فصلنامه فیزیک زمین و فضا
سال چهل و هشتم شماره 1 (بهار 1401)

Study of Moho in Middle East and surrounding region is of great importance for scientists, because it has a rich geological history and contains parts of the Eurasian, Indian, African and Arabian plates as the main plates and some small plates. According to complexity and different tectonic structures in Middle East using a proper method that yields a Moho depth model which is in accordance with these structures, has a great importance. In this paper we compare the Moho depth obtained from two different methods, 1) Gravity data inversion of spherical prisms (tesseroids) and 2) Moho depth evaluation using tesseroids and CRUST1.0 crustal model. Determining of Moho depth from gravity data is a nonlinear inverse problem. Regarding the extent of the study area we use an efficient inversion method (Uieda’s inversion method) in order to consider the earth's curvature by using spherical prisms instead of rectangular prisms. In this method one needs to minimize the  cost function, where is the fidelity term,  is the penalty term and  is regularization parameter. In this method in addition to Moho depth, we need to estimate three hyper parameters namely the regularization parameter ( ), Moho reference level ( ) and density contrast ( ). They are estimated in two steps during the inversion by holdout-cross validation methods.To estimate the relief of the Moho from gravity data, first one must obtain the gravitational effect of the target anomalous density distribution attributed to the Moho relief, this requires eliminating all gravity effects other than that of the target anomalous density from observed data. In the first method tesseroid modeling is used to calculate the gravity effect of the topography and sediments. The effect of topography and crustal sediments are removed using global topography and crustal models. In the second method first we extract Moho depth over the study region from CRUST1.0 model and then evaluate gravity effect arising from this anomalous Moho, then using inversion method to estimate the Moho depth from CRUST 1.0 model. According to the results, the minimum depth of Moho is about 12 km in some parts of Indian Ocean and the maximum depth is about 54 km in the west of Tibetan plateau from the first method which is in accordance with plate boundaries and correlates well with the prominent tectonic features of the Middle East region. The Moho depth obtained from the second method varies between 7.5 and 49 km where the minimum depth is related to the parts of Indian Ocean and maximum depth is appeared in parts of the Zagros in Iran. Comparing the results of two methods demonstrates the acceptable performance of the adapted inversion procedure and utilization of spherical prisms but the calculated Moho depth from second method failed to estimate acceptable Moho depth especially in divergent boundary at Red sea, Gulf of Aden and Indian Ocean. The results indicate that the CRUST1.0 model, at least over an area with large extent, is not a suitable model for gravity inversion and Moho depth estimation.

Atmospheric wet refractivity indices, which are dependent on the water vapor, are one of the most important parameters for analyzing climate change in an area. Wet refractivity indices can be estimated from Radiosonde stations measurement or calculated from numerical meteorological models. But due to low temporal and spatial resolution of radiosonde stations and severe variations of water vapor in the lower levels of Atmosphere, today’s numerical meteorological models provide low accuracy for atmospheric parameters. But nowadays, by growing number of stations that can use global positioning satellite measurements, atmospheric parameter can be estimated via remote sensing measurements in wide temporal and spatial resolutions. Wet refractivity indices cause delay in GPS measurement signals thus this delay have information about distribution of wet refractivity indices in atmosphere. By the use of global positioning satellites that can estimate atmospheric wet delay and tomography method, wet refractivity indices can be estimated. One of the growing methods for measuring the atmosphere parameters is the radio occultation technique. By increasing the number of low earth orbit satellites that carry GNSS receiver, this technique can provide observation in all of the globe, which its observations are obtained directly from the type of atmosphere parameters. The aim of this study is to use a combination of RO and GPS observation in 3D and 4D atmospheric tomography. But since tomography problem are ill-posed because of the poor distribution of GPS observations in network, a functional model has been implemented to estimate the wet refractivity indices from of the atmospheric tomography problem. By expanding tomography’s unknowns to base functions coefficients, the number of unknowns will be decreased and problem will become well-posed and unknowns can be estimated from inverse problem. In the three-dimensional functional model, combination of spherical cap harmonics and empirical orthogonal functions have been used to solve the inverse problem. Spherical cap harmonics are used to represent the wet refractivity indices in horizontal distribution and empirical orthogonal functions are used for the vertical distribution of the unknown coefficients. Eventually, the B-spline is used to represent the four-dimensional functional model and the dependence of coefficients to the time. After implementing 3D and 4D functional models, the relative weight of RO data with comparison to GPS data has been calculated using variance component method. The US region of California has been selected as the study network due to its high tectonic importance and the large number of GPS stations. The results in two considered tomography epochs have been validated with radiosonde station data in the network and also have been compared with ERA5 reanalysis data. Comparison of the profiles obtained from tomography and the ERA5 data profiles with the radiosonde wet refractivity indices shows that the results obtained from the functional model tomography are better than those of the ERA5 data. The results of the combination illustrate that using RO data in both 3D and 4D models, the RMSE has been decreased and showed improvement of about 7 to 10 percent compared to uncombined tomographic models. Also, it is seen that using RO data in the 4D model has higher accuracy compared to the 3D model due to the use of a time-dependent functional model that increases the functional model's accuracy.

Sea level anomaly as a parameter that expresses the difference between the instantaneous water level height and the average amount of water level in a period of time is of great importance in studying the water level situation in different regions. Predicting a time series requires that the series be static and that seasonal trends and changes be removed from the observations to eliminate the dependence of variance and mean on time. For this purpose, the use of various methods to static a time series has been suggested and used. Using the method of decomposition into the intrinsic modes of a signal that underlies the formation of intrinsic mode functions that include parts of the signal with approximately the same frequency; in order to analyze and isolate the trend and seasonal changes of the signal have been considered. Caspian sea as the largest lake in the world or the so-called largest enclosed water area in the world is located in northern Iran. This important water area has become one of the main sources of income for its peripheral countries. It has important oil and gas resources as well as the main source of sturgeon as one of the most expensive food sources in the world. This strategic region is known as a medium for connecting the East and the West of the world. In addition to the economic and commercial dimension, the Caspian Sea is of great importance from the military point of view, as numerous military maneuvers are held every year by the neighboring countries. For the above reasons; awareness of the water level and its changes has become increasingly important, especially over the past few decades, but despite this importance, not many studies have been conducted to study the water level. Therefore, in this research, using satellite altimeter data, the monitoring of water level changes in this area has been done. In this study a coverage of the sea anomaly parameter and its changes from 1993 to the present has been provided. The Caspian Sea water region as one of the two important water sources for Iran, is strategically important.For this purpose, in this study, using the transit data of 92 satellite altimetric missions passing through the Caspian Sea region, the changes in the sea level anomaly in this region since 1993 have been observed. This quantity is then analyzed using the method of analysis of intrinsic modes as an efficient method in separating the frequencies that make up a signal and then, using a neural network, a network of radial base functions has been created in order to predict sea level anomaly. 9 dominant frequencies along with a trend are the result of signal analysis considered in this study. Finally, it leads to the parameters of the mean square error of 0.029 m and 0.034 m with a correlation coefficient of 0.99 and 0.97, respectively, in the two stages of neural network training and testing.

In order to properly understand the subsurface structures, the issue of inversion of geophysical data has received much attention from researchers. Since accurate reconstruction of the shape and boundaries of the mass using gravimetric data is very important in some issues, it is important to use an effective and efficient method that has a high ability to draw and reconstruct the boundaries of a mass. In recent years, the level set method introduced by Asher and Stein has been widely used to solve this problem. From the expansion of the level set function in some bases of the problem, the effective number of parameters is greatly reduced and an optimization problem is created which its behavior is better than the least squares problem. As a result, the level set parameterization method will be presented for the reconstruction of inversion models. A common advantage of the parametric level set method is the careful examination of the boundary for optimum sensitivities, which significantly reduces the dimensional problem, and many of the difficulties of traditional level set methods, such as regularization, reconstruction, and basis function. Level set parameterization is performed by radial basis functions (RBF); which causes an optimal problem with an average number of parameters and high flexibility; and the computational and optimization process for Newton's method is more accurate and smooth. The model is described by the zero contour of a level-set function, which in turn is represented by a relatively small number of radial basis functions. This formulation includes some additional parameters such as the width of the radial basis functions and the smoothness of the Heaviside function. The latter is of particular importance as it controls the sensitivity to changes in the model. In this algorithm adaptively chooses the required smoothness parameter and tests the method on a suite of idealized Earth models. In this evolutionary approach, the reduction gradient method usually requires many iterations for convergence, and the functions are weakened for low-sensitivity problems. Although the use of Quasi- Newton methods to improve the level set function increases the degree of convergence, they are computationally challenging, and for large problems and relatively finer grids, a system of equations must be solved in each iteration. Moreover, based on the fact that the number of underlying parameters in a parametric approach is usually much less than the number of pixels resulting from the discretization of the level set function, we make a use of a Newton-type method to solve the underlying optimization problem.In this research, the algorithm is used to investigate its strengths and weaknesses for applying geophysical gravity data, coding and programming, and it is tested using several two-dimensional synthetic models. Finally, the method is tested on gravity data from the Mobrun ore body, north east of Noranda, Quebec, Canada.The results of this study show that the application of the optimization algorithm of the level set function will lead to a relatively more accurate and realistic detection of mass boundaries. It shows that the tested mass has spread from a depth of 10 meters to a depth of 160 meters.

Terrestrial gravimetry in large countries such as Iran with mountainous areas is time consuming and costly. Airborne gravimetry can be used to fill the data gravity gaps. Airborne gravity data are contaminated with different kinds of systematic and random errors that should be evaluated before use. In this study, the downward continued airborne gravity data is compared with existing terrestrial gravity data for detecting probable biases and measurement error. For this purpose, the efficiencies of the two least squares colocation and Poisson's integral methods are compared.Collocation is an optimal linear prediction method in which the base functions are directly related to the covariance functions. The covariance function can be derived from empirical covariance fitting. This method can be utilized for downward continuation (DWC) of gravity data with arbitrary distribution. Often the homogeneous and isotropic covariance functions are used in collocation. However, in reality the statistical parameters of gravity data change with location and azimuth. This is the main drawback of collocation with stationary covariance function. Based on the Dirichlet’s boundary values problem for harmonic functions, the downward continuation of airborne gravity data from the flight altitude to the geoid/ellipsoid surface is given by inverse of Poisson’s integral. Similar to collocation, this method can be utilized for DWC of gravity data with arbitrary distribution. Poisson’s integral as inverse problem is unstable in continuous form. However, for discrete data, the instability depends of the amplitude of high frequency components in the gravity observation such as error measurements.Numerical computations for this study were performed in the Colorado region and northern parts of New Mexico that is bounded by . In this region, 524,381 airborne data are available in 106 flight lines. The along track sampling is 1 Hz (about 128 meters) and the cross distance between lines is about 10 km. To reduce the edge effect, the final test area is reduced to  which includes 5494 ground gravity points. To improve the efficiency of the computations, the sampling interval is decreased to  Hz (about 2 km).We first demonstrate the applications of the DWC methods using simulated gravity data. Short wavelength of gravity disturbance related to degree 360-2190, was generated using experimental global gravity model 'refB' at the two true positions of airborne and ground data. Two (white) noise 1 and 2 mGal was added to airborne data. Using these simulated observations, the two aforementioned methods were employed to determine the terrestrial disturbances. The comparison of computed and simulated terrestrial disturbances show that the accuracy of the Poisson method for both noise levels is about 30% better than the collocation.For real data, the residual gravity data is computed by subtracting the long wavelengths up to degree 360 and corresponding residual topographical effect (RTM) from the real gravity observation. RTM is derived from the harmonic model (dV_ELL_Earth2014_5480) of spherical harmonic degrees between 360-5480. This model provides spherical harmonics of gravitational potential of upper crust. According to previous studies, the level noise of airborne gravity of Colorado is about 2.0 mGal. By introducing this noise into collocation, the problem becomes stable. In Poisson method, the iterative 'lsqr' method is used to solve the system of linear equations. To achieve stable solution, the iterations was terminate using discrepancy principal rule.The residual anomaly gravity at Earth's surface can be computed directly using collocation. But in the Poisson method, computation is performed in two steps: 1 the airborne gravity disturbances are downward continued to a  grid on the reference ellipsoid, 2- the terrestrial gravity disturbance is computed by upward continuation from ellipsoid disturbances. Despite of simulated data, the accuracy of the two methods is the same in terms of standard deviation of the differences. The mean and the standard values of difference is about 2mGal and 8mGal, respectively. According to a study by Saleh et al. (2013), the bias of in parts of Colorado reaches more than 2mGal. Therefore, due to the bias of terrestrial data, the estimated bias in airborne data cannot be confirmed.

The study of the simultaneous occurrence of cyclones and the changes of Tropopause Pressure's level (TPL) can provide useful insights into the characteristics of the precipitation, especially the widespread precipitation (WP) over Iran; as mid-latitude cyclones are one of the most critical factors associated with WP in Iran. Understanding the mechanisms and the features associated with the cyclones can be crucial for estimating and predicting cyclones and their consequences with precision. To this end, in the current study, we underlined the relationship between tropopause and cyclones affecting WP in the country.In the current study, two data sets were adopted. These data sets include daily precipitation data of Asfazary national data set (version 3) and atmospheric data (including temperature and geopotential height (GH) data of ERA-Interim base from the European Centre for Medium-Range Weather Forecasts (ECMWF)) with spatial resolution of 0.25 degrees for an area comprised 0 to 80° N and -10 to 120° E. The main aim of selecting the aforementioned area and the data was to identify all the cyclones which are originated from or pass through the Mediterranean Sea and are associated with WP over Iran. Accordingly, the associated pressure levels of the tropopause were examined.The Asfazary database from 1979 to 2015 was adopted to identify days with WP based on precipitation anomalies covering more than 10% of the country. Accordingly, a total of about 1189 days with WP was extracted for the intended period.In this study, regional variations of GH at the level of 1000 hPa have been used to identify cyclone centers. To this end, the GH of the pixel was evaluated in relation to the eight neighboring pixels; when the GH was lower than the neighboring ones, and the gradient of the GH was at least 100 geopotential meters per thousand kilometers, the pixel was considered as the center of the cyclone. Cyclones were tracked with respect to the days with WP, and their characteristics were investigated based on the day of cyclone activity and the day of WP.Using the thermal criterion defined by the World Meteorological Organization (WMO, 1957)), the tropopause was identified.The 1189 days with WP have been studied visually. Since it is not feasible to present all the days in this brief paper, a few samples were selected to identify the association of tropopause with cyclones on days with WP. The days were selected based on the highest percentage of the area covered for different months. Accordingly, for the entire period, 8 days were selected to represent January, February, March, April, June, October, November, and December. In May, July, August, and September, days with WP were not observed. In the present study, to investigate the relationship between tropopause and cyclones in eight WP samples, the features of tropopause and cyclones on the starting days and on the days with WP were considered.The spatial distribution of the TPL on the day of cyclone activity and the day with WP showed that on the day of cyclone activity, tropopause had certain characteristics; at this time, the tropopause pressure level showed larger values than those in the surrounding areas. Even on days when WP was observed in Iran and within the cyclone activity range, this anomaly was observed in the TPL. The tropospheric condition of the country compared to the day of the cyclone activity had significant differences; at the time of precipitation, tropopause level showed a larger numerical value in most areas compared to the beginning of the cyclone, especially in areas with heavy precipitation intensity. Tropopause at the time of the formation of the cyclone with WP on April 7, 2013, was different from other under study cases. In this case, at the beginning of the cyclone activity on the cyclone formation area, the tropopause did not have a significant anomaly; while on the day of WP in the south of Iran, the anomaly was significantly prominent. It seems that this difference can be due to the differences in the origin and the mechanism of cyclones in different areas. This probably explains the difference in the characteristics of tropopause on the day of cyclone activity. In the whole area under study, at latitudes above 30 degrees, in geographic locations where the cyclones emerged at the 1000 hPa, tropopause was broken. At this time, tropopause pressure levels showed larger values than the surrounding areas. Given this fact, it seems that there is a relationship between the two phenomena, cyclones and TPL.Based on the findings, in all eight samples of WP days, tropopause had special characteristics in the same area of cyclone; in addition, tropopause pressure levels in these areas were higher than their counterparts at the same geographical situation.

Land surface temperature (LST) estimation is widely used in many applied and environmental studies such as agriculture, climate change, water resources, energy management, urban microclimate and environment. LST, which is the result of atmospheric-earth interaction, due to the sensitivity and influence of land surface conditions such as soil cover, soil moisture, albedo, surface roughness and the interaction of these factors with the atmosphere, can well determine changes in land surface temperature conditions. In the present study, Modis nighttime sensor products of both Terra and Aqua satellites (MOD11C3 & MYD11C3) from http://reverb.echo.nasa.gov/reverb for LST estimation in the Jazmourian drainage basin (southeast of Iran), were used in the period 2013-2019. After providing the products with monthly and spatial time steps of 5 km, calculations on two matrices; One monthly with dimensions of 2784 x 204 (204 represents the number of observations in consecutive months of 17 years studied (17 x 12) and 2784 represents the number of gridded points (cells) in Jazmourian drainage basin area) and the other is a seasonal matrix with dimensions of 2784 x 68 (68 representing the number of observations in consecutive chapters (17 x 4) were performed. After performing the relevant statistical and spatial analyzes in Excel and GIS software environment, nighttime LST estimation was used. The results showed that the nighttime LST in the statistical period increased by about 1 degree Celsius and this increase was more in the minimum temperatures (cold period months of the year) than the maximum nighttime LST. According to the findings, the maximum nighttime LST has occurred in the low altitudes of the central and southern regions and the minimum LST has also occurred in the northern heights of the drainage basin. The seasonal spatial distribution of the Earth's nighttime LST indicates the distribution of nighttime LST in the range of -10 to +35°C in winter and summer, respectively. Extreme fluctuations in nighttime LST during the seasonal terrestrial surface well show the prominent role of altitudes and latitudes in the temperature distribution of the Jazmourian drainage basin. Also, the time analysis of the studied variable shows a positive trend of nighttime LST in all four seasons, among which the spring and winter seasons had a higher upward slope. In addition; spatial estimation of nighttime LST anomalies, while confirming its increasing trend, shows the maximum location of nighttime LST anomalies in the central and western parts and the minimum anomalies in the eastern parts and northern heights of the drainage basin. Also, the analysis of monthly anomalies of nighttime LST shows the maximum occurrence of positive anomalies with +0.07°C in September 2016 and the minimum anomalies with -0.01 °C. are in January 2008. In general, the values of the nighttime LST significantly increased from 2008 onwards, especially in the months related to the cold period of the year (with a greater increase in the minimum nighttime LST than the maximum nighttime LST). This indicates the nighttime LST trend of the cold period of the year towards a warmer pattern. These conditions can be considered as an indicator of climate change and lead to changes in some environmental parameters such as relative humidity, evapotranspiration, soil surface moisture, snow persistence, dew point temperature and nightly reflective energy. Considering the high capabilities of the Jazmourian drainage basin in agricultural products and also the capability of seasonal tourism in different areas of this drainage basin, the importance of investigating nighttime LST changes, in this regard, is undeniable. On the other hand, with the continuing increase of environmental sensitivities and the accelerating trend of continental climate in this drainage basin, it is suggested that in future research, while estimating other climatic variables, their correlations with LST are considered. This will provide more climate knowledge of the environmental changes that have occurred in this less studied drainage basin.

Protecting the electronic components against the space radiation is an important basic requirement in satellites designing and constructing. One of the most common radiation shields for satellites is the addition of aluminum to achieve the desired radiation levels. However, in environments such as the GEO circuit where electrons are predominant, thick aluminum walls are not the most effective beam shields, as they are not able to attenuate the secondary X-rays caused by the electrons colliding with the shielding material. In general, materials with higher atomic numbers, such as tantalum, can severely attenuate X-rays, but when used as their own electron shield, they generate more secondary X-rays and impose more weight on the system. Today, polyethylene is a well-known material in the field of protection due to its high level of hydrogen, low density, ease of use and reasonable price, and is used as a benchmark for comparing the efficiency and effectiveness of other protection materials. There is a lighter method of protection called multilayer which works well in electronic environments as well as protecting against energetic protons. In designing and manufacturing radiation protection, proper selection of material and layer thickness is very important in reducing the dose and optimizing the weight. This requires experimental or computational work. Despite the accuracy of the experimental method, because practical experiments are costly and require a long time to run, and due to lack of access to space radiation testing laboratories, using computational and simulation methods can save time and budget.In this work, the influence of different structures in space radiation shielding has been evaluated using MCNPX Monte Carlo code. Therefore, the induced dose was calculated in a silicon component. A graded-z shield consisting of aluminum, carbon and polyethylene was proposed. The operation of the graded-z shield in various dose ranges has been investigated and compared with aluminum and polyethylene. Due to the importance of weight factor in the design of space systems, this factor is considered as one of the criteria for optimizing the thickness of the designed protection layers in comparison with aluminum and polyethylene protection for low-risk, medium and high-risk periods. The energy and flux of space rays for a mission in the GEO orbit that began in early 2021 and lasts for 5 years is provided by the Space Environment Information System (SPENVIS). The results showed that by replacing the conventional aluminum shield with the graded-z shield in specified dose ranges, weight reduction of 22/12% will be achieved in maximum case. For medium and low risk ranges, the use of multi-layer protection is more sensible in terms of weight than aluminum protection. In addition, if it is not necessary to use aluminum boxes to place electronic components inside the satellite, use polyethylene shield in terms of weight budget in high risk mode with 17.65%, medium risk 13.16% and low risk with 19.23% difference compared to aluminum protection is cost effective. Advantage in the field of manufacturing new materials such as aerogels and the placement of these lightweight materials can lead to lighter shields.

In the present study, using the ERA-INTERIM reanalysis data for geopotential height, horizontal wind speed and relative vorticity at 300, 200, 150, 100 and 50hPa levels, the quasi geostrophic potential vorticity, the quasi geostrophic potential vorticity gradient ,the wave activity and wave activity flux for cyclonic and anticyclonic Rossby wave breaking events that occurred over Europe during the winter time 1979-2018 in the westerly and easterly phase of quasi biennial Oscillation, were calculated and analyzed. The mechanism of Rossby wave breaking during five days before to five days after the wave break were analyzed. The Results show that in the anticyclonic breaking event over west Asia in the QBOe, the poleward displacement of jet in the upstream of trough to upper latitude over the Europe is more consistent than for the QBOw. Whereas in the cyclonic break in the westerly phase, jet on the upstream of trough over the west of Mediterranean sea displace to lower latitude over the Europe more than that pf the easterly phase. Therefore in the anticyclonic wave breaking over the west Asia in the QBOe compared that of to QBOw, the amplitude of the waves increase. The QBOe in the anticyclonic breaking causes increasing altitude on the upstream trough over the Europe and decreasing altitude on the downstream trough over the east Europe and Mediterranean and also causes increasing altitude over the east of Atlantic ocean. In the cyclonic breaking, QBOe causes increasing altitude on upstream of trough over the west of Mediterranean and decreasing altitude on the downstream of trough over the east of Mediterranean region.In the anticyclonic wave breaking on the west Asia and east Mediterranean in the QBOe, anomaly jets velocity and following the formation of critical latitude on north Europe is stronger than the critical latitude in the QBOw. The QBOe causes poleward displacement the jets and critical latitude as compared to that of the QBOw. In the anticyclonic wave breaking over west Asia, formation of extended ridge over Atlantic ocean and Europe causes settlement of the narrow trough on the west Asia. In the QBOe, the jet intensifies over north of Europe and critical latitude on the upstream of trough form stronger, QBOw. Equatorward wave activity flux due to anticyclonic breaking in the QBOe is more than that of the QBOw. Therefore the anticyclonic wave breaking in QBOe is stronger than QBOw.   In the cyclonic waves breaking, jets on the upstream of trough over Europe and jet on the downstream of trough over east Mediterranean are formed across north westerly- south easterly. In the QBOe, jet on the upstream of trough intensifies on the upper latitude as compared to the QBOw. Following this the critical latitude have poleward displacement. In the QBOe, north westerly-south easterly slope of trough is more than QBOw and the trough on the Mediterranean and east Europe has lower altitude compared to that for the QBOw. The poleward wave activity flux due to cyclonic wave breaking is more in QBOe compared to that for the QBOw. Therefore the cyclonic wave breaking is stronger in QBOe compared to that for the QBOw.Whereas in the anticyclonic wave breaking over west Mediterranean in the QBOw compared to that for the QBOe and the meridional gradient of quasi geostrophic potential vorticity is stronger and meridional wave activity flux is more. Therefore the anticyclonic wave breaking over west Mediterranean in the QBOw is stronger compared to that for the QBOe.

The modification of laws of physics at short intervals is an important result of the theory of quantum gravity. For instance, commutative relations of standard quantum mechanics change on scales of length- called Planck length. It should be noted that these changes can be neglected at low energy levels but they are considerable only at high energy levels such as the initial universe. In this regard, the principle of uncertainty of standard quantum mechanics is changed with modified relations of uncertainty including a visible minimum of Planck order. Early moments of the universe, which included the inflation period, was a period with noticeable effects of quantum gravity due to the high energy level, and as such, the effects can be studied during this period. To do this, characteristics of the inflation period can be examined according to initial parameters of the universe such as the initial fluctuations in the formation of the universe structure and the spectral index. On the other hand, vector cosmology models have been taken into consideration by researchers. These models include an action in which a vector field (in addition to the scalar field) is included to investigate effects of violation of the Lorentz invariance in observations.The present paper investigated effects of quantum gravity (with effects on non-commutative geometry and generalization of the uncertainty principle) on parameters of a vector cosmological model. The vector model was used as this scenario had acceptable adaptation to parameters of cosmology after inflation (e.g. the transition from the Phantom boundary, etc.) (Nozari and Sadatian, 2009). Furthermore, the present study could test this vector model for determining parameters of the inflation period based on effects of quantum gravity. According to calculations in the present paper, we concluded that, first: the density of scalar perturbations decreased in the vector model based on effects of quantum gravity (the reduction of standard model was more considerable), and second: due to the ignorance of effects quantum gravity, the scalar spectral index parameter remained invariant as observations indicate, but due to large enough gravitational effects (depending on amount of  β), the spectral index parameter is not maintained its invariance scale. According to obtained modification in the present study, the quantum gravity can be tested for the density of scalar perturbation (which can be measured by observing the spectrum of cosmic microwave background radiation).In order to compare our results with other studies, we can refer to (Zhu et al, 2014) where they examined the spectral index in accordance with high-order correction mechanism. It also indicated that a single asymmetric approximation does not lead to a considerable error value for the spectral index, and the invariance scale is maintained. Furthermore, the paper (Hamber and Sunny Yu, 2019) found the same results for invariance scale of the spectral index according to the Wilson normalization analysis method. Therefore there was no need to have common assumptions in the inflation period.Finally, it should be noted that despite a great number of studies on effects of quantum gravity, the reviewed model of this paper considers a state in which the effects can be investigated at all stages of the universe evolution from inflation till now.

Drought is an extreme event and is a creeping phenomenon as compared with other natural disasters, which has great effects on the environment and human life. During 1997 to 2001, a severe 40-year return period drought affected half of Iran's provinces, with a loss in the agricultural sector estimated at more than US$ 10 billion (National Center for Agricultural Drought Management, http://www.ncadm.ir) and a Gross Domestic Product (GDP) reduction of about 4.4% was reported (Salami et al., 2009). A more severe drought period (2007–2009) devastated the country on a larger scale than the previous drought period. A 20% average reduction of precipitation has been reported for 2008 compared with a 30-year average (Modarres, et al. 2016). It was found that the longest and most severe drought episodes have occurred in the last 15–20 years (1998-2017) (Ghamghami and Irannejad, 2019). A drought is characterized by severity, duration and frequency. These characteristics are not independent of each other, and droughts cause significant economic, social and ecosystem impacts worldwide (IPCC, 2013). Probabilistic analysis of drought events plays an important role for an appropriate planning and management of water resources systems and agriculture, especially in arid or semi-arid regions. In particular, estimation of drought return periods can provide useful information for different water sectors under drought conditions. In this study, the capability of two CMIP5 GCMs in estimating the joint return period of severity and duration of drought using copula have been investigated in the Southern part of the Karun Basin.In this study, three type data have been used. These include monthly precipitation and temperature observed at synoptic stations and gridded data in 1975-2005 were obtained from IRIMO (the Iranian Meteorological Organization) and CRU (http: https://crudata.uea.ac.uk/cru/data) as well as the outputs of two GCM (HadGEM2-ES and IPSL-CM5A-MR) from CMIP5 (http;//cmip-pcmdi.llnl.gov/CMIP5/) in the period of 1975-2005 for historical. Following the Intergovernmental Panel on Climate Change (IPCC, 2013), the first ensemble member (r1i1p1) from two GCMs were selected. RCPs are estimation of radiative forcing (RF), where RCP2.6 and RCP4.5 represents 2.6 and 4.5 W.m-2 and RCP8.5 represents 8.5 W.m-2 at the end of the 21th century (Goswami, 2018). Assuming a drought period as a consecutive number of intervals where SPEI (Vicente-Serrano et al. 2010) values are less than −1, two characteristics are determined, namely: extreme drought length and severity. Hydrological phenomena are often multidimensional and hence require the joint modeling of several random variables. Copulas model have become a popular multivariate modeling tool in many fields where multivariate dependence is of interest and the usual multivariate normality is in question. Among the copula-based drought frequency analysis, Elliptical and Archimedean copulas have been the most popular used equations. In this paper, we focus on copulas based multivariate drought frequency analysis considering drought duration and severity. Return period is defined as ‘‘the average time elapsing between two successive realizations of a prescribed event’’ (Salvadori et al.,2011).

The ionosphere is a layer of the Earth's atmosphere that extends from an altitude of 60 km to an altitude of 1,500 km. Knowledge of electron density distribution in the ionosphere is very important and necessary for scientific studies and practical applications. Observations of global navigation satellite system (GNSS) such as the global positioning system (GPS) are recognized as an effective and valuable tool for studying the properties of the ionosphere. Studies on ionosphere modeling in the Iranian region have shown that the global ionosphere maps (GIM) model as well as empirical models such as IRI2016 and NeQuick have low accuracy in this region. The main reason for the low accuracy of these models is the lack of sufficient observations in the Iranian region. For this reason, this paper presents the idea of using learning-based methods to generate a local ionosphere model using observations of GNSS stations. Therefore, the main purpose of this paper is to use three models of artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM) to model and predict the time series of ionospheric TEC variations in Tehran GNSS station.An adaptive neuro-fuzzy inference system (ANFIS) is a kind of ANN that is based on Takagi–Sugeno fuzzy inference system. The technique was developed in the early 1990s (Jang, 1993). Since it integrates both neural networks and fuzzy logic principles, it has potential to capture the benefits of both in a single framework. Its inference system corresponds to a set of fuzzy IF–THEN rules that have learning capability to approximate nonlinear functions. Hence, ANFIS is considered to be a universal estimator. ANFIS architecture consists of five layers: fuzzy layer, product layer, normalized layer, defuzzy layer, and total output layer.In machine learning, support-vector machines (SVM) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. More formally, a SVM constructs a hyperplane or set of hyperplanes in a high- or infinite-dimensional space, which can be used for classification, regression, or other tasks like outliers detection (Vapnik, 1995). In SVM method, using nonlinear functions φ(x), the input vector (x) is depicted from N-dimensional space to M-dimensional space (M>N). The number of hidden units (M) is equal to the number of support vectors that are the learning data points, closest to the separating hyperplane.The results of this paper show that the SVM has a very high accuracy and capability in modeling and predicting the ionosphere TEC time series. This model has a higher accuracy in the period of severe solar activity than GIM and IRI2016 models, which are the traditional ionospheric models in the world. Due to the fact that global models in the region of Iran do not have acceptable accuracy due to lack of sufficient observations, therefore, the SVM can be used as a local ionosphere model with high accuracy. Using this model, the TEC value can be predicted with high accuracy for different times and during periods of severe solar activity. This model can be used in studies related to the physics of the ionosphere as well as its temporal variations.

Decadal prediction is a general term that encompasses predictions for annual, interannual, and decadal periods in which significant progress has been made over the years. Decadal climate prediction is made using a hindcast and the latest generation of climate models. It provides two categories of hindcast and prediction data. The purpose of this study is to evaluate the temperature from the DCPP and its prediction in Iran based on the available models of the DCPP project contribution to the CMIP6 project.The study area of this research is Iran. As mentioned, the purpose of this study is to predict the near-term temperature based on the output of the DCPP project. For this purpose, daily temperature from 42 synoptic stations was used as observation to evaluate the available models of the DCPP project. Unlike general circulation models (GCMs), the DCPP project has an initialization that includes a three-month time step for implementation of each year. Air temperature of two models BCC-CSM2-MR and MPI-ESM1-2-HR with a horizontal resolution of 100 km is available for the DCPP project from the CMIP6 series. Three statistics, Pearson correlation coefficient (PCC), root mean square error (RMSE) and mean bias error (MBE), were used to evaluate the selected models of the DCPP project using observational data (synoptic stations).In the study of the relationship between observation and hindcast of the two selected models, it is found that the BCC-CSM2-MR model shows a high correlation (0.99) in the mountainous areas of Zagros and Alborz and arid and semi-arid regions of the inland and east of Iran. However, the northern and southern coasts show a weak correlation (between 0.92 and 0.97). Examination of RMSE statistics for the BCC-CSM2-MR model also shows the maximum error between 1.2 to 2.2o in the coastal areas of the country (the Caspian Sea and the Oman Sea). The western and northern mountains of Iran show the minimum RMSE.The BCC-CSM2-MR model shows more bias than the MPI-ESM1-2-HR model in the northern regions of the country. Examination of the average monthly temperature anomaly across Iran in the predicted period compared to the hindcast period (1980-2019) showed that the monthly temperature anomaly is positive across the country compared to the normal period in all months of the year. This value is 1.03 degrees Celsius for the country-wide average. In other words, the temperature in Iran will increase by one degree for the bear term period (2021-2028) compared to the long-term period of the last 40 years (1980-2019).In this study, for the first time, a decadal climate prediction of Iran's monthly temperature is assessed using the output of two available models BCC-CSM2-MR and MPI-ESM1-2-HR from the DCPP contribution to the Coupled Model Intercomparison Project Phase 6 (CMIP6). The evaluation of the models using three statistical measures RMSE, MBE and PCC showed that the BCC-CSM2-MR model has the lowest performance in the coastal areas of Iran (the Caspian and the Oman Sea) and the highest performance in the highlands of Iran. The output of the MPI-ESM1-2-HR model during the hindcast period (1980-2019) show good performance of this model in determining the temperature patterns of the country. The minimum temperature is based on the output of this model in January with a value of -6.28o. Examination of the predicted temperature anomaly (2021-2028) compared to the hindcast period (1980-2019) shows that the average anomaly across the country for different months of the year during 2021-2028 compared to the hindcast period is 0.99o.

Due to increasing atmospheric disasters in the Iran, accurate monthly and seasonal forecasts of rainfall as well as temperature, can help decision makers to better plan for the future. Meanwhile, machine learning methods are widely used today in predicting temperature and precipitation. For this purpose, the outputs of climate models are processed with the help of observational data and machine learning methods and a more accurate forecast of temperature and precipitation (or other climatic variables) are provided. In the meantime, methods based on multilayer perceptron artificial neural networks are widely used.In a multi-layer perceptron artificial neural network, the design of the network architecture is very important and this design can directly affect the ability of the neural network to solve the problem. In designing network architecture, questions such as the number of neurons in each layer, the number of layers, network activity functions in each layer, etc. must be answered. In some cases, there are methods to answer each of the above questions, but in most cases, a suitable architecture for the specific problem under study must be found by trial and error. One of the important steps in using machine learning methods (in general) and especially the use of perceptron artificial neural network method is the training stage. During the neural network training process, which actually leads to solving a mathematical optimization problem, the optimal network weights are calculated as its adjustable parameters.Today, various types of artificial neural networks are used in various fields of atmospheric science and climatology for purposes such as classification, regression and prediction. But the fundamental question in the use of artificial neural networks is how they are designed and built. One of the important points in using artificial neural networks that should be considered by designers is choosing the right algorithm for network training. In this paper, six different methods are considered for training a multilayer perceptron neural network including: Bayesian Regularization algorithm, Levenberg-Marquatt algorithm, Conjugate Gradient with Powell/Beale Restarts, BFGS Quasi-Newton algorithm, Scaled Conjugate Gradient and Fletcher-Powell Conjugate Gradient methods for monthly forecasting of precipitation that are reviewed and compared. In mathematical optimization methods based on derivatives and gradient vectors, the second-order derivative of the objective function, called the Hessian matrix, and its inverse, play an essential role in the calculations. On the other hand, with increasing the number of variables, the size of the matrix increases and its inverse calculation is computationally time consuming. Therefore, in the improved optimization methods, it is tried to approximate the inverse matrix of the objective function with some tricks.Because the ECMWF model has six different lead times, 72 different models can be proposed for 12 different months of the year. For this purpose, data for the period 1993 to 2010 were used as network training data and data for the period 2011 to 2016 for testing. To evaluate the performance of different neural networks, three indices of correlation coefficient, mean square error and Nash-Sutcliffe index were used. Results indicated that the Bayesian Regularization and Levenberg-Marquatt, Conjugate Gradient with Powell/Beale Restarts outperforms other training algorithms.

Weather forecasting and monitoring systems based on numerical weather forecasting models have been increasingly used to manage issues related to meteorology and agriculture. Using more accurate minimum and maximum temperature forecasts can be helpful in this regard. But systematic and random errors in the model affect the accuracy of forecasts. In this study, the model errors during the 5 and 14 days training period in the same climate areas on the points of the network where the observations are available are calculated.Then the errors are generalized on all points of the network using the cokriging interpolation method. This, preserves the model forecasts for other points of the network and only error values are applied to them. To better evaluate the model, the spatial and temporal distribution of the maximum and minimum temperature forecast errors are also investigated in the country. Observed daily maximum and minimum temperatures data from 560 meteorological stations for the period 1/11/2019 to 1/2/2021 are used to evaluate the WRF model. The WRF model is run daily at 12UTC, with a forecast time of 120 hours. And first 12 hours of each run is consider as the model spin-up and is not used in errors calculation. In order to correct the maximum and minimum temperature forecast errors for next three days (forecasts of 36, 60 and 84 hours), the forecasts for each day in the period of 11/1/ 2019 to 1/2/2021, is extracted from the model outputs. In order to evaluate the error correction method, the skill score index is used. The validation results of the error correction method shows that the absolute mean error value, correlation coefficient and RMSE are improved after the error correction compared to results that were before the error correction. This shows that the error correction method can be used for other network points that do not contain observational data. The results shows that the RMSE of the raw model maximum (minimum) temperatures forecasts for next three days is approximately 6 degrees Celsius (5 degrees Celsius), which after error correction reaches 2 degrees Celsius (4 degrees Celsius). Also the value of correlation coefficient, after correcting for the model error, has a significant increase compared to the raw model output. The average skill score for the raw minimum and maximum temperature forecast for more than 50% of the days is more than -1 and -1.9, respectively, but after correction, the model skill scores become closer to one and for more than 75 percentage of days that reach above zero. Without exception, all climatic regions after error correction have a higher skill score than before error correction, so that the model skill score for most climatic regions after error correction reaches above zero for more than 75% of the days. Before error correction, the warm semi-humid zone has the lowest average skill score for forecasting maximum and minimum temperatures among climatic zones, but after error correction it reaches the highest value among other zones. In general, for areas with hot and dry climates, the raw output skill score for predicting the minimum temperature in July, August, and September is minimized. The 14-day error correction method did not improve the modeling skill score much compared to the 5-day error correction method, and they acted almost similarly. In areas with high elevation gradient, the model error increases. In general, model underestimates the maximum and minimum temperatures in most areas. Knowing the spatial and temporal distribution of model forecast error can be helpful for researchers to have an overview of the areas (and months) where the model forecast error is high.