In this paper GPS radio occultation has been introduced as a way to extract the electron density of the IONOSPHERE. A GPS radio occultation occurs when a receiver on-board a LEO spacecraft tracks a GPS satellite as it sets or rises through the earth’s atmosphere. The recorded occulted signal phase and amplitude of the tracked satellite can then be analyzed to derive atmospheric electron density. TEC Monitoring has been studied to correct the error caused by the ionospher electron density in the telecommunication satellite global positioning system are briefly introduced as a tool for this method. One occultation event studied near one of the brazilion oson destations and the electron density curves obtained from the data reported Brazil Ionosonde are compared with radio methods.

Background and Objectives: Radio sounding and tomography techniques play a crucial role in studying the structure and dynamics of the IONOSPHERE. Specifically, tomography is an advanced method for creating three-dimensional models of electron density within the ionospheric layer. By utilizing observational data, such as GPS measurements, tomography generates accurate maps of electron distribution. Ionospheric tomography provides high-precision insights into temporal and spatial variations in electron density. This precision is essential for applications like satellite navigation, radio communications, and meteorological predictions. Researchers focus on the upper layers of Earth’s atmosphere, using specialized radars called ionosondes to obtain precise information about electron density and the structure of ionized layers. Tomography, an imaging technique, relies on radio wave propagation through the IONOSPHERE. It produces two- or three-dimensional images of electron distribution within this layer. Widely used in weather forecasting, radio communications, and space studies, tomography significantly advances our understanding of ionospheric phenomena. Technological advancements, including satellite-based measurements, enable even more accurate analyses, ultimately enhancing global communication and aviation safety. In this paper, the existing method for how to obtain the electron density change of the IONOSPHERE layer based on the total electron content (TEC) parameter by using the phase difference analysis created in the communication signal of the global navigation satellite system GNSS when passing through different layers of the IONOSPHERE has been investigated and studied. For this purpose, communication signals from low-orbit and high-orbit satellites were studied, and the method of obtaining TEC from phase difference was explained for each. Then, we studied the existing methods and algorithms for converting TEC (Total Electron Content) data into tomographic images. At the end of this article, as an example, we implemented the radio tomography method to visualize plasma bubbles in the equatorial region and compared the results with images taken from optical instruments. It was shown that radio tomography can be used as an accurate method for visualizing the structure of plasma bubbles. At the end of this article, we compared the method studied here with methods such as all-sky imaging, incoherent scatter radars, etc., and discussed the advantages and disadvantages of these methods relative to each other.Methods: In current research on ionospheric sounding and tomography, significant progress has been made using the Global Navigation Satellite System (GNSS). Recent studies indicate that GNSS can model the ionospheric structure in three dimensions with high precision. Electron distribution in the IONOSPHERE is analyzed using radio data obtained from satellites at Low Earth Orbit (LO) and High Earth Orbit (HO). Collecting ionospheric information via GNSS is a complex and precise process that relies on advanced technology to measure and analyze various ionospheric parameters. These systems, which include Earth-orbiting satellites, transmit signals to ground-based receiver stations. These signals contain precise temporal and spatial information about the satellites, allowing accurate determination of receiver positions on Earth. The distribution of electron density in the ionospheric layer directly affects the propagation of GNSS radio waves, including their path, shape, and phase. Any disruption in the ionospheric layer significantly impacts satellite communications, precise navigation, and long-range communications. In fact, GNSS utilizes this capability to measure the Total Electron Content (TEC) of the IONOSPHERE, a key indicator for understanding its state. This process occurs through signals transmitted from satellites to ground stations. As these signals pass through the IONOSPHERE, they are influenced by electron density variations, which can be measured with high accuracy.Findings: In this comprehensive study, current research on ionospheric radio tomography using Total Electron Content (TEC) measurements from GNSS has been conducted. The concept of TEC and its impact on the phase and shape of signals received from the examined satellites has been explored. The application and methodology of using Low Earth Orbit (LEO) and High Earth Orbit (HEO) satellite data to obtain detailed TEC information are described. The validation and accuracy assessment of satellite data in ionospheric radio tomography, which is crucial for the reliability of the final product and the production process, have been addressed. Finally, a technique for reconstructing tomographic images using TEC measurements via GNSS signals is reviewed. It has been demonstrated that this reconstruction technique works well for imaging plasma bubbles. Horizontal distributions obtained from Vertical TEC (VTEC) depletions are compared with images captured by optical instruments, yielding similar results. Even in regions where GNSS signals are weak, this method can yield good outcomes if the bubble structures are sufficiently large.Conclusion: In summary, GNSS tomography represents a dynamic and evolving field with significant potential for improving accuracy and efficiency in weather predictions. As we continue our research and development efforts, we anticipate the emergence of new methods and technologies that can address existing challenges and enhance the quality and precision of tomographic models. These advancements hold promise for diverse applications of GNSS tomography, including meteorology, climate change studies, and disaster management.

The IONOSPHERE is the ionized region of the atmosphere which is situated between 80 and 1200 km. Ionospheric delay is the major resource of error in GNSS positioning, Therefore knowledge of the ionospheric behavior is an important factor in this field. Total Electron Content (TEC) values may be considered as a key parameter to monitor the behavior of the ionospheric medium. Nowadays, continuous GNSS observations can provide an efficient tool to monitor timely ionospheric irregularities. Many scientists have investigated global ionospheric models on the basis of different observations data. For example, IGS IONOSPHERE Working Group produced daily TEC maps for user services from GNSS data. In this paper, we intend to utilize dual frequency GPS observations provided by Iranian Permanent GNSS Network (IPGN) to calculate TEC maps in Iran. For this purpose, data of 43 IPGN stations and about 180 IGS stations were processed with Bernese GPS software. This process was based on the use of spherical harmonics expansion up to degree and order 15 like the global one, to provide a model of TEC. In the meantime of using GPS data to calculate TEC maps, other resource of errors in GPS positioning such as satellite and receiver clock biases, tropospheric error and multipath error must be either removed, or at least significantly reduced. For this purpose, we used the geometry free linear combinations of pseudo ranges and carrier phases. For reducing the noise level of pseudo range observations we used the carrier phase smoothed pseudo range data as well. The processing method consists of several steps; code smoothing with phase observations, estimation of Differential Code Biases (DCBs), estimation of spherical harmonic coefficients and generation of TEC maps. Before code smoothing, the phase observations were pre- processed to remove the cycle slips. The used model assumes that the whole free electrons are concentrated on a thin spherical layer to an altitude varying between 250 and 450km. We chose the altitude equals to 450km in this paper. The obtained results show that the maximal TEC value measured over Iran is about 22 TECU, this value corresponds to the noon period (midday), where the sun is close to the zenith. The minimal TEC value varied around 5 TECU, it corresponds to the midnight period, and such values were obtained for the day of Jun 22, 2009. Iranian IONOSPHERE Model (IRIM) was created and compared with the different solutions delivered by the several IGS IONOSPHERE Associate Analysis Centers (IAACs) which are CODE, ESA, JPL and UPC. Despite different IAACs use various approaches, they provide TEC maps with resolution of 2 hours, 5o and 2.5o in UT, longitude and latitude respectively. In order to compare our obtained results with different IAACs TEC maps, we chose TEHN station from IPGN stations to generate and display TEC profiles. The differences between the various models are less than 6 TECU. The IRIM results had minimum differences with CODE TEC maps which both use spherical harmonics as their basic functions. The remained differences caused by the fact that when CODE TEC maps are estimated, the data from IPGN stations are not used. Calculated TEC values were thereafter applied to correct and improve the quality of the single frequency solutions in absolute and relative positioning modes. It is noted that IONOSPHERE free (L3) solution results was considered as the reference solution. In absolute mode, we received the considerable improvements in horizontal and vertical components by using the IRIM instead of IGS models. In relative mode the comparison between the corrected L1 and L3 solutions showed that ignoring the ionospheric effects causes network contraction. Furthermore, the corrected L1 solution results using IRIM rather than IGS models were closer to the L3 solution results. Moreover, for baselines up to several hundreds of kilometers, deviations were better than 10cm in horizontal component.

The IONOSPHERE layer of atmosphere environment is a highly variable media that performs significant weather variations with altitude, latitude, longitude, universal time, solar cycle, season, and geomagnetic activity. Therefore, IONOSPHERE modeling and determining the total electron content (TEC) play an important role to know this layer of atmosphere specifications and control its effects on human activities. Different kinds of ionospheric models are widely used to monitor the changes in IONOSPHERE in which single layer model (SLM) of IONOSPHERE or TEC model has been always interesting for researchers. Complications of physical models for major of users, low accuracy of numerical (empirical) models for precise applications, the 24 hour delay in IGS daily global ionospheric map (GIM) propagation and the precise ionospheric information necessity in real-time and near real-time applications have been the reasons of development of new ionospheric models which is known as data assimilation models. These models combine measurements from observing system with the information obtained from background model trough the data assimilation technique. Assimilation algorithm involves a forecast step, in which a previous estimate of the state is evolved forward to the time of the observation, and an update or analysis step, where the evolved estimate of the state is updated using information from the observations. The outputs of assimilated models have parameters closer to the observations. The accuracy of the reconstructed IONOSPHERE depends on the amount of assimilated data, the diversity of the data types and the quality of the data. Assimilated data may have different sources such as GPS slant TEC, in situ electron densities, electron density profiles (EDPs) from ground-based radars and ionosonde data in IONOSPHERE data assimilation. In this study, precise TEC derived from dual frequency GPS observations are assimilated in to an international reference IONOSPHERE (IRI) numerical model in analysis and forecast steps of assimilation. Kalman filter is used to increase the accuracy of IRI extracted TEC in analysis step and Gauss-Marcov Kalman filter (GM-KF) is used to predict TEC in forecast step for real-time and near real-time applications. Observations of 40 stations of Iranian permanent GPS Network (IPGN) in May 03, 2016 are used to extract precise VTEC for assimilation in IRI model. The GPS observed VTEC are compared with TEC form IRI model, TEC from IGS GIM and assimilated IRI TEC in analysis step of assimilation. The rote mean square (RMS) of discrepancy between GPS VTEC and IRI TEC are reduced from 9.8 TECU to 1.47 TECU at t=10UT, from 3.16 TECU to 0.98 TECU at t=14 UT and from 4.59 TECU to 1.39 TECU at t=18UT, after assimilation. Comparing the IGS GIM and assimilated IRI TEC with GPS observed VTEC indicate that the assimilated model is more accurate than GIM in Iran region. The GPS observed VTEC are also compared with TEC form IRI model, TEC from IGS GIM and assimilated IRI TEC in prediction step of assimilation. This comparison shows 90% improvement in assimilated TEC respect to IRI TEC at t=10 UT for Dt=0.5, 1 hour prediction time intervals. This improvement at t=14, 18 UT is more than 50% for Dt=0.5, 1, 2 hour prediction time intervals. By increasing the prediction time interval to Dt=5 hour, the assimilation accuracy tends to IRI model. Therefore the assimilated model has a good accuracy for real-time and near real-time applications.

In this paper, 3-layer perceptron Neural Network has been used with 5 neuron in hidden layer for modeling the Ionospheric Total Electron Content (TEC) Over Iran. For this purpose, 25 GPS station from IPGN is used. These 25 stations are located within a range of approximately 24oN to 40oN and 44oE to 64oE. Evaluation of the results has been applied with 1 GPS station in Tehran. The station is equipped with ionosonde. So it is possible to calculate independently the TEC at the station. Minimum relative error obtained from evaluation is 0.73% and maximum relative error is 34.66 %. In this research, for the evaluation of artificial neural networks in estimating the TEC, a polynomial of degree 3 with 11 coefficients are used. Comparison of the relative error from polynomial model and relative error from neural network, illustrate the superiority of the neural model with respect to polynomial in this region. The number of neurons in hidden layer of neural network and the order and coefficients of the polynomial used in this paper is determined by trial and error, and by taking the minimum relative error for the results.

In the last two decades, knowledge of the distribution of the ionospheric electron density considered as a major challenge for geodesy and geophysics researchers. To study the physical properties of the IONOSPHERE, computerized IONOSPHERE tomography (CIT) indicated an efficient and effective manner. Usually the value of total electron content (TEC) used as an input parameter to CIT. Then inversion methods used to compute electron density at any time and space. However, CIT is considered as an inverse ill-posed problem due to the lack of input observations and non-uniform distribution of TEC data. Many algorithms and methods are presented to modeling of CIT. For the first time, 2-dimensional CIT was suggested by Austin et al., (1988). They used algebraic reconstruction techniques (ART) to obtain the electron density. Since, other researchers have also studied and examined the CIT. Although the results of all studies indicates high efficiency of CIT, but two major limitations can be considered to this method: first, due to poor spatial distribution of GPS stations and limitations of signal viewing angle, CIT is an inverse ill-posed problem. Second, in most cases, observations are discontinuous in time and space domain, so it is not possible determining the density profiles at any time and space around the world. In this paper, the method of residual minimization training neural network is proposed as a new method of ionospheric reconstruction. In this method, vertical and horizontal objective functions are minimized. Due to a poor vertical resolution of ionospheric tomography, empirical orthogonal functions (EOFs) are used as vertical objective function. To optimize the weights and biases in the neural network, a proper training algorithm is used. Training of neural networks can be considered as an optimization problem whose goal is to optimize the weights and biases to achieve a minimum training error. In this paper, back-propagation (BP) and particle swarm optimization (PSO) is used as training algorithms.3 new methods have been investigated and analyzed in this research. In residual minimization training neural network (RMTNN), 3 layer perceptron artificial neural networks (ANN) with BP training algorithm is used to modeling of ionospheric electron density. In second method, due to the use of wavelet neural network (WNN) with BP algorithm in RMTNN method, the new method is named modified RMTNN (MRMTNN). In the third method, WNN with a PSO training algorithm is used to solve pixel-based ionospheric tomography. This new method is named ionospheric tomography based on the neural network (ITNN). The GPS measurements of the Iranian permanent GPS network (IPGN) (1 ionosonde and 4 testing stations) have been used for constructing a 3-D image of the electron density. For numerical experimentation in IPGN, observations collected at 36 GPS stations on 3 days in 2007 (2007.01.03, 2007.04.03 and 2007.07.13) are used. Also the results have been compared to that of the spherical cap harmonic (SCH) method as a local ionospheric model and ionosonde data. Relative and absolute errors, root mean square error (RMSE), bias, standard deviations and correlation coefficient computed and analyzed as a statistical indicators in 3 proposed methods. The Analyzes show that the ITNN method has a high convergence speed and high accuracy with respect to the RMTNN and MRMTNN. The obtained results indicate the improvement of 0.5 to 5.65 TECU in IPGN with respect to the other empirical methods. The GPS measurements of the Iranian permanent GPS network (IPGN) (1 ionosonde and 4 testing stations) have been used for constructing a 3-D image of the electron density. For numerical experimentation in IPGN, observations collected at 36 GPS stations on 3 days in 2007 (2007.01.03, 2007.04.03 and 2007.07.13) are used. Also the results have been compared to that of the spherical cap harmonic (SCH) method as a local ionospheric model and ionosonde data. Relative and absolute errors, root mean square error (RMSE), bias, standard deviations and correlation coefficient computed and analyzed as a statistical indicators in 3 proposed methods. The Analyzes show that the ITNN method has a high convergence speed and high accuracy with respect to the RMTNN and MRMTNN. The obtained results indicate the improvement of 0.5 to 5.65 TECU in IPGN with respect to the other empirical methods.

IONOSPHERE is the upper part of the atmosphere that extends from 80 to 1200 km above the Earth’s surface. The existing free electrones and ions in the IONOSPHERE layer affect the signal propogation speed such as satellite positioning and satellite altimetry signals. Regardless of the fact that Dual frequency measurments can remove ionospheric delay effect, dual frequency observations of the permanent GPS stations can also be utilized to produce the IONOSPHERE maps including the vertical total electron content (VTEC) values. For instance, International GNSS service (IGS) sub-centers produce daily global IONOSPHERE maps (GIMs) using the GNSS data. The spatial resolution of GIMs in the latitude and longitude directions is 2.5 degree and 5.0 degree, respectively, and their temporal resolution is 2 hours. One of the IGS sub-centers, namely CODE produces the GIMs based on the spherical harmonic basis functions up to the degree and order 15. The aim of this research is to develop a local inosphere model based on the B-spline basis functions using the combined GPS and satellite altimetry observations over Iran. Accordingly, the potentiality of the B-spline basis functions for local inosphere modeling was studied at first. For this purpose, a local IONOSPHERE model (LIM) was produced based on observation data from 16 Iranian permanent GPS stations and 5 IGS ones and B-spline basis functions. My assumptions in this modeling are as follows: first, the IONOSPHERE is a thin shell that is located on 450 km above the Earth’s surface, second, the smoothed code station observations obtained by Bernese 5.0 software is considered as observation vector. Third, the weight matrix elements are proportional to the satellite elevation angle. Forth, the differential code biases (DCBs) for all satellites which are obtained from IGS precise products, are considered as known parameters in the equations. And the last assumption was that a simple cosine mapping function was used to convert the slant total electron content (STEC) to the VTEC. As a result, the comparison between the LIM and the GIM showed that the B-spline basis functions were more efficient than the spherical harmonic ones for local IONOSPHERE modeling. Following the first result, a new LIM, which is based on the B-spline basis functions, was produced by integration of permanent GPS station and Jason-2 satellite altimetry observations. The GPS and satellite altimetry observations were chosen from day 107 of year 2014, according to the latest maximum solar activity. The weight matrix of the GPS and satellite altimetry observations were determined based on the least-square varince component estimation (LS-VCE) method. The results showed that the local inosphere model derived from combination of the GPS and satelite altimetry observations were more accurate than the local inosphere model derived from the GPS observations only, this is due to the fact that the dual-frequency radar altimetry data are the main source of the ionospheric observations at sea, where there is no GPS permanent station, and can be used to improve the GIMs and LIMs. Finally As by-products, the DCB values for the permanent GPS recievers and the bias term between the GPS and satellite altimetry observations were determined.

IONOSPHERE is a layer in the upper part of the atmosphere wide-ranging from 60 km to 2000 km. It has a very significant role in radio wave propagation because of its electromagnetic attributes. IONOSPHERE is mainly affected by solar zenith angle and solar activity. In the daytime, ionization in IONOSPHERE is at the highest level and the ionospheric effects are stronger. In the night-time, ionization decreases and the effects of IONOSPHERE gets weaker. One of the most important parameters that defines the physical structure of IONOSPHERE is Total Electron Content (TEC). TEC is a line integral of electron density along signal path between satellite to the receiver on the ground. The unit of TEC is TECU and 1 TECU equals 1016 electrons/m2. The TEC values can be computed from dual frequency Global Positioning System (GPS) stations, which are the most available observations for studying the Earth’ s IONOSPHERE. However, because of scattered repartition of dual frequency of GPS stations, precise information on TEC over the favorable region is unknown. Fuzzy inference systems (FIS) take inputs and process them based on the pre-specified rules to produce the outputs. Both the inputs and outputs are real values, whereas the internal processing is based on fuzzy rules and fuzzy arithmetic. FIS is the key unit of a fuzzy logic system having decision making as its primary work. It uses the “ IF… THEN” rules along with connectors “ OR” or “ AND” for drawing essential decision rules. A FIS is defined according to the following five main sections: • Rule Base − It contains fuzzy IF-THEN rules; • Database − It defines the membership functions of fuzzy sets used in fuzzy rules; • Decision-making Unit − It performs operation on rules; • Fuzzification Interface Unit − It converts the crisp quantities into fuzzy quantities; and • Defuzzification Interface Unit − It converts the fuzzy quantities into crisp quantities. In this paper, the TEC of the IONOSPHERE is modeled using FIS. The fuzzy inference system uses the rules IF-THEN to recognize the characteristics of dynamic phenomena. This feature, along with the simplicity of computing, has made it possible for this model to study the temporal and spatial variations of the IONOSPHERE. In fact, the main innovation of the paper is the time series modeling of TEC in Iran using FIS. Hybrid particle swarm optimization training (BP-PSO) algorithm is used to train fuzzy network. This algorithm uses the PSO in the early stages of searching for solution and uses the back propagation (BP) near the optimal solution. From the observations of 2015, the Tehran GPS station, which is one of the IGS global stations, was used for evaluation of the proposed model. Also, the results were compared with the results of the global IONOSPHERE map (GIM) TEC as well as artificial neural network model (ANN). In order to evaluate the accuracy of the fuzzy model presented in this paper, 5 days of each season were selected as the test data and model validation was performed in these 20 days. Based on the results, the average relative error calculated in the 20 test days for FIS, ANN and GIM models compared to GPS were 11. 25%, 19. 68% and 16. 03%, respectively. Besides, the average absolute error calculated for FIS, ANN and GIM models compared to GPS in the 20 test days was 1. 32 TECU, 3. 33 TECU and 1. 98 TECU, respectively. The calculated correlation coefficients between TEC obtained from FIS, ANN and GIM compared to GPS were 0. 9474, 0. 6960 and 0. 831, respectively. The results of the analysis show that the FIS model is superior to the ANN and GIM models. Using the proposed model of this research, the time series of the IONOSPHERE TEC can be modeled and investigated with high accuracy. This model can also be a good alternative to the outputs of the IGS network in Iran.

Global positioning system (GPS) signals provide valuable information about IONOSPHERE physical structure. Using these signals, can be derived total electron content (TEC) for each line of sight between the receiver and the satellite. For historic and other sparse data sets, the reconstruction of TEC images is often performed using multivariate interpolation techniques. Recently it has become clear that the techniques derived from artificial intelligence research and modern computer science provide a number of system aids to analyze and predict the behavior of complex solar-terrestrial dynamic systems. Methods of artificial intelligence have provided tools which potentially make the task of ionospheric modeling possible. Artificial neural network (ANN) provides an inexplicit non-linear model to learn relations between inputs and outputs using training data. Neural network is an information processing system which is formed by a large number of simple processing elements, known as artificial nerves. 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 behavior of neural network is related to communication between nodes. Using training data, the designed ANN can be adjusted in an iterative procedure to determine optimal parameters of ANN. Then for an unknown input, we can compute corresponding output using the trained ANN. 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. Radial basis function neural network (RBFNN) is known from the approximation theory as it is applied to the real multivariate interpolation problem. RBFNN is popularized by Moody and Darken (1989), and many researchers suggested it as an alternative ANN structure to MLP. RBFNN is very useful for function approximation and classification problems because of its more compact topology and faster learning speed. RBFNN is configured with three layers. An input layer consists of source neurons and distributes input vectors to each of the neurons in the hidden layer without any multiplicative factors. The single hidden layer has receptive field units (hidden neurons) each of which represents a nonlinear transfer function called a basis function. The output layer produces a linear weighted sum of hidden neuron outputs and supplies the response of RBFNN. Due to the nonlinearity of IONOSPHERE physical properties, in this paper, multi-layer perceptron artificial neural networks (MLP-ANN) and RBFNN used to model and predict the spatial and temporal variations of vertical TEC (VTEC) over Iran. The used model is able to estimate and predict the VTEC within and also near the network. For this work, observations of 22 GPS stations in northwest of Iran (360).

The IONOSPHERE is the part of the atmosphere in which the number of free electrons is so high that it significantly affects the propagation of radio waves. Since the deactivation of SA, the accuracy of the differential positioning with GPS is mostly dominated by the refraction delay of the GPS carrier waves in IONOSPHERE. To improve the accuracy of real time positions, it is customary to use the existing models in order to account for various sources of errors, including the effect of IONOSPHERE. Application of the single layer model, in which the electron content of IONOSPHERE is assumed to be condensed on a thin shell at a specific height, is a well-established technique to account for the IONOSPHERE delay. In contrary to this model, multi-layer modeling can provide three-dimensional information on the spatial distribution of the electrons in atmosphere. In this method, spherical harmonics and empirical orthogonal functions are the base functions in use for modeling the horizontal and the vertical content of the electron density. This is at the cost of using regularization techniques for solving the corresponding problem. Therefore, the developed model should be calibrated, i.e. the corresponding regularization parameter should be chosen, based on the a-priori information of the electron content. In this study, a 3D-modeling of the electron content has been constructed using the GPS measurements over Iran. The Ionosonde Data in the Tehran station (f=50.64, l=35.87) have been used for choosing an optimum value for the regularization parameter. To apply the method for constructing a 3D-image of the electron density, GPS measurements of the Iranian Permanent GPS Network (at 3-day in 2007) has been used. The instability of solution has been numerically analyzed and the Tikhonov method has been used for regularizing the solution. To come up with an optimum regularization parameter, the relative error in the Electron density profile computed from Ionosonde measurements and their 3D model are minimized. The modeling region is between 24 to 40 N and 44 to 64 W. The result of 3D-Model has been compared to that of the International Reference IONOSPHERE model 2007 (IRI-2007).The result shows that the electron density has 6×1011ele/cm3 diurnal variation and 9×1011ele/cm3 seasonal variations especially in winter months. This method could recover 66% to 99% of the IONOSPHERE electron density.