Journal of the Earth and Space Physics
Year:2008 | Volume:34 | Issue:1|Papers Count:12

Dynamic processes in the sun, such as solar flares, transmit plasma of charged particles, especially electron and proton, and associated fields into the earth's surroundings that cause geomagnetic disturbances on the earth's surface, called magnetic storms. On magnetic quiet days, moving charged particles, called solar wind, can restrictedly enter into the earth's atmosphere in the Polar Regions. On disturbed magnetic days, more particles at high speed can influence the earth's atmosphere and affect the ionospheric layers.During a magnetic storm, energy enters into the ionosphere at high latitudes that can change some thermospheric parameters such as atomic and molecular composition, temperature and circulation. Composition changes directly affect the electron concentration in the F2 layer, and circulation propagates the warm gas into the lower latitudes. Increasing thermospheric temperature expands the F2 layer and then the layer establishes at higher altitude and the electron concentration decreases (Campbell, 1997). The most intensive disturbances of the ionospheric layers occur during magnetic disturbances. They have a catastrophic effect in the auroral zone and the application of the normal laws is impossible. During such a disturbance, corpuscular emission, originating on the sun penetrates the atmosphere of the earth, usually in a belt between 55° to 80° of geomagnetic latitude. It generally hits the night side of the earth. Chapman and Ferraro (1930) have shown that the corpuscular emission must consist of an equal number of charges of both signs and therefore appear in general as conducting but uncharged. A magnetic disturbance in the auroral zone begins usually in the evening, with oscillations of the magnetic field. Simultaneously, aurorae borealis occur which are usually band-shaped. Strong reflections from the E-level appear in a wide frequency range often attaining 10 MHz. F-reflections do not occur since the auroral E-layer blankets the higher layers.In the course of the disturbances, the penetrating corpuscular emission often becomes harder, ionization shifts downward and the damping increases. For periods of a few minutes no reflections at all can be observed (polar blackout). Such conditions may predominate for many hours, especially during very serious disturbances. The reflections from the E-level also cease in the case of less serious disturbances shortly after midnight due to the decrease in corpuscular emission. The auroral lights diminish simultaneously and then cease entirely. During this second phase of the disturbance which lasts at least until dawn, no echoes at all can be observed. The E-reflections stop with the cessation of corpuscular emission; F-echoes also do not exist anymore as this layer was dissolved in the course of the disturbance (Rawer, 1956). In the Polar Regions the main effects of ionospheric storms are the increases in the electron concentrations in the E and D regions: they can be understood in outline in terms of the energetic electrons that also produce the aurora. At lower latitudes, however, the changes in the D region take a different form and there are conspicuous changes in the F layer.A storm usually results in significant changes in the F layer all over the world; at most latitudes the concentration of electrons is decreased, although, within a few degrees of the geomagnetic equator, it is often increased. These changes have not been properly explained. The decrease is the most surprising. One type of explanation ascribes it to an increase in the rate of loss of electrons. In the F region that loss involves reactions of the type O+N2®N+2+, with a rate k[N2][O+]: this rate could be increased during a storm either because k increases or because [N2] increases. Laboratory measurements suggest that k increases with temperature and since the temperature is known to increase during a storm the decrease of electron concentration would be explained.Those who ascribe the storm phenomenon to an increase in the concentration of molecular nitrogen suggest that during a storm the entry of particles into the low polar ionosphere excites gravity waves that then travel into the F region at lower latitudes where they mix the atmospheric constituents.Still another suggestion is that the modification of the ionospheric current system that shows itself as the polar electrojet is accompanied by an electric field that extends into the F region at lower latitudes and there causes the ionospheric plasma to move downwards to levels where the rate of loss is greater. To explain an increase in the F region electron content, of the kind that is often observed near the equator during storms, it has been suggested that at those times the ionosphere is moved upwards to places where the rate of loss of electrons is smaller. The movement might be caused by the electric field of the dynamo below, so that the F region acts like an atmospheric motor. It might also be caused by a wind in the neutral air, blowing from the poles towards the equator, so as to move the ionization upwards along the sloping lines of force (Ratcliff, 1972).

In this paper by using MM5 modeling system, the effects of the Alborz mountain range on the development of synoptic weather systems on the leeward side of the mountains have been studied. The primary aim was to see if the cyclones are produced and intensified (cyclogenesis), however the cyclogenesis was found not to be marked and hence the focus of the study was on the effects of the mountain on weather systems reaching this area.The extent of the simulation domain is from 24°E to 74°E and 14.6°N to 53.6°N with horizontal grid resolution of 25 kilometer, and 23 half-sigma levels in the vertical. The effect of terrain elevation was considered by smoothing the elevation typically by 50 percent (near the peaks by 70 percent and about 10 percent near the topographic borders). We found that the north-westerly weather system (24th to 27th Dec.2005) crossing the Alborz mountain range showed a sharp difference between the case with topography and that without it. These changes were particularly observed in potential temperature field, Froude number, vertical speeds and precipitation. The vertical speeds as well as potential temperatures were found to be reduced markedly. Also the 48-hours accumulative precipitation was found to be reduced too. By removing the topography, the Froude number of the flow was substantially increased; indicating that wave activity over the area was reduced. Typical Froude numbers for the case with topography is about 0.9, which is a favorable condition for formation of Lee waves, but without topography it appears to be much larger than 1, which is not a favorable condition for wave formation.We also found that the westerly and south-westerly weather systems moving to this area were hardly influenced by the Alborz mountain range. This may be due to the fact that these systems are greatly weakened by the smaller topographic features along their path as well as losing most of their moisture over the Zagros mountain range, although this needs further study.

Orography plays a substantial role in the formation and evolution of many atmospheric phenomena. Observations indicate that two mountain ranges to the southwest of the Red Sea and west of Iran (Zagross mountain range) play a crucial role in the formation, evolution and activities of synoptic weather systems over Iran. Observations also, indicate that there are considerable differences between the amount of precipitation over the west and east of the Zagross mountain range.In this paper, the effects of those two mountain ranges on the formation, evolution and activities of a synoptic system over Iran between 23rd and 30th of December 2004 have been studied. To do this fifth-generation of PSU (Pennsylvania State University) /NCAR (National Center for Atmospheric Research) Meso- and Micro-scale Modeling system (MM5) was used. To provide a clear picture of the development and evolution of the system an area bounded to 10 to 50 degrees north and 20 to 70 degrees east was selected. A rectangle grid by 101 zonal grid points and 81 meridional grid points with a distance of 55.5km between the grid points was implemented for simulation and modeling purposes and the study of dynamical and thermodynamical processes involved in the development and evolution of the system. Betts-Miller, Grell, Blackadar, and MRF schemes were used to model different physical, thermodynamical and dynamical processes.Model results show that the elimination of the mountain range to the southwest of the Red Sea leads to widen the existing low pressure over the southwest area of the Red Sea towards the east. It also leads to a decrease in the small high pressure cell that is believed to play a substantial role in the formation of the so-called Red Sea trough towards the north of the Red Sea and southeast of the Mediterranean Sea, where many of the weather systems that pass over Iran during the winter form. Therefore, it can be concluded that elimination of that mountain range will destroy part of the mechanism essential for the formation of some weather systems that are important for Iran.Model results also state that the elimination of Zagross mountain range could lead to the enhancement of precipitation over the central part of the country, east of the range.

Direct numerical weather prediction model forecasts of near surface parameters often suffer from systematic errors mainly due to the low resolution of the model topography and inaccuracies in the physical parameterization schemes incorporated in the model. In this paper a simple objective algorithm based on Kalman filtering have been implemented to correct the maximum and minimum temperature model forecasts.In the last couple of years different methods for a postprocessing the model outputs have been developed. Kalman filter is one of them which provides a practical tool that combines the observed data and predictions of the model using a simple algorithm to reduce the systematic errors of the direct model outputs without the need for long historical data archives.This paper is organized as follows. In Section 2, we introduce a simple Kalman filter. In Section 3 and 4, we show that how the filter is applied on the model outputs for 2 meter minimum and maximum temperature for 117 meteorological stations. In Section 5, statistical results are presented and finally the paper is concluded in section 6. Simple Kalman Filter: The Kalman filter theory provides equations for recursively updating estimates of an unknown process through combining observations related to the process and time evolution of the process. Let xt be a vector describing the state of the unknown process at time t that, in this paper, is considered to be the systematic deviation between the observed and predicted temperatures. The state vector at time t is related to the state at time t−1 through the system equation: xt=ft.xt-1+wt where Ft describe the systematic change in xt and wt denotes the random part of the evolution of t x from time t−1 to time t and is known as the noise vector of the process. The state xt is related to the observation(s) yt through the observation equation: yt=ht.xt+vtwhere Ht is the observation matrix and vt is the noise vector of the observed data. wt and vt are assumed to be Gaussian white noise with zero mean processes and to have covariance matrixes Q and R respectively. Kalman Filter has two main steps; first step includes predictor equations which preestimate the state and its corresponding error covariance matrix:xt/t-1 = Ft . Xt-1 Pt/t-1 = Ft. Pt-1 .FTt +Q   xt/t-1 is the pre-state and P is its error covariance matrix. The next step includes the corrector equations which update the pre-state using recent observation: xt = xt/t-1 + Kt (yt-Ht.xt/t-1) Kt = pt/t-1 .Ht/Ht .pt/t-1. HTt+R Pt= (I-H .Kt) Pt/t-1 where kt is Kalman gain. Procedure: Since there is not sufficient information about the dynamics of the system, a number of simplifying assumptions have been made; we consider F and H as constant unit matrixes.Estimates of initial state x0 and P0 and also wt and vt are required before running the filter. Initial values of x0 and P0 are not effective in the filter performance after some iterations and their consequence is lost. But the main problem in applying the filter is determination of noise vectors, wt and vt. The method proposed by Galanis and Anadranistakis for calculating wt and vt is used here and a training period of seven days was selected to train the filter using outputs of the MM5 modeling system. The Kalman filter was applied on model outputs for minimum and maximum temperature forecasts for 117 meteorological stations over Iran during 120 days and some statistical scores were calculated.

Due to various effects the geoid does not coincide with the Mean Sea Level (MSL) but is deviated from it by a separation that is called “Sea Surface Topography” or in short SST. One of the SST computations techniques is the “Local Response”, which is one of the “response analysis” methods. Using the aforementioned technique the response of the sea surface to various factors such as atmospheric pressure, temperature, wind, etc. can be used for the SST determination. Namely, the sea can be considered as a physical system where the environmental phenomena are its input function and the instantaneous sea level is the output function. Knowing the input and output functions in spectral form, to be determined, is the system itself, which reveals the output upon the frequency character of the input. More specifically, we are going to use the “Cross-Spectral Analysis” for the SST computations and for the case study four coastal stations along the Persian Gulf will be considered. These stations are located at: (1) Shahid-Rajaie Port, (2) Kangan Port, (3) Bushehr Port, and (4) Imam Hassan Harbor. According to the numerical results of this study, the overall effect of the environmental factors on the sea level within the wintertime is between +8 and –33 cm. The maximum amount of SST is associated with the Bushehr Port and the minimum with Kangan Port. The maximum SST in Bushehr Port shows the subsidence of the MSL with respect to the geoid and the minimum SST in Kangan Port is associated with the rise of MSL. This finding is consistent with the prevailing sea dynamics in the Persian Gulf and the Oman Sea, i.e. Shahid-Rajaie Port being closer to the Oman Sea is more strongly affected by the sea topography and the oceanic currents than Bushehr Port which is away from the Oman Sea. For the practical application of the current study we may refer to the determination of national height datums with respect to the geoid, which are established traditionally with respect to the MSL, by sea level observations at the fundamental tide gauge stations. Knowing the separation of the MSL from the geoid, i.e. SST, we may arrive at a solution to the “height datum unification” problem.

Because of urgent needs of groundwater resources in the Tochal region of north Tehran, an integration of electric and electromagnetic methods was applied to investigate the location and the direction of water bearing fractures. First, the VLF electromagnetic profiling was performed on suitable places and two locations were detected for supplementary resistivity surveys. Then, a number of vertical electrical soundings using Schlumberger array were carried out on suitable locations. With the presumption of a layered earth, three soundings in every two locations were merged and the 2D electrical resistivity cross-sections were made and the best location for groundwater- bearing fracture zone was detected.The VLF method has been applied successfully to map the resistivity contrast at boundaries of fractured zones having a high degree of connectivity (Parasnis, 1973). Further, the VLF method yields a higher depth of penetration in hard rock areas because of their high resistivity. Therefore, a combined study of VLF and DC resistivity has the potential to be successful (Bernard and valla, 1991; Benson et al., 1997). For tilt angle measurements, magnetic field coupling with the fracture zone is important. Therefore, the VLF-transmitter should be located along the strike of the target. The depth of investigation is dependent on the frequency used and the resistivity of the host medium. Sharma and Baranwal (2005) suggested a method using real component curve of VLF measurements to locate water bearing fractures. In this method, every significant leap in the data curve indicates a conductive anomaly referring to a fracture in a hard rock medium.Integrated geophysical studies were performed on the Tochal telecabin region, north of Tehran, Iran. Tochal telecabin is a cultural-sporting complex that begins from the end of Velenjak Street and continues to the vertex of Tochal Mountain. This study was accomplished by a VLF survey followed by a resistivity survey using Schlumberger array to find a suitable location for drilling a well in the Tochal telecabin region. In order to find the locations that contain water-bearing fractures, nine VLF profiles were performed to study along the main road of the Tochal telecabin. First a suitable transmitter with sufficient power and admissible direction was found and then the measurements with 5m station spacing were done. It is important to note that almost all of the measurements were taken before sunrise when the city wireless transmitters had not begun to work. Because city wireless transmitters generate noise, the instrument cannot get a suitable signal. High noise level is a serious problem on a cold and snowy day. Moreover, the active installations and high voltage transporter cables affect the signal and increase the noise level.VLF data were collected using an ABEM-WADI instrument. With regard to the pseudo current-density section of first VLF profile, we focused on two major zones along the road. For zone 1, since the probable fault direction in the formation was approximately in the E–W direction, the HWU transmitter in this direction with a frequency of 18.3 kHz located in Le Blance, France was used. For zone 2, since the probable fault direction in the formation was approximately in the N–S direction, the RCV transmitter in this direction with a frequency of 27.1 kHz located in Russia, was used. Next, the other profiles were surveyed along hydrogeologically suitable locations with probable fractures and fault.Six Schlumberger resistivity soundings were performed in two zones using a DC resistivity meter. Sounding locations were selected by detailed study of the area with a VLF survey as well as by their hydrogeological suitability. After gathering the measured data, for further detailed information of the subsurface, the measured real anomaly was filtered using the approach of Karous and Hjelt (1983). This process yields pseudo-section of relative current density variation with depth. A higher value of relative current density corresponds to conductive subsurface structures. According to Sharma and Baranwal (2005), every leap of real anomaly may indicate subsurface water-bearing fractures in a hard rock medium. Apparent current density cross-section also gives a rough idea about the dip direction; however, exact dip angle cannot be estimated due to the vertical axis variable being a pseudo depth only. The current density pseudo-section of three VLF profiles in Zone-2 has an obvious anomaly that is located between 1925m to 1960m along the profile with the dipping angle about 45 o. This is the best anomaly found with the VLF method due to its concentration of current density and low noise.As noted previously, Schlumberger soundings in two Zones were performed (three soundings in each Zone). With the presumption of a layered earth, the soundings were measured in the field, then assuming a 2D-interpretation by group of 3 VES merged with the resistivity cross-sections of two VES groups. The anomaly observed in Zone-1 is about an underground pipeline that crosses the line of profile and is not a natural anomaly. In Zone-2, a low resistivity fracture zone is check located at 1930m and continued to depth of 50m and higher which is a natural anomaly. The existence of water-bearing fractures as previously suggested at a distance of 1930m along the road was proved, making it a suitable place to drill a well. Also with the acceptable results that were given by this method, in order to give more accuracy and speed to geophysical prospecting in the hard rocks that contain probable groundwater bearing fractures, first we suggest using the VLF method as a fast and low cost way in order to gather some useful information about the formation and anomalous zones. Then with the results of VLF pseudo current-density cross sections, the locations for electrical resistivity surveys can be determined. 

The problem of downward continuation of the gravity field from the Earth’s surface to the reference ellipsoid arises from the fact that the solution to the boundary value problem for geoid determination without applying Stokes formula is sought in terms of the disturbing potential L dW (X) on the ellipsoid but the disturbing gravity observations dG(X) are only available on the Earth’s surface. Downward continuation is achieved via Abel-Poisson integral and its derivatives. Using discrete observations, the Abel-Poisson integral has to be transformed into a summation form:b=Ax     bÎRm    xÎRnWhere the matrix A is the design matrix and b stands for the disturbing gravity observations vector. The downward continuation problem is an inverse problem. Inverse problems are ill-posed, like any ill-posed problem it must be regularized. The objective of this paper is the comparison between direct and iterative methods for solving downward continuation of the gravity field from the Earth’s surface to the reference ellipsoid for geoid determination without applying Stokes formula.Direct regularization methods are methods where the solution is directly derived. In this contribution truncated method, standard Tikhonov method and generalized Tikhonov method using discretized norms at Sobolov subspaces W12 (a,b), W22(a,b) and Sobolov semi norms ‖L1‖2 and ‖L1‖2 are implemented.

Due to the spherical divergence and specifically absorption in the earth, amplitude of a propagating seism wave varies as a function of time. This stretches the wavelet in time and reduces the time (or vertical) resolution of the seismic sections. To overcome the problem one has to apply so called spatial migration or deconvolution on data. Usually the least square Wiener deconvolution is used to boost up the attenuated frequency components. Unfortunately, the Wiener based deconvolution methods assume that the source generated seismic wavelet is stationary (i.e. its frequency content remains unchanged within the record). A method of deconvolution in the Gabor domain is applied in this paper that considers the seismic data as a non-stationary phenomenon.The Gabor transform (Equation 1) is a windowed or short time Fourier transform, where the window used to isolate the frequency content of input record in time, is a Gaussian type. According to the uncertainty principle, the Gabor transform has the least uncertainty among other windowed Fourier transforms.Please clik on PDF icon to viwe the complet abstract.

The travel time observations contain information about the location and time of origin of the seismic source and the velocity structure in the region between the source and receivers. So, the travel-time residuals as criteria are used in the seismic topographical studies and locating seismic events, etc. There are many possible factors that create systematic bias in travel-time residuals. Some of the important systematic factors which might lead to such bias include: azimuth variation due to geological structure; systematic errors in recording system or instrumental error; phase picking error; location error and etc.Systematically biases and instrumental errors in seismic stations are very difficult to address in many earthquake location methods. Minimizing the effects of these errors is very important to improve earthquake locations. In order to identify and minimize them, we analyze the time residuals of the first P-wave arrivals at 9 stations of the Tehran network using the data base from 1996 through 2005.The azimuth variation can be due to a combination of earth structure near the source and receiver. In order to identify earth structure near the stations we used the data of events which occurred within 100 kilometers of each station. Also, to discriminate different source regions we divided the Earth's surface into 5° in azimuth by 100 km in distance relative to the station.In this analysis time residual versus back azimuth data of stations from events located within 100 kilometers are sorted, then averaged over 5° back azimuthal intervals. In each bin, we have an average residual and an azimuth centered on the bin. Then, for the average residual versus the corresponding back azimuth, a least square curve was fitted due to dependent equation of the form (Dziewonski and Anderson, 1983): dt=A°+A1 Cos (q-A2)+A3Cos2 (q-A4) Where q is the azimuth from the station (back azimuth) to the event and dt is the average P-wave travel time residual into the bin 5°×100 km. A0, A1, A2, A3 and A4 are constants.The value of A0 can be jointly related to instrumental error, phase picking error, location error and etc. Since separation of these biases is difficult, and A0 value indicates the total shift of data, we consider A0 as instrumental error or station offset. Therefore, the constant term in azimuth dependent equation is attributed to the average systematic errors at the stations. The value of A0 can indicate an operational quality of a station. By using this procedure we can identify "pathological" stations. As a result, DMW, RAZ and FIR stations show significant azimuthal biases and FIR and AFJ stations have had considerable systematic errors. QOM, VRN and MHD stations have had reliable records according to the stability of the azimuthally dependent curve. Also, all of the stations are graded in 4 azimuth windows according to their functional quality.

CO is the important air pollutant in Tehran. Two forecasting techniques are presented in this paper for prediction of average daily CO concentration. One of them, Multivariate Linear Regression (MLR) is based on Principal Component Analysis (PCA). The other technique is Artificial Neural Network (ANN) model. With this regard to six pollutants, i.e., PM10, NOx, SO2, THC, CH4 and O3, and six meteorological variables, i.e., wind speed, wind direction, temperature, air pressure, humidity, solar radiation are used. These variables were measured daily throughout 2004 and 2005 at Gholhak Monitory Station, one of the eleven monitory stations in the Tehran area. Among all the ANNs available paradigms, a Feed-Forward Multi-Layer Perceptron (FFMLP) was considered to be the best choice for this study because it is the most popular architecture for an ANNs. Therefore, in our research, one hidden layer FFMLP was used for the average daily CO concentration prediction. The activation functions chosen were the sigmoid hyperbolic tangent function in the hidden and output layers. The error correction learning with the Levenberg–Marquardt (L–M) algorithm was chosen for training the networks.Regression model in matrix form can be shown as: Y=Xb +e (1)where b is regression coefficient matrix, e is fitting error matrix and Y is response matrix. By solving equation for b we will have:b=(X'X)-1 (XY')where X' is transpose of X.For calculating the inverse of (X'X), the independent variables should not have high relativity, because in this situation (X'X) matrix can not become inverse and we will have more error in the data and calculations. To solve this problem, we should remove the multicolinearity between independent variables with PCA approach. In this research after removing the problem of multicolinearity on independent variables by the PCA, an appropriate model (PCA-MLR) was developed for predicting CO concentration. However, in the MLR calculation, stepwise algorithm has been used. In this method, entering the variables to the MLR is step by step condition, from the most important of them to the less important of them. To achieve the best network structure for estimating CO concentration, various structures of FFMLP was investigated. Finally, a 13-22-1 architecture was selected for the best architecture of the network. Also, after removing the multicolinearity between independent variables, an appropriate PCA-MLR model was developed for prediction of CO concentration by stepwise algorithm.In this step by performing PCA from 12 Principal Components (PCs), just 8 PCs were meaningful to enter the model. It estimates the CO concentration the regard to these new input variables. Finally, a PCA-MLR model is constructed that its equation is given below:CO 4.92 0.60 (PC1)-0.57 (PC9) 0.35 (PC6)-0.29 (PC5)-0.24 (PC3) 0.24 (PC11) 0.16 (PC2) 0.13 (PC7) For better judgment and selection of one on them, the Threshold Statistic (TS) index of testing step calculated and presented. For example this index shows that Absolute Relative Error (ARE) for 75% prediction of testing stage in ANN is 20%. This value (ARE) is 25% in the PCA-MLR model. However 90% of prediction of testing stage in ANN and PCA-MLR models are ARE equal to 41% and 53% respectively. Finally, the use of FFMLP in prediction of average daily CO concentration in Tehran is offered.