Journal of the Earth and Space Physics
Year:2016 | Volume:42 | Issue:1|Papers Count:22

In the last 150 year, most destructive earthquakes of Kopeh Dagh occurred near Quchan. These earthquakes caused large damages to Quchan (Tchalenko, 1975; Ambraseys and Melville, 1982). The Kopeh Dagh zone accommodates a motion, by a combination of slip-partitioning in the NW, thrust faulting in the SE, and anticlockwise block rotation in the Central Kopeh Dagh (Hollingsworth et al.2006, 2008). The system of NNW–SSE right-lateral strike-slip faults in the Bakharden–Quchan fault zone between Bojnurd and Quchan is one of the most prominent structural and topographic features of the central Kopeh Dagh (Hollingsworth et al., 2006). The Kopeh Dagh is made up of a sequence of mostly conformable and complete Mesozoic–Tertiary sedimentary rocks (Stocklin, 1968; Berberian, 1976). The Kopeh Dagh form a linear intracontinental fold and thrust belt trending NW–SE between the stable Turkmenistan platform and Central Iran (Hollingsworth et al., 2006). Shortening in Iran accommodates the northward motion of the Arabian shield into Eurasia. Recent GPS measurements (McClusky et al., 2003; Vernant et al., 2004) indicate that Arabia moves approximately northwards, with respect to Eurasia, at ~ 23 mma−1 at the longitude of the Kopeh Dagh. The Kopeh Dagh fold belt as a part of Alpine-Himalayan mountain belt in western Asia, constitutes the north-eastern border of the Iranian plateau and lies on the south-western margin of the Turan (Turkmenistan) continental crust, forming its epi-Hercynian (Early Kimmerian) cover (Berberian, 1981; Nabavi, 1983).In this study, the Double-Difference earthquake location algorithm was applied to the relocation of a large set of seismic events that occurred in Quchan region and recorded by Quchan and Mashhad seismic networks affiliated with the Iranian Seismological Center (IRSC) during the period from 1996 to 2012. The study area extends from 35.5°N to 39°N and 56°E to 60.5°E and is located in the Kopeh Dagh major seism otectonic province. The purpose of this study is to improve earthquakes location by using Double-Difference method developed by Felix Wald hauser and William Ellsworth (2000). Relative earthquake location methods can locate earthquakes with higher accuracy by removing effects due to unmodeled velocity structure. HypoDD program determines relative locations within clusters using the Double-Difference algorithm. In order to estimate capability of Double-Difference technique in the area, we performed synthetic tests by which four datasets, each including 10 synthetic earthquakes, were considered along the Kashafrud, Quchan, Binalud and Robat-e-Qarabil faults. The single event method by hypo71 program was applied to determine initial locations. Then, Double-Difference technique by hypoDD program was used for relocating the events. The results showed significant decrease in errors using Double-Difference technique. By using the synthetic tests, capability of Double-Difference algorithm was demonstrated. Then, by putting constraints on primary data, a number of 2516 earthquakes, recorded by Quchan and Mashhad's seismic networks from 1996 to 2012, were chosen to be relocated by the latest version of hypoDD program using the Double-Difference algorithm and Mehraban’s 3D velocity model (2012). The distribution of the events in the central part of the Kashafrud fault shows that the fault is dipping northeast and the occurrence of earthquakes at different depths can be the representation of a high-angle thrust fault and the activity in the entire fault plane of this reverse fault. According to the relocation of the earthquakes and the cross sections in the north of the Shandiz-Sangbast and the west of the Quchan faults, the existence of seismic activities can represent hidden fault activity. The linearity of earthquakes to the south of Baghan-Garmab fault can be also the representation of the continuation of seismic activity in this fault, though the surface trace of this activity is not visible in the geologic maps. In the present study, the average RMS is 0.27 s in the initial locating and reaches to 0.09 s in the relocation by hypoDD using the 3D velocity model. The average of the relative horizontal and vertical uncertainties stood at 686 m and 721 m for relative relocation. The relocation using a 3D model could improve the depth distribution of earthquakes, which is more accurate than initial location. This means that it reveals the concentration of the events between the depths of 5 to 23 km. As for the constraints imposed on the initial data, we considered a minimum depth of 3 km, but the Double-Difference relocation of earthquakes using the 3D model shows that 73 earthquakes occur at depth less than 3 km with least errors.

Kuhbanan fault is in southeast margin of micro continent of Iran between Tabas and Yazd blocks. It directed from northwest of Kerman to northwest of Bahabad east of Bafgh (Yazd). Its length is about 300 km and its overall direction in Iran is in southeast – northwest. This fault has caused many historical deadly Earthquake and is recently more active at its southeast part near Zarand city. The most resent (2005) Earthquake of this fault costs 600 dead.We tried in this work to use specific parameters of this fault and stresses related to its Earthquakes for an attempt to Earthquakes prediction. Various methods have historically been used to find a way to Earthquake prediction. This has caused many researches to use different parameters in different ways. Although, there has been no considerable success except one or two cases, but attempts have opened many windows to search about.In this work, with respect to sensitivity of fiber optics to changes in physical parameters such as pressure and temperature, as a new approach we have theoretically investigated the possibility of using fiber optics in fault structure to find a precursor to seismic activities. In a special way, using Coulomb failure diagram and Mohr circle we could estimate stress distribution with time before the Earthquakes. The stress distribution around the focus of the quakes toward the surface inside the faults, where the cable may or can be used, was investigated. We fond from previous studies that there is no considerable differences between stresses at focal point and at points near surface.Depending on sensitivity of fiber optics to changes of pressure acting on and sensitivity of the cable to resulting changes of its length or its diameter, we found Bragg grating glass coated fiber is more useful to use in this range of stress changes. In such designed fibers the applied stresses will cause changes in the length of the fiber that is more effective in its sensitivity than changes in its diameter.As a reality, pre hazard stresses of the 2005 Zarand Earthquake related to the Kuhbanan fault structure, northern part of Kerman province (Iran), have been used to act on this kind of fibers to estimate changes in wavelength of output wave. This 2005 Earthquake of 6.4 magnitude, in Richter scale, has occurred two years and four months after the 2002 Earthquake of 5 magnitude in the same small area of Zarand. We could determine the increasing stresses between these two quakes in different times up to two days before the 2005 Earthquake.The stresses were applied to a glass coated fiber with a wave of 1550 nm wavelength passing through. As the stress is increasing during the time between the two quakes changes in wavelength (Dl) increase toward a maximum value of 6.8 nm at the time of 2005 Earthquake. This value of Dl is 6.57 nm 22 days and 6.16 nm about two months before the Earthquake, which are easily readable. That means in Kuhbanan fault structure when changes in output wavelength pass through 6.16 nm one should be careful.

Iran is a country in the middle part of the Alp-Himalayan orogeny system that have huge earthquakes. The risk of the earthquakes is the cause of the researches about the precursors. Because earthquakes occur suddenly, often with devastating consequences, earthquake prediction is a matter of great interest among the public and emergency service officials. A few observations suggest that some earthquakes may be preceded by detectable anomalies of geophysical signals as a precursors. One of the successive precursors is Seismic Electric Signals. Seismic Electric Signals (SES) are low frequency electric signals that have been recorded in Greece in first time (e.g. by VAN team at 1984) and Japan (e.g. Uyeda at 2000) to precede earthquakes. Now these signals are recording in NW of Iran (since 2014) for the first time. Varotsosa and his colleagues recording the electrotelluric potential in several stations in Greece and claim that their signals reveal systematic anomalies before major earthquakes. A possible correlation between SES characteristics and earthquake parameters was investigated by some scientists. There are some relationships between VAN seismic electric signals (SES) and earthquake parameters, such as magnitude, current time and location. In this research we install 5 VAN stations in NW of Iran and recording data since Mar 2014, that shown 4 SES. This signals and predicted earthquake parameters, reported to the government (East Azarbaijan disaster management organization) before shake and these earthquakes were successively predicted by this method.On Jun 19, 2015, an Ms=4.0 earthquake occurred in Basmenj.13 days before, the our group observed an electrotelluric anomaly (seismiec electric signals (SES)) at our station of University of Tabriz, located NE of Tabriz. The second SES is recorded 2 days after stop the 1st signal.13 days after recording of this signal the earthquake Ms=3.7 Kolvanag in the NE of Tabriz recorded. Third SES in 1/2/2015 recorded for 8 days. After 17 days the earthquake Ms=3.3 Khoy in 26/02/2015 is recorded.For the first time, in Iran for search and capability of recording the SES precursors since Mar 2014 in the NW of Iran installed 6 VAN stations help to recognize more than 6 earthquakes.3 of the 6 stations by recording the SES, changed to the main sensitive VAN stations in the NW Iran. Search and compatibility of recorded SESs by the results of the VAN group in Greece shown these signals are the same in both countries.Furthermore selectivity is one of the most important SES physical properties which refers to the experimental fact that a (sensitive) monitoring station is capable to detect SES only from a restricted number of seismic areas. A map showing the seismic areas that emit SES detectable at a given station is called selectivity map of this stations. Now the number of station in the NW of Iran is limited and therefore prediction the location of the earthquakes and make selectivity maps is difficult. Then it is necessary to install more VAN stations to be possible make selectivity map and locating precede earthquakes and their times.

Iran is situated in one of the world's seismic regions and the possibility of destructive earthquakes in most regions of the country has given great significance to recognition of Iranian seismic nature from a seismic and seism otectonic standpoint. Study of the crust and upper mantle velocity structure in the Iranian plateau provides better understanding of its evolution and tectonic history of seism otectonic zones. Crustal velocity structure is used as initial information for various geological and geophysical studies, and therefore it is a basic and important issue in seismology. Receiver functions show Earth local structure response to P-wave vertical arrival approximately beneath of a three-component seismometer and are sensitive to shear-wave velocity impedance. Depth-velocity trade-off in RFs information is causing of inversion non-uniqueness problem, but one can overcome to this limitation by incorporating information from absolute velocity from dispersion estimations and joint inversion of this two data sets. By this, more exact constraints are provided about crustal structure. In this study, crustal velocity structure and Moho discontinuity depth beneath of four broadband stations of Kerman seismological network have been investigated from joint inversion of P-wave receiver functions (RFs) and Rayleigh wave group velocity dispersion. The teleseismic waveformes in time interval more than two years was used to compute RFs from the time domain iterative deconvolution procedure Ligorria and Ammon (1999) which has higher stability with noisy data compared to frequency-domain methods. The 165 desired RFs were computed from these waveforms that have magnitude bigger than 5.5 and have recorded at four permanent stations in epicentral distance 25˚-90˚. To delete high frequencies, Gaussian parameter 1.0 used. For increasing signal to noise ratio, RFs clustered in 10˚ azimuthal and less than 15˚ epicentral distance ranges. Finally, the RFs were stacked. This work performed under software SAC. Due to changes in group and phase velocity of surface waves with depth for different periods and dispersion in these waves and sensitivity of the waves dispersion curve to shear wave velocity, inversion of dispersion curve is an efficient method for determining the average shear wave velocity in a vast region of the depth between two seismic stations. Group velocity dispersion curves were incorporated into our joint-inversion scheme from an independent regional fundamental-mode Rayleigh waves tomography images for within the 20–80s period range in Iran by Rahimi et al. (2014). Joint inversion of two independent data sets was performed with considering combination weighting parameter appropriate performed from Herrmann and Ammon program (2003). Minimizing standard error between real and predicted data is the criteria for getting to desired final and close to earth real model.The results from this study show that Moho discontinuity boundary is beneath of CHMN station at 52±2 km depth, beneath of KHGB station at 50±2 km depth, beneath of NGRK station at 54±2 km depth and beneath of TVBK station at 52±2 km depth. We used forward modeling test for error estimation and resulting models accuracy.Relative high crustal thickness in this region compared to other regions of central Iran can be attributed to abut the region to the Sanandaj–Sirjan zone (SSZ) and Urumieh–Dokhtar magmatic assemblage (UDMA) that under thrusting of the Arabian plate beneath Central Iran along the main Zagros thrust fault is caused of thickening. It can also attributed to exist of thick Magma masses in Urumieh–Dokhtar magmatic assemblage and increase the density and relative thickness of the area based on the Isostasy theory.

Luminescence dating is now an important element in the suite of Quaternary geochronological methods. The major area of growth in the past two decades has been its application to Quaternary sediments, where the method provides an absolute age for the last exposure of the constituent grains to daylight.Such deposits are generally composed of wind-blown sediment grains that have been exposed to sufficient sunlight to zero the optically stimulated luminescence (OSL) signal during the most recent sediment transport event. In such instances, the measured equivalent dose (D_ (e" ")) may closely approximate the true burial dose (D_ (b" ")). By contrast, water-transported sediment grains are frequently exposed to insufficient sunlight to fully erase the OSL signal, owing to the reduced efficiency of bleaching beneath a cover of water (among other factors).Duller (2008) differentiated between two types of partially bleached sediments: 'type A' where all the grains are partially bleached to the same extent (i.e. homogeneously bleached); and ' type B' where different grains have been exposed to sunlight of differing intensity and/or duration causing varying amounts of residual trapped charge to remain in the grains (i.e. heterogeneously bleached).This circumstantial result shows high variation in the range D_ (e" ") distribution. Understanding the dose distribution from a sample is essential for obtaining the appropriate burial dose (D_ (b" ")). To allow one to assess the shape of a sample’s dose distribution, a sufficient number of D_ (e" ") values must be obtained.The single aliquot regenerative dose (SAR) procedure for feldspar was used by Duller (1991) and further developed by Murray and Wintle (2000) describing how D_ (e" ") values were calculated. Using the SAR protocol, each aliquot provides an independent estimate of D_ (e" ") and by taking measurements on many separate aliquots the distribution of D_ (e" ") within a sample can be assessed.To deal with D_ (e" ") various possibilities, several informal approaches and parametric statistical models have been used to estimate the D_ (b" ") of interest, of which some were developed originally for fission track analysis (Galbraith and Laslett, 1993).The most commonly used models for OSL dating have been adopted from fission track analysis and are described in detail by Galbraith (2005). The Central Age Model (CAM) is appropriate for sediments which have been well bleached and the Minimum Age Model (MAM) assumes that only part of the sample were bleached at deposition, and that the remaining grains were bleached to differing degrees. The value appropriate for calculating the age of the sample is defined by the population of grains at the lower end of the distribution.The Eastern Mosha Fault (EMF) and the North Tehran Fault (NTF) are two major active faults of the southern central Alborz mountains, located in proximity of Tehran (population ~15 million). The Ira trench site is located at the linkage zone between the North Tehran Fault and Eastern Mosha Fault. This trench contains different kind of sediments including alluvial, and colluvial sediments. Therefore, CAM and MAM are suitable methods to be employed for dating samples collected from the Ira site. Dating these samples is useful to calculate kinematics on the two faults in their linkage zone (Ghasemi et al, .2014). The ages of samples from Ira trench can also be used for seismic hazard analysis for this heavily populated major city. As a case study the samples collected from this trench was employed and new ages were determined.

The presence of noise in geophysical measurements has undesirable effects on the seismic data. One of the important problems in seismic data processing is attenuation of the noise to the desired form and keeping the original signal. Contamination of seismic data with noise prevents obtaining a proper image of geological structures and seismic data interpretation. In some of the receivers the noise has erratic values and the amplitude is large in relation to other receivers, surprising and do not follow a Gaussian distribution. In reality, not all observed data follow the Gaussian distribution. There may be a group of atypical data that are far away from the majority of data. Atypical data are referred to as outliers or gross errors, which follow other distributions or there is no clear distribution to describe them. These are called erratic noises that do not follow the Gussian distribution. Conventional methods for noise suppression assume Gaussian noise distribution and their performance decreases in the case of erratic noise. The rank reduction based techniques are applied to attenuate weak random seismic noise in a least squares sense. The rank reduction methods are very sensitive to erratic noises and the different results provide. Even a little of erratic noises extremly degrades the performance of the rank reduction methods. More robust estimates are needed such that they are acceptable even when the data do not strictly follow the given distribution. The non-Gaussian and erratic noise are usually produced by wind, incorrect polarity, cultural and traffic noises and so on. In order to solve this problem a new filter based on repeating the reduction of the rank of Hankel matrix is introduced. The method is called iteratively reweighted rank reduction (IRRR). This method is combination of iterative weighted least squares procedure (IRLS) and weighting low-rank approximations (WLRA). In this method after transferring data into the frequency domain, for each constant frequency slice an individual Hankel matrix is created and then by using singular value decomposition (SVD) a rank reduced matrix is obtained. Later on using the iterative algorithm, until the desired convergence is achieved, the combined weight values are obtained from the original matrix and rank reduced matrix. Parameter that controls the convergence of the method is the weighting function. The role of weighting function is reducing or completely removing of the erratic noise from data. Here the weighting function we used was Tukey’s Biweight function. In order to maintain the statistical performance and the ability of the method we define regulation parameter τB. Regulation parameter is calculated based on the estimates to the median and the median absolute deviation. These two estimates are not sensitive to erratic noise. The advantage of this method in comparison to the other rank reducing methods is the attenuation of erratic noise and at the same time random noise. This method is application to 2D and 3D seismic data. Performance of the method was tested on synthetic and real seismic data. The results showed superior performance of the method in attenuating erratic noises.

Summary Based on the results of the analysis of site effects, mostly those with shear wave velocity and the fundamental period chosen as the basis for classification. In revision of Earthquake resistant design regulations for buildings, classification of lands should be subject to the field conditions, geological, geotechnical, geophysical, and laboratory tests.In this context, the average shear wave velocity of the soil to a depth of 30 m and soil normal period are important ones. In Iran, earthquake design rules (Iran standard 2800) and the classification of Komak Panah et. al. (2002) for the terrains are used in practice. The Komak Panah classificationis the only which applied he ground normal period. In this paper, we identify the dynamic characteristics of the land within the city of Ardakan. Adjustment of land classification according to the studies on the site effect base on geotechnical data, geophysical, and microtremors with dynamic design codes for buildings are shown. Evaluation of seismic wave propagation and its changes from the bedrock surface to the bedrock to determine the movement of the soil profile is used. The seismic wave propagation from the bedrock to the surface, such as amplitude and frequency content of the seismic wave changed so that the soft sedimentary deposits, certain frequencies of the ground motion amplification and dynamic behavior of soil, damaging effects of the earthquake.Regulations seismic design of buildings for a variety of land, measures considered. The Regulations, based on dynamic mechanical parameters of the soil, the Soil classification is the valid regulations as well as regulations for Euro Code (CEN, 2004) and US (ICC, 2006), the classification of the land and exercising their influence on the design spectrum, in order to achieve the above objectives walked. In these Regulations, in addition to conventional fields, categories for loose ground and in certain circumstances, such as liquefaction, is intended collapsible soils. Different methods estimate the parameters of geotechnical soil profile and map the dynamic and resonant, liquefaction and landslide hazards in different scales in the United States by (Street et al 1997, 2001; Bauer et al 2001; Broughton et al 2001; Rix et al 2001; (Cramer et al 2004, 2006). Is used. Shear wave velocity, density, frequency, nature and thickness of the layers of soil determines the period of hard or poor quality of the land area.Based on these characteristics, particularly shear wave velocity and the normal period, there have been many different categories. UBC classification of natural period as the basis for classification is used Uniform Building Code, (1979). Classification) 1991, Seed et al) in addition to the parameters of the seismic bed rock, sex and severity of vibration as well as the parameters of the auxiliary materials used. (Bray and Abrahamson 1999) of the two main parameters period and other parameters, such as the site and the average shear wave velocity and strength properties of soil deposits have been used. Regulations designed to Earthquake in Iran (2800) and classification help shelter al (2002) Komak panah et al, noted. The fourth edition rules of Earthquake in Iran, (2800) of three parameters characteristic of sediments, soil thickness and the average shear wave velocity in the upper 30 m was used to classify the land as the land into 4 groups less than 175, 375-175, 750-375 and more than 750 meters per second are separated.Classification Komak panah, et al, 2002, the natural frequency of the structure as the main parameter, gender, interests and geological conditions as auxiliary parameter is used for classification. In Europe Regulations (CEN, 2004) describes the layering of soil and soil classification based on the average of the three parameters of shear wave velocity, untrained shear strength and the standard penetration from the surface to a depth of 30 meters down. In the Ardekan city to improve ground classification, shear wave velocity and soil profile of 5 boreholes in addition to Micro tremor recorded data of 100 points analyzed based on H/V method (Nakamura, 1989 and 2000) of natural frequencies obtained in the matches Borehole with the natural frequency of the linear one-dimensional analysis of soil profiles were compared. According to the characteristics of geotechnical, geophysical, and the results of the analysis of the site effect and the natural frequency of the micro tremors data in the holes, the terrain was determined. Finally, Iran standard 2800 classification was slightly modified using fundamental frequency. These changes may efficiently represent the ground situation and better using of Iran Standard 2800 classification in this area.

In this paper, two methods have been used: multi-layer perceptron artificial neural network (ANN-MLP) and universal kriging to estimate of velocity field. Neural network is an information processing system which is formed by a large number of simple processing elements, known as artificial nerves. It is formed by a number of nodes and weights connecting the nodes. The input data are multiplied by the corresponding weight and the summation are entered into neurons. Each neuron has an activation function. Inputs pass to the activation function and determine the output of neurons. The number of neurons and layers could be obtained through trial and error according to a specific problem.One of the simplest and effective methods to use in modeling of real neurons is multi-layer perceptron neural network. This model has been established of one input layer, one or more hidden layers and one output layer. In this structure, all the neurons in one layer are connected to all neurons of the next layer. This arrangement is commonly called a network with full connectivity. Neuron numbers in each layer is determined independently. The neurons of input and output layers are determined according to the number of input and output parameters. The number of neurons in the hidden layer can be determined by trial and error through minimizing total error of the ANN. For this minimization, each ANN parameter’s share in the total error should be computed which can be achieved by a back-propagating algorithm.One of the most famous and simplest methods is back-propagation algorithm which trains network in two stages: feed-forward and feed-backward. In feed-forward process, input parameters move to output layer. In this stage, output parameters are compared with known parameters and the errors is identified. The next stage is done feed-backward. In this stage, the errors move from output layer to input layer. Again, the input weights are calculated. These two stages are repeated until the errors reaches a threshold expected for output parameters.Kriging is probably the most widely used technique in geostatistics to interpolate data. Kriging interpolation is a two-step process: first a regression function f (x) is constructed based on the data and a gaussian process Z is constructed through the residuals: Y (x) =f (x) +Z (x) where f (x) is a regression function and Z is a gaussian process with mean 0, variance σ2 and a correlation matrix y.Depending on the form of the regression function, kriging has been prefixed with different names. Simple kriging assumes the regression function to be a known constant, f (x) =0. A more popular version is ordinary kriging, which assumes a constant but unknown regression function f (x) =a0. In universal kriging, more complex trend functions such as linear or quadratic polynomials are used.In two methods, for testing and validation of results, 7 GPS station have been used. The velocity field of these stations is known with respect to Eurasia. The average relative error in test stations is obtained 13.48% for ANN-MLP and 25.38% universal kriging in northern component (VN). Also in eastern component (VE) the average relative error is obtained 18.12% for ANN-MLP and 28.61% for universal kriging. The results show the capability and efficiency of artificial neural networks approach for estimation of velocity field in this region. Another important result obtained from this research indicates that distribution and number of input points are very effective in training stage and coefficients determine.

Japan is a region of active plate tectonics, well known for its Japanese Trench a geological feature evolving as a result of North Pacific (NP) oceanic plate pushing and sub-ducting underneath the North American (NA) plate. Multiple plate tectonics have been continuously recorded in the region since 1979 by the network of GPS stations called GEONET. The network operating and monitoring more than 1200 GPS stations into which the movements in three dimensions in the GPS Cartesian coordinate system are continuously recorded. For the geodetic applications, the movements are transformed to the local North, East, and Upward directions at each station. In this research, the plate tectonics resulted in the disastrous March 11, 2011, M9.0 earthquake have been reviewed prior and after the earthquake and explained in the form of strain analysis using the accurately estimated type F3 temporal (daily) 3-D geodetic coordinates given in ITRF05 coordinates system of the GPS stations around the epicenter. Three periods of displacements comprehensively different in the pattern of motions of the plate were realized within 128 days before the disaster. The characteristics common to all periods is that the displacements at the stations are steady in time and almost in the same direction but may be slightly different in magnitude from place to place on the plate. Period 1 shows displacements all almost continued westward at the stations with the exception of a few stations close by volcanic activities. Period 2 show nonaligned displacements and negligible in magnitude pointing towards randomly distributed directions. It seems that the time of period 2 (30 days) is spent by the plate as the time needed to change its course of motion, so that when turning to period 3 (48 days), the course of motions are completely diverted from the motions in period 1. An strain analysis of the displacements is performed in a local 2-D horizontal Cartesian coordinates system defined at the center of the region, the unique system into which all GPS stations attain new horizontal positions while their displacements, already computed, remain unchanged. For the computation of strain elements (positional derivatives of displacements) at each station, the displacements in the neighboring stations are taken into account in a system of linear forms (Taylor expansion of displacements). Then, the least-squares optimization is applied to solve for the elements. Analysis of the diagonal strain elements separately in each period, show that the contraction phenomena happens during both periods 1 and 3 but the way (direction) contractions grow are completely different from period 1 to period 3. Also the phenomena of expansion show up and grow up along the east coast line of Japan explaining somehow overriding of the plate towards the PC ocean by the amount of 0.01 ppm before the disaster. The analysis of the displacements on the day of disaster show faster overriding of around 5 m in magnitude. Period 2 as a time interval could be assumed as the opportunity for the plate to change its course of action. It then may be considered as a precursor to the disaster of March 11, 2011.

Study of the potential fields at different altitudes, constituting a multiscale field is a class of interpretative methods which are used to approximate depth and geometry of sources. Interpretation of potential fields by this class of methods is mainly based on the recognition that the gravity or magnetic fields, generated by ideal sources (point mass, line of mass, sheet and contact) are homogeneous functions satisfying Euler homogeneous equation. In multiscale methods the potential fields have to be known at several altitudes. Because the direct measurement of the field at many altitudes is not often feasible, the upward-continuation algorithm is used to create a multiscale field. Fedi et al (2012) introduced a multiscale method to estimate, depth and the structural index of potential field sources. In this new method, depth to the source of homogeneous fields is determined by a geometric technique. According to the geometric approach, as a consequence of the dilation of potential fields versus the altitude, the maxima of the field modulus at various scales are located along the straight lines that are called ridges. The source depth (singular points of source) can be recovered by simply extrapolating the ridges below the measurement surface and then identifying their intersection point. Simple sources, such as spheres, horizontal cylinders and sills, have singular points corresponding to their center. Dikes, vertical cylinders and contacts have their singular points correspond to the top of the source. Besides, the independent estimate of the structural index is done by the ScalFun method (Fedi and Florio 2006; Florio et al.2009). The ScalFun method is based on the concept of the scaling function of potential fields which estimates both the structural index and the depth to source either independently or simultaneously. The scaling function is defined as the derivative of the logarithm of a potential field with respect to log (z) where z is altitude.Finally, the validity of the results is tested by a criterion, called ‘ridge consistency’ criteria. The criterion is based on the principle that the structural index estimations on all the ridges converging towards the same source should be consistent. If there exist some coalescence effects, the gravity or magnetic anomalies measured from high altitudes may not be sufficiently isolated, and the estimated structural index from different ridges will be significantly different. One solution can be testing the field derivatives of any order to lessen the interference effects from nearby sources or regional fields up to obtaining a consistent set of estimates. Discarding low enough levels eliminates the improved high frequency noises produced during the differentiation and improves the results as well. Increasing the resolution with differentiation warrants better depth estimation. As differentiation and upward continuation behave like high pass and low pass filters, respectively, a combined use of them makes the whole procedure a very stable process. Briefly, by the explained multiscale analysis method the interpretation is done in four main steps: 1. Generation of a multiscale data set through the upward continuation algorithm, 2. Estimation of the source position with a geometrical method, 3. Estimation of the structural index for each analyzed ridge by using ScalFun method, 4. validating the results by the ridge consistency criteria.The Depth from Extreme Points (DEXP) method of Fedi (2007) is the other multiscale method probed in this paper. DEXP approach is based on the explicit scaling of the upward continued field by a power law of the continuation height. The type of power law i.e., its exponent, can be either assumed or determined directly from the field data by the criterion of extreme point position invariance versus derivative order. There is a specific relationship between scaling exponent and source structural index. Moreover, similar to multiscale analysis field derivative of any order can be used. Therefore, in DEXP method, the scaling function is dependent on the structural index, upward continuation height and order of field derivative. Depths to sources are obtained from the position of the extreme points of the DEXP transformed field. As the main advantages, these multiscale methods are very fast and stable respect to noises even while applying to high order derivatives.In order to evaluate the capability of the studied methods, firstly the multiscale analysis and DEXP method are applied to a noise contaminated synthetic dataset due to three thin-magnetic dike. The results obtained by both methods are in a good agreement with the real ones. Finally, the practical utility of these multiscale methods are verified using a real profile extracted from an aeromagnetic data set acquired in Sweden. Also, in the real case the results of the studied methods are consistent.

Determination of boundary surface structures is commonly used for data interpretation. Horizontal and vertical derivatives are useful tool for determining the edges boundary. Using horizontal and vertical derivative in signal analysis method; that is effective method for interpretation of edges boundary, have been used commercially.In acquisition potential field data, using different methods of interpolation for transform data to the regular network and applying different filters on the regular network, then the edges of anomalies can be determined. In usual methods, applying the filters directly on interpolated data in the regular network. The result of this method it is reflecting that, increased the accuracy in determining the edges boundary of data anomalies, and causes the amplified noise also; finally the results are very complicated and it will be difficult to interpret.In this paper it is suggested that the first three spectrum of image (an image that has three main frequency spectrum red, blue and green) are prepared on a regular network of interpolated data, because each color of spectrogram have a defined wavelength and own frequency ranges. It's possible that breakdown potential field anomaly grid to three frequency spectrum, and applying different filters on each spectrogram. Using this technique for edge detection a potential field anomaly data, causes the noise and other unwanted elements that haven' t continuous spectrum, only amplified in specific range and not match with results of other spectrum (concept that the color code produce for storing in particular cell or pixel of image), so for discontinuous spectrum we have tree color cod for tree main spectrum that not match together and formation a meaningless color cod for storage in specific pixel. In this situation we have black color showing. For continuous spectrogram in tree main spectrum, tree spectrum match together and formation a meaningful color cod for storage in specific pixel. In this situation we have white color showing; usually potential field data have continuous spectrogram so this technique be able to decrease the noise effect and increase the accurately edge detection a potential field of anomaly data. In this method only the strongest range of continuous spectrum amplified, so the complexities of other factors that make difficult interpret filtered. Apply this method has two advantages, first that unwanted factors such as noise, which haven' t continuous range spectrum deleted and second the color spectrum that have continuous behavior (such as the survey anomaly) in three color spectra, depending on the filter used, amplified and improving the filter results.In this paper, we used eight filters with usual method and color spectrum analysis method, this filters shows the boundary and limited area of survey anomaly. Results shows the usual method have very complicated for interpretation but the color spectrum analysis method with elimination noise and discontinuous spectrum improve the result of potential field edge detection.Here, firstly this method applied on synthetic model with five percentage Gaussian noise and twenty percentage inverse Gaussian noise, then applied on the magnetic data of Ojat-Abad iron ore deposit, in Semnan.

Generally, the measured secondary field data is inverted into resistivity using two principal models; the homogeneous half-space model and the layered half-space model. While the homogeneous half-space inversion uses single frequency data, the inversion is done individually for each of the frequencies used, the multi-layer 1D inversion is able to take the data of all frequencies available into account. The resulting parameter of the half-space inversion is the apparent resistivity which is the inverse of the apparent conductivity. It's possible that using the fast method to calculate the apparent resistivity, if the distance between the HEM sensor and the top of the half-space is known. Unfortunately, the dependency of the secondary field on the half-space resistivity is highly non-linear. Thus, the inversion is not straightforward and the apparent resistivities have to be derived by the use of look-up tables, curve fitting or iterative inversion procedures (Fraser, 1978; Siemon, 1997; Siemon, 2001).The usual technique for inversion of airborne electromagnetic data frequency domain (HEM) data is a 1D single site inversion, because of the 2D and 3D inversion of HEM data wants very powerful computer hardware. Some inversion method for electromagnetic data inversion suggested. Usually this method updated for ground electromagnetic methods. One of the methods employed in the inversion of airborne electromagnetic data frequency domain (HEM), Levenberg-Marquardt method inversion (MLI) is looking for smoothing fitted to the data in the inversion algorithm; this inversion method based on least squares criteria, seeking a modelby minimizing the residuals of an objective function. Marquardt’s inversion only pursuits the largest fitting of simulation data to original measurements, and has the characteristics of simple algorithm and fast calculation. In this procedure usually HEM data smoothed and then used in the inversion procedure, but any variation in data change results. For stability of inversion procedure, it is suggested that stitched-together 1-D models along the profile that each sounding inverted by constrained neighbor sounding and each layer of each sounding inverted by depth constrained neighbor layers. In addition used smoothing constrained in inversion procedure instead of smoothing a data like Marquardt–Levenberg inversion.In this paper, Starting model determined for apparent resistivity with Mundry technique and for centroied depth with Weidelt technique. To using this method, the auto inversion cod written in MATLAB software environment that inputs are real and imaginary part of data with sensor altitude and output is inverted model with misfit. In the following this algorithm tested on standard synthetic data, the model chosen for the generation of synthetic data represents a layered earth structure having an inhomogeneous top layer in order to study the influence of shallow resistivity variations on the appearance of deep horizontal conductors in one-dimensional inversion results. The inversion of synthetic data results shown this technique for inversion HEM data improved the results and is much more accurate than Marquardt–Levenberg inversion. Finally the inversion algorithm used to invert a set of real DIGHEM field data from Mirgah Naqshineh area in Saqqez of Kurdistan and interpretation of results according to geology information of area.

Determination of the thickness of a mountains glacier like Alamkooh is very difficult due to its extremely cold climate conditions and high elevation, risk of falling large snow segments or balls and coarse topography of the glacier. In this research, due to the transparency of ice for electromagnetic (EM) waves, and also, that ground penetrating radar (GPR) method is non-destructive, fast and accurate, this method has been used for determination of thickness and basement topography of Alamkooh glacier. The GPR data acquisition has been made by 25 MHZ transmitter antenna using common offset method, and transmitter and receiver spacing or separation of 6 meters. Almost all of the glaciers in the west of the study area (including Alamchal, Takhtechal and Takhtesoleiman) have been surveyed by the GPR method. In this research, only the GPR data from 6 survey lines of a, b and c in Alamchal glacier have been processed and interpreted. The processing step has been made using static correction, signal saturation correction filter, gain functions, f-k migration filter, sequential average filter and topography correction in order to obtain a clear picture from the subsurface layers. After applying these processing methods on the acquired data, the EM waves reflections from the interfaces of different layers including the reflections from the glacier basement have been detected, and by assigning a suitable EM wave velocity in the ice (0.16 m/ns), the varying thickness of 50-94 m for the ice layer laid under the survey lines has been estimated. In this research, the debris sediments inside the ice, trough or bowl-shaped structures in the ice and parabolic reflections indicating the presence of boulders inside the ice, have also been detected. Considering the similarity between GPR and reflection seismic methods, we can use seismic processing methods to process GPR data, GPR attributes comprising of instantaneous amplitude, phase and frequency attributes have been extracted from the GPR data for better detection and interpretation of the subsurface layers. The instantaneous amplitude attribute has well distinguished the EM waves reflections from the interfaces of different layers including the EM reflections from the glacier basement, boulders and sediment debris inside the ice. Furthermore GPR data are demonstrated as a two-dimensional (2-D) time section, in which vertical axis is the two-way time of electromagnetic wave, and the horizontal axis is the location of the middle point between the transmitter and receiver, or the distance of this point from the starting of the survey line. The topography of the ground surface generally causes a mess in the GPR data, but the data presentation and interpretation is based on the flatness of the ground surface. To remove the undesired mess in the data due to the topography and putting the events on their real locations, it is necessary to apply topography correction on the data. The results obtained from this research work indicate that if relatively coarse topography exists in the survey area, applying topography correction on the data is needed. Finally, after investigating GPR depth sections, it has been observed that the topography of the glacier basement is a relatively accurate copy of the topography of the glacier surface.

Water crisis and drought are among the most important issues to which the human has been faced particularly in the recent years; hence, investigation of drought is so important in optimal water management. Drought has been known as an environmental phenomenon which is inseparable from climate changes which can occur in any geographical region. Various definitions have been presented since yet. By a general definition, drought includes abnormal shortage of precipitation in a long-term period so that, it causes soil moisture shortage and reduced current waters; therefore, human activities and natural life of plants are disturbed. Qarehsou watershed is among the places which are affected by drought issue. Considering the major role of this watershed on water supply of Kermanshah Province as well as recreational value of Qarehsou River; the occurred droughts can cause economic challenges and ultimately, social crisis in the region. The objective of the present study is to determine meteorological and hydrological wet and drought periods and investigating the relationship between them of which the results can be used for more appropriate water resources management in Qarehsou watershed. In the present study, the statistics of five rain-gauge stations, five hydrometric and 20 piezometric wells existing in the watershed were used for drought analysis. Drought indices RAI and SIAP were used to extract meteorological drought periods and also the indices SDI and SWI were used to analyze hydrological drought. According to the obtained amounts by each drought indices, the severest drought has occurred in 2007; hence, the hydrological and meteorological drought zoning maps of the year 2007-2008 were drawn. The results showed that, the drought severity has been increased gradually since 1999 and the severest drought was in 2007 and the most durable one has occurred during 2007 to 2012.The results indicated that, hydrological drought has been occurred since the cropping season 2007-2008 with one-year delay relative to meteorological drought. Also during the recent years, one or two years of delay has been observed between the occurrence of hydrological drought of underground waters and meteorological drought. The results demonstrated that, based on the index RAI, the maximum frequency was related to the stations Mahidasht, Pol Kohneh and Ravansar by 27% at a very severe drought class. Also based on the index SIAP, the maximum frequency was related to the station Doabmerk by 23% at a moderate drought class. Based on the index SDI, the maximum frequency was in the station Sarasiab by 45% at a moderate drought class and finally, based on the index SWI, the maximum frequency was related to the piezometric wells of Hashilan, QarehTapeh and Kahriz by 45% at a moderate drought class. Zoning maps of meteorological drought showed that, meteorological drought has occurred in all the region. According to the zoning map of hydrological drought, hydrological drought of the surface flows is at a moderate class throughout the watershed while, hydrological drought of the underground water resources at the north east and south west and somewhat central parts of the watershed is at a very severe class. Generally, results of the present study indicated that, duration and severity of the droughts and particularly hydrological drought are considerable during the recent years in QarehSou watershed. Hence, results of the present study can be useful for optimal management of water resources and water demand and supply planning, as well as the managers, lanners and experts who can be enabled to provide required strategies and practical solutions.

Introduction It is expected that projected changes in the frequency and severity of extreme climate events, such as increased frequency of heat stress, droughts and flooding will have significant consequences on water resources. Increasing pressure on water resources due to climatic and anthropogenic changes as well as increasing competition among users is recognized challenges worldwide. Global scale studies identified the Mediterranean region as one of the most vulnerable regions to climatic and anthropogenic changes and thus as one of the world’s water crisis hot-spots. So investigation about this phenomenon in these areas will be very crucial. Yazd is located in arid regions and due to increasing population and industrial growth, has been highly regarded by planners and authorities. Water shortages in the past decades, lead to the water transfer of from Isfahan. But recently, due to increasing in rising water demand, the inadequacy of this project clearly felt. In the other hand, ground water resources of Yazd province are recharged from Shirkouh region. So due to this issue, to determine water stress risks in study area, considering climate change projections in shirkouh and Yazd as water supply and water consumption area is necessary.Research Methodology In the present study, to assess future climate changes in the study area, historical data from the Yazd and Dehbala stations -as water recharge and water depletion areas of Yazd-Ardakan aquifer-were analyzed by Lars software.To do this, daily values of minimum and maximum temperature, precipitation, and sunshine were used. GCM model in this research is HadCM3. These data are derived from two scenarios, A2, A1B and B1 for 2010-2030 periods.Validation of the predicted values was conducted using the statistical parameters, including bias, P Value of t Student statistics. To better analyze the results using Excel software, the moving average of predicted parameters for every month of the year was calculated for the 2010 to 2030 period and the corresponding graphs were drawn.Discussions and Results Results of two climate scenarios were evaluated and summarized below.The results of statistical validation of the predicted values, showed no significant differences between historical and predicted values of precipitation, maximum temperature, minimum temperature and sunshine. Therefore, suitability of Lars model to simulate climatic data of the study area is confirmed. Investigation of the precipitation in the Dehbala station showed that in autumn season will be declining and in spring will be increasing. In other words, the distribution of precipitation in the future will have significant changes and as winter precipitation decreases the spring precipitation will have increasing trend. On the other hand, due to the significant decline in January precipitation it is expected that the proportion of precipitation falling as snow vs. rain decrease. Such a change would affect the hydrological response of the basins and increasing flooding in this season. Similar process will happen in plain area.Based on the results, it can be said that in future, mountain stations has more variations in precipitation parameter than plain stations. These differences may be related to the amount of rainfall, in other words, in areas with higher precipitation, more variations in rainfall and rainfall distribution will be happen.Results showed that in studying stations, monthly minimum and maximum temperature increasing in almost all months.Conclusion According to this study, recharge area of Yazd-Ardakan aquifer will experience climate change and changes in the type of precipitation. As a result, by increasing in rainfall to snow coefficient, there would be more flood and less aquifer recharge. In the other hand by increasing in maximum temperature in water consumption (plain) area in future, water needs will grow significantly. Therefore planners and authorities should consider this fact in future water resources allocation.

Weather predicts are influence in all aspects of life everyday. More importantly Weather warnings are important forecasts because they are used to protect life and property. Mesoscale numerical weather prediction models are key for weather forecasting, however the accuracy of the predictions are affected by the errors in the numerical models.Study of the clouds is challenging and there are many applications in which the prediction of cloud is essential, including the field of air transport, rainfall and water resources. Nowadays, the clouds and precipitation of its behavior are even more strategic. Severe water shortage in some areas and increase rainfall in other areas is due to climate changes, therefore research in cloud and its precipitations are so important. The errors in the cloud forecast can have widespread impacts on the quality and accuracy of other model outputs. One of the important influences on temperature is interaction between the cloud and radiation. Therefore, parameters of cloud models, are very important to evaluate the model used for the prediction. In this study cloud cover, predicted by the Weather Research and Forecast (WRF) model, is reviewed.The evaluating of clouds has always been a challenging task, due to the three dimensional (3D) structure and the need to finding adequate observations for the purpose. Historically conventional surface data have been used for verification purposes because of the ease of accessibility. This, at best provide point observations of low, medium and high cloud, total cloud and cloud base height. Recently Mittermaier (2012) reviewed the use of these observations for verification of Total Cloud Amount or cover (TCA) and cloud base height (CBH).The availability of two dimensional time-height observations from ground-based active remote sensing instruments such as vertically pointing cloud radar can provide vertical detail at a location over time, from which cloud profiles (cloud amount as a function of altitude) can be derived. These give a view of clouds “from below”. Satellite data can provide a view from above.As the first step for the evaluation of a numerical model of forecasting, the forecast data and observations must be prepared in which the observations should be temporally and spatially matched in an appropriate manner, as far as possible. The aim of the present study is to evaluate the cloud cover predicted by the model, and to do that, radar and satellite images have been used as observation data. The study has used table of agreement 3*3 for verification of cloud parameters in three categories of no clouds, partly cloudy and cloudy. The first WRF model is implemented in 5 days for ages predicting less than 24 hour and more than 24 and less than 48 hours. For running WRF model, 3 Domain was considered including 36 km horizontal range of parent domain and two nest domains with range of 12 and 4 km. It is worth noting that the relation between total cloud cover and images of Radar measurements was investigated in the time - period of 7/5/2009 - 12/5/2009. For evaluation of WRF used the assessment of the quantities, such as Bias (B), False Alarms Rate (FAR), Proportion Correct (PC), Kuipers Skill Score (KSS) and Heidke Skill Score (HSS). The 5 - day period were also evaluated with data obtained from satellite images. The results showed that verification of the model WRF for all ages of forecasting and for all domains is same. The model almost predicts all clear weather condition, however, it has come to overestimation. The number of times correct or incorrect partly cloudy weather is predicted by the model was very small so the output of the model for the partly cloudy is poor and quantity of cloudy weather was acceptable. The cases in which the weather was clear or partly cloudy, but the model predicted cloudy weather has very few occurrences. This result shows the amount of false alarms rate is low. In order to confirm these results, the evaluation was done for a longer period in three months, and the results are match.

Summery Output of global climatic scenarios resulted from large-scale predictions (Usually 125 to 500 km network) of GSM models are not appropriate for important applications. Because small-scale spatial variability due to factors such as land cover, topography, etc., has a significant impact on climatic variables of desired area and some processes such as runoff are sensitive to this variability. Therefore, to explore the values of climatic components and to achieve a clear picture of future changes in climate in different regions of the earth, Large-scale output of general circulation models of the atmosphere, are downscaled. In this regard, there are various statistical and dynamical methods for downscaling, each with their own strengths and weaknesses. This study tries to using a new statistical approach predict and downsize maximum monthly amount of components and minimum temperature at weather stations in Golestan province in the context of changes in climate caused by human activity. So therefore as much as possible, it tries to provide a clear picture of future climate change for the studied area.In this study, in order to climatic prediction of the output of general circulation model of climate, HadcM3 with two scenarios A and B were used. It should be noted that Atmospheric general circulation model outputs of HadcM3, include26 components of the general circulation of the atmosphere which have been provided for all past and future decades with a resolution of 3.75 ´ 2.5 ° for the entire globe.26-fold output of HadcM3 model includes sea level pressure, power flow, and the orbital velocity of the wind, meridional velocity, volubility, wind direction, divergence, high pressure, relative humidity, Specific humidity and temperature elevation of 2 meters above the ground which are considered for three levels of SLP, 850 and 500 hPa heights. It is noticeable that in the later stages, the 26 component will be used as predictors.Then it is noteworthy that 26 predictor variables in a space of 3.75 ´ 2.5 °, In order to predict the temperatures and the location could not be a suitable solution for prediction and downscaling. For example, in the synoptic view, detection of temperature changes in an area is not only dependent on the changes of pressure patterns in that specific pixel located in that station, but the temperature changes of the desired station is dependent on high and low pressure or heat waves and cold flaps that are part of this system whose dimensions are sometimes more than one pixel. Based on abovementioned facts, as it was described, 2 predicting component of a pixel cannot provide proper results, to do this, following successive tests of this output, it was concluded that the best range for downscaling of climatic parameters of Golestan province includes a range of 15 workspaces (i.e.15 pixels) that each pixel has a total of 26 variables, including 390 Total predictor variable.Based on the results of this study it was shown that in general, more areas of the province based on scenario B, in comparison with scenario A, will have an increase in temperature. While, in a comparison of different months in a year, t was concluded that May, August, September and Feb respectively, for the factors of maximum temperature based on scenario A and B, and minimum temperature based on those two scenarios have experienced the maximum temperature increase for future years and on the other hand, the maximum area of the province, in terms of temperature decrease considering two factors of minimum and maximum temperatures based on scenario A belongs to December and for two factors of maximum and minimum temperature based on scenario B belongs to September. Also, map output for temperature changes of future decades of Golestan, confirms this fact that the maximum of increase and decrease in temperature in different areas of province, based on different months in a year, do not follow a specific pattern, so that in each month, a different spatial patterns of temperature change can be seen. Thus it seems that for risk management in order to reduce the harmful effects of temperature changes in different areas, different models and scenarios should be defined separately for each month.Nevertheless, these inevitable uncertainties in climatic predictions result from different factors such as Uncertainty of the values of meteorological observations, the output of general circulation models of the atmosphere and the uncertainty arising from the use of stem downscaling methods. What is important in this context is to be aware of these uncertainties, as well as to make efforts to reduce them as much as possible and to consider them in regional planning which all these cases have been taken into in this study.

Lightning is a characteristic of severe weather and often associated with hail and heavy rainfall. It is a natural hazard with potential threat to human life and considerable damages to aviation structures. Therefore, lightning prediction is critical and the real-time lightning detection systems are able to determine the location of cloud-to-ground (CG) lightning strikes accurately.Generally many indices are used to predict the thunderstorms such as K-Index (KI), Convective Available Potential Energy (CAPE) and Cloud Physics Thunder Parameter (CPTP) that are based on thermodynamic instability parameters. Lightning Potential Index (LPI) is an advanced index for evaluating the potential for lightning activity introduced by Yair et al. (2010) based on the dynamics and microphysics of clouds. According to Yair et al. (2010), LPI is estimated within the charge separation zone of clouds, between 0oC and 20oC, where the non-inductive mechanism involving collisions of ice and graupel particles in the presence of super-cooled water is dominant (Saunders et al., 1991).In the current study, the meso-scale Weather Research and Forecasting (WRF) model has been used to predict LPI over the northern part of Iran for two case studies of thundercloud event on 9 December 2013 and 25 May 2014. The WRF model is a fully compressible, no nhydrostatic atmospheric model, which uses a terrain-following hydrostatic vertical pressure coordinates (Skamarock et al., 2008). In the present research, WRF version 3.6.1 is used to simulate historical thundercloud event in Iran region.The model was run at 36 km, 12 km, 4 km and 1.333 km grid spacing. The inner domain is containing Tehran urban area. The Rapid radiative transfer model (Mlawer et al., 1997) with the Dudhia scheme (Dudhia, 1989) was used to simulate the long- and short-wave radiation, respectively. The Monin-Obukhov scheme was used to simulate surface layer fluxes (Janjic, 1996) and the Mellor-Yamada-Janjic turbulent kinetic energy (TKE) scheme was used to simulate boundary layer fluxes (Mellor and Yamada, 1982; Janjic, 1990, 1994). The land surface fluxes were obtained from NOAA model (Chen and Dudhia, 2001, modified by Liu et al., 2006). The Kain-Fritsch scheme was used on the 36 and 12 km grids to parameterize moist convection (Kain and Fritsch, 1993) and the Thompson microphysical scheme was used on the 4 and 1.333 km grids to parameterize microphysical processes. The simulated values of mixing ratios of hydrometers and vertical velocity have been used to calculate the LPI.Results were evaluated using Cloud-to-ground (CG) lightning flash data from NASA Lightning Imaging Sensor (LIS) and one of the common indices used for forecasting thunderstorms which rely on stability and thermodynamical indices such as K index. Results show that there is a good consistency of both the location of lightning occurrence between the model outputs and LIS data for both understudied cases. Besides, the LPI gives more localized estimation of the location of lightning occurrence compared to the KI. Since K index is not derived from the microphysical fields, it seems to be much less useful for accurate prediction of lightning. Thus LPI provided important information to predict the potential for lightning.