مجله ژئوفیزیک ایران
سال شانزدهم شماره 1 (پیاپی 54، بهار 1401)

In this paper, using reanalysis ERA5 data and observational data, the effect of Low-Level Jet (LLJ) on dust in the west and south-west of Iran during the period 2007-2017 is studied. The vertical wind shear transfers the momentum from the jet level to the surface and then transfers dust from the sources, so to identify the LLJ besides the maximum wind speed, the vertical wind shear is also considered. Moreover, the horizontal and vertical range of LLJ, average of long term frequency of LLJ and monthly and seasonal frequency of dust are calculated in this study. The result of the comparison of observational data with ERA5 data shows that 10 m wind speed of ERA5 is larger than of observational data. Strong north to south pressure gradient due to the high pressure system over Iraq and Zagros and the low pressure system over the south-west of Iran, is the most important synoptic forcing that causes strong north and north-westerly and LLJ winds over the study area. Furthermore, region topography, land-sea breeze, weak surface tension over the Persian Gulf, temperature differences and special heat capacities between land and sea are other factors that form and affect the intensity of LLJ. The monthly wind speed distribution shows the maximum average wind speed 10 m/s in July and its minimum 2 m/s in January. The average height of the boundary layer varies from 2400 meters by days to 150 meters at nights, which is one of the main causes of LLJ. The highest frequency of dust in Ahvaz was in July 2009 with 30 days of dust

Selection of ground motion time series provides a critical link between site-specific probabilistic seismic hazard analysis (PSHAs) and dynamic seismic response analysis to quantify the seismic performance of a system of interest. The conditional mean spectrum (CMS) can be used for selecting the target earthquake records and dynamic analysis of structures. The conditional mean spectrum gives the mean spectral form associated with the spectral acceleration of the target Sa(T*), so that the seismic records that match the target spectral shape can be considered as the representative of the seismic target records of the spectrum Sa(T*). In this paper, in order to extract the combinations of magnitude, distance and epsilon of the earth’s movements, seismic hazard analysis and deaggregation in the selected site of Bakhtiari Dam has been done based on the natural period of the structure and the return period of 475 years. After preparing the seismic data bank and near-fault data using Iran’s data (Zagros region) and regarding the seismic characteristics of the selected site, appropriate classification of the database based on the magnitude and distance has been done. The ultimate goal of this article is to prepare and present the conditional mean spectrum of the area near the fault for the selected structure of Bakhtiari Dam. For structures with short rotation times, the correlation relationship can have a significant effect on the final response spectrum.   The results indicate that by selecting high periods as the target period, the difference between the uniform risk spectrum and the conditional mean spectrum increases, so the shape of the conditional mean spectrum is more sensitive to tall structures. In fact, the shape of these spectra is highly dependent on the target period. This point is more important in the dynamic analysis of structures with several degrees of freedom because in these structures, considering only one period of target rotation gives rise to downstream results. In the conditional mean spectrum, the closer the two cycles to each other, the higher the correlation of the epsilon values and the less scatter. This means that the farther apart the two rotations are, the less similar the spectral acceleration values are to each other.   We know three methods by which ground motions can be selected for dynamic seismic response analyses of engineered systems when the underlying seismic hazard is quantified via ground motion simulation rather than empirical ground motion prediction equations. Even with simulation-based seismic hazard, a ground motion selection process is still required in order to extract a small number of time series from the much larger set developed as part of the hazard calculation.

Aerosols can act as cloud condensation nuclei in inhomogeneous nucleation. Thus, changes in the concentration of aerosols can affect cloud microphysics and precipitation by changing the number and concentration of cloud droplets. Using the two-moment Thompson cloud microphysics for two nested domains in the weather and research forecasting (WRF) model, the impacts of aerosols on cloud microphysics and precipitation are investigated for a convective system in northern Iran on 17-23 April 2019, in which the first 24 hours are considered as spin-up of the model. Aerosols are obtained from the Goddard chemistry aerosol radiation and transport (GOCART) model and used in the WRF model. Mass mixing ratios are of sulfate, dust, black carbon, organic carbon, and sea salt, although black carbon is ignored in the cloud condensation nuclei activation. Dust aerosols larger than 0.5 μm in diameter are accumulated into the ice-nucleating mode, while all other aerosol types mentioned above are combined into the cloud-droplet-nucleating mode. Three numerical experiments have been conducted. In the control experiment, the extracted aerosols from the GOCART model are used in the WRF model. In the polluted experiment, the number of hygroscopic aerosols in all model grid points that contain aerosols is increased by a factor of five. The third experiment is similar to the second experiment in terms of the number of hygroscopic aerosols. However, relative humidity is increased by 10 percent in all grid points with relative humidity between 0 and 90 percent. It is changed to 100 percent in all grid points with relative humidity higher than 90 percent. This experiment is referred to as the polluted-humid experiment. The maximum number of ice crystals is found in the polluted-humid experiment. The increase of relative humidity in the polluted-humid experiment also leads to the formation of more cloud droplets, and thus more release of latent heat of condensation, which results in higher cloud tops and the formation of more ice crystals. The lifted condensation level (LCL) is also shifted to lower heights in the polluted-humid experiment. In the polluted experiment, the accumulated precipitation in the innermost domain is decreased, which can be due to a decrease in the size of cloud droplets, associated with which is less collision of cloud droplets, which results in a delay in warm precipitation. Nevertheless, higher relative humidity in the polluted-humid experiment and associated larger cloud droplets are accompanied by higher accumulated precipitation.

Global warming has a significant impact on weather and climate change. These changes, and especially changes in climate extremes, have a great impact on human society and ecosystems. Future changes in extreme climate events, including precipitation extreme, will cause great damage to society, the economy, and ecosystems because of their potentially severe effects. The purpose of this study is to investigate the performance of NASA earth exchange global daily downscaled projections (NEX-GDDP) in simulating precipitation and its long-term projection in Iran. For this purpose, the nine models of NEX-GDDP were selected based on climate sensitivity. Precipitations from 49 ground stations during the historical period (1980-2005) were used to evaluate the precipitation output of the mentioned models using RMSE and MBE statistics. The Bayesian model averaging (BMA) method was used to generate an ensemble model from nine models. Intensity of precipitation with two indices SDII and RX1day is projected during the three periods of near future (2026-2050), medium future (2051-2075) and far future (2076-2100) under two scenarios RCP4.5 and RCP8.5. The results showed that NEX-GDDP models did not have much bias compared to observation and most models with low relative error have good performance in reproducing the spatial pattern of precipitation in Iran. Among the nine selected models, MPI-ESM-LR model has shown the maximum overestimation and IPSL-CM5A-LR model has shown the maximum underestimation in Iran. Compared to other GCMs in the historical period, NEX-GDDP models show less uncertainty at the regional scale;therefore, NEX-GDDP simulations are much more reliable. The precipitation intensity projections show that in the future, precipitation will occur more intensively throughout Iran. The RX1day and SDII indices will increase by about 4 to 13 percent for the average area of Iran by the end of the century, which indicates an increase in flooding in Iran in the coming decades. Projections of precipitation intensity in Iran based on two indices RX1day and SDII from the set of precipitation index of ETCCDI working group by ensemble model NEX-GDDP-MME showed that with the continuation of global warming, precipitation intensity will increase significantly throughout Iran. The maximum one-day precipitation amount (RX1day) will increase between 4.42 to 13.08 percent for the area-averaged by the end of this century compared to 1980-2005. Moreover, the SDII index will increase between 4.45 to 13.96% for area-average of Iran. The highest increase in precipitation intensity generally occurs in the coastal region of southern Iran, especially in the coasts of the Persian Gulf and western Iran, while the lowest increase is generally observed in the northwestern region. IntroductionGlobal warming has a significant impact on weather and climate change. These changes, and especially changes in climate extremes, have a great impact on human society and ecosystems. Future changes in extreme climate events, including precipitation extreme, will cause great damage to society, the economy, and ecosystems because of their potentially severe effects. The purpose of this study is to investigate the performance of NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) in simulating precipitation and its long-term projection in Iran.Materials and methodsFor this purpose, the nine models of NEX-GDDP were selected based on climate sensitivity. Precipitation from 49 ground stations during the historical period (1980-2005) were used to evaluate the precipitation output of the mentioned models using RMSE and MBE statistics. The Bayesian model averaging (BMA) method was used to generate an ensemble model from nine models. Intensity of precipitation with two indices SDII and RX1day is projected during the three periods of near future (2026-2050), medium future (2051-2075) and far future (2076-2100) under two scenarios RCP4.5 and RCP8.5.Results and discussionThe results showed that NEX-GDDP models did not have much bias compared to observation and most models with low relative error have good performance in reproducing the spatial pattern of precipitation in Iran. Among the nine selected models, MPI-ESM-LR model has shown the maximum overestimation and IPSL-CM5A-LR model has shown the maximum underestimation in Iran. Compared to other GCMs in the historical period, NEX-GDDP models show less uncertainty at the regional scale, and therefore NEX-GDDP simulation are much more reliable. The precipitation intensity projections show that in the future, precipitation will occur more intensively throughout Iran. The RX1day and SDII indices will increase by about 4 to 13 percent for the average area of Iran by the end of the century, which indicates an increase in flooding in Iran in the coming decades.ConclusionProjections of precipitation intensity in Iran based on two indices RX1day and SDII from the set of precipitation index of ETCCDI working group by ensemble model NEX-GDDP-MME showed that with the continuation of global warming, precipitation intensity will increase significantly throughout Iran. The maximum one-day precipitation amount (RX1day) will increase between 4.42 to 13.08 percent for the area-averaged by the end of this century compared to 1980-2005. Also, the SDII index will increase between 4.45 to 13.96% for area-average of Iran. The highest increase in precipitation intensity generally occurs in the coastal region of southern Iran, especially in the coasts of the Persian Gulf and western Iran, while the lowest increase is generally observed in the northwestern region.Keywords

The Caspian Sea is the greatest lake in the world. This basin plays a significant role in the climate of the countries located in the vicinity of it. This water body is divided into three basins, including the northern, middle, and southern parts. The Iranian coasts are located in the southern part. This paper uses the ROMS model to investigate the seasonal changes in physical oceanography phenomena in the Southern Caspian Sea. To deal with this, the model was run for seven years. The GEBCO data are utilized to make the grid file with the resolution of 30 seconds. Three-hourly ECMWF (ERA Interim) data was applied to the model. Furthermore, the Kura and the Sepidrud rivers were considered for simulation. The climatology and initial conditions data were extracted from World Ocean Atlas 2013 and ICOADS, respectively. In this research, the horizontal resolution of 2.5 km and 16 layers in vertical grids were applied to the model. The model outputs are validated with observation data and other simulations in this basin. The results show that the ROMS can be an appropriate model for simulation in this region, particularly in the southern part, as the outputs are compatible with the observation data. Moreover, the results indicate that the seasonal changes of temperature are remarkable compared to salinity.     While the model has recorded the mean value of 16°-18° C for temperature, this value for salinity is 13.5 PSU. The typical surface currents are counter-clockwise as the dominant winds are from the north to the south towards the Iranian coasts. The topography of the southern part controls these currents because most eddies are formed in the vicinity of the deep part of the south part. Some eddies are dipoles that can be observable in most seasons. The strongest eddies are formed in this basin in the fall, particularly in December. The most important finding of this research can be the fluctuation of the surface water, which varies from 5 to 10 cm, due to these cyclonic and anticyclonic eddies. When eddies are cyclonic, they form the center of cold water. This water mass can be 0.5°-1° C colder than the surrounded water. Herein, we conclude that these eddies can have two considerable effects on sea surface temperature distributions and sea surface height. Thus, this model shows the behaviors of eddies correctly. It should be noted that eddies can play a significant role in the propagation of oil spills in the southern parts, which is discussed in this paper.

Fault slip distribution plays an important role in earthquake studies. Because faults are loaded at very slow rates in continental interiors, interaction among faults and resulting slip distribution can give rise to earthquakes on other faults after a long period of quiescence and seismicity can migrate from one fault to the other.    In this research, slip partitioning was done along the North Tabriz Fault (NTF). First, an elastic and homogeneous half space was considered for the study area. Then geometric data of NTF collected from geological and geophysical references including fault length, width, dip, and locking depth. For Lame coefficients, we used average global values. Both mentioned geometrical and physical data were kept fixed in the modeling process.    Then, displacement gradient tensor that best fits the study area estimated using GPS data by least squares method. Next, strain rate tensor and finally stress rate tensor were estimated using generalized Hook’s law. Stress rate tensor acts as a boundary condition in the model. As other boundary conditions, the NTF was locked in normal direction but it was allowed to slip freely in strike and dip directions under the influence of boundary conditions.    Our problem involves a medium containing NTF. Each fault section has two surfaces or boundaries, one effectively coinciding with the other. A boundary element method called displacement discontinuity can cope with this problem. It is based on the analytical solution (Green’s function) to the problem of a constant discontinuity in displacement over a finite line segment in a plane of a half space elastic solid. Analytical solution of Okada (1985) is used as Green’s function for modeling.    Regarding the strike changes of NTF, the fault surface was divided by different segments in strike direction with constant strike and dip. As a result, we had eleven fault segments. Next, fault segment surfaces were divided into elements. Finally, we had free slipping elements in strike and dip directions as input for modeling. The results indicate the dependency of the distributed slip rate on the boundary conditions and confirm the existence of interaction among different parts of fault. Also, partitioned slip rate shows that NTF is right-lateral strike slip fault in all cases. Moreover, it is almost symmetric and reaches its maximum near the Tabriz metropolis. We show that the maximum slip rate in the fault plane is reduced by partitioning, which it will be definitely closer to reality. According to the meshing done for slip rate partitioning, we get a maximum slip rate of 5.5 mm/year in the northern part of Tabriz city.The proximity of the partitioned slip rate to the paleo-seismic values indicates the closeness of the partitioning results to reality with the Boundary Elements Method compared to other analytical and numerical methods.

Flood is one of the natural disasters which leaves behind many humans, financial and ecosystem losses. Consideration of situations and conditions in the basins has a high priority in reducing the effects of these losses. Study and research on Terrestrial Water Storage (TWS) point out the capacity of water storage in a basin and consider its potential for occurring possible floods. TWS is defined as the summation of all water stored above and below on the earth’s surface (e.g., lakes, rivers, soil moisture, snow, ice and waters inside the vegetation). In this study, using Terrestrial Water Storage Anomaly (TWSA) data attained from twin GRACE satellites and using monthly precipitation data acquired from Global Precipitation Climatology Project (GPCP), the capacity of water storage in basin and the Flood Potential Index (FPI) are calculated. FPI is defined as a storage capacity quantity of basin and has been utilizing in assessing the potential of flood occurrence. The more FPI is near to 1.0, the more possibility of flood event will be. Regarding previous flood events happened during March 2019 in Iran, FPI values related to Karun basin within interval of October 2018 and August 2019 are considered specially. FPI values of 0.21 in March and 0.42 in April show flood events in March and April (2019), respectively. Based on the proposed method, rising value of FPI causes more potential for occurring a flood event. On the contrary, falling value of FPI makes flood events less likely. Concerning results point out higher positive values of FPI connected to more precipitations and in reverse, lower values of FPI related to lower precipitations. Therefore, using study of FPI during desired time interval and regarding the increasing of its index value, forecasting the flood event is possible even several months before its happening and prevents the possible losses. However, temporary and small-scaled floods are not recognized by GRACE data and FPI is less effective. By the way, in some cases it is possible that FPI increases and causes a wrong flood forecasting. For example, in our case study within specific time intervals in May (FPI = 0.68) and June (FPI = 0.27) the FPI values are positive, while there has not occurred any flood. Therefore, definitely, this index is not merely capable of flood forecasting and it is necessary to use supplementary information utilizing other sources and methods to forecast it more precisely.

In the present study, using the ERA-Interim reanalysis data for geopotential height, horizontal wind speed and relative vorticity at 300, 200 and 150 hPa 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 West Asia during the winter time 1979-2018 were calculated and analyzed. The mechanism of Rossby wave breaking during five days before to five days after break was analyzed. From three to five days before Rossby wave breaking events, the formation and development of wave were initiated. In the five days before anticyclonic breaking, ridge and trough informed with vertical axis potential vorticity penetration across the axis of the trough in the 200hPa about 5PVU. From four days before breaking, the ridge penetrated to north of Europe. It caused to intensify jet and form critical latitudes. Equatorward reflection of ridge and trough caused trough to penetrate to lower latitude with anticyclonic circulation. Anticyclonic circulation reinforcement caused formation of equatorward wave activity flux and divergence of wave activity flux in the two regions of lower latitude. Through the anticyclonic breaking, the NE-SW slope of axis of trough increased and potential vorticity rose to 7PVU. In the breaking days, the weakening of jet was intiated in the upstream of trough on the north of Europe. Through two days after anticyclonic breaking, equatorward wave activity flux in the downstream of trough was weakened and divergnce region of wave activity flux was split into areas in Europe and Mediterranean regions. From three days after breaking, weakening of jet in the downstream of trough was initiated. First the upper part of wave passed from middle latitude and then the lower part passed from subtropical latitude. The process of cyclonic breaking was intiated approximately five days before breaking. Four days before cyclonic breaking, ridge and trough informed with NW-SE axis potential vorticity penetration across the axis of trough in 200hPa about 3PVU. In cyclonic breaking, the formation of the ridge in the midlatitude caused zonal velocity to intensify and the formation of critical latitude over Europe. During three days before wave breaking, the jet in the upstream extended to downstream of trough, the NW-SE slope of the axis of trough increased and potential vorticity rose to its maximam about 6.5PVU in the midlatitude. During these days, poleward reflection of wave activity flux caused the divergence of the wave activity flux in the downstream of trough and cyclonic circulation to increase. Rossby wave breaking mechanism was similar in the different areas of East Atlantic Ocean until West Asia. In these regions, the meridional wave activity flux in the anticyclonic wave breaking was more than in the cyclonic wave breaking. However, equatorward (poleward) wave activity flux in the anticyclonic (cyclonic) wave breaking on the east of Atlantic Ocean and Europe was similar to (twice as many) on the east of Mediterranean and West Asia. Approximately, zonal wave activity flux in wave breaking on the downstream of troughs on East Atlantic Ocean and Europe was twice as many on East Europe and West Asia; just as the wave amplitude on Europe and east of Atlantic Ocean which was higher than on the east of Mediterranean and West Asia. Due to wave breaking, wave activity flux on the east Atlantic and Europe was stronger than on the east Mediterranean and West Asia.

Being an inseparable part of environmental data, errors are generated due to several reasons, either natural or artificial. The first is produced from natural phenomena such as animal activities, storms, floods, etc. The later can be generated via human activities during data collecting, entering and processing that can be intentional or unintentional. Since errors can affect results of any analysis, distinguishing them via quality control is a prerequisite of any data usage. Because of unknown truth, this seemingly simple task becomes challenging. Although many efforts have been devoted to develop tests and tools for distinguishing errors in data, none of them can guarantee that all errors can be found. It is important as much as orthogonal testing to find more errors. Here we used a tool named AutoQA4Env, which has been developed for an automated quality control of environmental data. This tool consists of a series of statistical tests which have been used in various communities and organizations such as World Meteorological Organization and Environmental Protection Agency. The tests have been classified in several groups, based on their strictness. The tool has a setting menu by which users can add tests and modify the thresholds. Two versions of the tool, namely basic and advanced flagging system are open source and accessible via b2share. The tool was tested for the quality control of a set of data series of surface ozone measured at the pollution monitoring stations in the city of Tehran. These data are an important source to get information about the pollution levels and trends in Tehran; thus knowing their quality can improve and reduce the uncertainties in the results. The results indicate that gross errors exist in the most of the stations’ data, even though these data are published and are publicly available. Applying the tool in the basic state finds most of the errors. About 0.02% of the data were erroneous for three years of data at 15 stations. Binary flagging system of the tool labels these failure data as an unacceptable data, although they were in fact acceptable. The advanced state of the tool was more moderate than the basic one and corrected these labels. In this state, 57.7% of the unacceptable data in the basic state were distinguished as a suspected value and only 5.6% of them were unacceptable. Therefore, we can conclude that the AutoQA4Env even at this stage could find and flag most of the data errors, at least gross errors. Besides, the advanced flagging system of the tool reduces errors in labeling.

Today, many earthquakes occur in most parts of the world that are very different in terms of intensity, duration, energy and other seismic characteristics, so the amount and extent of damage to different structures are also affected by the characteristics of the earthquake. In this regard, the study of the effects of earthquake frequency content on the behavior and response of reinforced soil walls is also important. In this research, using numerical simulation with two-dimensional modeling of geocell-reinforced soil in Flac-2D finite difference software and seismic analysis with nonlinear dynamic time history of earthquake, the effect of earthquake frequency content on the behavior and seismic response of reinforced soil and also the role of various factors such as embankment geometry and reinforcement characteristics were investigated. The most important results can be summarized as follows: In seismic conditions, lateral displacement is significant compared to vertical displacement and its maximum value is above the wall. By increasing the frequency range of the earthquake around the natural frequency range of the structure, the strain energy in the embankment increases and the reliability and stability decrease consequently. By increasing the frequency range and strain energy, the amount of unbalanced internal forces of the embankment is increased. It creates a large deformation and displacement during the earthquake by creating a cumulative plastic strain and will lead to rupture and instability of the embankment. With increasing altitude and subsequent geometric dimensions of the embankment, the weight of the mass increases and the seismic acceleration leads to large and permanent displacement during the earthquake, which is a significant increase in earthquakes with wide frequency content (around the natural frequency range of the structure). As the length of the reinforcement increases, the displacement of the embankment during the earthquake decreases due to the increase in internal capacity. It is a tangible improvement in earthquake behavior with wide frequency content. The minimum length of the reinforcement in proportion to the height of the embankment and the frequency range of the earthquake is recommended. As the geocell height and reinforcement thickness increase, the axial and flexural stiffness increase and the embankment displacement during the earthquake decreases in proportion to this. It improves the behavior in proportion to the frequency content of the earthquake. The proposed height and thickness of the geocell is proportional to the frequency content of the earthquake and to the average dimension of soil particles (D50). The effect of earthquake frequency content on embankment behavior depends on the natural frequency of the embankment, in which various factors such as embankment geometry, arrangement and reinforcement geometry, as well as the physical and mechanical characteristics of the embankment are involved and cannot be easily calculated.

Drought and regime changes in precipitation and cloudy changes due to climate change and global warming in many world regions have been considered by scientific communities. The impact of solar activities on atmospheric processes, such as solar radiation, sunspots, solar flares, magnetic storms and other cosmic rays, geophysical phenomena on climate change and global warming, is one of the significant issues. Many studies have been conducted to evaluate the role and influence of the Sun and galactic cosmic rays (GCRs) on global warming. The fluctuation of geomagnetic activity is accompanied by solar activity, which includes the transfer of electrons and protons from the earth's radiation belts to the ionosphere. This hypothesis is based on the excitation of Rydberg states of atoms and molecules irradiated by solar radiation. A Rydberg atom is an excited atom that has one or more electrons with very high principal quantum numbers, n. The high electric dipole moment of Rydberg systems is a distinguishing characteristic of these systems. The large dipole moment of Rydberg atoms causes a strong interaction of dipole-dipole radiation in the system. The orientation of two molecules that have a large dipole moment with each other induces dipole-dipole interaction.     The ionosphere totally absorbs magnetic storm and solar wind energy fluxes in the UHF, SHF, and EHF bands, resulting in microwave emissions. In a lower atmosphere, this event reduces the separation of the cluster ions. The amount of created water clusters is highly dependent on the orbital quantum number of Rydberg states with the values of (n≥10). In other words, the probability of cluster ion separation is lower for higher-orbital quantum numbers than for smaller quantum numbers. The effect of microwave irradiation on clustering, water vapor concentration, and the generation of thin and young clouds are investigated in this research. It will be explained that such radiations easily penetrate the ionosphere and induce aggregation of molecules in Rydberg states, resulting increase in supramolecular stability. Furthermore, microwave radiation affects clustering and water vapor concentration, increasing the concentration of water vapor clusters. As a result, the probability of recently formed clusters separating decreases considerably, resulting in the accumulation of these clouds with concentrations of less than 10-15 cm-3.     The current work is a part of the scientific investigations of scientific research and techniques in human capabilities in understanding the impact of solar activity and magnetic storms on atmospheric processes and cloud formation. Ionospheric microwave radiation in the framework of excitation of atomic and molecular Rydberg states has been considered for this purpose. The thin and young clouds generated by solar activity can produce a greenhouse effect, which may eventually play a significant role in periodic global warming on the earth. In recent years, researchers have been attracted to pursue a practical approach, a knowledge-based and reasonable solution to this critical issue. Hence, the main purpose of this study is to provide an overview of the basic concepts and physical principles of solar wind and magnetic storms in climate change and improve atmospheric conditions.

Inversion of gravity data is one of the important steps in the interpretation of practical gravity data. The goal of 3D inversion is to estimate the density distribution of an unknown subsurface model from a set of known gravity observations measured on the surface. The inversion result can be obtained by minimization of Tikhonov objective function. Inversion of gravity data is an underdetermined and ill-posed problem. In addition, the non-uniqueness of the solution is the main issue of the inversion. One way to achieve a suitable model result in the inversion is to carry out the inversion with smoothness and smallness constraint. The determination of an optimal regularization parameter is highly important in gravity data inversion. Regularization parameter makes a trade-off between misfit and regularization function. In this paper, an attempt has been made to use Unbiased Predictive Risk Estimator (UPRE) method in selecting the best regularization parameter for 3D inversion of gravity data using Lanczos bidiagonalizatoin (LSQR) algorithm. The UPRE method has been adapted for the solution of inverse problems. The UPRE method is based on a statistical estimator of the mean squared norm of predictive value. In this method, the optimal regularization parameter minimizes the UPRE function. We have developed an algorithm for 3D inversion of gravity data that uses the UPRE method for choosing optimal regularization parameter, and then, the inverse problem is solved by the LSQR algorithm. To evaluate the reliability of the introduced method, the gravity data of a synthetic model contaminated by 5 percent random noise has been inverted using the developed method. The discrepancy principle method was also applied for comparison of its results with the UPRE results. Then, the algorithm was used for inversion of real gravity data obtained from San Nicolas deposit in Mexico. The results of three-dimensional (3D) inversion of gravity data from this sulfide deposit show that the LSQR algorithm can provide an adequate estimate of gravity density and geometry of subsurface structures of mineral deposits. A comparison of the inversion results with geological information clearly indicates that the proposed algorithm can be used for 3D inversion of gravity data to estimate precisely the density distribution and geometry of ore bodies. The obtained results indicate that the discrepancy method is weaker than UPRE method to choose regularization parameter, but the UPRE method finds a unique optimal regularization parameter. Finally, the introduced algorithm has been used for 3D inversion of gravity data from sulfide deposit in San Nicolas. The results are consistent with geological information.

Reanalysis data have been considered as an important source of atmospheric information in a variety of applications such as climate studies, hydrological modeling and numerical weather prediction. Evaluating the effectiveness of the reanalysis products in each area before use is of great importance. With the advent of advanced reanalysis such as ERA5 and ERA5-Land, the interest of many researchers in using these data sources has increased.     To date, several studies have been conducted in the country to statistically compare the reanalysis products with other meteorological data sources, each of which has its limitations and does not provide a comprehensive evaluation of the reanalysis data across the region. In other words, most of these studies are related to the evaluation of one of the meteorological variables such as precipitation and have been done in a specific location of the country or have used a limited number of ground stations in statistical comparison.     In this work, the quality of 2m temperature, surface pressure, 10m wind speed and dew point temperature of ERA5-Land are evaluated temporally and spatially over Iran. For this purpose, sub-daily observations of 406 synoptic stations from 1999 to 2019 were used. The bilinear method was used to spatially interpolate the meteorological values obtained from ERA5-Land at the station locations. After preparing the ERA5-Land sub-daily time series and the corresponding actual observations, the error statistics required to evaluate the ERA5-Land data were calculated. Statistical comparisons between ERA5-Land products and ground observations of 2m temperature, surface pressure, 10m wind speed and dew point temperature parameters are done with a 3-hour temporal resolution.     In the whole region, ERA5-Land products and local measurements of 2m temperature, surface pressure, 10m wind speed and dew point temperature showed agreement about 0.97, 0.98, 0.49 and 0.88, respectively. Also, compared to the actual observations, the mean RMSE for the above ERA5-Land data products achieved 2.87°C, 19.42 hPa, 2.52 m/s and 4.12°C, respectively.     The study of bias values showed that in the region of Iran, ERA5-Land, on average, estimates all the studied variables less than the observed value. In addition, the study of the relationship between height difference of ERA5-Land grid points and station height with estimated error values showed that with increasing altitude difference, the size of negative bias and root mean square error of 2m temperature and the surface pressure of ERA5-Land increases significantly. Therefore, it is suggested eliminating the existing systematic errors in the area before applying this data.

In-situ observations underlie a wide range of planning, applied studies and modeling in various fields and sciences. Using this data in studies and planning without ensuring the accuracy and homogeneity of them can lead to uncertainty in the results. The major problems that researchers face are the poor data quality, missing data, outliers and in-homogeneity in time series. Therefore, in this paper, the minimum and maximum daily temperature series and daily rainfall series were analyzed at 37 weather stations in Iran for outliers and homogeneity over the period 1959-2018. In this regard, the World Meteorological Organization in cooperation with the Climatology Commission has provided instructions for data homogenization (e.g. WMO/TD Document No. 1186, Guidelines on climate metadata and homogenization; WMO Document No. 1203, WMO Guidelines on the Calculation of Climate Normal).     The main steps in data homogenization are: Metadata analysis and data quality control; Creating a reference series; Detection of break points; Data correction.     To do this, in the initial clustering, according to the previous activities and studies in this field which have mostly used empirical and quantitative methods, including principal components and cluster analysis, Iran was divided into 5 clusters based on the climatic characteristics. After initial clustering, the daily maximum and minimum temperatures and daily rainfall series were statistically analyzed using SPSS software and the percentage of missing data was determined for each station. Then, Climatol package in R software was used to study outliers, in-homogeneity and homogenization. In each cluster, the series are re-clustered based on the variability of desired parameter, and for each station, the other stations with similar variability belonging to that cluster are considered as reference stations.     Based on this algorithm, first the desired series is estimated and standardized by reference series using type (II) regression method. After estimating the series, the standardized anomaly series are calculated, in which the difference between the observed and estimated values is calculated. For detecting outliers, two steps were followed. Original data corresponding to the standardized anomalies greater than the prescribed thresholds were detected as outliers. In the second step, in order to confirm the outliers, the detected outliers in the first step were compared with the values of the days before and after for temperature series. If they differed significantly, they would be accepted as outliers and deleted. For the precipitation series, the atmospheric condition of the desired dates would be checked. For detection of in-homogeneity, the standard normal homogeneity test (SNHT) was performed on the monthly series. If the SNHT test statistic was greater than the prescribed threshold, the series was split at the point of the maximum SNHT and all the data before the break point were transferred to a new series with the same geographic coordinates. This process was repeated until all series were homogeneous. If break points were confirmed by metadata, they would then be accepted as non-climatic breaks. Finally, all the missing data in every homogenous series are estimated using same estimation procedure. The only difference is that the fragments of series are used as references. Given the large number of missing and suspicious data in 1959, we considered the beginning of the statistical period from 1960. Investigations have shown that on some dates, all stations in a cluster lack data, possibly due to glitches in the MESSIR-CLIM system of Meteorological Organization through which data is received. In such cases, the average data of the days before and after the mentioned stations was used to estimate the data on that dates. MESSIR-CLIM is the database of IRIMO including climatic database management system that is based on PostgreSQL. The main functions of the system receive and store all kinds of weather and climatic data. The system is able to collect and process massive amounts of information and provide meteorological products (such as charts, maps, tables, and reports).     For the maximum temperature in the stations of the Caspian region (Cluster 4), 18 dates and for the minimum temperature, 13 dates in the mountainous areas (Cluster 5), in all the cluster stations were missing data.     The maximum and minimum temperature and daily precipitation series for 37 weather stations of Iran have an average of 5%, 7% and 2% missing values, respectively. In the 60-year time series (1959-2018) after deleting the 1959 data, the percentage of missing data at maximum and minimum daily temperature and daily precipitation decreased by an average of about 0.3%. In this time series, excluding 1959 data, 7 outliers were detected for the maximum temperature parameter. For the minimum temperature, this number reached 7 and for the precipitation parameter, 8 outliers were identified. In 8 cases, due to the lack of atmospheric data on the desired dates, it was not possible to make a definitive judgment about the accuracy of precipitation data outliers. In terms of daily temperature series, with the exception of Tabas station, out of 36 stations, 16 stations were homogeneous and 20 stations had one or two or three breakpoints. For the precipitation parameter, 5 in-homogeneous stations were identified.    Unfortunately, due to the lack of a comprehensive metadata bank, there was no definitive reason for many of these fractures at some stations.