cell was evaluated and in addition to that, their share in the supply of precipitation days and precipitation amount of each cell were also calculated. Then, the most important persistence of Iran's precipitations (one-day persistence) was identified and their importance was investigated. Yearly and monthly time series of one-day precipitation' share in the supply of precipitation days and precipitation amount were entered in a trend analysis for evaluating and understanding its changes and its results were considered. In spatial analyses including identification of climatologically variables trend, more confident way is that firstly, spatial interpolation is done; then, an appropriate trend test is performed on the data on the nodes. The results obtained from such analyses not only enjoy higher degree of spatial attribution, but based on closeness principle, spatial order of points themselves provide intuitional reason for accepting or rejecting trend analysis. One-day precipitations supply more share of Iran's precipitation days compared to remaining precipitation persistencies in such a manner that it may be noted that in all regions of Iran, the frequency of one-day precipitations is maximum compared to remaining precipitation persistence. In contrast, Iran's precipitation is provided by different persistencies and the share of one-day precipitations in precipitation supply is maximum only in Western half (Central and Southeastern parts). However, although oneday precipitations do not have much importance throughout Iran, the degree of their importance in Eastern half is maximum compared to Western half. The share of on-day precipitations in the supply of Iran's precipitation days and precipitation amount has changed with time. The results of yearly changes of share of on-day precipitations in the supply of Iran's precipitation days and precipitation amount indicate that their share in the supply of precipitation days decreases in one quarter of Iran's area and only in 3% of Iran's area, their share increases. Given that Western and Central Iran's maximum precipitation days are provided by one-day precipitations, precipitation days of Eastern Iran have decreased. In addition, their share in the supply of precipitation days decreases in 1/5 of Iran's area and only in 6% of Iran's area, their share has increased. On the other hand, Given that Central Iran's maximum precipitation days are provided by one-day precipitations, their share in the supply of precipitation days has decreased; just in discrete regions and along with Zagros and Alborz unevennesses, their share increases. The results of yearly changes of share of one-day precipitations in the supply of Iran's precipitation indicate that their negative trend in all rainfall months is greater than their positive trend. Looking more generally into the share of monthly changes of one-day precipitations in the supply of Iran's precipitation, the aspects of Iran's precipitation concentration becomes evident, especially in Eastern and Central Iran.
Journal of the Earth and Space Physics (25383906)39(2)pp. 171-186
One of the effects of climate change is the possible increase in both frequency and intensity of extreme weather events. Extreme weather and climate events have a major impact on ecosystems and human society due to their severity and the fact that they often occur unexpectedly. In warmer climates and during transition seasons, cold extremes have agricultural impacts that are manifested in the damage of crops due to frost. The identification of teleconnections and the analysis of their impact on the atmospheric circulation can be very useful for the understanding of anomalous events at many regions of the planet when one assumes that local forcing may influence the atmosphere circulation at remote locations. Teleconnection patterns are simultaneous correlations in the fluctuations of large scale atmospheric parameters at points on the Earth that are wide apart. The effect of these patterns could be significant throughout the dominant modes of the atmospheric variability. Teleconnection patterns reflect large-scale changes in the atmospheric wave and jet stream patterns, and influence temperature intensity over vast areas. Thus, they are often the culprit responsible for abnormal weather patterns occurring simultaneously over seemingly vast distances. The objective on this study is to clarify whether the frequency of extreme cold temperatures occurrence in Iran during cold period have correlation with North Sea-Caspian pattern (NCP) and East Europe-Northeast Iran (ENEI) . In order to study the relation between the monthly numbers of extreme cold temperature day number of Iran during cold period with North Sea-Caspian pattern (NCP) and East Europe-Northeast Iran (ENEI), temperature data of 663 synoptic and climatic stations during 1/1/1962 to 31/12/2004 has been used. Then temperature on 15×15 kilometer pixels by using Kriging method interpolated over Iran. A matrix that was 7853×7187 has been created that for this period (7853) located on the rows and pixels on the columns (7187). There is no single definition of what constitutes an extreme event. In defining an extreme event some factors that may be taken into consideration include its magnitude, which involves the notion of the exceeding a threshold. The most general and simple, and so more wide used method for defining an extreme event of temperature is based on the definition of frequency of occurrence of the event. In this study, at first the extreme cold days during cold period recognized with Fumiaki Index. Then for each month during cold period, the number of extreme cold temperature occurrence was calculated. Monthly data during cold period of North Sea-Caspian pattern (NCP) and East Europe-Northeast Iran pattern during study period extracted from NCEP/NCAR data site of United States National Oceanic and aAtmospheric Center. The correlation between the monthly numbers of extreme cold temperature days in Iran during the cold period with North Sea-Caspian pattern (NCP) and East Europe-Northeast Iran (ENEI) was calculated. After extracting the number of extreme cold day's occurrence for each month during the cold period of the year during the study period, the correlation was calculated with North Sea-Caspian pattern (NCP) and East Europe-Northeast Iran (ENEI). Also, the magnitude of explanation coefficient has been calculated. The map of correlation and explanation coefficient are showed in figures 2 to 6. There is a significant correlation between monthly numbers of extreme cold days during cold period with NCP and ENEI at the 95% confidence level. The results showed that there is a positive correlation between the monthly numbers of extreme cold temperature days in Iran during cold period with an North Sea-Caspian pattern. The positive phase results in increase of cold extreme days in western part of Iran. The positive phase of North Sea-Caspian pattern (NCP) accompany with positive anomaly of the 500 hPa geopotential height level in the North Sea and negative anomaly in Caspian Sea. This indicates in cold air advection towards Iran especially in the western parts. In January, the correlation for 95% of Iran area is significant and positive. The highest explained coefficient is observed for the west and northern part of Iran.
