فصلنامه فیزیک زمین و فضا
سال چهل و هفتم شماره 2 (تابستان 1400)

Increasing demands of raw materials and energy resources has led to a fast growth in the geophysical studies. Due to the properties of minerals and geological conditions, there are various geophysical methods. Among these methods, magnetic method is capable of exploring the magnetic mineralization of rocks with relatively high or low magnetic properties. In this method, the magnetic field variations of the ground are measured. Sonajeel is located 17 kilometers from Harris, East Azarbaijan Province. The main stone units in this area are from the old to the new: volcanic and volcanoclastic rocks, Sonajeel porphyry stock, Incheh granitoid stock, and Okuzdaghi volcanic rocks. In this study, the magnetic method is used as an indirect method for identification of copper ore deposits. Based on the magnetic method, information can be obtained about the gradient, depth, shape, and extension of the source of anomalies. There are several examples for using this method (especially the airborne magnetic method) to explore the copper deposits. Including the copper project in the Cadia region of Australia, as well as the use of magnetism to explore the mineralization of copper and gold in the polymetal exploration area of Bashmaq Hashtrood. For the aim of identification of copper mineralization in the study area, the magnetic data along 19 survey lines were carried out. The length of each line was considered to be1000 meters and magnetic measurements were made at magnetic stations having distance intervals of 20 meters. The distance between the successive survey lines was 50 meters, except the distance between survey lines 19 and 18 that were located 30 meters from each other. The total survey area was about 1 km2. After applying diurnal correction on the magnetic data, the processing of the data was made by applying various filters such as reduction to the pole (RTP) to remove the effect of the inclination angle and to locate the subsurface position of the anomaly that is assumed to be symmetrically placed on the creator mass, upward continuation filter to study the process of mineralization in depth, and also, vertical derivatives and analytical signal processing methods were used to estimate the anomalous boundaries. Three-dimensional (3D) modeling of the magnetic data was also carried out using the Mag3d software. The results indicated that the mineralization process was extended in the north and north-east to the south-east of the study area. Upward continuation filtering was applied to the data at altitudes of 20, 40, 80 meters. The maps resulting from this filtering represented the root of the subsurface anomaly in the southeastern of the region. As a result of comparison of the various magnetic images with the 3D model, obtained from modeling the magnetic data using the Mag3d software, we found out that the copper mineralization in the study area is scattered but covers a large range of the area. Moreover, according to the results of 3D modeling of the data, the magnetite susceptibility in north and northeast of the study area is more than that in south and southeast of the area. The contrast of the magnetic susceptibility in north of the study area from the depth of 100 m to 270 was high, however, in the east and southeastern parts of the study area, from the depth of more than 100 meters, there was a high magnitude of magnetic susceptibility. Hence, it can be concluded that in the northern parts of the study area, potassic alteration was closer to the ground surface. It should be mentioned that the potassic alteration is a good place for copper and magnetite mineralization as the copper and magnetite mineralization is located in the center or middle of the potassic alteration. By comparing and interpreting the magnetic results and assessing these results with the geological data or information from the study area, the probability of occurrence of the magnetite mineral and, consequently, the copper mineralization or deposits in the Sonajeel area is highly indicated.

The Moho discontinuity is a boundary between the crust and upper mantle that reveals the difference between them with changes in seismic velocity, density, chemical structure, and constituents. Estimating the Moho depth and studying its lateral changes is one of the important goals of geophysical studies. The current study aims to estimate the depth and topography of the Moho discontinuity in the southwestern part of the Baltic Sea, including parts of the central European system, the Trans-European Suture Zone, Caledonian Crustal Suture, and the Ringkobing-Fyn High. This area has been one of the most attractive regions for Geoscientists in the last decades due to its complicated geological structures caused by different tectonic events. For this purpose, a three-dimensional model of the crustal structures based on gravity data forward modeling in the study area has been presented. Previous seismic / non-seismic results have been used to constrain the model and reduce its degree of freedom. This model includes sedimentary sequences, crustal thickness, Moho topography, and high-velocity lower crust expansion in the region and shows the tectonic structures of the study area. This study used a combination of marine, land, and EGM2008 gravity data and modeled them with IGMAS+, Interactive Gravity and Magnetic Application System. The interactive modeling program allows the user to change the geometry as well as the density and susceptibility of the primary model and observe results quickly during the processing. In the software, the model structure could be be more user friendly by eliminating additional details and dividing the whole model into vertical sections. Our primary model consists of three main layers of sediments, crust and upper mantle. The sedimentary layer is divided into two major parts, pre-Permian and post-Carboniferous. Also, the crustal layer is divided into the upper crust and the high-density lower crust. Besides, the upper crust is composed of the upper crust of the Baltica and the upper crust of Avalonia. The last layer of the model is a part of the upper mantle. The model space consists of 16 vertical planes stretching 385 kilometers east-west with an equal distance of 15 kilometers, covering the entire study area. The initial model was developed based on seismic sections and previous models, and it has been improved using interactive forward modeling of gravity data. The result shows a good agreement between the measured and modeled Bouguer anomaly, and the Root Mean Square Error of the model is 1.12 mGal. The model correlates clearly with major tectonic units. It indicates that the Caledonian collision resulted in the amalgamation of Baltica and Avalonia is the most prominent tectonic event in the area, and the Caledonian crustal suture between them is interpreted from changes in physical parameters at crustal levels. There is a relatively thick crystalline crust in the area, and the depth of Moho discontinuity varies from 26 to 42 km. The results also indicate that the transition from the Paleozoic crust of the Central European Basin to the Precambrian crust of the Eastern European Craton occurs within the Tornquist Zone.

For a long time, two very important issues have been raised for humans in relation to the phenomenon of earthquakes: 1) Predicting the exact time of an earthquake, 2) Controlling the conditions caused by an earthquake. In many advanced societies, valuable work has been done on the second, which has reduced the loss of life and property, but the actions taken on the first have brought us closer to our goal. These measures include studying the tectonic activity of the Earth's crustal plates, investigating changes in wave velocity (P, S) in an area, installing sensors on the ocean floor, monitoring active faults by satellite and using Doppler shift frequency in satellites, studying the behavior of some animals and etc. The mentioned study has laso pointed out that some of them were able to inform us even 15 minutes before the earthquake. But we are looking for a way that, in addition to being efficient, accurate and comprehensive, can cover a wider area and give us more time before the main earthquake occurs. The study of changes in the characteristics of VLF / LF waves such as signal amplitude, phase signal, temporal and spatial transmissions of the signal along the transmitter-transmitter are cases that have been followed more seriously by Hayakawa et al. since 1995. Since most of the studies have been with the help of VLF wave propagation and less LF waves have been used for investigation and pre-marking, so we want to analyze the first VLF / LF signals arrived at Tehran station in 2019 and also match them with daily density change diagrams. Electrons in terms of time obtained during the signal propagation path from the experimental IRI model associated with each month of the year. The proposed approach in this paper allows us to examine the ability of the IRI model in explaining the temporal evolution of the received signal. Here is a comprehensive way to advance IRI estimates of the current state of the ion sphere. This technique is proven to not only validate the experimental observations of recorded LF and VLF signals at the Tehran station, but also to propose a new approach for improving the estimate of the current state of the ionosphere using the IRI model. More observations could lead to a better estimation of averaged ionospheric densities along the signal propagation path at the morning and evening termination time. By examining the changes in amplitude and phase of the signal, we examine the amount of charge density and the condition of the lower layer of the sphere ion (layer D) along the propagation path of the waves. We are looking for signs to reach a premonition for other earthquakes that will occur in the future. This approach could be used as an indicator of pre-seimic activities produced through the well-known Lithosphere-Atmosphere -Ionosphere coupling (LAIC) process. Such a methodology could lead to a solid approach for earthquake prediction in Iran using the physics-based analogy. Therefore, this study investigated a new technique for ionosphere remote sensing as well as a new approach for earthquake prediction in Iran.

The passage of satellite signals through the atmosphere with variable nature of its troposphere will have a significant delay in the movement of these signals. This effect is commonly known as tropospheric delay. It can be divided into wet and dry components. The dry component is usually modeled using devices that measure air pressure. Unlike the dry component, the wet component of tropospheric refraction cannot be modeled using air pressure measuring devices. This component depends on the water vapor (WV) and moisture content of the troposphere. The WV is one of the key parameters in climate system analysis and a major factor in atmospheric events. Using the observations of local and regional GNSS networks, it is possible to estimate the slant tropospheric delay (STD) and subsequently, the slant wet delay (SWD) for each line of sight between the receiver and the satellite. The SWD observations are used to model horizontal and vertical WV variations in the upper atmosphere of the study network. This is done with a tomography technique. In tomography, the horizontal variations of tropospheric wet refractivity are modeled with the polynomial in degree and rank of 2 with latitude and longitude as variables. Also, altitude variations are modeled in the form of discrete layers with constant heights. The main innovation of this paper is in estimating the tropospheric parameters for each line of sight between the receiver and the satellite by the adaptive neuro-fuzzy inference system (ANFIS). The SWD obtained from GPS observations for the different signals at each station is compared with the SWD generated by the ANFIS (SWDGPS-SWDANFIS). The square of the difference between these two values is introduced as the cost function in the ANFIS. By calculating the value of the cost function at each step, the weights associated with the ANFIS network are corrected by the back-propagation (BP) method. In the next step, using the estimated wet refractivity, the value of slant water vapor (SWV) is calculated. To evaluate, GPS observations from 27-31 October 2011 and Tabriz radiosonde observations are used. For a more detailed evaluation, 2 test stations are selected and ANFIS zenith wet delays (ZWDANFIS) are compared with the ZWDGPS. Observations of test stations are not used in modeling step. In order to further examine the accuracy of the proposed method, the results of this study have been compared with the results of voxel-based tomography (TomoVoxel) method and troposphere tomography using artificial neural network (TomoANN). Also, relative error, mean square error (RMSE), standard deviation, and correlation coefficient were used to evaluate the results. At the Tabriz Radiosonde station, the correlation coefficient for the ANFIS, TomoVoxel and TomoANN have been calculated as 0.9131, 0.8863 and 0.9006, respectively. The minimum relative error for the TomoANFIS, TomoANN and TomoVoxel are 8.31%, 8.55% and 8.71%, respectively. Also, the maximum RMSE for three models is 0.9718, 1.0281 and 1.2346 mm/km, respectively. The results of this paper indicate the very high capability of the TomoANFIS model in showing the temporal and spatial variations of SWV. This method can be used to discuss the behavior of the atmosphere in real time and near to real time applications.

In this research, we study the oscillating properties of the solar spicules in the line of sight with spectral measurements recorded by Interface Region Imaging Spectrograph (IRIS) on August 17, 2014. The primary purpose of IRIS is the observation of the movement of materials, fluctuations, and energy absorption and heat production in the lesser- known region of the solar atmosphere which affect the behavior of the Earth's atmosphere, the performance of satellites, power transmission networks and radio communications. The transmission of energy through waves and oscillations can play an important role in understanding of the solar dynamics, and responding to the problems about the sudden rise of the solar atmosphere temperature to several million Kelvin from the transition layer to the solar corona. The source of energy required to heat the solar corona plasma to a temperature of one million Kelvin in the Sun's dynamic photosphere is a matter of debate in solar physics. One of the mechanisms of energy transfer is the propagation of magneto-hydrodynamic waves. These waves in photospheric magnetic tubes can be generated by granular shock motions and then propagate along the chromospheric magnetic field and penetrate the corona to transfer energy in the form of heat. Therefore, observations of oscillating motions in the chromosphere are a crucial test for the theory of corona heating. Quasi-periodic fluctuations in spicules appear mainly as displacement of these structures in image observations or periodic shifts in spectral lines. We use Interface Region Imaging Spectrograph (IRIS) to measure the spectrum around a narrow slit. By fitting a Gaussian profile of the Si IV profiles, we can calculate Doppler velocity shifts up to an altitude of 4200 km along the spicules. The Doppler velocity range from the edge of the sun to an altitude of 4200 km was obtained from 12 to 15 kms-1 (blue- shift), and from10 to 15 kms-1 (red- shift). For determining the dominant periods of Doppler shift oscillations, it is needed that the maximum intensity positions of 150 spectral profiles are collected, and a set of temporal signals is generated as a temporal signal. Any physical quantity that changes according to an independent parameter or variable is called a signal. If the parameter is a time variable, it is called a temporal signal, and if it is a position, the signal is called a spatial signal. These signals contain information about their sources, for example, period. So by processing signals, the behavior of resources can be studied and predicted. After processing temporal signals, we apply the wavelet analysis. Wavelet analysis is a useful method for simultaneous diagnosis of the power in time and frequency domains for temporal signals. The results of wavelet analysis revealed Doppler shift fluctuations with dominant periods of 3, 5 and 8 minutes. According to the results of this study, it is suggested that the main contribution of Doppler shift fluctuations in the solar spicules, observed transversely perpendicular to the axis of the solar spicules, is due to kink and alfven waves. These waves can play an essential role in heating the solar corona to millions of Kelvin.

Representing the boundary layer processes is crucial in simulating atmospheric phenomena in operational hydrostatic weather forecast models. Moreover, evaluating the performance of different physical processes in a variety of numerical models is an essential subject of its own. This paper presents an objective assessment of a planetary boundary layer scheme based on turbulent kinetic energy in a single-column version of the innovative atmospheric general circulation model developed based on potential vorticity at the University of Tehran, which is called UTGAM. Single-column models are a complementary tool to the atmospheric general circulation models that provide a simple framework to investigate the fidelity of the simulated physical processes.  The reliable parameterization of the boundary layer processes has got significant impacts on weather forecasts. Most of the hydrostatic models have got deficiencies in the representation of these unresolved processes, especially in stably stratified conditions, and it seems that this problem is continuing in the forthcoming future. Here we have utilized the first GABLS intercomparison experiment set up as a simple tool to evaluate the performance of the diffusion scheme in the UTGAM. Two different sigma-theta and sigma-pressure single-column grid staggering combined with, respectively, 33 and 14 vertical levels below 3 km height have been used for the low- and high-resolution simulations. The GABLS1 LES results have been used as a benchmark for comparison. The boundary layer scheme that has been explored here is the same as the one in the ECHAM model, but some simplifications have been made. For instance, in this simulation, the effects of tracers have been ignored to circumvent the complexity of the problem. Results depict subtle nuances between the sigma-theta and sigma-pressure coordinates in intercomparison between the low and high vertical resolutions separately, which are more apparent in the lower vertical resolution. Nevertheless, it seems that the diffusion processes have been simulated rather more accurately in the high-resolution sigma-pressure vertical coordinate. The boundary layer scheme analogous with most of the operational models in the GABLS1 intercomparison experiment overestimate the momentum and the heat diffusion coefficients. The wind profile with height, depicts maxima that are higher than the corresponding LES profile. It is inferred that the scheme mixes momentum over a deeper layer than the LES, but the simulated wind profile is better in comparison with the other operational models in GABLS1. Considering the vertical profiles of potential temperature revealed that the amount of heat mixing is not suitable in this experiment, and it causes a negative bias in the lower part of the simulated boundary layer. The simulated amounts of surface friction velocity have proved significant differences with the LES results in all separate experiments. However, the latter large amounts seem unlikely to have a detrimental effect on forecast scores in the operational model. Moreover, the sensitivity of the scheme to the lowest full level has been partially explored. Decreasing the lowest full-level height concurrent with increasing the vertical resolution leads to a modest influence on the simulation of the boundary layer processes. All the results confirm notable improvements by increasing the vertical resolution in both sigma-theta and sigma-pressure coordinates.

The main purpose of this study is to detect precipitating clouds and to analyze their vertical structures in the south and southwest of Iran using CALIPSO and CloudSat satellite observations. At First, events with high precipitation rates using the daily precipitation data of the synoptic stations in the area of interest during the statistical period from 2006 to 2016 were selected. The selection of these samples is based on two parameters: the average precipitation of the synoptic system and the number of stations involved in precipitation. The average precipitation of the system was calculated by the ratio of the total precipitation of all stations in one day to the number of stations involved in precipitation on the same day. In order to eliminate light precipitating samples, a precipitation threshold was set for the mentioned parameters. So that at least in one of the days of precipitating system activity, the number of stations involved in precipitation is not less than 15 stations and the average precipitation of the system is not less than 15 mm. This threshold is defined as the day of peak precipitation. In total, 74 precipitating systems that lasted from one day to one week were determined and 107 days of precipitation with the above specifications were selected. In order to ensure the occurrence of precipitation at the same time as the satellite orbit passing through the area, TRMM satellite level 3B precipitation data was used. These data have precipitation values in a temporal interval of 30 minutes and spatial resolution of 0.1 by 0.1 degrees. Considering the network precipitation values of peak days, three precipitating samples in three different paths where the precipitation occurred along the satellite path, were selected to analyze their cloud structures. Precipitation characteristics of the mentioned systems were extracted based on station and network precipitation values. In the next stage, three features including the total attenuated backscatter at 532 nm, the depolarization ratio and the color ratio were obtained by the use of CALIOP lidar level 1B data. The radar reflectivity feature was also extracted using data of CPR sensor of CloudSat. Then, using layers extracted from CALIOP and CPR sensors, the clouds of these samples were compared and analyzed in terms of cloud thickness and precipitation intensity. The results of the analysis showed that in the first sample (Path A), despite the large thickness of the cloud (approximately 10 km), the amount of precipitation is less than the other two samples. The cloud of this sample is different from the other two samples. Cloud layers in the vertical direction are not dense and integrated enough. Also, aerosol particles and ice crystals in the cloud are fewer and smaller. While in the other two samples, especially in path C, while the thick and dense cloud covers the atmosphere of the region, the concentrations of aerosols and ice crystals are much higher.

Missing data are common issue in climate data. Also precipitation is a very important part of the hydrological cycle and meteorological and hydrological studies of watersheds, initially depend on the quantity and quality of recorded rainfall data and its distribution in the area. Complete and reliable sets of climatic and hydrological data are required for planning and design of these projects. Therefore for treatment of precipitation missing data, various methods have been developed and applied. Normal ratio method, linear regression, multivariate regression and inverse distance weighting (IDW) have a wide applications in natural resources study in our country. Therefore, it is necessary to determine the ability of these methods, especially in relation to the precipitation parameter, which plays a crucial role in the study of natural resources. In this study, the capability of each mentioned methods for infilling missing data of daily, monthly and annual precipitation time series in the arid regions of Iran was investigated for varying proportion of missing data from 5 to 50% of total data. In fact, the main purpose of this study is to answer the question of which of the four mentioned methods are more effective for infilling precipitation missing data. The daily data of Iran’s synoptic meteorological stations were used for the present study. Using the Run homogeneity test, the data homogeneity was investigated. Also, using graphical exploring data, and especially boxplot diagrams, outlier data were identified and flagged as missing data. The average annual precipitation and temperature of 400 stations were determined, and then based on these data their de Martonne coefficients were computed. In the next step, stations with de Martonne coefficient less than 10 were selected as arid climate. Among them, 73 stations that had sufficient data from 1986 to 2017 were distinguished. To evaluate each of the data reconstruction methods, part of the actual data was deliberately discarded from the original data and then reconstructed. Due to high volume of calculations, this process was programmed in MATLAB software. The results showed that each method had different functionality according to the conditions. Daily data are not well estimated using the normal ratio method to estimate the missing data less than the actual one. The use of linear regression method showed that in daily time scale, unlike the normal ratio method, the model accuracy in data reconstruction is higher. For linear regression approach, the distance between the fitted line between the observed and estimated data is small at first, and as the precipitation increases, this distance increases, indicating that the model is less accurate in estimating the extreme values. Given that the fitting line is below the 1:1 line, the linear regression method estimates the actual values below normal. The same results can be found for IDW producer. The multivariate regression method is more accurate for daily time series when the proportion of missing data are not considerable, but is generally very sensitive to the proportion of missing data. The normal ratio method is not suitable for reconstructing daily missing values, however it is more stable than other methods when missing data increase. In monthly time series, the performance of the IDW method and then the normal ratio is better. In annual series, linear correlation, normal ratio, and IDW have better performances, respectively. The findings of this study show that in general, the accuracy of reconstructions on annual scales is more than monthly and on monthly scales is higher than daily. This is due to smoother time series in the monthly and annual time series than the daily ones. Also it should be noted that the scale of current studies is in Iran. If the data from the reserved rain-gauge stations of the Meteorological Organization and the Ministry of Energy are added to this data, the accuracy of the methods is expected to increase. As the results of the present study show, the accuracy of the models decreases with increasing incomplete data ratio. Therefore, if new data is included in missing data processing, there is an expectation of better performance of each of these methods. Finally it should be considered that each method should be used in accordance with the given conditions, and therefore it is recommended to develop a software package for infilling missing data in Iran.

Mineral suspended particles, in addition to being important components of the Earth's atmosphere, play an important role in the atmosphere-Earth energy interactions and geochemical cycles of the Earth system. The meteorological and climatic importance of atmospheric particulate matter can be attributed to its effects on the energy level of the Earth-Earth system, physical, dynamic, and chemical changes in the atmosphere at regional and global scales, absorption and emission of radiation in the atmosphere, micro physical changes and radiative properties of clouds and changes in snow and ice levels that occur. Fine particles smaller than 2.5 microns are one of the most important air pollutants with a wide variety, complexity and diffusion. Dust events are one of the most important natural sources of particulate matter in the atmosphere. In recent decades, air pollution in many parts of the world has raised public concerns about their health effects. Epidemiological studies have shown that lung disease, cardiovascular disease, and their mortality are associated with particulate matter. Although the effects of particles on both climate and air quality has been evident over the past few decades, continuous monitoring will still be important. In recent years, techniques, and models based on satellite data has made significant contributions to the monitoring of particles. Different versions of the MERRA-based satellite model have excellent capabilities in the study of particles and its time series analysis. The MERRA-2 model (the Modern-Era Retrospective analysis for Research and Applications, Version 2 called MERRA-2) is based on the analysis of satellite data (Moloud et al., 2012) and is one of the most reliable models for assisting various environmental scientists to answer questions related to climate research and climate change, to make optimal use of the created satellite observations. This study aims to investigate the spatio-temporal density and dispersion of PM2.5 suspended particles due to dust events in the Iranian atmosphere during the statistical period (1980-2019) based on the MERRA-2 based satellite model. Here, the meaning of column PM2.5 suspended particles is PM2.5 suspended particles of dust in a vertical column from the ground. Relevant data was prepared with monthly, seasonal, annual and spatial time steps of 0.5°x 0.625°and after applying the necessary preprocessing, they were identified and analyzed. The results show good fluctuations in PM2.5 particulate matter density during the statistical years studied. But in general, the density of PM2.5 suspended particles increasing and its upward trend is observed especially in the last years of the statistical period. The results showed that MERRA-2 model has a good performance in monitoring the concentration of PM2.5 particulate matter in the vertical column of the atmosphere. The average of particulate matter PM2.5 in the atmosphere of this area is 61/23 Mg/m2, which indicates the high concentration of these particles in the atmosphere compared to other parts of the world, including the United States (Bouchard et al., 2016a), Taiwan (Provence et al., 2017a) and Europe (Provence et al., 2017b). On the other hand, the highest concentrations of these particles are in the southwest of Iran, southern coastal areas, eastern regions, deserts of central Iran and part of northern Iran and the lowest is estimated over the Zagros highlands. The spatial distribution of PM2.5 suspended particles over the Iran area depends on the frequency of dust events, distance from emission centers, seasons, rainfall and other climatic parameters (soil surface temperature, soil moisture, etc.). In this sense, in the warm months and seasons of the year, which are associated with the increasing land surface temperature, decreasing rainfall and, consequently, decreasing soil surface moisture, the conditions for the formation of the dust events are the release of suspended particles into the atmosphere. So that among the months of the year, May/December and between the seasons, summer/winter had the highest/the lowest value of column concentration of PM2.5 suspended particles in the over Iran. Analysis of correlation values based on Pearson linear regression relationship between PM2.5 suspended particles in the atmosphere (response variable) with some meteorological parameters (independent variables) such as; precipitation, soil surface moisture and soil surface temperature in the geographical area of Iran, well indicate the significant relationship between this variable and the above parameters. So that in the meantime; the amount of correlation between PM2.5 suspended particles over Iran with soil surface temperature indicates a significant positive relationship (R = 81%), a strong negative relationship with soil surface moisture (R = - 76%) and a significant relationship with monthly precipitation, negative (R = - 61%). This means that the concentration of PM2.5 suspended particles over Iran is strongly influenced by environmental parameters, so that in the time series analysis, the presence of seasonal behavior indicates a relatively stable time pattern of PM2.5 suspended particles distribution in the atmosphere over Iran.

Water vapor is the dominant greenhouse gas in the Earth’s atmosphere and, at the same time, highly variable in the atmosphere. Observations of its spatial and temporal variations is a major objective of climate. It is important in several major areas in the atmospheric sciences, on scales from turbulence to synoptic-scale systems, and including cloud formation and maintenance, radiation and climate. The intent of this paper is to demonstrate the ability of MODIS PWV products for use in monthly and daily variability of climatological scales over Iran. Therefore, the results are presented in two sections. The first section compares the long term (2003-2015) Monthly mean MODIS Level 3 and ERA-Interim PWV data sets. The second section validates the level 2 MODIS PWV products by Radiosonde daily data. For a better comparison of MODIS level 2 PWV products with Radiosonde data, we used data from 10 Radiosonde stations over Iran. We consider the sky conditions (cloudiness and visibility) in our comparison. There are no microwave radiometers (MWR) and Global Positioning System (GPS) sites in Iran hence, in the absence of these data, we used the measurements of Radiosonde and ERA-Interim as reference data for the comparison of the MODIS PWV estimates. These data were obtained at monthly and daily scales. In the first section, long-term (2003- 2015) spatial and temporal characteristics of monthly mean PWV are investigated over Iran. For this, Level-3 MODIS terra (MOD08_M3) products and ERA-Interim data were obtained with the 1-degree resolution for Iran. In the second section, January (as a month with low values of PWV and unstable atmosphere) of 2004 and July (as a month with high values of PWV and Stable atmosphere) of 2008 were selected for comparison of MODIS daily (MOD05-L2) PWV product with Radiosonde data for 10 Radiosonde stations in Iran. The average annual MODIS and ERA-Interim PWV data are 12.248 and 12.243 mm, respectively.These values are very close to each other. These values are also close to those derived by Asakereh et al. (2015) from NCEP data reanalysis (about 14.3 mm). Also, Ferencz and Pongra (2008) concluded that the ERA-Interim and the MODIS PWV fields are very similar.The maximum and minimum values of PWV for both data sets is observed during July and January, respectively. Tuller (1968) indicated that February and July are the months of highest and lowest precipitable water at most stations. At some, August replaces July, and at a smaller number, January replaces February. Also, our result is the same with the study of Maghrabi & Dajani, (2014) over Saudi Arabia. They reported that the lowest PWV values were in December and January, whereas the highest values were in June and July. They pointed that during warm periods, increases in the temperature and height of constant pressure levels result in an increased capacity for water vapor of the air mass, keeping it away from the saturation point and consequently preserving high PWV values. In contrast, in cold periods, the decrease in the height of constant- pressure levels, reduce the capacity for water vapor of the air mass and facilitates the condensation process, resulting in a decrease in the amount of PWV. The topography is a key factor in the spatial distribution of PWV. PWV from both data sets has a significant negative relationship with the distribution of topography in all months. This means that the concentration of PWV is high in the highland regions and vice versa. During January 2004, the ranges of errors are in the best case 5.53 mm (Tabriz) and in the worst case (Ahwaz) 16.02 mm. In all stations, the coefficient of determinations are negligible. While in the suitable weather condition, RMSE is decreased in all stations. During July of 2008 at many stations such as Zahedan, Kerman and Esfahan cloud cover and visibility condition have been appropriate, while in Bandar Abbas in all days the visibility was poor (less than 5 KM). It seems that the cloud cover and visibility conditions result in the high coefficient of determinations in Esfahan, Kerman and Zahedan (77, 80 and 66%, respectively) and with high error in Bandar Abbas station. Annual average MODIS PWV and ERA-Interim are close to each other (12.24), in addition, MODIS has a higher negative correlation coefficient with topography compared to ERA-Interim PWV data. This suggests that MODIS level-3 monthly PWV data are valuable for the monthly long-term climatology of PWV over Iran. In daily scale, a comparison of MODIS and radiosonde PWV data in different atmospheric conditions are significantly different. During clear days with appropriate visibility (despite the time lag between two data sets) values of R2 is higher compared to cloudy days with poor visibility. Hence, accuracy of the MODIS PWV data over Iran is strongly dependent on weather conditions.

Prediction of tropical cyclone (TC) intensity has been considered in numerous research studies, due to TC destructive effects. Hence, various parameters were combined in potential intensity relations to show the maximum probable intensity that a TC can achieve. The relations of potential intensity are different, since each relation has been suggested based on various factors affecting TC intensity. In this research, the validity of five potential intensity relations, defined by other researchers for the other basins, was verified for all TCs formed over the northwest of the Indian Ocean from 1990 to 2019. In this duration, sixteen cyclonic storms, nine sever cyclonic storms, ten very sever cyclonic storms and ten extremely severe cyclonic storms occurred. In this research, two sets of data reported by India Meteorological Department (IMD) and reanalysis data from the fifth generation of the European Center for Medium Range Weather Forecast (ECMWF, ERA5) with the horizontal resolution of 0.25 degrees were used. The IMD data included position (latitude/longitude) of the TC’s eye and maximum wind speed. The reanalysis data consisted of meteorological parameters from sea level to the tropopause level, including relative and specific humidity, temperature, pressure, dew point temperature and horizontal wind vector. The first relation for the potential intensity is based on the difference between convective available potential energy values at the radius of maximum wind using saturated and unsaturated air mass. The second one considers the difference between saturated entropy at sea level and environmental value of entropy. The third relation consists of the ratio of difference between upper-level and lower-level temperature to the outflow temperature and also the discrepancy between saturated and unsaturated enthalpy. The fourth relation includs difference of saturated and unsaturated values of equivalent potential temperature at the radius of maximum wind. The last relation not only uses the ratio of temperature of inflow and outflow and discrepancy between surface and boundary layer entropy, but also emphasizs on surface temperature. The ratio of the enthalpy and drag coefficients is used in the all relations, while thermodynamic efficiency is included in some recent relations. The potential intensity values achieved using the empirical relations, were evaluated using the maximum wind speed reported by IMD. The comparison was done based on some statistical indexes and the Taylor diagrams. The statistical indices include (I) index of agreement (IOA), (II) standard deviation, (III) root mean square deviation and (IV) correlation coefficient. For the intensity of depression and deep depression states, the minimum value of IOA was achieved using the first relation, while the other relations produced the close values of around 0.7. For the CS-Category intensity, the first two relations produced the lowest IOA values. For the SCS- Category the last two relations did the best performance, while for VSCS- and ESCS-Categories, the second relation produced the most consistent results. The results from IOA showed that the fifth relation produced the highest agreement with the IMD data. This showed that the discrepancy between sea surface temperature and tropopause temperature and the difference between environmental entropy and inner-core entropy played the most important role in intensification for the first four categories of intensity. However, for the last two categories of intensity the discrepancy between the saturated entropy at surface and entropy of boundary layer produced IOA of 0.73 and 0.75, respectively. It is notable that the difference between saturated equivalent potential temperature and potential temperature of boundary layer, and also difference between temperature of inflow and outflow produced the same results for the beginning state. The other statistical indices were analyzed based on the Taylor diagram focusing on all considered tropical cyclones that were intensified to the various intensities. Conclusions demonstrated that the last and the second potential intensity relations produced the best performance in the all categories for the TCs formed over the northwest of the Indian Ocean during 2019-1990.

In-situ observations underlies a wide range of planning, applied studies and modeling in various fields and sciences, and using this data in studies and planning without ensuring the accuracy and homogeneity of them, can lead to uncertainty in the results. Major problems that researchers face are the poor quality data, missing data, outliers and in-homogeneity in time series. Inappropriate co-locating of stations, human errors in reading and recording data, errors in measuring equipment, changes in measurement tools, different methods of observation, non maintenance and calibration of equipment, constructions around the stations, changes in the type of instruments and sensors for atmospheric parameters measurements and station relocation during the statistical period are problems that affect the accuracy and homogeneity of the meteorological data. Therefore, in this paper, the minimum and maximum daily temperature series and daily rainfall series at 134 weather stations in Iran were analyzed for outliers and homogeneity over the period 1989-2018. First, Iran was divided into 5 clusters based on climatic characteristics. After clustering, the daily maximum and minimum temperatures and daily rainfall data were statistically analyzed using SPSS software and the percentage of missing data was determined separately 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 needed parameter, and for each station, the other stations belonging to that cluster are considered as reference stations. Based on this algorithm, first the desired series is estimated and standardized by reference series by type (II) regression method. After estimating the series, the standardized anomaly series is calculated, in which the difference between the observed and estimated values is calculated. For detecting outliers, two steps were followed. Original data corresponding to standardized anomalies that were greater than the prescribed thresholds, were detected as outliers. In the second step, in order to ensure the correct detection of the outliers, for temperature series the detected outliers in the first step were compared with the values of the days before and after. If they differed significantly, they would be accepted as outliers and deleted. For the precipitation series, the atmospheric condition of the desired dates were 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 maximum SNHT and all the data before the break 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 all homogeneous series and subseries infilled with the same data estimation procedure using only the reference of their own other fragments. The maximum, minimum temperaturs and precipitation series for 134 weather stations of Iran have an average 3%, 4% and 2% missing values, respectively. In this time series, 63 outliers were detected for the maximum temperature parameter that 53 of them were related to the Geophysics station of the University of Tehran. For the minimum temperature, this number reached 50 that 11 of them belong to the Geophysics station and for the precipitation parameter 13 outliers were identified that 5 of them are related to the Geophysics station. For the daily temperature series (excluding Geophysics station), 89 stations were homogeneous and 44 stations had one or two break points, and for the precipitation series, 15 stations were identified as in-homogeneous.