ali akbar rasouly

For the first time, Faddingham presented a geographic weight regression model. He tried to study the aspects of space heterogeneity. After that, Bronson examined the relationship between housing prices and areas. Which encountered a number of issues in relation to the model, which included the selection of variables, bandwidth and spatial correlation errors. Using the GWR, Franklin analyzed the spatial characteristics of the rainfall along with the elevation changes. Elvi also used this model to study the spatial factors that affect land prices. The GWR produces spatial information that expresses spatial variations between variables' relationships. Therefore, the maps produced from these analyzes play a key role in the spatial non-static description and interpretation of variables (Mennis 2006) and an equation Generates a separate regression for each observation instead of calibrating an equation, so it allows the parameter values ​​to be continuously changed in the geographic space. Each of the equations is calibrated using a different weight of the observations contained in the total data. And more relative weights are assigned to closer observations and less or zero weights to those who are far away. 

The Surface Energy Balance Algorithm for Land (SEBAL) calculates the surface heat flux instantaneously as well as 24-hour. The latent heat flux shows the energy required for true evapotranspiration and is calculated as the remainder of the equilibrium energy equation (Mobasheri, 2005). In remote sensing estimates of surface Albedo, surface temperature and surface leakage in the thermal infrared region, reflectance is used to calculate spatial variations in short-wave radiation and long-wave radiation emitted from the surface of the earth. A combination of short-wave and long-wave radiation combines the ability to calculate the pure absorbed surface radiation for each image pixel. Each of the equations is calibrated using a different weight of the observations contained in the total data. And more relative weights are assigned to closer observations and less or zero weights to those who are far away. In other words, the GWR only uses geographically close observations to estimate local coefficients. This method of weighting is based on the idea that the use of geographically close observations is the best way to estimate local coefficients. The GWR method not only does not consider the effects of self-variables on the independent variable, but also the effects of neighboring situations. The values ​​of the geographic weighting model can be used to describe the spatial correlation of the factors used. Therefore, we extend the study area to several sections We divide the values ​​of the geographic weight coefficients in each of the sections in relation to each of the environmental parameters. Unlike regular regression models, they provide an equation for describing general relationships between variables. GWR allows the parameter values ​​to be changed continuously in the geographic space. Each of the equations is obtained using a different weight of the observations contained in the total data.

The analysis of the relationships between selected indices by geographic weighted regression model and the classification of output values ​​through the normalization of data in seven categories. The values ​​obtained vary between 1 and 1, and the smaller the index, the spatial disjunction is variable, and the larger it shows the presence of spatial clusters. It was found that all three indexes of evapotranspiration, surface temperature and vegetation index have cluster spatial pattern. Therefore, the null hypothesis is based on the spatial correlation itself, and as a result, three of the above indicators can be used for spatial analysis of the actual evaporation. Based on the correlation between the factors affecting the macroeconomic factors, the factor of vegetation index has the most effect on the magnitude of the spatial distribution in the studied area (53% with an area of ​​471782864 square meters). However, as the results are clear, this number is an overall number and covers the overall situation in the area. And does not refer to spatial features of the area. In the results of weighted regression, the effect of elements can be observed spatially. Accordingly, according to the geographic weighted regression method, the relationship between evapotranspiration and surface temperature was negatively affected and negatively affected. The relationship between dehiscence and vegetation index was studied in different years. The highest digit on the seventh floor is 13/99 and in the area of ​​266611500, which shows a high positive effect. The relationship between evapotranspiration and the Albedo shows the highest value in the first and second classes. The values ​​of 18 and 10 in the area of ​​490428000 and 1170753300 m 2, respectively, show a very negative impact and a significant negative effect.

Geographic weighted regression method is a statistical method that is adapted to study local patterns. This method is, in fact, a technical technique that analyzes the relationship between spatial variables in a hypothetical unpopular space. In this research, we tried to express the effect of several indicators on actual evaporation. These indicators are not all indicators that have had an impact on actual evapotranspiration Because actual evapotranspiration is closely related to other climatic factors. Because of the unique ability of spatial weighted regression to identify and analyze the relationships between variables, it is recommended to use it in quantitative analyzes. The Z classes resulting from the GWR analysis of the actual evapotranspiration in different years have different states that indicate the spatial effect of the surface temperature in different conditions.

Introduction:

 Snow is an important component of the climate system over the mid- and high-latitude regions of the Earth. Its high shortwave albedo and low heat conductivity modulate heat and radiation fluxes at the Earth’s surface and thus directly modulates regional temperature evolution and ultimately atmospheric circulation patterns. Moreover, because snow acts as a temporary water reservoir, snow variability impacts soil moisture, evaporation and ultimately precipitation processes). As a result, snow cover has an essential influence on ecological) and economical systems. Vice versa, snow cover itself is determined by climate variations. Recent Arctic warming has severely impacted spring snow cover. This study aimed to evaluate the snow depth it the north of Iran. The results of this study can be used in the field of water resources, flooding and climate change will be useful. 

Materials and methods:

The present study aimed at evaluating Modeling and spatial analysis of snow depth of the European Centre for Medium-Range Weather Forecasts (ECMWF) of the ERA-Interim version with a 0.125 × 0.125 arc-spatial resolution in a survey has been designed and implemented. In this regard, the temporal and temporal changes of the snow depth of the North Country were also evaluated. This study, the monthly data of the 6-level 3 product (MYD08_M3_6), Normalized Difference Vegetation Index (NDVI) of the ،Terra Satellite were used. Modeling of spatial relationships between snow depth and Geographic Parameters and NDVI index was obtained by using OLS and GWR models. The coefficients of regression equations obtained for the relationships were used in the area studied after calculation. Several criteria have been proposed for selecting the appropriate bandwidth. In this study, the Akaike information criterion (AIC), was used to select the core bandwidth.

Results and discussion:

 studied after calculation. Several criteria have been proposed for selecting the appropriate bandwidth. In this study, the Akaike information criterion (AIC), was used to select the core bandwidth. Results and discussion The average depth of snow in the northern zone of Iran ranges from 0.006 to 1. 748 cm for winter, April and autumn, respectively. The northern area of Iran in this season is 1.34 cm. In winter, the maximum average snow depth in the northern zone of Iran in February is 1.748 cm. The maximum amount of standard deviation occurred in the same season. In general, in winter and year, the maximum snow depth in the northern zone of Iran is more than in the other months of the year. The third quartile can be considered as the maximum snowfall and the first quartile is the northern border of the northern northwest of Iran, which can then be classified as the northern part of Iran's snowfall. In winter one-fourth of the year, the northern zone of Iran has a snow depth of more than 1.98 cm. The significant difference between Moran's I and Geary's c expected and Moran's I and Geary's c measured has shown that the spatial autocorrelation values calculated for each month really fluctuate and the value cannot be due to the magnitude of the data and changes caused by around the mean.

 Conclusion:

 The results showed that the winter season with mantle cubes is 1.34 cm maximum snow depth during the seasons. Winter also has the highest snow depth variability. The highest snow depth was obtained with an average of 1.74 cm in February. Based on the results of the study, using quartz statistics, in winter one-quarter of the northern zone of Iran has a snow depth of more than 1.98 cm, which is the maximum value among seasons. The spatial dependence of the depth of snow on universal Moran methods has been rejected by the hypothesis that there is no relation between the depth of snow in the northern zone of Iran, and the Geary's c method has also shown that snowfall areas with high snow depth are relatively relative in terms of geographic patterns and a behavior They show clusters of their own. Correlations obtained with snow depth with longitude and vegetation index of NDVI have a significant reverse relationship and its relationship with latitude and elevation is a significant direct relationship. Modeling with GWR and OLS has also shown that the GWR method has a higher ability to justify the spatial association of snow depth with geographic parameters. The results of the GWR model show that the relationship between snow depth and geographic parameters, especially elevation, does not follow a linear model. Altitude in the mountain range of Alborz and northwestern Iran is mountainous areas that have shown significant snow depth. 

Conclusion:

 The results showed that the winter season with mantle cubes is 1.34 cm maximum snow depth during the seasons. Winter also has the highest snow depth variability. The highest snow depth was obtained with an average of 1.74 cm in February. Based on the results of the study, using quartz statistics, in winter one-quarter of the northern zone of Iran has a snow depth of more than 1.98 cm, which is the maximum value among seasons. The spatial dependence of the depth of snow on universal Moran methods has been rejected by the hypothesis that there is no relation between the depth of snow in the northern zone of Iran, and the Geary's c method has also shown that snowfall areas with high snow depth are relatively relative in terms of geographic patterns and a behavior They show clusters of their own. Correlations obtained with snow depth with longitude and vegetation index of NDVI have a significant reverse relationship and its relationship with latitude and elevation is a significant direct relationship. Modeling with GWR and OLS has also shown that the GWR method has a higher ability to justify the spatial association of snow depth with geographic parameters. The results of the GWR model show that the relationship between snow depth and geographic parameters, especially elevation, does not follow a linear model. Altitude in the mountain range of Alborz and northwestern Iran is mountainous areas that have shown significant snow depth.

1

Snow is a vital component of the Earth's climate system because of its interaction with the energy flux and surface moisture on a local to global scale. This parameter significantly increases the relationships with radiation at higher latitudes. Analyzing changes in the amount of snow is essential for the assessment of the impacts of climate variability of a region. Snow cover has major effects on surface albedo and energy balance, and represents a major storage of water. The snow pack strongly influences the overlying air, the underlying ground, and the atmosphere downstream. Snow cover duration influences the growing season of the vegetation at high altitudes. A shortening snow season enhances soil warming due to increased solar absorption. While the importance of information on mountain snowpack is clear, obtaining these measures remains challenging. This is in part because snow depth and snow water equivalent (SWE) are both spatially and temporally variable, and mountain regions are generally difficult to access. Snow depth is one of the key variables for understanding the relationship between hydrological cycles. The flow of many rivers, especially during the warm period of the year, is mainly due to snow accumulation, which varies depending on the amount of snow melting in the time series. As mentioned, snow is an important hydrologic variable and acts as a water source in many parts of the world, especially Iran. In Iran, mountainous regions act as water suppliers for arid and semi-arid areas around them, and the coincidence of these conditions is one of the most important reasons for the creation of aqueducts in the country. This study, using the ECMWF data base of the ERA Interim, evaluates the trend and slope trend of snow depth (SD) in northern Iran. The achievements of this research can be useful for studies on climate change, water resources, flood, and agriculture. As a step toward addressing this challenge, we evaluated Methods to increase the efficiency of snow surveys and to enhance remotely derived estimates.
2

In this study eleven districts of North Khorasan, Golestan, Mazandaran, Gilan, Tehran, Alborz, Qazvin, Zanjan, Ardebil, East Azarbaijan and West Azarbaijan have been studied. Interim was produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). The database is available as an hourly basis since 1979. In this study, the spatial resolution of 0.125 × 0.125 degrees arc for the period 1980-2016 was used. Non-parametric Mann-Kendall and Sen's Slope methods were used to evaluate the trend and trend slope of snow depth.
3

The assessment of the depth of snow in northern Iran in January shows that only 0.063% of the northern zone of the country has a significant increase in the level. These areas are more in the northwest of Iran on the border with Turkey, areas with no significant trend. An increase of 3.82% of the total study region has come from this month. These areas are located in the North Khorasan Provinceand near Bojnurd. Areas with increasing trend at 0.05, 0.01 and 0.001 levels have not been observed in northern zone of the country. The northwest regions of Iran on the border between Iran and Turkey, which show an increasing depth of snow, can be attributed to climate change affecting the systems leading to northwest Iran, with snow depth rising. January showed the lowest amount of snow depth for me-Kendall in winter. In this month, the maximum declined trend was 5.58 and the average trend was -14.3. Also, the average slope of the calculated trend in January was 0.03. This indicates that the depth of the snow with a negative slope of 0.07 cm is decreasing.
4

The results show that snow depth in the north of Iran in winter is more than 96% of the studied area with decreasing trend. The significant decrease trend at the level of 0.001 in the winter is the maximum trend, and from January to March, the size of areas under the territory of this level increase the meaning of the trend, so that in January, February and March, respectively, 47.99, 56.08, 71.82 percent of the area of the northern zone of Iran has fallen into a declining trend at a probability level of 99.99 percent. Winter season of the Iran regions in the northwest and east, the increasing snow depth was observed that this trend is not incremental but significant. The maximum decreasing trend is snow depth in the provinces of Tehran, Qazvin, Zanjan and East Azarbaijan. In end of April areas with no significant decreasing trend with more than 51% of the same areas. The pattern of snow depth in the spring follows the same pattern in the winter. The average slope of the trend has also declined in line with the trend slowdown in April. On the contrary, the decreasing trend in autumn is based on the statistical results obtained in the study period. Snowfall increases in autumn in October and November, unlike other months in the northern regions of Tehran and southern Mazandaran province, especially in the central Alborz region.

Lightning is a sudden electrostatic discharge during an electrical storm between electrically charged regions of a cloud (called intra-cloud lightning), between two clouds (CC lightning), or between a cloud and the ground (CG lightning).This phenomenon is one of the most important featureswhich are associated with extreme storms and seize the life of about 2,000 people in the world each year.The occurrence of lightning is related to the cloud microphysics in the mixed-phase layer becauselightning is frequent in convective clouds that contain many large hydrometeors in the mixed-phase layer.Also, air on the windward side of a mountain is forced to rise; anditoften leads tothecloudand lightning.In fact, lightning activities are highly variable on many spatial and temporal scales, and to some extent depend on the local convective regime.Lightning activities are not registered in the synoptic stations, but the thunder day statistics determined by human observers and compiled by the World Meteorological Organization (WMO) are one of the best sources of proxy information concerning lightning activity worldwide.Lightning day might include more than one hundred events; therefore, it cannot be a good representative for the lightning activitywhilethese stationsdo notshow offa good distribution.Accordingly, using remote sensing technology can be accurately measured lightning activity. Several space borne instruments have measured the global distribution of lightning one of which is Lightning Image Sensor (LIS).In this paper, diurnal, spatial, and temporaldistribution of the lightning phenomenon in Iran werestudied using LIS data.
Study Area: Iran, with an area of 1,648,195 km2 is located in the southern part of the temperate zone of the northern hemisphere.It is situated between 25° and 47°northern latitudes, and 44° to 63° eastern longitudes.This area is generallymountainous and semi-arid.The lowest area is 28 m lower than sea level located on northern Iranand its highest peak is Damavand with an altitude of 5,671 m/ASL.Existence of this diversity in roughness of the ground causes different climatic characteristics in various parts of the country.
Material and Two satellite-based lightning sensors have been successfully used by NASA since April1995. The sensors can detect the total lightning activities (cloud-to-ground flash and intra-cloud flash) on a global scale. One of these sensors is Lightning Image Sensor (LIS) which was launched on November 28, 1997 aboard the Tropical Rainfall Measuring Mission (TRMM). The LIS sensor detects lightning with storm-scale resolution of 3∼6km over a region of 600km×600kmof the earth’s surface. The LIS circles the earth with a velocity of 7km·s−1, and observes a point on theearth or a cloud for about 90s. This short sampling time during the satellite overpass limits the data usage for forecast and requires several years to compute high resolution climatology. Nowadays, LIS has collected lightning measurements for over 16 years making possible the compilation of total lightning climatology maps in high resolution such as 0.250 and 0.100 of horizontal resolution. In this paper, diurnal, spatial and temporal distribution of the lightning phenomenon in Iran werestudied using LIS data. Some geoprocessing functions in ArcGIS were applied tocalculate statistical values and to identify the locations of statistically significant lightning clusters. For generalizing geographic locations of lightning occurrence to an entire area a Kernel Density Interpolation estimator was introduced. Basically, a Kernel density tool calculates the density of point features such as lightning occurrence locations in a radius searcharound all similar features. Conceptually, a smooth, curved surface is fitted over each lightning flash pointincidentin Kernel density procedure regarding all observations. The surface value is highest at the location of the occurrence point and diminishes with increasing distance from the point, reaching zero at the search radius distance from the point. In practice, the density rate at each output raster cell is calculated by adding the values of all the Kernel surfaces where they overlay the raster cell centre, based on a quadratic Kernel function.
The result showed that diurnal cycle of lightning display a local maximum in flash rate in early afternoon (between 12 and 15 local time) and local minimum in flash rate in early morning to late morning (between 01 and 11 local time). Monthly variation of lightning indicated that maximum frequency of lightning occurs in April whereas the minimum happensin January and September. Annual distribution of lightning data indicated that the maximum frequency of lightning coincides with mountain areas. A majority of the lightning activities over the mountain region occurs primarily in southern slopes ofthe mountains. More specifically, this maximum occurs over the south and southeast facing slopes of the mountainous areaslikeZagros, Alborz, Binalud, Barez, etc. Western and south-western slopes of the Zagros Mountains have the highest rate of annual lightning in Iran.Central regions of Iran have the lowest frequency of lightningwhich are generally flat and arid.
The result of Kernel density function showed that distribution of lightning in January, November and December are alike and maximum density of lightning occurs in southwest of Iran (between Khozestan and Lorestan provinces). The maximum density of lightning in February, March and October are also in southwest of Iran but the lightning occurred in a wider area. The peakfrequency oflightningactivityoccurs inApril and Mayanditsspreadismuch more thanother months. In these months, west, southwest and northeast of Iran have maximum frequencies of lightning. In June, July, August and September, the distribution of lightning activities are different from other months and the maximum density of lightning are in southern Kerman, Sistan and Baluchestan and some areas of Hormozgan province.
Although lightning activity occurs in all regions, it appears that some areas havemore favorable conditions for the occurrence of this phenomenon. This study investigated diurnal, spatial and temporal distribution of lightning activity with 16 years (1998–2013) of LIS.The results provide valuable information on the distribution of lightning activity in Iran, sinceno study had been carried outbefore the distribution of this phenomenon in Iran.Results of diurnal cycle indicated that there was a marked daily distribution of lightning frequency during the afternoons peaking between 3PM and 5PM hours. These results nearly match the pervious findings; in such studies it was shown that all maximums in lightning were observed during the afternoons between 3pm and 7pm (EST). The increase in storms during this period is primarily due to the proliferation in energy provided by the sun during the warmer spring and summer months. The monthly distribution of lightning showed a distinct tendency indeed for all lightning to occur during March to May.The increase instorms during this period is primarily due to the increase in energy provided by the sun during the warmer spring. The result of lightning distribution analysis indicated that a majority of the lightning activity over the mountain region occurs primarily over the southern slopes ofthe mountains. Western and south-western slopes of the Zagros Mountains have the highest rate of annual lightning in Iran. Maximum frequency of lightning in January, February, March, October, November and December are also in this region but in warm season (June, July, August and September), south and southeast of Iran have maximum frequency of lightning activity.