University of Tehran PressJournal of the Earth and Space Physics2538-371X51320251216Optimal Site Selection for Aquaculture Using Spatial Analysis of Satellite Data (Case Study: Persian Gulf and Gulf of Oman)Optimal Site Selection for Aquaculture Using Spatial Analysis of Satellite Data (Case Study: Persian Gulf and Gulf of Oman)51752910427910.22059/jesphys.2025.393689.1007680FAReyhanehMardaniDepartment of Surveying and Geomatics Engineering, Faculty of Engineering, University of Tehran, Tehran, Iran.SaeedFarzanehDepartment of Surveying and Geomatics Engineering, Faculty of Engineering, University of Tehran, Tehran, Iran.AliSam KhanianiDepartment of Surveying Engineering, Faculty of Civil Engineering, Noshirvani University of Technology, Babol, Iran.Journal Article20250503Aquaculture has become a key strategy in meeting the growing global demand for animal protein, especially as natural fish stocks face overexploitation and ecological degradation. Among modern aquaculture methods, cage farming stands out for its cost-effectiveness, environmental sustainability, and potential to boost regional economies. In this context, the current study focuses on identifying optimal sites for the development of sea bass (Lates calcarifer) cage aquaculture in the southern coastal waters of Iran, particularly in the Persian Gulf and the Gulf of Oman. The study employs satellite remote sensing data, geospatial analysis, and spatial multi-criteria evaluation (SMCE) to propose a systematic approach for aquaculture site selection. To achieve this objective, data were collected from environmental and oceanographic multiple satellite and modeling sources, including MODIS (for temperature and dissolved oxygen), SMOS (for salinity), GEBCO (for bathymetric depth), and CMEMS (for variables such as chlorophyll concentration, pH, nitrate, phosphate, sea surface height, and wind speed). All data were obtained for the year 2024 and processed using the Kriging interpolation method to create continuous raster layers with a spatial resolution of one kilometer. The study identifies ten criteria, crucial for sea bass aquaculture: depth, water temperature and salinity, wind speed, sea surface height, chlorophyll-a concentration, pH, dissolved oxygen, nitrate, and phosphate levels. These variables were selected based on biological needs of sea bass and insights from prior studies. For each parameter, suitable ranges were defined—such as water depth between 20–50 meters, salinity between 8.8–39 ppt, and surface temperature between 13–28°C—ensuring conditions that support optimal growth and health of farmed fish. Each criterion was classified into three suitability levels (high, moderate, and low) and then standardized using a ranking method. The relative importance of each criterion was determined using the Analytic Hierarchy Process (AHP), a well-established decision-making framework. Through pairwise comparisons and consistency checks (with a consistency ratio of 0.076), the study assigned the highest weight to depth (0.3342), followed by temperature (0.2061) and salinity (0.1216), while phosphate received the lowest weight (0.0277). Using a Weighted Linear Combination (WLC) approach, the standardized criteria layers were integrated to generate a final suitability map. The results were categorized into three classes—highly suitable, moderately suitable, and unsuitable—for sea bass cage farming. Spatial analysis revealed that 15 counties along the southern coast of Iran hold significant potential for sea bass cage aquaculture. Among them, Gachsaran, Bushehr, Tangestan, Ganaveh, Qeshm, Bandar Abbas, and Minab showed the highest suitability scores. To validate the model, the study compared the identified suitable zones with actual cage farming sites reported by Iran’s Fisheries Organization and the species,’ recorded habitat range from the OBIS (Ocean Biodiversity Information System) database. The overlap between current farms and high-scoring zones confirmed the reliability of the methodology. Furthermore, areas with high suitability also aligned well with recorded sea bass presence in global biodiversity datasets. This research underscores the significant potential of remote sensing and geospatial analysis in aquaculture planning. By leveraging freely available satellite datasets and integrating them into a structured spatial decision-making framework, the study presents a replicable and scalable method for aquaculture site selection. The approach significantly reduces the need for time-consuming and costly field surveys, while also enabling wide-area assessments that account for environmental variability. In addition to environmental considerations, the study also highlights economic and operational benefits. The proposed locations support optimal growth conditions, reducing the risks of fish mortality, disease, or operational failure. Moreover, by identifying regions with low concentrations of pollutants (e.g., nitrate and phosphate) and stable physical conditions (e.g., low wind and wave height), the model aids in promoting sustainable aquaculture practices. In conclusion, the integration of satellite-based environmental data, spatial interpolation techniques, and multi-criteria decision-making tools offers a robust solution for site selection in marine aquaculture. Given the increasing global emphasis on food security, environmental sustainability, and efficient resource use, such methodologies can play a pivotal role in guiding policy and investment in the aquaculture sector—especially in coastal regions like southern Iran, where natural conditions are favorable for fish farming.Aquaculture has become a key strategy in meeting the growing global demand for animal protein, especially as natural fish stocks face overexploitation and ecological degradation. Among modern aquaculture methods, cage farming stands out for its cost-effectiveness, environmental sustainability, and potential to boost regional economies. In this context, the current study focuses on identifying optimal sites for the development of sea bass (Lates calcarifer) cage aquaculture in the southern coastal waters of Iran, particularly in the Persian Gulf and the Gulf of Oman. The study employs satellite remote sensing data, geospatial analysis, and spatial multi-criteria evaluation (SMCE) to propose a systematic approach for aquaculture site selection. To achieve this objective, data were collected from environmental and oceanographic multiple satellite and modeling sources, including MODIS (for temperature and dissolved oxygen), SMOS (for salinity), GEBCO (for bathymetric depth), and CMEMS (for variables such as chlorophyll concentration, pH, nitrate, phosphate, sea surface height, and wind speed). All data were obtained for the year 2024 and processed using the Kriging interpolation method to create continuous raster layers with a spatial resolution of one kilometer. The study identifies ten criteria, crucial for sea bass aquaculture: depth, water temperature and salinity, wind speed, sea surface height, chlorophyll-a concentration, pH, dissolved oxygen, nitrate, and phosphate levels. These variables were selected based on biological needs of sea bass and insights from prior studies. For each parameter, suitable ranges were defined—such as water depth between 20–50 meters, salinity between 8.8–39 ppt, and surface temperature between 13–28°C—ensuring conditions that support optimal growth and health of farmed fish. Each criterion was classified into three suitability levels (high, moderate, and low) and then standardized using a ranking method. The relative importance of each criterion was determined using the Analytic Hierarchy Process (AHP), a well-established decision-making framework. Through pairwise comparisons and consistency checks (with a consistency ratio of 0.076), the study assigned the highest weight to depth (0.3342), followed by temperature (0.2061) and salinity (0.1216), while phosphate received the lowest weight (0.0277). Using a Weighted Linear Combination (WLC) approach, the standardized criteria layers were integrated to generate a final suitability map. The results were categorized into three classes—highly suitable, moderately suitable, and unsuitable—for sea bass cage farming. Spatial analysis revealed that 15 counties along the southern coast of Iran hold significant potential for sea bass cage aquaculture. Among them, Gachsaran, Bushehr, Tangestan, Ganaveh, Qeshm, Bandar Abbas, and Minab showed the highest suitability scores. To validate the model, the study compared the identified suitable zones with actual cage farming sites reported by Iran’s Fisheries Organization and the species,’ recorded habitat range from the OBIS (Ocean Biodiversity Information System) database. The overlap between current farms and high-scoring zones confirmed the reliability of the methodology. Furthermore, areas with high suitability also aligned well with recorded sea bass presence in global biodiversity datasets. This research underscores the significant potential of remote sensing and geospatial analysis in aquaculture planning. By leveraging freely available satellite datasets and integrating them into a structured spatial decision-making framework, the study presents a replicable and scalable method for aquaculture site selection. The approach significantly reduces the need for time-consuming and costly field surveys, while also enabling wide-area assessments that account for environmental variability. In addition to environmental considerations, the study also highlights economic and operational benefits. The proposed locations support optimal growth conditions, reducing the risks of fish mortality, disease, or operational failure. Moreover, by identifying regions with low concentrations of pollutants (e.g., nitrate and phosphate) and stable physical conditions (e.g., low wind and wave height), the model aids in promoting sustainable aquaculture practices. In conclusion, the integration of satellite-based environmental data, spatial interpolation techniques, and multi-criteria decision-making tools offers a robust solution for site selection in marine aquaculture. Given the increasing global emphasis on food security, environmental sustainability, and efficient resource use, such methodologies can play a pivotal role in guiding policy and investment in the aquaculture sector—especially in coastal regions like southern Iran, where natural conditions are favorable for fish farming.https://jesphys.ut.ac.ir/article_104279_b9cac96b9aac49cef117d00fe2f7b53d.pdfUniversity of Tehran PressJournal of the Earth and Space Physics2538-371X51320251216Integration of Global Navigation Satellite System Observations for Generating Network Corrections Usin g a Virtual Reference Station MethodIntegration of Global Navigation Satellite System Observations for Generating Network Corrections Usin g a Virtual Reference Station Method53154810430610.22059/jesphys.2025.394061.1007682FASajadNafarDepartment of Surveying and Geomatics Engineering, Faculty of Engineering, University of Tehran, Tehran, Iran.0009-0001-2048-7878SaeedFarzanehDepartment of Surveying and Geomatics Engineering, Faculty of Engineering, University of Tehran, Tehran, Iran.Journal Article20250511The rapid advancement of Global Navigation Satellite Systems (GNSS) has revolutionized the field of geospatial data acquisition significantly, enhancing the accuracy and efficiency of positioning operations. This study investigates an innovative approach to improving positional accuracy by integrating observations from multiple GNSS constellations — specifically GPS (USA), Galileo (EU), and BeiDou (China) — within a Network-based Real-Time Kinematic (NRTK) positioning framework using a Virtual Reference Station (VRS) method. Traditionally, RTK positioning techniques relied on a single base station to provide corrections, which are effective only within a limited radius due to the degradation of accuracy with increasing distance from the base. The NRTK technique, and particularly the VRS method, emerged as a superior solution by establishing a network of reference stations that deliver spatially interpolated corrections to a virtual station near the user, significantly reducing the baseline length and associated atmospheric and orbital errors. This research implements a dual-frequency double-differencing observation model to mitigate receiver and satellite clock errors, followed by ambiguity resolution using the Melbourne-Wübbena combination and an ionosphere-free linear combination. These steps are vital in estimating the double-differenced integer ambiguities for each frequency, which are subsequently utilized to compute atmospheric delays with high precision. The use of the MLAMBDA algorithm for ambiguity fixing further enhances the reliability of the results.<br />The methodology includes the interpolation of network corrections using four distinct models: linear combination model (LCM), linear distance-based interpolation model (DIM), least squares model (LSM), and linear interpolation model (LIM). Each model offers different strategies to compute tropospheric and ionospheric corrections at the user's location based on surrounding reference station data. Notably, DIM, while computationally simpler, showed limitations due to its unidirectional approach that neglects two-dimensional spatial variability in atmospheric conditions. To assess the performance of the proposed VRS-based NRTK system, data from six permanent GNSS stations from the UNAVCO network were used, with a focus on a single baseline for clarity. The results were evaluated across multiple scenarios: single-system, dual-system, and triple-system integrations, with and without applying network corrections. The empirical data showed that the integration of multiple GNSS systems significantly reduces positional errors. For instance, without integration, GPS yielded horizontal and vertical errors of 33 cm and 52 cm, respectively, while Galileo and BeiDou had higher error margins. However, combining all three systems reduced these errors to just 6 cm horizontally and 17 cm vertically. Analysis of atmospheric corrections revealed that both ionospheric and tropospheric corrections are highly sensitive to satellite geometry, particularly around the times of satellite rise and set. These periods showed an exponential increase in correction values, indicating the need for a threshold-based correction exclusion strategy. A threshold of 15 cm for dispersive (ionospheric) and 10 cm for non-dispersive (tropospheric) corrections was found effective; for fewer systems, thresholds up to 30 cm were tested to maintain a non-singular solution matrix.<br />The results highlight that among all the interpolation methods, LSM, LIM, and LCM performed comparably well, while DIM often lagged, especially under multi-dimensional spatial variability. The dual-system integration of GPS and Galileo consistently outperformed GPS and BeiDou, likely due to structural similarities between GPS and Galileo and the higher noise levels in BeiDou observations. The final outcomes underscore that the optimal configuration for high-precision real-time positioning involves the integration of all three GNSS systems with a robust interpolation model that can accurately model the spatial behavior of atmospheric corrections. This integrated approach not only improves the accuracy of the mobile receiver's coordinates but also enhances the resilience and reliability of the positioning system by reducing dependency on any single GNSS constellation. In conclusion, the study demonstrates that multi-GNSS integration within a VRS-based NRTK framework significantly enhances positional accuracy. The selection of an appropriate interpolation method and the implementation of correction magnitude thresholds are crucial for optimizing performance. This methodology, when localized and adapted with native correction software, offers a promising direction for future GNSS applications in geodetic and cadastral surveying.The rapid advancement of Global Navigation Satellite Systems (GNSS) has revolutionized the field of geospatial data acquisition significantly, enhancing the accuracy and efficiency of positioning operations. This study investigates an innovative approach to improving positional accuracy by integrating observations from multiple GNSS constellations — specifically GPS (USA), Galileo (EU), and BeiDou (China) — within a Network-based Real-Time Kinematic (NRTK) positioning framework using a Virtual Reference Station (VRS) method. Traditionally, RTK positioning techniques relied on a single base station to provide corrections, which are effective only within a limited radius due to the degradation of accuracy with increasing distance from the base. The NRTK technique, and particularly the VRS method, emerged as a superior solution by establishing a network of reference stations that deliver spatially interpolated corrections to a virtual station near the user, significantly reducing the baseline length and associated atmospheric and orbital errors. This research implements a dual-frequency double-differencing observation model to mitigate receiver and satellite clock errors, followed by ambiguity resolution using the Melbourne-Wübbena combination and an ionosphere-free linear combination. These steps are vital in estimating the double-differenced integer ambiguities for each frequency, which are subsequently utilized to compute atmospheric delays with high precision. The use of the MLAMBDA algorithm for ambiguity fixing further enhances the reliability of the results.<br />The methodology includes the interpolation of network corrections using four distinct models: linear combination model (LCM), linear distance-based interpolation model (DIM), least squares model (LSM), and linear interpolation model (LIM). Each model offers different strategies to compute tropospheric and ionospheric corrections at the user's location based on surrounding reference station data. Notably, DIM, while computationally simpler, showed limitations due to its unidirectional approach that neglects two-dimensional spatial variability in atmospheric conditions. To assess the performance of the proposed VRS-based NRTK system, data from six permanent GNSS stations from the UNAVCO network were used, with a focus on a single baseline for clarity. The results were evaluated across multiple scenarios: single-system, dual-system, and triple-system integrations, with and without applying network corrections. The empirical data showed that the integration of multiple GNSS systems significantly reduces positional errors. For instance, without integration, GPS yielded horizontal and vertical errors of 33 cm and 52 cm, respectively, while Galileo and BeiDou had higher error margins. However, combining all three systems reduced these errors to just 6 cm horizontally and 17 cm vertically. Analysis of atmospheric corrections revealed that both ionospheric and tropospheric corrections are highly sensitive to satellite geometry, particularly around the times of satellite rise and set. These periods showed an exponential increase in correction values, indicating the need for a threshold-based correction exclusion strategy. A threshold of 15 cm for dispersive (ionospheric) and 10 cm for non-dispersive (tropospheric) corrections was found effective; for fewer systems, thresholds up to 30 cm were tested to maintain a non-singular solution matrix.<br />The results highlight that among all the interpolation methods, LSM, LIM, and LCM performed comparably well, while DIM often lagged, especially under multi-dimensional spatial variability. The dual-system integration of GPS and Galileo consistently outperformed GPS and BeiDou, likely due to structural similarities between GPS and Galileo and the higher noise levels in BeiDou observations. The final outcomes underscore that the optimal configuration for high-precision real-time positioning involves the integration of all three GNSS systems with a robust interpolation model that can accurately model the spatial behavior of atmospheric corrections. This integrated approach not only improves the accuracy of the mobile receiver's coordinates but also enhances the resilience and reliability of the positioning system by reducing dependency on any single GNSS constellation. In conclusion, the study demonstrates that multi-GNSS integration within a VRS-based NRTK framework significantly enhances positional accuracy. The selection of an appropriate interpolation method and the implementation of correction magnitude thresholds are crucial for optimizing performance. This methodology, when localized and adapted with native correction software, offers a promising direction for future GNSS applications in geodetic and cadastral surveying.https://jesphys.ut.ac.ir/article_104306_a566d090cd600f5e84f8e288aed41416.pdfUniversity of Tehran PressJournal of the Earth and Space Physics2538-371X51320251216Stabilization of Vertical Gradient Calculation Against Random Noise in Potential Field Data Using Tikhonov RegularizationStabilization of Vertical Gradient Calculation Against Random Noise in Potential Field Data Using Tikhonov Regularization54957010430710.22059/jesphys.2025.394254.1007685FAMinaAmirniaDepartment of Petroleum and Geophysics, Faculty of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran.AminRoshandel KahooDepartment of Petroleum and Geophysics, Faculty of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran.HamidAghajaniDepartment of Petroleum and Geophysics, Faculty of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran.Journal Article20250525Accurate calculation of vertical gradients in potential field data plays a vital role in geophysical surveys and subsurface explorations. These gradients, which are derived from gravity or magnetic field measurements, serve to enhance the resolution of anomaly boundaries and help differentiate between geological structures located at various depths. By sharpening the edges of anomalies, vertical gradients allow geophysicists to better understand the geometry and distribution of subsurface features such as faults, intrusive bodies, or mineral deposits. However, calculating these gradients is highly sensitive to the presence of random noise, which is an inevitable component of real-world data. This sensitivity becomes increasingly problematic when computing higher-order vertical gradients, where noise amplification can dominate the signal, leading to unstable, distorted signals and hence unreliable interpretations.<br />This study addresses the critical need to stabilize vertical gradient calculations in the presence of noise. It begins by analyzing the standard method of calculating vertical gradients in the frequency domain using Fourier transforms. While this approach is efficient and mathematically straightforward, it inherently increases the influence of high-frequency components, many of which represent noise rather than useful geological information. As a result, the final gradient estimates obtained through this method may be contaminated, especially in areas with complex geological settings or low signal-to-noise ratios. To overcome these limitations, the paper investigates two advanced stabilization techniques: Tikhonov regularization and upward continuation filtering.<br />Tikhonov regularization is a well-established method for addressing ill-posed inverse problems. In this context, it introduces a stabilizing constraint that penalizes overly sharp or erratic variations in the gradient estimates. This is achieved through the minimization of a cost function that includes both a data fidelity term and a regularization term that suppresses rapid changes. The strength of this regularization is controlled by a parameter that must be carefully selected. The paper evaluates two methods for determining this parameter: the norm C method and Morozov’s discrepancy principle. These approaches help ensure a balance between suppressing noise and preserving meaningful geological detail in the gradient results.<br />Upward continuation filtering is another effective technique explored in this study. It works by projecting the data to a higher elevation, which naturally smooths out high-frequency (i.e., shallow or noisy) components. This filtering effect reduces the influence of noise and results in more stable and geologically interpretable gradient estimates, particularly for deeper structures.<br />The effectiveness of these methods is demonstrated using both synthetic and real geophysical datasets. A synthetic model involving a Bouguer anomaly at a 10-meter depth shows that Tikhonov regularization and upward continuation outperform conventional Fourier-based gradient calculations in terms of accuracy and noise resistance. Furthermore, real-world gravity data from Slovakia are used to validate the practical utility of these approaches. The improved clarity in detecting geological features such as faults and shear zones, underscores the value of stabilized gradient methods.<br />In conclusion, the study highlights the importance of using stabilization techniques for vertical gradient computation. Both Tikhonov regularization and upward continuation significantly improve the reliability and interpretability of gradient estimates, making them essential tools for modern geophysical data analyses and subsurface mapping.Accurate calculation of vertical gradients in potential field data plays a vital role in geophysical surveys and subsurface explorations. These gradients, which are derived from gravity or magnetic field measurements, serve to enhance the resolution of anomaly boundaries and help differentiate between geological structures located at various depths. By sharpening the edges of anomalies, vertical gradients allow geophysicists to better understand the geometry and distribution of subsurface features such as faults, intrusive bodies, or mineral deposits. However, calculating these gradients is highly sensitive to the presence of random noise, which is an inevitable component of real-world data. This sensitivity becomes increasingly problematic when computing higher-order vertical gradients, where noise amplification can dominate the signal, leading to unstable, distorted signals and hence unreliable interpretations.<br />This study addresses the critical need to stabilize vertical gradient calculations in the presence of noise. It begins by analyzing the standard method of calculating vertical gradients in the frequency domain using Fourier transforms. While this approach is efficient and mathematically straightforward, it inherently increases the influence of high-frequency components, many of which represent noise rather than useful geological information. As a result, the final gradient estimates obtained through this method may be contaminated, especially in areas with complex geological settings or low signal-to-noise ratios. To overcome these limitations, the paper investigates two advanced stabilization techniques: Tikhonov regularization and upward continuation filtering.<br />Tikhonov regularization is a well-established method for addressing ill-posed inverse problems. In this context, it introduces a stabilizing constraint that penalizes overly sharp or erratic variations in the gradient estimates. This is achieved through the minimization of a cost function that includes both a data fidelity term and a regularization term that suppresses rapid changes. The strength of this regularization is controlled by a parameter that must be carefully selected. The paper evaluates two methods for determining this parameter: the norm C method and Morozov’s discrepancy principle. These approaches help ensure a balance between suppressing noise and preserving meaningful geological detail in the gradient results.<br />Upward continuation filtering is another effective technique explored in this study. It works by projecting the data to a higher elevation, which naturally smooths out high-frequency (i.e., shallow or noisy) components. This filtering effect reduces the influence of noise and results in more stable and geologically interpretable gradient estimates, particularly for deeper structures.<br />The effectiveness of these methods is demonstrated using both synthetic and real geophysical datasets. A synthetic model involving a Bouguer anomaly at a 10-meter depth shows that Tikhonov regularization and upward continuation outperform conventional Fourier-based gradient calculations in terms of accuracy and noise resistance. Furthermore, real-world gravity data from Slovakia are used to validate the practical utility of these approaches. The improved clarity in detecting geological features such as faults and shear zones, underscores the value of stabilized gradient methods.<br />In conclusion, the study highlights the importance of using stabilization techniques for vertical gradient computation. Both Tikhonov regularization and upward continuation significantly improve the reliability and interpretability of gradient estimates, making them essential tools for modern geophysical data analyses and subsurface mapping.https://jesphys.ut.ac.ir/article_104307_f8f05b0a37f0d7a698e18c6b11753c2a.pdfUniversity of Tehran PressJournal of the Earth and Space Physics2538-371X51320251216Inverse Modeling of Time-Domain Induced Polarization Data with the Aim of Imaging Mineralization ZonesInverse Modeling of Time-Domain Induced Polarization Data with the Aim of Imaging Mineralization Zones57159210463210.22059/jesphys.2025.401218.1007718FAAmir HosseinZiaDepartment of Earth Physics, Institute of Geophysics, University of Tehran, Tehran, Iran.RezaGhanatiDepartment of Earth Physics, Institute of Geophysics, University of Tehran, Tehran, Iran.MahdiFallahsafariDepartment of Earth Physics, Institute of Geophysics, University of Tehran, Tehran, Iran.Journal Article20250902Time-domain induced polarization (TDIP) measurements provide valuable information about the degree of polarization of subsurface geological layers. This geophysical technique plays a crucial role in a wide range of applications, including mineral exploration (for determining bedrock depth, identifying cavities, fracture zones, and faults), geotechnical investigations (such as assessing the stability of dams, airport runways, and other infrastructures), as well as in environmental studies (as detecting and monitoring contaminated areas). The primary objective of TDIP measurements is to determine the spatial distribution of the subsurface’s electrical properties, whether within a buried object along its boundaries, or in the surrounding medium. These electrical characteristics are typically influenced by factors such as mineral composition, porosity, fluid saturation, and grain surface chemistry. Mathematically, the behavior of the electric potential in the earth is governed by Poisson’s equation, which must be solved under appropriate boundary conditions. By solving this equation, a model of the subsurface can be generated, offering deeper insight into its geological structure. In the forward modeling process, for a two-dimensional environment with an arbitrary distribution of electrical conductivity, the governing partial differential equation is solved numerically—commonly using the finite difference method (FDM). Based on the conductivity distribution, apparent induced polarization (IP) responses are calculated by incorporating the relationship between conductivity and IP parameters such as chargeability. These apparent IP responses include the calculation of Frechét derivatives, which form the elements of the sensitivity matrix (Jacobian matrix). This matrix plays a key role in defining the objective function for the inversion process. Inversion of TDIP data is inherently nonlinear and typically involves a two-step procedure. In the first step, the direct current (DC) resistivity data is used to estimate the distribution of electric potential within the medium, which leads to the estimation of the background conductivity. In the second step, using this conductivity model as a fixed or initial condition, the main goal is to recover the chargeability distribution that best explains the observed IP data. This is achieved by solving the inverse problem using suitable numerical optimization methods. The final chargeability model is considered acceptable when it produces simulated data that adequately match the measured field data within a defined error tolerance.<br />To evaluate the effectiveness and robustness of the proposed inversion algorithm, both synthetic datasets and field measurements from the Borzamin area were utilized. Numerical results demonstrate that the algorithm can provide reasonable reconstructions of synthetic models. However, as the complexity of the IP model increases (e.g., due to heterogeneity or anisotropy), the uncertainty in precisely determining the chargeability distribution also increases. Field data inversion results indicate the presence of chargeable anomalies across multiple profiles in the study area. When interpreted alongside lithological information, these anomalies suggest the potential existence of mineralized zones within the Borzamin region. Such integrated geophysical and geological interpretations can significantly enhance exploration strategies and reduce the uncertainty in locating economically viable ore deposits.Time-domain induced polarization (TDIP) measurements provide valuable information about the degree of polarization of subsurface geological layers. This geophysical technique plays a crucial role in a wide range of applications, including mineral exploration (for determining bedrock depth, identifying cavities, fracture zones, and faults), geotechnical investigations (such as assessing the stability of dams, airport runways, and other infrastructures), as well as in environmental studies (as detecting and monitoring contaminated areas). The primary objective of TDIP measurements is to determine the spatial distribution of the subsurface’s electrical properties, whether within a buried object along its boundaries, or in the surrounding medium. These electrical characteristics are typically influenced by factors such as mineral composition, porosity, fluid saturation, and grain surface chemistry. Mathematically, the behavior of the electric potential in the earth is governed by Poisson’s equation, which must be solved under appropriate boundary conditions. By solving this equation, a model of the subsurface can be generated, offering deeper insight into its geological structure. In the forward modeling process, for a two-dimensional environment with an arbitrary distribution of electrical conductivity, the governing partial differential equation is solved numerically—commonly using the finite difference method (FDM). Based on the conductivity distribution, apparent induced polarization (IP) responses are calculated by incorporating the relationship between conductivity and IP parameters such as chargeability. These apparent IP responses include the calculation of Frechét derivatives, which form the elements of the sensitivity matrix (Jacobian matrix). This matrix plays a key role in defining the objective function for the inversion process. Inversion of TDIP data is inherently nonlinear and typically involves a two-step procedure. In the first step, the direct current (DC) resistivity data is used to estimate the distribution of electric potential within the medium, which leads to the estimation of the background conductivity. In the second step, using this conductivity model as a fixed or initial condition, the main goal is to recover the chargeability distribution that best explains the observed IP data. This is achieved by solving the inverse problem using suitable numerical optimization methods. The final chargeability model is considered acceptable when it produces simulated data that adequately match the measured field data within a defined error tolerance.<br />To evaluate the effectiveness and robustness of the proposed inversion algorithm, both synthetic datasets and field measurements from the Borzamin area were utilized. Numerical results demonstrate that the algorithm can provide reasonable reconstructions of synthetic models. However, as the complexity of the IP model increases (e.g., due to heterogeneity or anisotropy), the uncertainty in precisely determining the chargeability distribution also increases. Field data inversion results indicate the presence of chargeable anomalies across multiple profiles in the study area. When interpreted alongside lithological information, these anomalies suggest the potential existence of mineralized zones within the Borzamin region. Such integrated geophysical and geological interpretations can significantly enhance exploration strategies and reduce the uncertainty in locating economically viable ore deposits.https://jesphys.ut.ac.ir/article_104632_b39f1d3b4ae0d920eb4a01507609eb8a.pdfUniversity of Tehran PressJournal of the Earth and Space Physics2538-371X51320251216Evaluation of Soil Spectral Indices and Surface Albedo in Desertification Monitoring of Western Khuzestan Province using Remote Sensing DataEvaluation of Soil Spectral Indices and Surface Albedo in Desertification Monitoring of Western Khuzestan Province using Remote Sensing Data59361110429110.22059/jesphys.2025.386172.1007647FAMohammadAbiyatDepartment of Soil Science and Engineering, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran.MostefaAbiyatDepartment of Human Geography, Faculty of Geographical Sciences and Planning, University of Isfahan, Isfahan, Iran.MortezaAbiyatDepartment of Human Geography, Faculty of Geographical Sciences and Planning, University of Isfahan, Isfahan, Iran.Journal Article20241224Desertification is a form of land degradation, resulting from both natural processes and human activities. In this regard, the use of remote sensing indicators to prepare desertification maps can be an efficient approach to assess this process. The goal of this study is to evaluate the potential of soil spectral indices and surface albedo in modeling desertification intensity in the western Khuzestan Province. Initially, we extracted indices including BSI, BI, SI, IFe2O3, NSMI, TGSI, CLI, CI, SCI, and Albedo from Landsat 8 satellite images. Then, soil spectral indices were calculated using the band calculation tool available in ENVI software, and raster maps of these indices were generated. To create feature space models and identify correlations between the variables, we also extracted the surface albedo index values from the satellite images.<br />The maximum and minimum values of surface albedo index were 37518.1 and 8338.75, respectively. Next, we randomly selected spectral values from 753 locations on the soil spectral index map and extracted their corresponding values from the surface albedo index map. To ensure comparability, the index values were standardized through data normalization. After normalization, scatter plots of the spectral pixel value densities were generated in the SAGA 9.5 software environment, and best-fit line equations were determined. Linear regression analysis was then conducted to examine the correlation between the soil spectral indices and Albedo.<br />In this study, all soil spectral indices were considered independent variables, while the surface albedo index was treated as the dependent variable. Desertification intensity was classified by dividing the feature space perpendicular to the trend of desertification change. Subsequently, the Jenks Natural Breaks method was applied to classify the data values into five desertification classes: areas without desertification, areas with low desertification intensity, areas with moderate desertification intensity, areas with high desertification intensity, and areas with very high desertification intensity.<br />The results of the correlation analysis of the indices show that the BSI, BI, SI, NSMI, TGSI, CLI, and SCI indices are positively correlated with the Albedo index; so with the increase in the BSI, BI, SI, NSMI, TGSI, CLI, and SCI indices, the Albedo variable also increases. While the IFe2O3 and CI indices were negatively correlated with the Albedo index, such that an increase in IFe2O3 and CI indices resulted in a reduction of the Albedo variable. An analysis of the correlation coefficients between the indices revealed that the BI, SI, and TGSI indices exhibited the strongest correlation with the Albedo index, with correlation coefficients of 0.985, 0.909, and 0.850, respectively. Conversely, the IFe2O3, CLI, and NSMI indices showed the weakest correlation with the Albedo index, with correlation coefficients of -0.026, 0.106, and 0.110, respectively. Therefore, the Albedo-BI, Albedo-SI, and Albedo-TGSI models, due to their highest correlations with the variables, were considered suitable criterion for evaluating and classifying desertification in the region, given its rather arid climate.<br />Following the classification of desertification intensity, the accuracy of the Albedo-BI, Albedo-SI, and Albedo-TGSI feature space models was assessed using the error matrix, overall accuracy, and the kappa coefficient. The Albedo-BI model achieved an overall accuracy of 98.23% and a kappa coefficient of 0.97; the Albedo-SI model yielded 94.45% accuracy and a kappa value of 0.92; and the Albedo-TGSI model recorded 90.50% accuracy with a kappa coefficient of 0.86. These results indicate that the Albedo-BI model provides the highest classification accuracy among the three models. Furthermore, analysis of the models suggests that the eastern parts of the study area exhibit higher desertification intensity than the western regions.Desertification is a form of land degradation, resulting from both natural processes and human activities. In this regard, the use of remote sensing indicators to prepare desertification maps can be an efficient approach to assess this process. The goal of this study is to evaluate the potential of soil spectral indices and surface albedo in modeling desertification intensity in the western Khuzestan Province. Initially, we extracted indices including BSI, BI, SI, IFe2O3, NSMI, TGSI, CLI, CI, SCI, and Albedo from Landsat 8 satellite images. Then, soil spectral indices were calculated using the band calculation tool available in ENVI software, and raster maps of these indices were generated. To create feature space models and identify correlations between the variables, we also extracted the surface albedo index values from the satellite images.<br />The maximum and minimum values of surface albedo index were 37518.1 and 8338.75, respectively. Next, we randomly selected spectral values from 753 locations on the soil spectral index map and extracted their corresponding values from the surface albedo index map. To ensure comparability, the index values were standardized through data normalization. After normalization, scatter plots of the spectral pixel value densities were generated in the SAGA 9.5 software environment, and best-fit line equations were determined. Linear regression analysis was then conducted to examine the correlation between the soil spectral indices and Albedo.<br />In this study, all soil spectral indices were considered independent variables, while the surface albedo index was treated as the dependent variable. Desertification intensity was classified by dividing the feature space perpendicular to the trend of desertification change. Subsequently, the Jenks Natural Breaks method was applied to classify the data values into five desertification classes: areas without desertification, areas with low desertification intensity, areas with moderate desertification intensity, areas with high desertification intensity, and areas with very high desertification intensity.<br />The results of the correlation analysis of the indices show that the BSI, BI, SI, NSMI, TGSI, CLI, and SCI indices are positively correlated with the Albedo index; so with the increase in the BSI, BI, SI, NSMI, TGSI, CLI, and SCI indices, the Albedo variable also increases. While the IFe2O3 and CI indices were negatively correlated with the Albedo index, such that an increase in IFe2O3 and CI indices resulted in a reduction of the Albedo variable. An analysis of the correlation coefficients between the indices revealed that the BI, SI, and TGSI indices exhibited the strongest correlation with the Albedo index, with correlation coefficients of 0.985, 0.909, and 0.850, respectively. Conversely, the IFe2O3, CLI, and NSMI indices showed the weakest correlation with the Albedo index, with correlation coefficients of -0.026, 0.106, and 0.110, respectively. Therefore, the Albedo-BI, Albedo-SI, and Albedo-TGSI models, due to their highest correlations with the variables, were considered suitable criterion for evaluating and classifying desertification in the region, given its rather arid climate.<br />Following the classification of desertification intensity, the accuracy of the Albedo-BI, Albedo-SI, and Albedo-TGSI feature space models was assessed using the error matrix, overall accuracy, and the kappa coefficient. The Albedo-BI model achieved an overall accuracy of 98.23% and a kappa coefficient of 0.97; the Albedo-SI model yielded 94.45% accuracy and a kappa value of 0.92; and the Albedo-TGSI model recorded 90.50% accuracy with a kappa coefficient of 0.86. These results indicate that the Albedo-BI model provides the highest classification accuracy among the three models. Furthermore, analysis of the models suggests that the eastern parts of the study area exhibit higher desertification intensity than the western regions.https://jesphys.ut.ac.ir/article_104291_343c91f1a3a7e99d9d67f393c5f9b500.pdfUniversity of Tehran PressJournal of the Earth and Space Physics2538-371X51320251216Variability analysis of effective teleconnection signals on Iran's climateVariability analysis of effective teleconnection signals on Iran's climate61363310445910.22059/jesphys.2025.388254.1007661FAAbolfazlNeyestaniDepartment of Physics, Faculty of Science, Razi University, Kermanshah, Iran.Journal Article20250106Temporal variations in local and global climate are often influenced by changes in teleconnection patterns. These patterns are key phenomena arising from fluctuations in variables such as sea surface temperature (SST) and sea level pressure (SLP), which exhibit spatio-temporal variability, governed by stable and recurring atmospheric and oceanic processes. Consequently, teleconnection signals can induce oscillations in the climate system. Among the most influential indices are the East Atlantic-West Russia (EA-WR) pattern, the Mediterranean Oscillation (MO), the North Atlantic Oscillation (NAO), the Quasi-Biennial Oscillation (QBO), and the Southern Oscillation Index (SOI), all of which can significantly affect atmospheric variability across different regions of Iran.<br />Identifying the dominant variabilities within each climate index and examining the potential relationships among these signals is a critical first step in assessing their impact on regional climate variables. This is important because some indices may share common fluctuations within specific time scales (i.e. frequency bands), potentially producing similar effects on climatic variables such as precipitation, temperature, and pressure. By accounting for these shared fluctuations, the net influence of each teleconnection index can be more accurately evaluated.<br />Given the importance of teleconnection indices and their role in shaping various climatic variables in Iran, a comprehensive understanding of the patterns embedded in these signals — across high-, medium-, and low-frequency oscillations — is essential. Moreover, elucidating the temporal relationships between pairs of global circulation indices is critical for understanding climate evolution at multiple frequencies, particularly in the context of Iran climate.<br />In this study, monthly data were obtained from relevant international archives, and the signals were decomposed into distinct frequency bands using optimal digital filters. Correlation analyses, including auto- and cross-correlation functions, were applied to examine the linear relationships between similar variabilities across the signals at different time lags. Additionally, power spectral density (PSD) analysis was used to compare the strength of each teleconnection signal within selected frequency bands.<br />The results reveal significant correlations among certain teleconnection signals at specific time scales. For example, at the annual time scale (10–14 months), the corresponding components of the EA-WR and NAO signals exhibit a strong direct correlation at zero lag (correlation coefficient: 0.564). The variance distribution across frequency bands is also distinctive for each index. Specifically, over 80% of the variability in the monthly QBO signal occurs quasi-cyclically on time scales of 14 months to 3 years, primarily around 28 months. For the EA-WR and NAO indices, a substantial portion of variability (45%–55%) occurs on the 2–5 month scale (high-frequency component), with limited correlation between these short-term fluctuations. High-frequency variations dominate the MO signal, whereas the SOI exhibits substantial variability across most time scales except for the annual scale. For low-frequency variability (time scales greater than 7 years), a significant negative correlation exists between SOI and the other teleconnection indices, that can have implications for climate patterns over Iran.Temporal variations in local and global climate are often influenced by changes in teleconnection patterns. These patterns are key phenomena arising from fluctuations in variables such as sea surface temperature (SST) and sea level pressure (SLP), which exhibit spatio-temporal variability, governed by stable and recurring atmospheric and oceanic processes. Consequently, teleconnection signals can induce oscillations in the climate system. Among the most influential indices are the East Atlantic-West Russia (EA-WR) pattern, the Mediterranean Oscillation (MO), the North Atlantic Oscillation (NAO), the Quasi-Biennial Oscillation (QBO), and the Southern Oscillation Index (SOI), all of which can significantly affect atmospheric variability across different regions of Iran.<br />Identifying the dominant variabilities within each climate index and examining the potential relationships among these signals is a critical first step in assessing their impact on regional climate variables. This is important because some indices may share common fluctuations within specific time scales (i.e. frequency bands), potentially producing similar effects on climatic variables such as precipitation, temperature, and pressure. By accounting for these shared fluctuations, the net influence of each teleconnection index can be more accurately evaluated.<br />Given the importance of teleconnection indices and their role in shaping various climatic variables in Iran, a comprehensive understanding of the patterns embedded in these signals — across high-, medium-, and low-frequency oscillations — is essential. Moreover, elucidating the temporal relationships between pairs of global circulation indices is critical for understanding climate evolution at multiple frequencies, particularly in the context of Iran climate.<br />In this study, monthly data were obtained from relevant international archives, and the signals were decomposed into distinct frequency bands using optimal digital filters. Correlation analyses, including auto- and cross-correlation functions, were applied to examine the linear relationships between similar variabilities across the signals at different time lags. Additionally, power spectral density (PSD) analysis was used to compare the strength of each teleconnection signal within selected frequency bands.<br />The results reveal significant correlations among certain teleconnection signals at specific time scales. For example, at the annual time scale (10–14 months), the corresponding components of the EA-WR and NAO signals exhibit a strong direct correlation at zero lag (correlation coefficient: 0.564). The variance distribution across frequency bands is also distinctive for each index. Specifically, over 80% of the variability in the monthly QBO signal occurs quasi-cyclically on time scales of 14 months to 3 years, primarily around 28 months. For the EA-WR and NAO indices, a substantial portion of variability (45%–55%) occurs on the 2–5 month scale (high-frequency component), with limited correlation between these short-term fluctuations. High-frequency variations dominate the MO signal, whereas the SOI exhibits substantial variability across most time scales except for the annual scale. For low-frequency variability (time scales greater than 7 years), a significant negative correlation exists between SOI and the other teleconnection indices, that can have implications for climate patterns over Iran.https://jesphys.ut.ac.ir/article_104459_7447f427752ca194e57729f810732e47.pdfUniversity of Tehran PressJournal of the Earth and Space Physics2538-371X51320251216Entropy-Based Analysis of Summer Heatwave Intensity Percentiles Using Parametric Functions across IranEntropy-Based Analysis of Summer Heatwave Intensity Percentiles Using Parametric Functions across Iran63564710431610.22059/jesphys.2025.396221.1007697FAMojtabaShokouhiResearch Institute of Meteorology and Atmospheric Science (RIMAS), Tehran, Iran.MehdiMesrizadehResearch Institute of Meteorology and Atmospheric Science (RIMAS), Tehran, Iran.EbrahimAsadi OskoueiResearch Institute of Meteorology and Atmospheric Science (RIMAS), Tehran, Iran.0000-0002-5603-765XJournal Article20250620Global warming has contributed to an increase in both the intensity and frequency of extreme climatic events, including heat waves. The impacts of heatwaves are influenced by various parameters such as duration, frequency, intensity, magnitude and the spatial extent of the affected area. In this study, a nonlinear index was developed to quantify heatwave intensity, based on the probability distribution of daily temperatures in each region. The proposed Heatwave Intensity Index (HII) is derived using the concept of entropy and is proportional to the deviation from a temperature threshold. This threshold is determined from a parametric probability distribution function fitted to daily average temperature data for each region of Iran during the period 2011–2021. The index was evaluated in terms of intensity, duration, and spatial extent.<br />Daily average temperature data at a 2-meter height were employed over the entire area of Iran, with each grid cell treated as an individual study unit. A heatwave event was defined as a period of at least three consecutive days with daily average temperatures exceeding the 95th percentile of the warm-season (June–September) temperature distribution. The 95th percentile threshold for each region was derived from the best-fitting continuous parametric probability distribution function. Four distribution types were tested: normal, log-normal, Weibull, and gamma, and the one that best represented the observed data in each selected region.<br />The Weibull distribution provided the best fit for more than 85% of Iran’s territory. In contrast, less than 10% of the areas, primarily along the northern and southern coasts, as well as Ardabil and large parts of East Azerbaijan provinces, were best described by normal or log-normal distributions. The highest temperature thresholds, exceeding 43°C, were observed in southwestern Iran, particularly in Khuzestan, southern Ilam, and central regions of the Lut Desert. Given the geographical diversity and varying climatic conditions across Iran, applying a single, fixed temperature threshold for the entire country is not appropriate. Instead, region-specific thresholds should be used to accurately identify heatwave events. In areas with historically low heatwave frequency, return periods are estimated to range from 4 to 5 years, whereas regions with higher frequencies may experience heatwaves at least three times every two years. Except for southeastern Iran, the highest frequency and broadest spatial extent of heatwaves were observed in July. Except for one central region, the average heatwave duration in most areas did not exceed four days. In the years 2019 and 2021, the intensity and frequency of heatwave events were higher compared to other years. The results show that during the heatwave days in 2021, the heatwaves were more intense, and a larger area was affected by it than that in 2019. It can be said that, given the rising temperature trend in the later years of the study period, the magnitude of heatwave intensity has also increased. The geometric mean of the HII varied significantly across Iran, with the highest values exceeding 80 units recorded along the southern coastline. In recent years, both the spatial extent and the intensity of heatwave events have increased. The most expansive heatwaves primarily affected the central and eastern border regions of the country.Global warming has contributed to an increase in both the intensity and frequency of extreme climatic events, including heat waves. The impacts of heatwaves are influenced by various parameters such as duration, frequency, intensity, magnitude and the spatial extent of the affected area. In this study, a nonlinear index was developed to quantify heatwave intensity, based on the probability distribution of daily temperatures in each region. The proposed Heatwave Intensity Index (HII) is derived using the concept of entropy and is proportional to the deviation from a temperature threshold. This threshold is determined from a parametric probability distribution function fitted to daily average temperature data for each region of Iran during the period 2011–2021. The index was evaluated in terms of intensity, duration, and spatial extent.<br />Daily average temperature data at a 2-meter height were employed over the entire area of Iran, with each grid cell treated as an individual study unit. A heatwave event was defined as a period of at least three consecutive days with daily average temperatures exceeding the 95th percentile of the warm-season (June–September) temperature distribution. The 95th percentile threshold for each region was derived from the best-fitting continuous parametric probability distribution function. Four distribution types were tested: normal, log-normal, Weibull, and gamma, and the one that best represented the observed data in each selected region.<br />The Weibull distribution provided the best fit for more than 85% of Iran’s territory. In contrast, less than 10% of the areas, primarily along the northern and southern coasts, as well as Ardabil and large parts of East Azerbaijan provinces, were best described by normal or log-normal distributions. The highest temperature thresholds, exceeding 43°C, were observed in southwestern Iran, particularly in Khuzestan, southern Ilam, and central regions of the Lut Desert. Given the geographical diversity and varying climatic conditions across Iran, applying a single, fixed temperature threshold for the entire country is not appropriate. Instead, region-specific thresholds should be used to accurately identify heatwave events. In areas with historically low heatwave frequency, return periods are estimated to range from 4 to 5 years, whereas regions with higher frequencies may experience heatwaves at least three times every two years. Except for southeastern Iran, the highest frequency and broadest spatial extent of heatwaves were observed in July. Except for one central region, the average heatwave duration in most areas did not exceed four days. In the years 2019 and 2021, the intensity and frequency of heatwave events were higher compared to other years. The results show that during the heatwave days in 2021, the heatwaves were more intense, and a larger area was affected by it than that in 2019. It can be said that, given the rising temperature trend in the later years of the study period, the magnitude of heatwave intensity has also increased. The geometric mean of the HII varied significantly across Iran, with the highest values exceeding 80 units recorded along the southern coastline. In recent years, both the spatial extent and the intensity of heatwave events have increased. The most expansive heatwaves primarily affected the central and eastern border regions of the country.https://jesphys.ut.ac.ir/article_104316_a99d9838e01dcc9386c7e04435abed5b.pdfUniversity of Tehran PressJournal of the Earth and Space Physics2538-371X51320251216Atmospheric conductivity and effects of global electrical current on clouds layer electricityAtmospheric conductivity and effects of global electrical current on clouds layer electricity64966310429610.22059/jesphys.2025.397565.1007700FAJafarBorhanianDepartment of Physics, Faculty of Science, University of Mohghegh Ardabili, Ardabil, Iran.Journal Article20250701The atmospheric conductivity, the most important parameter in atmospheric electricity, is modeled using up to date data and models for ionization agents. Then the space charge production in clouds layer is investigated based on this conductivity model. In the troposphere and the lower stratosphere, cosmic rays and radioactive materials are primarily sources of ionization and ions production in the Earth’s atmosphere. Here, the ionization rate induced by cosmic rays is calculated based on the Monte Carlo CRAC: CRII model that is able to compute it in the atmosphere at any given location and time, provided the energy spectrum of incoming cosmic rays is known. The ionization rate caused by radioactive materials, mainly in atmospheric boundary layer, is determined in virtue of a semi-empirical model which is based on more recent data presented in literature. The profile of ionization rate induced by cosmic rays as well as total ionization rate are provided and their dependence on latitude is thoroughly investigated. In order to calculate the atmospheric conductivity, the data was obtained for ionization rate is utilized alongside the existing models for ion-ion recombination coefficient, ion attachment coefficients to aerosols and droplets, and ions mobility. Dependence of atmospheric conductivity on various parameters is inspected. It is shown that, close to the Earth’s surface, conductivity decreases when the altitude increases, but at higher altitudes its behavior is different; conductivity increases with altitude and reaches a maximum at top of the troposphere. One of the main goals of this paper is to investigate the space charge production at the boundaries of clouds layer (strati-form). To do so, the charge continuity and Poisson equations are employed to obtain an equation that relates space charge to vertical derivative of atmospheric conductivity. Then, it is shown that the flow of global electric current through the cloud causes space charges to be created at the upper and lower boundaries of clouds layer. This occurs because cloud commutativity is about an order of magnitude greater than the conductivity of clear air. Then, when global current flows through the boundaries, according to Gauss’s law, space charge accumulation takes place because of existence of a gradient in electric field inside and outside of cloud boundaries. Calculations show that the upper cloud edge acquires positive charge and the lower cloud edge obtains negative charge, with the upper layer charge density being slightly larger. It is also shown that the amount of space charge in both boundaries depends on latitude as well as to cloud altitude and to its thickness. The different charge accumulation at upper and lower boundaries of cloud layer bears a resemblance to an electric dipole moment that can be defined using standard definition. The values of these dipole moments depend on the characteristics of the cloud and the dipole moments per square meter of various clouds that are calculated for various clouds. Finally, a brief discussion is presented based on the findings. The presence of space charge at cloud layer boundaries is equivalent to charging of droplets there. Charging of droplets can affect cloud microphysics, i.e. it can affect droplet activation, droplet evaporation, and droplet-droplet collision which usually results in collection efficiency enhancement. Enhancing the collection efficiency can also shorten the droplet growth time scale. Moreover, the acquired electrical structure of clouds layer reinforces the fact that they should be considered as an important element in modeling global electric circuit.The atmospheric conductivity, the most important parameter in atmospheric electricity, is modeled using up to date data and models for ionization agents. Then the space charge production in clouds layer is investigated based on this conductivity model. In the troposphere and the lower stratosphere, cosmic rays and radioactive materials are primarily sources of ionization and ions production in the Earth’s atmosphere. Here, the ionization rate induced by cosmic rays is calculated based on the Monte Carlo CRAC: CRII model that is able to compute it in the atmosphere at any given location and time, provided the energy spectrum of incoming cosmic rays is known. The ionization rate caused by radioactive materials, mainly in atmospheric boundary layer, is determined in virtue of a semi-empirical model which is based on more recent data presented in literature. The profile of ionization rate induced by cosmic rays as well as total ionization rate are provided and their dependence on latitude is thoroughly investigated. In order to calculate the atmospheric conductivity, the data was obtained for ionization rate is utilized alongside the existing models for ion-ion recombination coefficient, ion attachment coefficients to aerosols and droplets, and ions mobility. Dependence of atmospheric conductivity on various parameters is inspected. It is shown that, close to the Earth’s surface, conductivity decreases when the altitude increases, but at higher altitudes its behavior is different; conductivity increases with altitude and reaches a maximum at top of the troposphere. One of the main goals of this paper is to investigate the space charge production at the boundaries of clouds layer (strati-form). To do so, the charge continuity and Poisson equations are employed to obtain an equation that relates space charge to vertical derivative of atmospheric conductivity. Then, it is shown that the flow of global electric current through the cloud causes space charges to be created at the upper and lower boundaries of clouds layer. This occurs because cloud commutativity is about an order of magnitude greater than the conductivity of clear air. Then, when global current flows through the boundaries, according to Gauss’s law, space charge accumulation takes place because of existence of a gradient in electric field inside and outside of cloud boundaries. Calculations show that the upper cloud edge acquires positive charge and the lower cloud edge obtains negative charge, with the upper layer charge density being slightly larger. It is also shown that the amount of space charge in both boundaries depends on latitude as well as to cloud altitude and to its thickness. The different charge accumulation at upper and lower boundaries of cloud layer bears a resemblance to an electric dipole moment that can be defined using standard definition. The values of these dipole moments depend on the characteristics of the cloud and the dipole moments per square meter of various clouds that are calculated for various clouds. Finally, a brief discussion is presented based on the findings. The presence of space charge at cloud layer boundaries is equivalent to charging of droplets there. Charging of droplets can affect cloud microphysics, i.e. it can affect droplet activation, droplet evaporation, and droplet-droplet collision which usually results in collection efficiency enhancement. Enhancing the collection efficiency can also shorten the droplet growth time scale. Moreover, the acquired electrical structure of clouds layer reinforces the fact that they should be considered as an important element in modeling global electric circuit.https://jesphys.ut.ac.ir/article_104296_7c833bd38d56cc9ecdcbeb75284a9135.pdfUniversity of Tehran PressJournal of the Earth and Space Physics2538-371X51320251216Spatial-temporal patterns of the Northern Hemisphere jet streams from the western Atlantic to western AsiaSpatial-temporal patterns of the Northern Hemisphere jet streams from the western Atlantic to western Asia66568710463310.22059/jesphys.2025.398471.1007702FABahramAsefiDepartment of Physical Geography, Faculty of Geography, University of Tehran, Tehran, Iran.GhasemAziziDepartment of Physical Geography, Faculty of Geography, University of Tehran, Tehran, Iran.MostafaKarimiDepartment of Physical Geography, Faculty of Geography, University of Tehran, Tehran, Iran.MasoumehMoghbelDepartment of Physical Geography, Faculty of Geography, University of Tehran, Tehran, Iran.0000-0001-9393-9954FaramarzKhoshakhlaghDepartment of Physical Geography, Faculty of Geography, University of Tehran, Tehran, Iran.Journal Article20250720Jet streams are narrow and powerful bands of air flows located in the upper troposphere and lower stratosphere. They are formed as a result of meridional temperature gradients between polar and tropical air masses and also baroclinic instability and play a key role in atmospheric dynamics, weather systems formation, and the transfer of energy and momentum within the Earth's atmosphere (Barnes and Screen, 2015). In the Northern Hemisphere, the polar and subtropical jet streams are globally significant due to their influence on weather phenomena such as storms, heatwaves, and cold spells (Francis and Vavrus, 2012; Screen and Simmons, 2014). This study identifies daily jet stream patterns from the Atlantic to the Middle East for the first time using hierarchical clustering. The objective is to classify and analyze the spatiotemporal patterns of the jet stream across the North Atlantic to the Middle East region and to investigate their climatic trends using NCEP/NCAR reanalysis data and a hierarchical clustering method.<br />Daily zonal (u) and meridional (v) wind components at the 250 hPa level were obtained from the NOAA database, covering a 31-year period from January 1, 1985, to December 31, 2015. The study domain extends from 10° to 80°N and 80°W to 80°E with a spatial resolution of 2.5°. The dataset was processed in MATLAB, converting spatial map data into numerical matrices: 11,322 rows representing daily records and 1,885 columns representing grid points, forming a matrix of over 21 million data points.<br />Jet streams were defined as bands with wind speeds exceeding 30 m/s. Hierarchical clustering was performed using Euclidean distance for intra-cluster similarity and Ward’s method for inter-cluster linkage. A dendrogram was constructed, and a cut-off threshold was set to obtain nine optimal clusters. The spatial and temporal characteristics of each cluster were analyzed in Excel and GIS to compute variance, frequency, and the 95th percentile of wind speed values. Seasonal and monthly trends were assessed, and inter-cluster correlation matrices were generated using SPSS. For each cluster, a representative day was identified based on maximum internal correlation, and wind fields for those days were visualized using GrADS software.<br />Autumn patterns (Clusters 1 and 2) and spring patterns (Clusters 5 and 7) demonstrate high variability in jet stream intensity and position. Cluster 1 exhibits a decreasing trend and features a strong polar jet over the North Atlantic alongside a weakened subtropical jet over the Middle East, consistent with the projected weakening of subtropical jets under global warming (Archer & Caldeira, 2008; Overland & Wang, 2010). Cluster 2, is characterized by a strong subtropical jet over southern Iran, and reflects complex interactions between subtropical and polar jets and Rossby wave propagation, consistent with Hoskins and Ambrizzi (1993). Significant negative correlations—such as those between Cluster 1 and Clusters 5 and 7, or between Cluster 3 and Cluster 9—suggest opposing atmospheric regimes (Michelangeli et al, 1995; Corti et al. 1999). Observed trends including declines in Clusters 1, 8, and 9 and an increase in Cluster 3—support previous findings regarding the weakening of the polar vortex (Kim et al., 2014). The results are also consistent with the findings of Thompson & Wallace (2000) and Ambaum et al. (2001).<br />This research shows a gap in the direct analysis of jet stream patterns by, for the first time, applying spatiotemporal clustering across the North Atlantic to the Middle East. The main innovation of this study lies in the identification of nine distinct and recurrent jet stream patterns, each representing a specific atmospheric circulation regime in the region. These findings enhance our understanding of both seasonal and long-term atmospheric dynamics. The most significant quantitative outcome is the detection of contrasting and statistically meaningful trends in the frequency of these patterns over 31 years (1985-2015). Specifically, the increasing trend of cluster 3 (the winter pattern associated with a strong south-ward shifted polar jet) and the decreasing trends of clusters 1, 8, and 9 (autumn and winter patterns) provide a strong evidence of changing atmospheric circulation regimes in the region. The increased frequency of the pattern associated with extreme weather events (cluster 3) may represent one of the direct consequences of climate change on regional atmospheric dynamics. Furthermore, the discovery of significant negative correlations between certain clusters (e.g., clusters 3 and 9, and clusters 1 with 5 and 7) indicate competing and replacement among atmospheric regimes across different seasons in this area.Jet streams are narrow and powerful bands of air flows located in the upper troposphere and lower stratosphere. They are formed as a result of meridional temperature gradients between polar and tropical air masses and also baroclinic instability and play a key role in atmospheric dynamics, weather systems formation, and the transfer of energy and momentum within the Earth's atmosphere (Barnes and Screen, 2015). In the Northern Hemisphere, the polar and subtropical jet streams are globally significant due to their influence on weather phenomena such as storms, heatwaves, and cold spells (Francis and Vavrus, 2012; Screen and Simmons, 2014). This study identifies daily jet stream patterns from the Atlantic to the Middle East for the first time using hierarchical clustering. The objective is to classify and analyze the spatiotemporal patterns of the jet stream across the North Atlantic to the Middle East region and to investigate their climatic trends using NCEP/NCAR reanalysis data and a hierarchical clustering method.<br />Daily zonal (u) and meridional (v) wind components at the 250 hPa level were obtained from the NOAA database, covering a 31-year period from January 1, 1985, to December 31, 2015. The study domain extends from 10° to 80°N and 80°W to 80°E with a spatial resolution of 2.5°. The dataset was processed in MATLAB, converting spatial map data into numerical matrices: 11,322 rows representing daily records and 1,885 columns representing grid points, forming a matrix of over 21 million data points.<br />Jet streams were defined as bands with wind speeds exceeding 30 m/s. Hierarchical clustering was performed using Euclidean distance for intra-cluster similarity and Ward’s method for inter-cluster linkage. A dendrogram was constructed, and a cut-off threshold was set to obtain nine optimal clusters. The spatial and temporal characteristics of each cluster were analyzed in Excel and GIS to compute variance, frequency, and the 95th percentile of wind speed values. Seasonal and monthly trends were assessed, and inter-cluster correlation matrices were generated using SPSS. For each cluster, a representative day was identified based on maximum internal correlation, and wind fields for those days were visualized using GrADS software.<br />Autumn patterns (Clusters 1 and 2) and spring patterns (Clusters 5 and 7) demonstrate high variability in jet stream intensity and position. Cluster 1 exhibits a decreasing trend and features a strong polar jet over the North Atlantic alongside a weakened subtropical jet over the Middle East, consistent with the projected weakening of subtropical jets under global warming (Archer & Caldeira, 2008; Overland & Wang, 2010). Cluster 2, is characterized by a strong subtropical jet over southern Iran, and reflects complex interactions between subtropical and polar jets and Rossby wave propagation, consistent with Hoskins and Ambrizzi (1993). Significant negative correlations—such as those between Cluster 1 and Clusters 5 and 7, or between Cluster 3 and Cluster 9—suggest opposing atmospheric regimes (Michelangeli et al, 1995; Corti et al. 1999). Observed trends including declines in Clusters 1, 8, and 9 and an increase in Cluster 3—support previous findings regarding the weakening of the polar vortex (Kim et al., 2014). The results are also consistent with the findings of Thompson & Wallace (2000) and Ambaum et al. (2001).<br />This research shows a gap in the direct analysis of jet stream patterns by, for the first time, applying spatiotemporal clustering across the North Atlantic to the Middle East. The main innovation of this study lies in the identification of nine distinct and recurrent jet stream patterns, each representing a specific atmospheric circulation regime in the region. These findings enhance our understanding of both seasonal and long-term atmospheric dynamics. The most significant quantitative outcome is the detection of contrasting and statistically meaningful trends in the frequency of these patterns over 31 years (1985-2015). Specifically, the increasing trend of cluster 3 (the winter pattern associated with a strong south-ward shifted polar jet) and the decreasing trends of clusters 1, 8, and 9 (autumn and winter patterns) provide a strong evidence of changing atmospheric circulation regimes in the region. The increased frequency of the pattern associated with extreme weather events (cluster 3) may represent one of the direct consequences of climate change on regional atmospheric dynamics. Furthermore, the discovery of significant negative correlations between certain clusters (e.g., clusters 3 and 9, and clusters 1 with 5 and 7) indicate competing and replacement among atmospheric regimes across different seasons in this area.https://jesphys.ut.ac.ir/article_104633_a04127196207f781f8648782ed30dc19.pdfUniversity of Tehran PressJournal of the Earth and Space Physics2538-371X51320251216Identification of Clutter in Bushehr Weather Doppler Radars and Their RemovalIdentification of Clutter in Bushehr Weather Doppler Radars and Their Removal68970110427810.22059/jesphys.2025.398518.1007703FAMahnazKarimkhaniResearch Institute of Meteorology and Atmospheric Science, Tehran, Iran.MehdiRahnamaResearch Institute of Meteorology and Atmospheric Science, Tehran, Iran.Journal Article20250714The word Radar from the term Radio Detection and Ranging means target radio tracking and ranging, an electromagnetic system that is used to detect and determine the position and speed of a target. Most meteorological radars are of Pulse-Doppler type, which have the ability to detect the movement of raindrops and the intensity of precipitation. Both types of this information can be analyzed to determine the structure of the storm and its potential for producing severe weather. Also, Doppler type radars measure rainfall and wind direction and speed at certain times and in a wide area. These types of radars display the position of the target and reflectivity with the help of a computer by combining colors on the display screens (Reinhart, 1997). Ground echo is a challenge for meteorological radar data analysis, especially in hydrology and precipitation estimation. Therefore, the removal of ground clutter is a prerequisite for the use of meteorological radar for both quantitative and qualitative purposes. An important part in the quality control of radar data is the removal of clutter.<br />Several researches have been conducted to provide different algorithms for removing ground clutter and abnormal echoes emitted by radar data (Gabela and Notarpietro, 2002; Layton et al., 2013; Zheng et al., 2016; Kaman et al., 2018; Proft et al., 2019; Adaba et al., 2021; Xie et al., 2021; Wang et al., 2022; Yan et al., 2020).<br />The aim of the current research is to present a simple and fast algorithm by selecting the most appropriate thresholds to remove ground clutter and unusual echoes presented in radar reflection data, so that it has the least impact on showing meteorological phenomena.<br />In the present study, the effectiveness of removing ground clutter as well as determining the best thresholds has been quantitatively evaluated on the meteorological Doppler radar of Bushehr, located in the southwest of Iran.<br />This algorithm has been used in rainy condition (squall line event On March 19, 2017 at 04:00UTC) using the Bushehr Doppler Weather Radar data which includes spatial-proximity filter and compactness test. The spatial-continuity filter that is applied to each pixel eliminates data that are weakly and spatially correlated to the surrounding. Therefore, when the difference between each pixel and the surrounding pixels is less than a certain threshold , its value is assumed to be a meteorological echo. The compactness test identifies adjacent pixels of not null intensity; thus, choosing a threshold level slightly greater than one, will eliminate most of the unwanted clutter. To determine the best threshold, for 3 to 8 values and between 1.1 to 2.6 are examined.<br />Removal of ground noises in Bushehr weather Doppler radar reflection data equal to 3, 4, 5, 6, 7 and 8 and with a threshold of 1.1 for 04UTC on March 19, 2017 showed that the difference in does not make a significant change in the removal of clutter.<br />Due to the fact that the change in does not have much change in the removal of clutter, so in the following, is equal to 8 and the change in is examined. The results showed that the present algorithm has a significant ability to remove the ground clutter from the Doppler radar reflectivity data. Varying the values from 3 to 8 did not have a significant effect on noise removal. This is likely due to the saturation of after reaching a certain threshold, which may result from the homogeneity of the area's texture (coastal region). Also, in the examined thresholds, equal to 8 and equal to 1.8 has the best result by removing most ground clutters and unusual echoes in the radar reflectivity data, as well as preserving storm convective cells.The word Radar from the term Radio Detection and Ranging means target radio tracking and ranging, an electromagnetic system that is used to detect and determine the position and speed of a target. Most meteorological radars are of Pulse-Doppler type, which have the ability to detect the movement of raindrops and the intensity of precipitation. Both types of this information can be analyzed to determine the structure of the storm and its potential for producing severe weather. Also, Doppler type radars measure rainfall and wind direction and speed at certain times and in a wide area. These types of radars display the position of the target and reflectivity with the help of a computer by combining colors on the display screens (Reinhart, 1997). Ground echo is a challenge for meteorological radar data analysis, especially in hydrology and precipitation estimation. Therefore, the removal of ground clutter is a prerequisite for the use of meteorological radar for both quantitative and qualitative purposes. An important part in the quality control of radar data is the removal of clutter.<br />Several researches have been conducted to provide different algorithms for removing ground clutter and abnormal echoes emitted by radar data (Gabela and Notarpietro, 2002; Layton et al., 2013; Zheng et al., 2016; Kaman et al., 2018; Proft et al., 2019; Adaba et al., 2021; Xie et al., 2021; Wang et al., 2022; Yan et al., 2020).<br />The aim of the current research is to present a simple and fast algorithm by selecting the most appropriate thresholds to remove ground clutter and unusual echoes presented in radar reflection data, so that it has the least impact on showing meteorological phenomena.<br />In the present study, the effectiveness of removing ground clutter as well as determining the best thresholds has been quantitatively evaluated on the meteorological Doppler radar of Bushehr, located in the southwest of Iran.<br />This algorithm has been used in rainy condition (squall line event On March 19, 2017 at 04:00UTC) using the Bushehr Doppler Weather Radar data which includes spatial-proximity filter and compactness test. The spatial-continuity filter that is applied to each pixel eliminates data that are weakly and spatially correlated to the surrounding. Therefore, when the difference between each pixel and the surrounding pixels is less than a certain threshold , its value is assumed to be a meteorological echo. The compactness test identifies adjacent pixels of not null intensity; thus, choosing a threshold level slightly greater than one, will eliminate most of the unwanted clutter. To determine the best threshold, for 3 to 8 values and between 1.1 to 2.6 are examined.<br />Removal of ground noises in Bushehr weather Doppler radar reflection data equal to 3, 4, 5, 6, 7 and 8 and with a threshold of 1.1 for 04UTC on March 19, 2017 showed that the difference in does not make a significant change in the removal of clutter.<br />Due to the fact that the change in does not have much change in the removal of clutter, so in the following, is equal to 8 and the change in is examined. The results showed that the present algorithm has a significant ability to remove the ground clutter from the Doppler radar reflectivity data. Varying the values from 3 to 8 did not have a significant effect on noise removal. This is likely due to the saturation of after reaching a certain threshold, which may result from the homogeneity of the area's texture (coastal region). Also, in the examined thresholds, equal to 8 and equal to 1.8 has the best result by removing most ground clutters and unusual echoes in the radar reflectivity data, as well as preserving storm convective cells.https://jesphys.ut.ac.ir/article_104278_3c713e166f483b5bb10931daa86f2ad0.pdfUniversity of Tehran PressJournal of the Earth and Space Physics2538-371X51320251216Application of Remote Sensing and Machine Learning for the Identification of Dust Sources in Khuzestan ProvinceApplication of Remote Sensing and Machine Learning for the Identification of Dust Sources in Khuzestan Province70371710427710.22059/jesphys.2025.398558.1007704FASeyed MohsenHesamForecasting, and Crisis Management of Atmospheric Hazards, Khuzestan Province Meteorological Department, Ahvaz, Iran.MohammadSabzehzariKhuzestan Province Meteorological Department, Ahvaz, Iran.Journal Article20250721Khuzestan Province, due to its arid climate and unique geographical location, is one of the main regions affected by dust storms in Iran. This study utilized satellite data and the Random Forest machine learning algorithm to identify dust source areas in the province during the period from January 26 to April 26, 2025. Sentinel-2, Sentinel-5P, and SMAP satellite data were used to calculate the Normalized Difference Vegetation Index (NDVI), Absorbing Aerosol Index (AAI), and surface soil moisture respectively. These indices monitored vegetation cover, the presence of dust-related aerosols, and soil dryness.<br />Google Earth Engine (GEE) was employed for data processing. The steps included defining the study area (Khuzestan, with a surface area of 64,057 km², based on FAO/GAUL/2015 data), filtering satellite images with cloud cover less than 10%, calculating the average values of NDVI, AAI, and soil moisture, and integrating these layers into a composite image. A Random Forest classifier with 50 decision trees was used to categorize regions into source and non-source classes. Sample points included 200 source points (e.g., Hoor-Al-Azim wetland) and 200 non-source points (urban and agricultural areas). Of these points, 70% were used for model training, 15% for validation, and 15% for final testing and evaluation. NDVI, Soil Moisture and AAI time series were also analyzed to assess aerosol variation trends, and model accuracy was evaluated using a confusion matrix. The results obtained from the validation data (overall accuracy = 0.95, F1-score = 0.95, Cohen’s Kappa = 0.90) and the final test (overall accuracy = 0.967, F1-score = 0.967, Cohen’s Kappa = 0.933) indicate a very strong and stable performance of the mode. The results indicated that the southern and western parts of Khuzestan—especially areas around Hoor-Al-Azim, southeastern Ahvaz, and the regions of Mahshahr, Hendijan, Omidiyeh, Susangerd, and Hoveyzeh are major dust sources. The classification map (Figure 8) highlighted these areas in red and non-source areas in green. The NDVI map (Figure 2) showed low vegetation coverage (values less than 0) in the southwest, particularly near Hoor-Al-Azim, indicating dry, bare-soil conditions. The AAI index (Figure 3) confirmed high aerosol values (0.2 to 0.5) in the central and southern regions, especially around Ahvaz and Hoor-Al-Azim. Soil moisture values (Figure 4) were also low in the south and central areas (below 0.1 cm³/cm³), indicating high potential for wind erosion. Key contributing factors to dust source activation included drought, reduced rainfall, the drying of the Jarrahi and Karun rivers, and desiccation of the southern parts of Hoor-Al-Azim.<br />This study successfully identified dust source areas in Khuzestan and is consistent with previous research, such as that by Rangzan et al. (1393) The inclusion of soil moisture as a new variable improved identification accuracy. The Random Forest algorithm provided reliable performance, due to its ability to model nonlinear relationships and handle heterogeneous data. However, limitations included the lack of field data for validation and the influence of atmospheric conditions on satellite observations.<br />Future studies are encouraged to incorporate field observations for validation and increase the number of sample points. Moreover, more advanced methods such as deep learning and the integration of satellite data with meteorological models (e.g., wind speed and direction), could improve dust storm forecasting accuracy. This approach especially with the flexible coding environment of Google Earth Engine offers an effective tool for monitoring and managing dust sources and can be applied in wetland restoration and soil stabilization efforts in desert regions.Khuzestan Province, due to its arid climate and unique geographical location, is one of the main regions affected by dust storms in Iran. This study utilized satellite data and the Random Forest machine learning algorithm to identify dust source areas in the province during the period from January 26 to April 26, 2025. Sentinel-2, Sentinel-5P, and SMAP satellite data were used to calculate the Normalized Difference Vegetation Index (NDVI), Absorbing Aerosol Index (AAI), and surface soil moisture respectively. These indices monitored vegetation cover, the presence of dust-related aerosols, and soil dryness.<br />Google Earth Engine (GEE) was employed for data processing. The steps included defining the study area (Khuzestan, with a surface area of 64,057 km², based on FAO/GAUL/2015 data), filtering satellite images with cloud cover less than 10%, calculating the average values of NDVI, AAI, and soil moisture, and integrating these layers into a composite image. A Random Forest classifier with 50 decision trees was used to categorize regions into source and non-source classes. Sample points included 200 source points (e.g., Hoor-Al-Azim wetland) and 200 non-source points (urban and agricultural areas). Of these points, 70% were used for model training, 15% for validation, and 15% for final testing and evaluation. NDVI, Soil Moisture and AAI time series were also analyzed to assess aerosol variation trends, and model accuracy was evaluated using a confusion matrix. The results obtained from the validation data (overall accuracy = 0.95, F1-score = 0.95, Cohen’s Kappa = 0.90) and the final test (overall accuracy = 0.967, F1-score = 0.967, Cohen’s Kappa = 0.933) indicate a very strong and stable performance of the mode. The results indicated that the southern and western parts of Khuzestan—especially areas around Hoor-Al-Azim, southeastern Ahvaz, and the regions of Mahshahr, Hendijan, Omidiyeh, Susangerd, and Hoveyzeh are major dust sources. The classification map (Figure 8) highlighted these areas in red and non-source areas in green. The NDVI map (Figure 2) showed low vegetation coverage (values less than 0) in the southwest, particularly near Hoor-Al-Azim, indicating dry, bare-soil conditions. The AAI index (Figure 3) confirmed high aerosol values (0.2 to 0.5) in the central and southern regions, especially around Ahvaz and Hoor-Al-Azim. Soil moisture values (Figure 4) were also low in the south and central areas (below 0.1 cm³/cm³), indicating high potential for wind erosion. Key contributing factors to dust source activation included drought, reduced rainfall, the drying of the Jarrahi and Karun rivers, and desiccation of the southern parts of Hoor-Al-Azim.<br />This study successfully identified dust source areas in Khuzestan and is consistent with previous research, such as that by Rangzan et al. (1393) The inclusion of soil moisture as a new variable improved identification accuracy. The Random Forest algorithm provided reliable performance, due to its ability to model nonlinear relationships and handle heterogeneous data. However, limitations included the lack of field data for validation and the influence of atmospheric conditions on satellite observations.<br />Future studies are encouraged to incorporate field observations for validation and increase the number of sample points. Moreover, more advanced methods such as deep learning and the integration of satellite data with meteorological models (e.g., wind speed and direction), could improve dust storm forecasting accuracy. This approach especially with the flexible coding environment of Google Earth Engine offers an effective tool for monitoring and managing dust sources and can be applied in wetland restoration and soil stabilization efforts in desert regions.https://jesphys.ut.ac.ir/article_104277_25e291e57a71b6568d707a87ad67c229.pdfUniversity of Tehran PressJournal of the Earth and Space Physics2538-371X51320251216Evaluation of the ERA Reanalysis Data Accuracy for Temperature and Humidity Parameters Based on Upper-Air Soundings in Iran (1990–2020)Evaluation of the ERA Reanalysis Data Accuracy for Temperature and Humidity Parameters Based on Upper-Air Soundings in Iran (1990–2020)71973710502310.22059/jesphys.2025.400092.1007713FANafisehPegahfarAtmospheric Science Research Center, Iranian National Institute for Oceanography and Atmospheric Science, Tehran, Iran.Journal Article20250820In this study, upper-air sounding observations from nine radiosonde stations across Iran were used to evaluate performance of the ERA5 reanalysis data in reproducing temperature and humidity parameters during the period 1990-2020. Observations were taken twice daily at 00:00 and 12:00 UTC. Temperature-related parameters included air temperature, dew point temperature (at 850, 700, and 500 hPa), and lapse rate (in three layers: the first 1-km, the first 3-km, and the 3–6 km layer from the surface). Humidity parameters include specific humidity (from 1000 to 200 hPa) and mixing ratio (at 100, 300, and 500 meters above ground level). Four statistical indicators were employed including bias, correlation at the 95% confidence level, root mean square error and Index of Agreement (IoA). Results indicate that ERA5 reanalysis data generally overestimate temperature at the lower tropospheric level at night time, while underestimations are found at daytime across most stations and seasons. At 700 hPa, Tehran, Kermanshah, and Isfahan show warm bias at daytime in all seasons, whereas Mashhad show a cold bias. At night times, all stations exhibited warm biases.<br />At the mid-troposphere, warm biases were observed across all stations and both times and seasons. Despite these biases, the correlation coefficients and IoA values for temperature across all levels exceeded 0.9, indicating generally good agreement. In addition, monthly mean lapse rate trends from observations and ERA5 reanalysis data showed distinct station-specific and climate-related variability. Although the frequency of surface cooling and temperature inversions-which ERA5 reanalysis fails to represent-decreased with altitude, discrepancies between observed and reanalyzed lapse rate trends were evident even in the 3–6 km layer. For example, observational and ERA5 reanalysis trends of lapse rate in 1-km layer differed at Shiraz, and also that for 3–6 km layer in Ahvaz, and Zahedan differed. Only in the 0–3 km layer both trends aligned across all stations. On a seasonal scale, reanalysis data underestimated lapse rates at all three layers at night time, while at daytime it overestimated lapse rates in the 1-km and 3-km layers and underestimated them in the 3–6 km layer. RMSE calculations revealed consistent overestimation of lapse rates across all stations, seasons, and times, with errors more pronounced in the lowest layer. IoA values indicated reasonable agreement between reanalysis and observations, particularly at daytime. Seasonal bias distributions of variables at 850 hPa were not deemed climatologically robust due to the limited number of soundings reaching that level at some stations with higher elevation. However, at 700 and 500 hPa—where more observations were available—88% of reanalysis dew point temperature estimates showed overestimation, with cold bias occurring in only 1% of cases. RMSE values of dew point temperature were higher at 500 hPa than that at 700 hPa, likely due to limited observational data at mid-troposphere levels, insufficient representation of upper-air dynamics in boundary layer schemes, and greater dependency on model-based estimates at higher altitudes. Nonetheless, IoA values showed high accuracy across all stations for reanalysis dew point temperature at 700 and 500 hPa at night and exceeded 0.8–0.9 in 89% of cases at daytime. Vertical profiles of seasonal specific humidity showed that, with the exception of Ahvaz station, the highest bias occurred near the surface and diminished with altitude. Larger biases were observed at night compared to daytime, particularly in the lower troposphere. Comparing mixing ratio estimates from reanalysis with observational data revealed overestimation at all three levels (100, 300, and 500 meters) in all stations except Ahvaz. The overestimation was more significant at night. Ahvaz was the only station where reanalysis consistently underestimated the mixing ratio during both day and night. The findings indicate that reanalysis data provide relatively acceptable performance in estimating mid- to upper-tropospheric temperature parameters (air temperature, dew point, and lapse rate), particularly during daytime and at higher atmospheric layers. However, near-surface layers, especially during nighttime, reanalysis data exhibited larger biases and errors, mainly due to reanalysis limitations in capturing surface cooling, temperature inversion, and moist boundary-layer conditions. Humidity parameters, such as specific humidity and mixing ratio, showed more significant overestimations near the surface and were generally less accurate than temperature parameters, with the exception of stations with unique climate characteristics, such as Ahvaz. In conclusion, although, reanalysis datasets cannot fully replace observational data, they offer valuable insights for climate studies, particularly in data-sparse regions and for mid- to long-term analyses. It is recommended that future studies integrate reanalysis with surface observations and satellite data to reduce uncertainties and gain a more comprehensive understanding of atmospheric processes.In this study, upper-air sounding observations from nine radiosonde stations across Iran were used to evaluate performance of the ERA5 reanalysis data in reproducing temperature and humidity parameters during the period 1990-2020. Observations were taken twice daily at 00:00 and 12:00 UTC. Temperature-related parameters included air temperature, dew point temperature (at 850, 700, and 500 hPa), and lapse rate (in three layers: the first 1-km, the first 3-km, and the 3–6 km layer from the surface). Humidity parameters include specific humidity (from 1000 to 200 hPa) and mixing ratio (at 100, 300, and 500 meters above ground level). Four statistical indicators were employed including bias, correlation at the 95% confidence level, root mean square error and Index of Agreement (IoA). Results indicate that ERA5 reanalysis data generally overestimate temperature at the lower tropospheric level at night time, while underestimations are found at daytime across most stations and seasons. At 700 hPa, Tehran, Kermanshah, and Isfahan show warm bias at daytime in all seasons, whereas Mashhad show a cold bias. At night times, all stations exhibited warm biases.<br />At the mid-troposphere, warm biases were observed across all stations and both times and seasons. Despite these biases, the correlation coefficients and IoA values for temperature across all levels exceeded 0.9, indicating generally good agreement. In addition, monthly mean lapse rate trends from observations and ERA5 reanalysis data showed distinct station-specific and climate-related variability. Although the frequency of surface cooling and temperature inversions-which ERA5 reanalysis fails to represent-decreased with altitude, discrepancies between observed and reanalyzed lapse rate trends were evident even in the 3–6 km layer. For example, observational and ERA5 reanalysis trends of lapse rate in 1-km layer differed at Shiraz, and also that for 3–6 km layer in Ahvaz, and Zahedan differed. Only in the 0–3 km layer both trends aligned across all stations. On a seasonal scale, reanalysis data underestimated lapse rates at all three layers at night time, while at daytime it overestimated lapse rates in the 1-km and 3-km layers and underestimated them in the 3–6 km layer. RMSE calculations revealed consistent overestimation of lapse rates across all stations, seasons, and times, with errors more pronounced in the lowest layer. IoA values indicated reasonable agreement between reanalysis and observations, particularly at daytime. Seasonal bias distributions of variables at 850 hPa were not deemed climatologically robust due to the limited number of soundings reaching that level at some stations with higher elevation. However, at 700 and 500 hPa—where more observations were available—88% of reanalysis dew point temperature estimates showed overestimation, with cold bias occurring in only 1% of cases. RMSE values of dew point temperature were higher at 500 hPa than that at 700 hPa, likely due to limited observational data at mid-troposphere levels, insufficient representation of upper-air dynamics in boundary layer schemes, and greater dependency on model-based estimates at higher altitudes. Nonetheless, IoA values showed high accuracy across all stations for reanalysis dew point temperature at 700 and 500 hPa at night and exceeded 0.8–0.9 in 89% of cases at daytime. Vertical profiles of seasonal specific humidity showed that, with the exception of Ahvaz station, the highest bias occurred near the surface and diminished with altitude. Larger biases were observed at night compared to daytime, particularly in the lower troposphere. Comparing mixing ratio estimates from reanalysis with observational data revealed overestimation at all three levels (100, 300, and 500 meters) in all stations except Ahvaz. The overestimation was more significant at night. Ahvaz was the only station where reanalysis consistently underestimated the mixing ratio during both day and night. The findings indicate that reanalysis data provide relatively acceptable performance in estimating mid- to upper-tropospheric temperature parameters (air temperature, dew point, and lapse rate), particularly during daytime and at higher atmospheric layers. However, near-surface layers, especially during nighttime, reanalysis data exhibited larger biases and errors, mainly due to reanalysis limitations in capturing surface cooling, temperature inversion, and moist boundary-layer conditions. Humidity parameters, such as specific humidity and mixing ratio, showed more significant overestimations near the surface and were generally less accurate than temperature parameters, with the exception of stations with unique climate characteristics, such as Ahvaz. In conclusion, although, reanalysis datasets cannot fully replace observational data, they offer valuable insights for climate studies, particularly in data-sparse regions and for mid- to long-term analyses. It is recommended that future studies integrate reanalysis with surface observations and satellite data to reduce uncertainties and gain a more comprehensive understanding of atmospheric processes.https://jesphys.ut.ac.ir/article_105023_3c7000d50ea6dfe1e3e87e6a10a026c2.pdf