مجله ژئوفیزیک ایران
سال هفدهم شماره 4 (پیاپی 61، پاییز 1402)

The distribution of stations network is not uniform in Iran. The number of stations is more in densely populated areas and less around a large part of the central areas of Iran. Also, these stations have been operating for various periods. Therefore, in situ observation cannot lead to robust results and conclusions for the climate model evaluation and climate change studies. In recent years, reanalysis products have been considered as pseudo-observations, especially for climate studies and weather forecasting. The evaluation of reanalysis products is fundamental and critical because of the uncertainty due to the interpolation and to the data assimilation and forecasting models. In this paper, the reanalysis data of CFS-v2, MERRA-2, and ERA-5 climate databases are evaluated, using the data of 143 meteorological stations over the period 1991-2020. Reanalysis data are compared using Pearson's correlation coefficient (R), root mean square error (RMSE), normalized mean square error (NRMSE), Nash-Sutcliffe efficiency coefficient (NSC), bias index (BIAS), mean absolute error (MAE) and Kling–Gupta efficient (KGE), in the 6 regions representative of climate diversity in Iran. Also, the seasonal accuracy, annual trend, and probability distribution of the reanalyzed data were analyzed in comparison with the observations. The results show that CFS-v2, MERRA-2, and ERA-5 have high accuracy for estimating the temperature in Iran. In general, based on all statistical indicators and in all regions, ERA-5 is the most accurate; MERRA-2 and CFS-v2 were ranked second and third, with MAE values of 1.39, 1.71, and 2.077, respectively. In the spring, for the hot cluster (southwest), CFS-v2 data; in the summer, for the hot (southwest) and very cold (northwest) clusters, MERAA-2 data; and in autumn and winter, for the warm coastal cluster (coasts of Oman Sea and Persian Gulf), MERAA-2 data; are most accurate. In terms of annual trends, in the very cold cluster (northwest), CFS-v2 data; in the hot cluster (southwest), MERRA-2 data; and in other places, the data of ERA-5 have the closest trend to the trend of observations.

Solar radiation is very important as one of the important variables in energy balance models and plant growth simulation. Although the measurement of this variable has a relatively long history in Iran, due to the high costs of measuring devices, there is no pyranometer in many existing stations in the country, and there are problems such as its recalibration, water, and dust accumulation that exists on the sensor. Even in meteorological stations that measure radiation, there are days when radiation data is not recorded or unrealistic values outside the expected range are observed due to equipment violations or other problems.This research investigated the performance of nine machine learning algorithms including linear regression (LR), Least Absolute Shrinkage and Selection Operator (Lasso), Elastic Net (EN), K-Nearest Neighbors (kNN), Decision Tree (DT), Support Vector Regression (SVR), Random Forest (RF), Extra Trees (ET) and Gradient Boosting Machine (GBM) to estimate solar radiation in Yazd synoptic station between 2005 and 2021 with cross-validation method (kfold). The parameters of average temperature, minimum temperature, maximum temperature, sunny hours, relative humidity, and solar radiation are obtained from the National Meteorological Organization on a daily basis and extraterrestrial radiation variables, relative distance from the earth to the sun, solar inclination angle, and maximum sunny hours are calculated with existing relationships and were selected as input for the prediction models. The evaluation criteria for solar radiation estimation were MSE (Mean Square Error), MAPE (Mean Absolute Error), and determination Coefficient (R2).The results showed the coefficient of determination (R2) varies between 0.716 and 0.870 depending on the algorithm used in the training phase. In other words, in terms of the determination coefficient, all the used algorithms showed good results for predicting solar radiation. According to the results of all three criteria, it can be seen that the Support Vector Regression (SVR) algorithm has performed better than other algorithms. After the support vector regression (SVR) algorithm, the linear regression (LR) algorithm was ranked next with the MAPE of 5.04, the MSE of 1.13, and the R2 of 0.867. Also, the elastic pure regression algorithm (EN) with the highest mean absolute value of error (MAPE), the highest mean squared error (MSE), and the lowest coefficient of explanation (R2) ranked last among the nine used algorithms. After the data training phase, using the K-fold cross-validation method, the remaining 20% of the data were tested. As the results show, the MSE changes for nine machine learning algorithms are high and vary from about 3 to 9 Mj/m2/day. Its highest value was observed in the DT algorithm and its lowest value was observed in the support vector algorithm. The average value of the absolute value of the error was also in the range of 0.8 to 2.2 Mj/m2/day, and also the values of the R2 were different in the range of 0.7 to 0.9. In general, and according to the results of all three evaluation criteria, the support vector machine algorithm showed the best results in the data test stage as well as in the training stage.

Pardis New Town located at a distance of 17 km from Tehran is bounded by the Alborz mountain range from the north, the Jajrud River from the west, Karasht, Siahsang and Taherabad villages from the south, and Bumehen from the east. It is located in the catchment area of the Jajrud River and has about 3600 hectares area. It includes 9 phases, 6 of which are residential regions and the other three are for research, industrial and tourism purposes.Since Pardis New Town is located in Tehran province and in proximity to the Mosha and North Tehran faults which have historical earthquakes, this study investigates its seismicity and seismic risk analysis by probabilistic method.   After estimating the parameters of each seismic source, namely β, λ and the expected maximum magnitude, the peak ground acceleration of the sources was obtained for the return period of 475 years. For an earthquake with a return period of 475 years, the maximum value of ground acceleration in bedrock was equal to 0.34 g, and when we take into account the soil effect using the existing boreholes that indicate the geotechnical characteristics of the soil of the site, the maximum acceleration of 0.37 g in the eastern area of Pardis was obtained.

Studying the bedrock geometry in mining and oil exploration operations to obtain its 2D pattern requires nonlinear reverse computations. Local optimization methods for solving nonlinear inverse problems are based on linearizing the changes of the model similar to a primary model and finding an objective function of minimum error from the parameters of the model; however, these optimization methods are not able to select a suitable primary function that is close enough to the general optimal value. That is to say, every objective function can have several minimum and maximum solutions. The lowest minimum is called the global minimum while the rest of them are named local minima. Therefore, in local inverse methods, the goal is to find the minimum of an objective function, and also an objective function might have a few local minima with different values. In this case, it is not suitable to use gradient-based methods for exploration purposes, unless the primary model is very close to the actual answer, which is outside the control of geological structures or the geometry of the subsurface. Despite the easy execution and high convergence rate of the local methods, there is the possibility of being trapped in local minima because these methods are dependent on the primary model, and also finding more than one optimized point in 2D or 3D simulations; this is why local optimization methods are considered deterministic algorithms. Multi-objective metaheuristic optimization algorithms are capable of searching the feasible region and they also provide a solution independent of the primary model. Searching the feasible region means finding all the feasible solutions for a problem. Each point in this region is representing a solution that can be ranked based on its value. One of the important differences between local optimization and metaheuristic methods is constraining. Constraining metaheuristic global optimization methods are only used for constraining the feasible region based on previous knowledge or estimation relations, which is different from constraining local optimization that is used for stabilizing inverse simulation. The algorithm used in the present work includes non-dominated sorting genetic algorithm (NSGA-II). The NSGA-II is commonly used to solve problems with multiple, typically conflicting objective functions. This algorithm is capable of being developed and also has a high potential for solving unbounded multi-objective problems. In the present study, NSGA-II algorithm was verified and validated using the data produced by an imaginary and complex synthetic model. In the present research work, a hybrid technique of NSGA-II and TOPSIS algorithms was introduced and utilized as a viable search method for nonlinear modeling of the gravity data, and a substitute for the optimization methods. In order for a more precise examination of the performance of this algorithm, the imaginary synthetic data were used both with no noise and with up to 10% Gaussian white noise (GWN). Based on the gravimetric data of the Moghan basin and Atacama Desert, Chile, the results obtained from algorithm indicated good performance of the NSGA-II and NSGAII-TOPSIS algorithms.

Magnetometry is one of the geophysical methods that has wide applications. In traditional magnetometry, one sensor so-called magnetometer is used for measurements. In order to obtain the gradient in a specific direction, mathematical derivation can be used. Alternatively, A more accurate method of obtaining the gradient is using two sensors simultaneously measuring the magnetic field. In this case, the gradient is obtained in the direction where the two sensors are placed relative to each other. To obtain the Full Magnetic Tensor Gradient (FMTG) of the Earth’s magnetic field, although direction derivatives can be used, the more accurate method is to use four sensors that measure the Earth’s magnetic field simultaneously. In this way, the FMTG matrix can be obtained. So far, Superconducting Quantum Interference Device (SQID) sensors have been used the most in obtaining the FMTG. But these sensors are very expensive and have accurate performance in a small range of temperatures. By developing the Micro Electro Mechanical System (MEMS) sensor with its variety of applications, geophysicists are also becoming interested in using such sensors. In this study, one of the precise MEMS magnetometers has been selected. Although the MEMS sensors have low sensitivity compared to SQUID magnetometers, due to their availability, being cheap, and lightness, they are a suitable option for FMTG measurements. Although these sensors do not have the resolution of SQUID magnetometers and they cannot be used on small anomalies that have caused changes of less than 160 nT on the Earth’s magnetic field, these kinds of sensors are able to measure the magnitude of the magnetic field in three perpendicular directions and have an acceptable sensitivity. In this study, four sensors of this type have been set up in a cross arrangement. In order to solve the problem of the low sensitivity of these sensors compared to SQUID sensors, the distance between the sensors was chosen to be one meter so that the gradient value would be larger enough. Then, a survey has been performed on an iron ore deposit near Jafar Khan village, Saqqz City, Kurdistan province. In order to validate elements of the FMGT matrix, they have been compared with the directional derivatives obtained from a sensor. This comparison shows that the elements Signal-to-noise ratio of elements Gxx, Gyx, Gzx, Gzy, and Gzz have increased around zero, 4.79, 10.80, 83.83, and 8.46, respectively, compared to their corresponding directional derivatives. This issue shows the ability of MEMS sensors to capture the full tensor of the magnetic gradient.

The near-surface temperature in the Arctic has increased about four times greater than the global average temperature in recent decades, the feature called rapid Arctic warming or Arctic amplification. Different dynamical pathways have been hypothesized through which Arctic amplification may influence the midlatitude weather. There is also some evidence that extreme weather in midlatitudes of the Northern Hemisphere is linked to rapid Arctic warming. We analyzed the ERA5 data for the period 1965–2021 to investigate the impact of rapid Arctic warming on weather patterns in West Asia. We defined pre-Arctic and post-Arctic amplification periods as 1965–1991 and 1995–2021, respectively. We analyzed wind speeds between 400 and 100 hPa to investigate the response of the upper-tropospheric jet stream in West Asia to rapid Arctic warming. We defined the meridional extent of an isopleth of the geopotential height at 500 hPa (Z500) as the difference between the maximum and minimum latitudes reached by a single Z500 isopleth each day. By averaging these daily values in different seasons during the pre-Arctic and post-Arctic amplification periods, we obtained seasonal averages of the meridional wave extent in these two time periods. In winter, the altitude of the maximum geopotential height in the middle troposphere has increased in the post-Arctic amplification period. Moreover, the latitudinal extension of the maximum amplitude of Rossby waves has decreased. These two changes imply that the speed of the eastward propagation of Rossby waves has intensified in the post-Arctic amplification period.In summer, the subtropical jet stream has wakened and shifted toward the equator in the post-Arctic amplification period. Due to the weakening of the meridional temperature gradient, both the subtropical and subpolar jet streams become weaker and shift poleward in summer. The poleward shift of the subtropical jet stream has also occurred in the post-Arctic amplification period, while the jet has weakened compared to the pre-Arctic amplification period. The analysis of the meridional circulation index (MCI) shows significant positive and negative values in most parts of West Asia in the post-Arctic amplification period, implying that Rossby waves have become wavier and their speed has decreased, associated with which is a higher possibility for the occurrence of extreme weather events in summer. In autumn, in the post-Arctic amplification period, the jet stream has intensified in the eastern parts of West Asia compared to the climatology and the pre-Arctic amplification period. Between 50 and 75 ºE, the subtropical jet stream in West Asia has shifted toward the equator in the post-Arctic amplification period. As the subtropical jet stream separates the warmer tropical air from the colder air of higher latitudes, its equatorward shift in the post-Arctic amplification period implies the development of negative temperature anomalies in most parts of West Asia.

Accidents and accidents in the aviation industry are mainly affected by meteorological and aviation factors. Research has indicated that weather conditions have a powerful impact on military and civilian aviation operations. The goal of this study is to investigate the impact of aviation parameters on flight safety using a well-known numerical model in meteorology, namely the mid-scale research and weather forecasting (WRF) model. This research identifies and analyzes the influential factors in the crash of the Tehran passenger plane to Yasuj near the Dena Mountain range. For this purpose, by calculating the load factor, angle of attack, and stall speed for the desired aircraft, the effect of each factor has been investigated. Considering the sub-zero temperature prevailing in the region, which was obtained by checking the temperature at different altitudes and times for the flight path, the phenomenon of freezing can occur on the body and wings of the plane if the plane is not equipped with a de-icing system. With the formation of ice, the smooth surface necessary for air flow is lost and the drag force increases, and the ability to produce power decreases, which leads to a decrease in height. To compensate for this additional drag, the propulsion force of the aircraft must also be increased. The nose of the aircraft must be raised to produce more thrust and prevent altitude loss, which means the angle of attack must be increased. But as the angle of attack increases, the amount of ice on the wings and fuselage increases. By calculating and checking the angle of attack on the flight path of the plane and the place where the plane hit the mountain, it is shown that the angle of attack first decreases and then increases as it approaches the place of impact. The angle of attack has a direct relationship with the drag coefficient, and with the decrease of the angle of attack, the drag coefficient also decreases, and as a result, the drag force also decreases, and the plane faces a decrease in altitude. At the point of impact, the angle of attack increases, and the plane tries to increase the height, but unfortunately, it hits the mountain. Stall speed is the minimum speed at which the aircraft must fly to maintain proper flight altitude. According to the minimum values of stall speed along the flight path and at altitudes close to the flight height, the plane flies at a speed lower than the minimum stall speed at an altitude of 4467 meters. This causes a loss of height in the flight path. By calculating and checking the load factor in the flight range for different altitudes and times with different coefficients, we reached a negative load factor in the range of the plane hitting the mountain, which indicates a decrease in altitude for the plane and an increase in drag force.