University of Tehran PressJournal of the Earth and Space Physics2538-371X52120260530Improving VTEC-derived IRI empirical model using COSMIC2 Radio Occultation observationsImproving VTEC-derived IRI empirical model using COSMIC2 Radio Occultation observations11510652910.22059/jesphys.2026.396551.1007695FAZahraForoodiDepartment of Geodesy, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran.0009-0004-0050-5612YazdanAmerianDepartment of Geodesy, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran.0000-0003-4854-3402Journal Article20250610Due to the inadequate spatial distribution of data in certain regions, empirical ionospheric models suffer from limited accuracy. To address this limitation, the present study developed a method to enhance the Total Electron Content (<em>TEC</em>) predictions of the International Reference Ionosphere 2020 (IRI-2020) model using ionospheric profile (ionPrf) products derived from the Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC2) measurements. COSMIC2 represents the next-generation follow-on to the original COSMIC mission. It consists of six satellites in orbits with a 24-degree inclination, primarily providing coverage between 30°N and 30°S latitudes. By contrast, the original COSMIC satellites operated at an 87-degree inclination, achieving near-global coverage. The proposed enhancement approach comprises two main components: a background model and correction terms. The IRI-2020 serves as the background, while the corrections are modeled via spherical harmonics expansion, yielding 256 unknown coefficients per time interval. The resulting improved TEC maps, guided by the spatial distribution of COSMIC2 observations, offer coverage from 30°S to 30°N across all longitudes. However, COSMIC2 data remain sparse near 45°N and 45°S latitudes. To assess the method, January 14, 2022, was selected as the test date. Geomagnetic conditions remained quiet (steady state) from 00:00 to 21:00 UT, shifting to disturbed (storm) conditions from 21:00 to 24:00 UT, while solar activity stayed stable throughout the day. The generated TEC maps feature a spatial resolution of 5° in longitude and 2.5° in latitude, with a temporal resolution of 2 hours. This interval was chosen because a 2-hour period typically contains more than 256 COSMIC2 observations, sufficient for reliable estimation of the spherical harmonic coefficients. Estimating these coefficients involves solving an ill-posed inverse problem using regularization techniques. Two approaches were compared in this study: the direct Tikhonov regularization and the recursive Least Squares with Quadratic Regularization (LSQR). For validation, modeled Vertical Total Electron Content (<em>VTEC</em>) values were compared against independent measurements from five International GNSS Service (IGS) GPS stations—two situated over the ocean and three on land. Performances were evaluated separately for quiet and storm geomagnetic conditions using Root Mean Square Error (<em>RMSE</em>) and Normalized Root Mean Square Error (<em>NRMSE</em>). Overall, the proposed method substantially reduced RMSE at both land-based and ocean-based stations, even during geomagnetic storms, although its effectiveness depended on the proximity and distribution of Radio Occultation (RO) observations within the region. Relative to the original IRI-2020 model, the recursive LSQR method reduced <em>NRMSE</em> by approximately 1.75% in quiet conditions and 3.86% during storm conditions, while the direct Tikhonov method achieved reductions of 1.34% and 3.68%, respectively. In most 2-hour intervals, LSQR outperformed Tikhonov at individual GPS stations, irrespective of land or sea location. Nevertheless, in one case during storm conditions, neither method could produce a reliable correction, owing to the unfavorable spatial distribution of available RO observations.Due to the inadequate spatial distribution of data in certain regions, empirical ionospheric models suffer from limited accuracy. To address this limitation, the present study developed a method to enhance the Total Electron Content (<em>TEC</em>) predictions of the International Reference Ionosphere 2020 (IRI-2020) model using ionospheric profile (ionPrf) products derived from the Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC2) measurements. COSMIC2 represents the next-generation follow-on to the original COSMIC mission. It consists of six satellites in orbits with a 24-degree inclination, primarily providing coverage between 30°N and 30°S latitudes. By contrast, the original COSMIC satellites operated at an 87-degree inclination, achieving near-global coverage. The proposed enhancement approach comprises two main components: a background model and correction terms. The IRI-2020 serves as the background, while the corrections are modeled via spherical harmonics expansion, yielding 256 unknown coefficients per time interval. The resulting improved TEC maps, guided by the spatial distribution of COSMIC2 observations, offer coverage from 30°S to 30°N across all longitudes. However, COSMIC2 data remain sparse near 45°N and 45°S latitudes. To assess the method, January 14, 2022, was selected as the test date. Geomagnetic conditions remained quiet (steady state) from 00:00 to 21:00 UT, shifting to disturbed (storm) conditions from 21:00 to 24:00 UT, while solar activity stayed stable throughout the day. The generated TEC maps feature a spatial resolution of 5° in longitude and 2.5° in latitude, with a temporal resolution of 2 hours. This interval was chosen because a 2-hour period typically contains more than 256 COSMIC2 observations, sufficient for reliable estimation of the spherical harmonic coefficients. Estimating these coefficients involves solving an ill-posed inverse problem using regularization techniques. Two approaches were compared in this study: the direct Tikhonov regularization and the recursive Least Squares with Quadratic Regularization (LSQR). For validation, modeled Vertical Total Electron Content (<em>VTEC</em>) values were compared against independent measurements from five International GNSS Service (IGS) GPS stations—two situated over the ocean and three on land. Performances were evaluated separately for quiet and storm geomagnetic conditions using Root Mean Square Error (<em>RMSE</em>) and Normalized Root Mean Square Error (<em>NRMSE</em>). Overall, the proposed method substantially reduced RMSE at both land-based and ocean-based stations, even during geomagnetic storms, although its effectiveness depended on the proximity and distribution of Radio Occultation (RO) observations within the region. Relative to the original IRI-2020 model, the recursive LSQR method reduced <em>NRMSE</em> by approximately 1.75% in quiet conditions and 3.86% during storm conditions, while the direct Tikhonov method achieved reductions of 1.34% and 3.68%, respectively. In most 2-hour intervals, LSQR outperformed Tikhonov at individual GPS stations, irrespective of land or sea location. Nevertheless, in one case during storm conditions, neither method could produce a reliable correction, owing to the unfavorable spatial distribution of available RO observations.https://jesphys.ut.ac.ir/article_106529_61630e38498e89989cd8411bc0c22dfc.pdfUniversity of Tehran PressJournal of the Earth and Space Physics2538-371X52120260530Analysis of Seismicity Parameters b-value and Fractal Dimension (Dc-value) in the Zagros Collision ZoneAnalysis of Seismicity Parameters b-value and Fractal Dimension (Dc-value) in the Zagros Collision Zone173110549410.22059/jesphys.2026.402700.1007725FAMuhammed HosseinMousaviDepartment of Physics Education, Farhangian University, Tehran, Iran.0009-0003-2282-8757ParvaSadeghi AlavijehDepartment of Physics, Faculty of Science, University of Kashan, Kashan, Iran.0000-0002-5064-9209AmirTalebiDepartment of Physics, Faculty of Science, Arak University, Arak, Iran.0009-0003-5402-0728MohammadKaramiDepartment of Earth Physics, Institute of Geophysics, University of Tehran, Tehran, Iran.0009-0005-2048-0448Journal Article20250930The Zagros Fold-and-Thrust Belt (ZFTB), as one of Iran's most active seismic zones, is situated at the convergent boundary between the Arabian Plate and the Central Iranian Block. Characterized by a high convergence rate, complex tectonic structures, and a rich seismic history, it has consistently been a focal point for tectonic and seismological research. This study aims to provide a comprehensive spatiotemporal analysis of the seismic indices b-value and Dc-value within this region, utilizing the earthquake catalog compiled by the Iranian Seismological Center (IRSC) from January 2006 to November 2024. The catalog, containing over 40,000 events with magnitudes greater than 1.5, served as the basis for analysis after refinement and removal of duplicate data. The b-value was estimated using the Gutenberg-Richter relationship coupled with the maximum likelihood method. To investigate structural changes in faults, the Dc-value parameter was also calculated. Event clustering was performed using an enhanced version of the Uhrhammer algorithm to optimally distinguish seismic clusters and remove aftershocks and foreshocks from the dataset. Spatial results revealed significant heterogeneity in the b-value across the Zagros, with values in the southern Zagros being substantially lower (0.25–0.55) on average compared to the central and northern sections. This pattern indicates higher stress concentration and a greater potential for larger earthquakes in the southern Zagros. In contrast, the higher b-values in the northern parts may reflect smaller-scale fracturing and more distributed stress. Temporal analysis of the b-value demonstrated a significant decrease during specific periods, particularly preceding large earthquakes (Mw > 6). A prominent example of this behavior was recorded before the Mw 7.3 Sarpol-e Zahab earthquake (2017), where the b-value decreased from approximately 0.85–1.0 to about 0.55–0.7 during the foreshock stage. This drop could be attributed to increased stress concentration, changes in fracture density, or crustal fluid migration, suggesting its potential as a possible precursor signal. During the co-seismic stage of this event, the lowest b-values (0.6) coincided with the main rupture, reflecting the sudden and intense stress release along the High Zagros Fault zone. In the post-seismic phase following the mainshock, a trend of relative b-value (1.2) recovery was observed, likely resulting from tectonic relaxation processes, stress redistribution, and aftershock activity. Analysis of the Dc-value, which expresses the correlation length of fractures and fault structure, revealed a complementary pattern to the b-value. Regions with low b-values exhibited higher Dc-values (1.6-2). This inverse correlation suggests that as a fault approaches its ultimate rupture stage, the fracture structure becomes more coherent and correlated, with energy release concentrating on larger scales. The combined spatiotemporal results for the b-value and Dc-value provide a comprehensive picture of the stress state and seismic dynamics of the Zagros. These findings indicate that a decrease in b-value accompanied by an increase in Dc-value can be considered a warning pattern for elevated seismic hazard. If confirmed by independent data over longer timeframes, such a pattern could be integrated into seismic monitoring systems and earthquake hazard assessment frameworks for Iran. From a tectonic perspective, the spatial heterogeneity of the b-value and Dc-value reflects differences in convergence rates, fault geometry, crustal rheological properties, and the role of hydrothermal processes in different segments of the Zagros. The southern Zagros, with its higher convergence rate and presence of long, active faults such as the MFF (Mountain Front Fault) and MZF (Main Zagros Fault), is prone to stress concentration and larger events, whereas the northern and central parts, with more complex structures and distributed fracturing, exhibit a different pattern of seismicity. In summary, this study, employing advanced statistical analyses and seismic clustering, has established a significant correlation between variations in b-value and Dc-value and the active tectonic processes in the Zagros. The obtained results not only contribute to a better understanding of stress distribution and seismic dynamics in this region but also provide a basis for developing predictive seismic hazard models and designing risk mitigation strategies for high-risk areas in Iran.The Zagros Fold-and-Thrust Belt (ZFTB), as one of Iran's most active seismic zones, is situated at the convergent boundary between the Arabian Plate and the Central Iranian Block. Characterized by a high convergence rate, complex tectonic structures, and a rich seismic history, it has consistently been a focal point for tectonic and seismological research. This study aims to provide a comprehensive spatiotemporal analysis of the seismic indices b-value and Dc-value within this region, utilizing the earthquake catalog compiled by the Iranian Seismological Center (IRSC) from January 2006 to November 2024. The catalog, containing over 40,000 events with magnitudes greater than 1.5, served as the basis for analysis after refinement and removal of duplicate data. The b-value was estimated using the Gutenberg-Richter relationship coupled with the maximum likelihood method. To investigate structural changes in faults, the Dc-value parameter was also calculated. Event clustering was performed using an enhanced version of the Uhrhammer algorithm to optimally distinguish seismic clusters and remove aftershocks and foreshocks from the dataset. Spatial results revealed significant heterogeneity in the b-value across the Zagros, with values in the southern Zagros being substantially lower (0.25–0.55) on average compared to the central and northern sections. This pattern indicates higher stress concentration and a greater potential for larger earthquakes in the southern Zagros. In contrast, the higher b-values in the northern parts may reflect smaller-scale fracturing and more distributed stress. Temporal analysis of the b-value demonstrated a significant decrease during specific periods, particularly preceding large earthquakes (Mw > 6). A prominent example of this behavior was recorded before the Mw 7.3 Sarpol-e Zahab earthquake (2017), where the b-value decreased from approximately 0.85–1.0 to about 0.55–0.7 during the foreshock stage. This drop could be attributed to increased stress concentration, changes in fracture density, or crustal fluid migration, suggesting its potential as a possible precursor signal. During the co-seismic stage of this event, the lowest b-values (0.6) coincided with the main rupture, reflecting the sudden and intense stress release along the High Zagros Fault zone. In the post-seismic phase following the mainshock, a trend of relative b-value (1.2) recovery was observed, likely resulting from tectonic relaxation processes, stress redistribution, and aftershock activity. Analysis of the Dc-value, which expresses the correlation length of fractures and fault structure, revealed a complementary pattern to the b-value. Regions with low b-values exhibited higher Dc-values (1.6-2). This inverse correlation suggests that as a fault approaches its ultimate rupture stage, the fracture structure becomes more coherent and correlated, with energy release concentrating on larger scales. The combined spatiotemporal results for the b-value and Dc-value provide a comprehensive picture of the stress state and seismic dynamics of the Zagros. These findings indicate that a decrease in b-value accompanied by an increase in Dc-value can be considered a warning pattern for elevated seismic hazard. If confirmed by independent data over longer timeframes, such a pattern could be integrated into seismic monitoring systems and earthquake hazard assessment frameworks for Iran. From a tectonic perspective, the spatial heterogeneity of the b-value and Dc-value reflects differences in convergence rates, fault geometry, crustal rheological properties, and the role of hydrothermal processes in different segments of the Zagros. The southern Zagros, with its higher convergence rate and presence of long, active faults such as the MFF (Mountain Front Fault) and MZF (Main Zagros Fault), is prone to stress concentration and larger events, whereas the northern and central parts, with more complex structures and distributed fracturing, exhibit a different pattern of seismicity. In summary, this study, employing advanced statistical analyses and seismic clustering, has established a significant correlation between variations in b-value and Dc-value and the active tectonic processes in the Zagros. The obtained results not only contribute to a better understanding of stress distribution and seismic dynamics in this region but also provide a basis for developing predictive seismic hazard models and designing risk mitigation strategies for high-risk areas in Iran.https://jesphys.ut.ac.ir/article_105494_1914d7e92a067810a1f9dd9f37a32870.pdfUniversity of Tehran PressJournal of the Earth and Space Physics2538-371X52120260530An Automated Method for Picking the Fundamental Mode Dispersion Curve and Reliably Estimating the Phase Velocity of This Mode in Surface WavesAn Automated Method for Picking the Fundamental Mode Dispersion Curve and Reliably Estimating the Phase Velocity of This Mode in Surface Waves334310655010.22059/jesphys.2026.408599.1007745FAFaruqMohammadyanDepartment of Earth Physics, Institute of Geophysics, University of Tehran, Tehran, Iran.0009-0004-0759-3010Hamid RezaSiahkoohiDepartment of Earth Physics, Institute of Geophysics, University of Tehran, Tehran, Iran.0000-0002-9227-2972Journal Article20251227Surface waves are type of seismic waves that provide valuable information about near-surface Earth structures in seismic studies, particularly non-invasive seismic surveys with geological, engineering, and earthquake engineering objectives. One of the main challenges in surface wave analysis is the extraction of dispersion curves, which represent the relationship between the phase velocities and corresponding frequency components. Conventional methods for extracting these curves are often done manually via point-vise picking by the user, which, in addition to being time-consuming, increases the possibility of erroneous identifying of the desired dispersion mode.
In this study, an automatic method for identifying dispersion curves is presented. The method automatically finds the optimal path along the fundamental dispersion mode through intelligent search and extracts the dispersion curve. An important feature of this algorithm is its simplicity and lack of need for complex configurations, such that it can search for the dispersion curve based on the position of local energy maxima in the phase-velocity spectrum without user involvement through two different search strategies so called low-frequency search strategy and high-frequency search strategy with minimum manual adjustments. Additionally, in the low-frequency search strategy, resolution limits based on survey profile length have been incorporated as a stopping criterion for the algorithm. This constraint ensures that the final dispersion curve is confined to a frequency that is physically measurable and reliable, and prevents the algorithm from deviating toward erroneous points at low frequency part. The proposed algorithm, by utilizing abovementioned two strategies, is capable of automatically and accurately picking of the fundamental mode dispersion curve.
The efficiency of the proposed method has been evaluated by applying it on synthetic and real seismic data. According to the results obtained from synthetic data, the proposed method possesses high accuracy in automatically identifying of dispersion curves, even in the presence of severe noise. In clean (noise-free) data, the mean square error between the theoretical dispersion curve and the dispersion curve picked by the proposed method was 6.3 (m/s)<sup>2</sup>, and the maximum relative error was 1.8%. For comparison of the propose method, and to demonstrate the efficiency of the proposed method, an automatic peak-value picking method was also used to extract the fundamental mode dispersion curves, where the mean square error between the theoretical dispersion curve and the picked one was 161830 (m/s)<sup>2</sup> and the corresponding maximum relative error was 116%, indicating the significant superiority of the proposed method over automatic peak-value picking technique. Furthermore, under stronger noise conditions with a signal-to-noise ratio of -25 dB the mean square error obtained by the proposed method was 156 (m/s)<sup>2</sup> and the maximum relative error was 6.9%, which still maintains considerably higher accuracy compared to the automatic peak-value picking method. The proposed method can significantly reduce the runtime and improves the accuracy of dispersion curve extraction as well as shear-wave velocity distribution model estimation.Surface waves are type of seismic waves that provide valuable information about near-surface Earth structures in seismic studies, particularly non-invasive seismic surveys with geological, engineering, and earthquake engineering objectives. One of the main challenges in surface wave analysis is the extraction of dispersion curves, which represent the relationship between the phase velocities and corresponding frequency components. Conventional methods for extracting these curves are often done manually via point-vise picking by the user, which, in addition to being time-consuming, increases the possibility of erroneous identifying of the desired dispersion mode.
In this study, an automatic method for identifying dispersion curves is presented. The method automatically finds the optimal path along the fundamental dispersion mode through intelligent search and extracts the dispersion curve. An important feature of this algorithm is its simplicity and lack of need for complex configurations, such that it can search for the dispersion curve based on the position of local energy maxima in the phase-velocity spectrum without user involvement through two different search strategies so called low-frequency search strategy and high-frequency search strategy with minimum manual adjustments. Additionally, in the low-frequency search strategy, resolution limits based on survey profile length have been incorporated as a stopping criterion for the algorithm. This constraint ensures that the final dispersion curve is confined to a frequency that is physically measurable and reliable, and prevents the algorithm from deviating toward erroneous points at low frequency part. The proposed algorithm, by utilizing abovementioned two strategies, is capable of automatically and accurately picking of the fundamental mode dispersion curve.
The efficiency of the proposed method has been evaluated by applying it on synthetic and real seismic data. According to the results obtained from synthetic data, the proposed method possesses high accuracy in automatically identifying of dispersion curves, even in the presence of severe noise. In clean (noise-free) data, the mean square error between the theoretical dispersion curve and the dispersion curve picked by the proposed method was 6.3 (m/s)<sup>2</sup>, and the maximum relative error was 1.8%. For comparison of the propose method, and to demonstrate the efficiency of the proposed method, an automatic peak-value picking method was also used to extract the fundamental mode dispersion curves, where the mean square error between the theoretical dispersion curve and the picked one was 161830 (m/s)<sup>2</sup> and the corresponding maximum relative error was 116%, indicating the significant superiority of the proposed method over automatic peak-value picking technique. Furthermore, under stronger noise conditions with a signal-to-noise ratio of -25 dB the mean square error obtained by the proposed method was 156 (m/s)<sup>2</sup> and the maximum relative error was 6.9%, which still maintains considerably higher accuracy compared to the automatic peak-value picking method. The proposed method can significantly reduce the runtime and improves the accuracy of dispersion curve extraction as well as shear-wave velocity distribution model estimation.https://jesphys.ut.ac.ir/article_106550_df5f981137c55afd7c053de8741af5b3.pdfUniversity of Tehran PressJournal of the Earth and Space Physics2538-371X52120260530Investigation of the Ionospheric Response over Iran to Solar and Geomagnetic Indices Using the SAMI2 Numerical ModelInvestigation of the Ionospheric Response over Iran to Solar and Geomagnetic Indices Using the SAMI2 Numerical Model456410530610.22059/jesphys.2025.396785.1007696FAMasoumehMohammadiDepartment of Space Physics, Institute of Geophysics, University of Tehran, Tehran, Iran.0009-0008-5131-5252Ali RezaMahmoudianDepartment of Space Physics, Institute of Geophysics, University of Tehran, Tehran, Iran.0000-0002-0095-458XJournal Article20250707In this study, the effects of the solar radiation index F10.7 and the geomagnetic index Ap on ionospheric characteristics were examined using two-dimensional SAMI modeling, with a focus on the critical frequency foF2, the peak electron density height hmax, and the electron density distribution. In this analysis, F10.7 values of 40, 100, 200, 300, and 400 were investigated for two different days of the year—Day 15 (January 15, winter) and Day 196 (July 15, summer)—and at two times of day, morning (09:00) and afternoon (15:30), with a fixed geomagnetic index of Ap = 100 at a longitude of 60° and latitude of 36°. These two days and two times intervals were selected to compare the effects of solar radiation and geomagnetic activity under varying seasonal and diurnal conditions; Day 15 represents winter conditions, while Day 196 corresponds to summer, allowing observation of the ionosphere’s differing responses to solar radiation.
The results indicate that foF2 increases approximately linearly and positively with increasing solar radiation, whereas hmax exhibits a nonlinear, exponential response, showing particularly significant growth during afternoon hours and at high levels of radiation. Furthermore, hmax was found to be more sensitive to time of day and season than foF2.
In the SAMI2 modeling section, the combined effects of geomagnetic index Ap (100, 200, and 400) and solar radiation F10.7 (100 and 200) on electron density distribution were examined for two days of the year—Day 196 (summer) and Day 349 (winter)—at a longitude of 51°. Day 196 represents summer conditions, and Day 349 represents winter. Selecting these two days enabled comparison of ionospheric behavior under different levels of solar input energy and plasma dynamics.
The results show that increasing Ap produces two differential density peaks near the equator and minima at mid-latitudes, while increasing F10.7 not only enhances the baseline density but also amplifies the magnitude of Ap-induced variations. Comparison of the two days revealed that Day 349 exhibits a stronger response to geomagnetic disturbances than Day 196, demonstrating the dependence of ionospheric structure on temporal conditions and the combined influence of solar radiation and geomagnetic activity.
These findings highlight the importance of accurate and comprehensive ionospheric modeling that accounts for the effects of solar radiation and geomagnetic activity, and they can be applied to improve communication systems, navigation, and space weather monitoring. Moreover, this research represents the first detailed simulation-based investigation of the ionospheric response over Iran under combined geomagnetic and solar-radiation conditions, addressing a critical regional gap in space-weather studies. Understanding these dynamics is essential for countries located in low- and mid-latitude zones, where ionospheric variability strongly affects HF communication, GNSS performance, and technological infrastructure. The results therefore offer valuable insights for enhancing national space-weather forecasting capabilities and strengthening the resilience of communication and navigation systems.In this study, the effects of the solar radiation index F10.7 and the geomagnetic index Ap on ionospheric characteristics were examined using two-dimensional SAMI modeling, with a focus on the critical frequency foF2, the peak electron density height hmax, and the electron density distribution. In this analysis, F10.7 values of 40, 100, 200, 300, and 400 were investigated for two different days of the year—Day 15 (January 15, winter) and Day 196 (July 15, summer)—and at two times of day, morning (09:00) and afternoon (15:30), with a fixed geomagnetic index of Ap = 100 at a longitude of 60° and latitude of 36°. These two days and two times intervals were selected to compare the effects of solar radiation and geomagnetic activity under varying seasonal and diurnal conditions; Day 15 represents winter conditions, while Day 196 corresponds to summer, allowing observation of the ionosphere’s differing responses to solar radiation.
The results indicate that foF2 increases approximately linearly and positively with increasing solar radiation, whereas hmax exhibits a nonlinear, exponential response, showing particularly significant growth during afternoon hours and at high levels of radiation. Furthermore, hmax was found to be more sensitive to time of day and season than foF2.
In the SAMI2 modeling section, the combined effects of geomagnetic index Ap (100, 200, and 400) and solar radiation F10.7 (100 and 200) on electron density distribution were examined for two days of the year—Day 196 (summer) and Day 349 (winter)—at a longitude of 51°. Day 196 represents summer conditions, and Day 349 represents winter. Selecting these two days enabled comparison of ionospheric behavior under different levels of solar input energy and plasma dynamics.
The results show that increasing Ap produces two differential density peaks near the equator and minima at mid-latitudes, while increasing F10.7 not only enhances the baseline density but also amplifies the magnitude of Ap-induced variations. Comparison of the two days revealed that Day 349 exhibits a stronger response to geomagnetic disturbances than Day 196, demonstrating the dependence of ionospheric structure on temporal conditions and the combined influence of solar radiation and geomagnetic activity.
These findings highlight the importance of accurate and comprehensive ionospheric modeling that accounts for the effects of solar radiation and geomagnetic activity, and they can be applied to improve communication systems, navigation, and space weather monitoring. Moreover, this research represents the first detailed simulation-based investigation of the ionospheric response over Iran under combined geomagnetic and solar-radiation conditions, addressing a critical regional gap in space-weather studies. Understanding these dynamics is essential for countries located in low- and mid-latitude zones, where ionospheric variability strongly affects HF communication, GNSS performance, and technological infrastructure. The results therefore offer valuable insights for enhancing national space-weather forecasting capabilities and strengthening the resilience of communication and navigation systems.https://jesphys.ut.ac.ir/article_105306_cfd6d3115e60f798fa4828d91e61caf2.pdfUniversity of Tehran PressJournal of the Earth and Space Physics2538-371X52120260530Study of Aerosols Dispersion due to Zanjan Specialized Town of Zinc ActivityStudy of Aerosols Dispersion due to Zanjan Specialized Town of Zinc Activity658710533810.22059/jesphys.2026.380433.1007698FAMinooNoorDepartment of Environmental Sciences, Faculty of Sciences, University of Zanjan, Zanjan, Iran.0009-0002-6424-4920YousefaliAbediniDepartment of Physics, Faculty of Sciences, University of Zanjan, Zanjan, Iran.0000-0003-3051-155XAminAbediAir Pollution Lab., Environmental department of Zanjan, Zanjan, Iran.0009-0004-3300-964XAbbas AliZamaniDepartment of Environmental Sciences, Faculty of Sciences, University of Zanjan, Zanjan, Iran.0000-0001-9703-3025ParhamAbediniDepartment of Biology, Faculty of Life Sciences and Biotechnology, Shahid Beheshti University, Tehran, Iran.0000-0002-5652-6306Journal Article20250701Particulate pollutants, or aerosols, are tiny solid or liquid particles suspended in the air, which are considered one of the most important air pollutants due to their environmental and health effects. On the other hand, due to aerosols optical properties in terms of absorption and scattering of solar radiation, they alter the radiation budget of the atmosphere and the Earth. Therefore, aerosols are important components in climate studies, and are important factors of uncertainty in climate change models. Key sources of aerosols include natural sources such as wind erosion, salt particles from ocean waves, volcanic ash, as well as man-made sources such as fuel combustion in transportation, industry, and services. Meteorological conditions affect the emission and dispersion of particulate pollutants, so one of the main issues in the study of air pollution is how this emission occurs and the possibility of estimating and predicting the concentration of pollutants. This can be achieved by air pollution models. Air pollution models are generally divided into two categories: destination-oriented models and source-oriented models. In this work, both methods were employed. The first, is measuring the particulate pollutants concentration by sampling at different distances and geographical directions from the Zanjan specialized town of Zinc as an air pollutant source (destination-oriented model) and the second is utilizing emission data from the pollution source along with meteorological inputs such as wind and temperature in an air pollution dispersion model (source-oriented model) to estimate aerosol concentrations and their dispersion patterns. For the first method, 18 air sampling stations were established at appropriate points around the Zanjan specialized town of Zinc, after a previous study and visit to the area, as a synoptic measurement network with the aim of determining the concentration of particulate pollutants. Then, for a year, air sampling was carried out simultaneously, twice a month, for 72 hours at each station. After each sampling, the filters installed in the devices were collected and transferred to the Zanjan Department of Environment air pollution measurement laboratory, and after the drying stage, they were weighed to determine the concentration of airborne particles. For the second method, first the necessary data such as the characteristics of each unit's chimney, the output values of each chimney (by measurement) were prepared with helping of Zanjan Department of Environment, as well as meteorological data through the Zanjan Department of Meteorology. Then, using source-oriented models such as the fixed and variable box model and Gaussian models for point and surface sources, the concentration values of particulate pollutants were determined and their distribution were examined.
The results showed that the measured concentrations of particulate pollutants decrease with distance according to the concentration changes diagram and their classification maps with distance from the specialized settlement. By comparing the concentration values with the primary and secondary standards of the particulate pollutants, it is observed that respectively 4 and 7 stations out of 18 stations in the measurement network are above the permissible limit, and the remaining stations are below the permissible limit of the standard. Observation and study of pollution rose wind and distribution patterns derived from the Gaussian model also confirm the decrease in aerosols with distance and show that aerosol concentration values and their dispersion depend on meteorological variables, especially wind direction and speed. Given the location of Zanjan city in the northeast of the Zanjan Specialized Town of Zinc<strong> </strong>and the prevailing northwesterly wind, the concentration of particles in the central strip of Zanjan city extending from southwest to northeast is high, and the further away from the strip, the particle concentration is lower.Particulate pollutants, or aerosols, are tiny solid or liquid particles suspended in the air, which are considered one of the most important air pollutants due to their environmental and health effects. On the other hand, due to aerosols optical properties in terms of absorption and scattering of solar radiation, they alter the radiation budget of the atmosphere and the Earth. Therefore, aerosols are important components in climate studies, and are important factors of uncertainty in climate change models. Key sources of aerosols include natural sources such as wind erosion, salt particles from ocean waves, volcanic ash, as well as man-made sources such as fuel combustion in transportation, industry, and services. Meteorological conditions affect the emission and dispersion of particulate pollutants, so one of the main issues in the study of air pollution is how this emission occurs and the possibility of estimating and predicting the concentration of pollutants. This can be achieved by air pollution models. Air pollution models are generally divided into two categories: destination-oriented models and source-oriented models. In this work, both methods were employed. The first, is measuring the particulate pollutants concentration by sampling at different distances and geographical directions from the Zanjan specialized town of Zinc as an air pollutant source (destination-oriented model) and the second is utilizing emission data from the pollution source along with meteorological inputs such as wind and temperature in an air pollution dispersion model (source-oriented model) to estimate aerosol concentrations and their dispersion patterns. For the first method, 18 air sampling stations were established at appropriate points around the Zanjan specialized town of Zinc, after a previous study and visit to the area, as a synoptic measurement network with the aim of determining the concentration of particulate pollutants. Then, for a year, air sampling was carried out simultaneously, twice a month, for 72 hours at each station. After each sampling, the filters installed in the devices were collected and transferred to the Zanjan Department of Environment air pollution measurement laboratory, and after the drying stage, they were weighed to determine the concentration of airborne particles. For the second method, first the necessary data such as the characteristics of each unit's chimney, the output values of each chimney (by measurement) were prepared with helping of Zanjan Department of Environment, as well as meteorological data through the Zanjan Department of Meteorology. Then, using source-oriented models such as the fixed and variable box model and Gaussian models for point and surface sources, the concentration values of particulate pollutants were determined and their distribution were examined.
The results showed that the measured concentrations of particulate pollutants decrease with distance according to the concentration changes diagram and their classification maps with distance from the specialized settlement. By comparing the concentration values with the primary and secondary standards of the particulate pollutants, it is observed that respectively 4 and 7 stations out of 18 stations in the measurement network are above the permissible limit, and the remaining stations are below the permissible limit of the standard. Observation and study of pollution rose wind and distribution patterns derived from the Gaussian model also confirm the decrease in aerosols with distance and show that aerosol concentration values and their dispersion depend on meteorological variables, especially wind direction and speed. Given the location of Zanjan city in the northeast of the Zanjan Specialized Town of Zinc<strong> </strong>and the prevailing northwesterly wind, the concentration of particles in the central strip of Zanjan city extending from southwest to northeast is high, and the further away from the strip, the particle concentration is lower.https://jesphys.ut.ac.ir/article_105338_6f721d117e3c78a131c9eec45ddab162.pdfUniversity of Tehran PressJournal of the Earth and Space Physics2538-371X52120260530Growth of urban aerosol particles due to moisture absorption and its effect on horizontal visibilityGrowth of urban aerosol particles due to moisture absorption and its effect on horizontal visibility8910610533710.22059/jesphys.2026.398653.1007706FASeyedeh FatemehMirshafieeDepartment of Space Physics, Institute of Geophysics, University of Tehran, Tehran, Iran.0009-0007-9195-3702Abbas AliAliakbari-BidokhtiDepartment of Space Physics, Institute of Geophysics, University of Tehran, Tehran, Iran.0000-0003-4841-2218MaryamGharaylouDepartment of Space Physics, Institute of Geophysics, University of Tehran, Tehran, Iran.0000-0002-0014-9409Journal Article20250720The size, chemical composition, and concentration of aerosols significantly impact horizontal visibility. The horizontal visibility can decrease due to the scattering and absorption of visible light by the particles and gases existing in the atmosphere. Horizontal visibility is influenced not only by aerosols but also by meteorological conditions—particularly relative humidity—which can impact visibility both directly and indirectly. Increased relative humidity significantly increases hygroscopic particles, thereby increasing the scattering cross-section and consequently reducing horizontal visibility. In some cases, changes in horizontal visibility are primarily determined by the concentration of PM2.5, meaning visibility can serve as an indicator of air quality. However, when relative humidity dominates changes in visibility, horizontal visibility may not be an accurate indicator of air quality. Overall, horizontal visibility is influenced by both human and natural factors. The concentration of suspended particles and relative humidity are key contributors to visibility changes, each with its own relative proportion. The relationship between PM2.5 concentration and horizontal visibility varies with relative humidity and requires further investigation. In order to examine factors such as the concentration levels of PM2.5 aerosols, and meteorological parameters including relative humidity, on the temporal changes in horizontal visibility in the metropolitan city of Tehran, which has distinct topography, daily average data from the Meteorological Organization's Mehrabad and Geophysics stations for variables like horizontal visibility, wind speed, relative humidity, temperature, and precipitation were collected. Also daily average pollutant data related to PM2.5 from Tehran’s Air Quality Control Center over a 10-year period (2013–2023) for the Sharif University and Tarbiat Modares stations, were collected. Although the Mehrabad and Geophysics stations are geographically close and share similar meteorological conditions, the characteristics of air pollution and atmospheric visibility in the two areas may differ due to different emission properties and levels of urbanization.
The results showed that increased relative humidity intensifies the impact of PM2.5 on visibility reduction, and at humidity levels above 90%, horizontal visibility significantly decreases with increased PM2.5 concentration. Additionally, a nonlinear negative relationship was observed between horizontal visibility, PM2.5 concentration, and relative humidity. Wind speed and average temperature play a positive role in improving visibility. As relative humidity increases, especially for greater values than 20 percent, the Volume Growth Fraction (VGF) of fine aerosols rises. Furthermore, at lower PM concentrations, VGF shows greater significance and variability, highlighting the role of other factors in moisture absorption by PM2.5 aerosols. It should be emphasized that the particle size distribution, the optical properties, and the chemical composition of aerosols also affect horizontal visibility. Therefore, the impact of aerosols and meteorological conditions on atmospheric visibility is relatively complex. This subject could be explored in future studies using a comprehensive numerical air quality model along with precise meteorological and environmental observations.The size, chemical composition, and concentration of aerosols significantly impact horizontal visibility. The horizontal visibility can decrease due to the scattering and absorption of visible light by the particles and gases existing in the atmosphere. Horizontal visibility is influenced not only by aerosols but also by meteorological conditions—particularly relative humidity—which can impact visibility both directly and indirectly. Increased relative humidity significantly increases hygroscopic particles, thereby increasing the scattering cross-section and consequently reducing horizontal visibility. In some cases, changes in horizontal visibility are primarily determined by the concentration of PM2.5, meaning visibility can serve as an indicator of air quality. However, when relative humidity dominates changes in visibility, horizontal visibility may not be an accurate indicator of air quality. Overall, horizontal visibility is influenced by both human and natural factors. The concentration of suspended particles and relative humidity are key contributors to visibility changes, each with its own relative proportion. The relationship between PM2.5 concentration and horizontal visibility varies with relative humidity and requires further investigation. In order to examine factors such as the concentration levels of PM2.5 aerosols, and meteorological parameters including relative humidity, on the temporal changes in horizontal visibility in the metropolitan city of Tehran, which has distinct topography, daily average data from the Meteorological Organization's Mehrabad and Geophysics stations for variables like horizontal visibility, wind speed, relative humidity, temperature, and precipitation were collected. Also daily average pollutant data related to PM2.5 from Tehran’s Air Quality Control Center over a 10-year period (2013–2023) for the Sharif University and Tarbiat Modares stations, were collected. Although the Mehrabad and Geophysics stations are geographically close and share similar meteorological conditions, the characteristics of air pollution and atmospheric visibility in the two areas may differ due to different emission properties and levels of urbanization.
The results showed that increased relative humidity intensifies the impact of PM2.5 on visibility reduction, and at humidity levels above 90%, horizontal visibility significantly decreases with increased PM2.5 concentration. Additionally, a nonlinear negative relationship was observed between horizontal visibility, PM2.5 concentration, and relative humidity. Wind speed and average temperature play a positive role in improving visibility. As relative humidity increases, especially for greater values than 20 percent, the Volume Growth Fraction (VGF) of fine aerosols rises. Furthermore, at lower PM concentrations, VGF shows greater significance and variability, highlighting the role of other factors in moisture absorption by PM2.5 aerosols. It should be emphasized that the particle size distribution, the optical properties, and the chemical composition of aerosols also affect horizontal visibility. Therefore, the impact of aerosols and meteorological conditions on atmospheric visibility is relatively complex. This subject could be explored in future studies using a comprehensive numerical air quality model along with precise meteorological and environmental observations.https://jesphys.ut.ac.ir/article_105337_28ff0c55ae771b9d184970a808b2d0a6.pdfUniversity of Tehran PressJournal of the Earth and Space Physics2538-371X52120260530Multiscale entropy analysis and long-range correlation of different sunspot cyclesMultiscale entropy analysis and long-range correlation of different sunspot cycles10712010530710.22059/jesphys.2025.398875.1007710FAMohsenJavaherianResearch Institute for Astronomy and Astrophysics of Maragha (RIAAM), University of Maragheh, Maragheh, Iran.0000-0003-0464-1561Journal Article20250719The sunspot system represents a quintessential example of astrophysical complexity, where nonlinear interactions within the solar dynamo generate emergent patterns across multiple spatiotemporal scales. Our investigation of ten solar cycles (15-24) through the lens of multiscale entropy (MSE) analysis reveals profound insights into this complex system. We collected sunspot data (the number of daily sunspots and daily standard deviation of sunspot counting) from the Royal Observatory of Belgium's SILSO database, and decomposed the full time series into individual cycles. Then, we applied MSE approach to time series of individual cycles of sunspots. MSE analysis has two main steps: 1-coarse-graining the time series, and 2-calculating sample entropy for each coarse-grained series. The MSEs of cycles are computed up to scale factor <em>τ</em>=20 and tolerance <em>r</em>=0.15–parameters carefully chosen to capture how information complexity evolves under progressive coarse-graining procedure. This approach is particularly suited to non-stationary systems where traditional entropy measures fail, as it quantifies how structural regularity changes when observed at different temporal resolutions.<br />For studying the long-range correlated behavior of solar cycles, we employed rescaled range (R/S) analysis. <strong>It calculates the Hurst exponent (H), which characterizes the system's fractal geometry and persistence (0.5<H<1) or anti-persistence (H<0.5).</strong> Using this approach, obtained Hurst exponents ranged in 0.81-0.86 across all different cycles, providing robust mathematical evidence of persistent long-range memory. This persistent behavior which is valid for Hurst exponents ranged in (0.5, 1) signifies fractal organization where correlations follow a power-law decay. Such scaling behavior implies that sunspot dynamics exhibit 1) statistical self-similarity across observational timescales, and 2) information encoding where subsystem behavior reflects global organization. These characteristics align precisely with self-organized criticality (SOC) – a universal mechanism where driven-dissipative systems spontaneously evolve toward critical states characterized by scale-invariant fluctuations. So, within this framework, it is revealed that the different cycles of solar activity have long-term memory in their time series. Since the sunspots have the magnetic origin, we can say that the solar magnetic field operates near a critical threshold, enabling energy release through avalanches of magnetic reconnection events.<br />Furthermore, it was discovered that neither mean daily sunspot number nor mean standard deviation of daily sunspot number have no meaningful relation with MSE-derived complexity. Quantifying complexity through the area under MSE curves revealed Cycle 20 as exhibiting peak complexity; while Cycle 24 showed minimal complexity. Using proposed methods, we found that among all cycles of solar activity with long-range correlated behaviors, Cycles 20 and 24 had the maximum and minimum long-range memory in their time series, respectively. The power-law exponential function as is fitted to ensemble-averaged entropy profiles. <strong>The exponential term induces an overall decreasing trend, yet its form creates a dynamic equilibrium between growth and decay up to scale factor <em>τ</em>=20. This functional behavior captures an initial growth phase followed by saturation, potentially indicating system stability. Crucially, it highlights that while complexity evolves differently across scales in various systems, it is universally moderated by a limiting factor.</strong>The sunspot system represents a quintessential example of astrophysical complexity, where nonlinear interactions within the solar dynamo generate emergent patterns across multiple spatiotemporal scales. Our investigation of ten solar cycles (15-24) through the lens of multiscale entropy (MSE) analysis reveals profound insights into this complex system. We collected sunspot data (the number of daily sunspots and daily standard deviation of sunspot counting) from the Royal Observatory of Belgium's SILSO database, and decomposed the full time series into individual cycles. Then, we applied MSE approach to time series of individual cycles of sunspots. MSE analysis has two main steps: 1-coarse-graining the time series, and 2-calculating sample entropy for each coarse-grained series. The MSEs of cycles are computed up to scale factor <em>τ</em>=20 and tolerance <em>r</em>=0.15–parameters carefully chosen to capture how information complexity evolves under progressive coarse-graining procedure. This approach is particularly suited to non-stationary systems where traditional entropy measures fail, as it quantifies how structural regularity changes when observed at different temporal resolutions.<br />For studying the long-range correlated behavior of solar cycles, we employed rescaled range (R/S) analysis. <strong>It calculates the Hurst exponent (H), which characterizes the system's fractal geometry and persistence (0.5<H<1) or anti-persistence (H<0.5).</strong> Using this approach, obtained Hurst exponents ranged in 0.81-0.86 across all different cycles, providing robust mathematical evidence of persistent long-range memory. This persistent behavior which is valid for Hurst exponents ranged in (0.5, 1) signifies fractal organization where correlations follow a power-law decay. Such scaling behavior implies that sunspot dynamics exhibit 1) statistical self-similarity across observational timescales, and 2) information encoding where subsystem behavior reflects global organization. These characteristics align precisely with self-organized criticality (SOC) – a universal mechanism where driven-dissipative systems spontaneously evolve toward critical states characterized by scale-invariant fluctuations. So, within this framework, it is revealed that the different cycles of solar activity have long-term memory in their time series. Since the sunspots have the magnetic origin, we can say that the solar magnetic field operates near a critical threshold, enabling energy release through avalanches of magnetic reconnection events.<br />Furthermore, it was discovered that neither mean daily sunspot number nor mean standard deviation of daily sunspot number have no meaningful relation with MSE-derived complexity. Quantifying complexity through the area under MSE curves revealed Cycle 20 as exhibiting peak complexity; while Cycle 24 showed minimal complexity. Using proposed methods, we found that among all cycles of solar activity with long-range correlated behaviors, Cycles 20 and 24 had the maximum and minimum long-range memory in their time series, respectively. The power-law exponential function as is fitted to ensemble-averaged entropy profiles. <strong>The exponential term induces an overall decreasing trend, yet its form creates a dynamic equilibrium between growth and decay up to scale factor <em>τ</em>=20. This functional behavior captures an initial growth phase followed by saturation, potentially indicating system stability. Crucially, it highlights that while complexity evolves differently across scales in various systems, it is universally moderated by a limiting factor.</strong>https://jesphys.ut.ac.ir/article_105307_43b7abbe66bd6851d2e87b56e226fff9.pdfUniversity of Tehran PressJournal of the Earth and Space Physics2538-371X52120260530Analysis of atmospheric weighted mean temperature models based on radiosonde observations over IranAnalysis of atmospheric weighted mean temperature models based on radiosonde observations over Iran12113710616410.22059/jesphys.2026.399918.1007712FAAliSam-KhanianiDepartment of Surveying Engineering, Faculty of Civil Engineering, Babol Noshirvani University of Technology, Babol, Iran.0000-0002-3771-6761Mohammad RezaHossien-ZadeDepartment of Surveying Engineering, Faculty of Civil Engineering, Babol Noshirvani University of Technology, Babol, Iran.0009-0001-4540-4119Journal Article20250809The Global Navigation Satellite System (GNSS) has been in existence for several decades, serving the purpose of position determination and finding applications in navigation, military operations, mapping, and earth sciences. Nevertheless, for almost thirty years, this system has also been utilized in meteorology, leading to the establishment of a field known as GNSS metrology. Signals transmitted from GNSS satellites must pass through all layers of the atmosphere to reach receivers located on the ground or at higher altitudes. When passing through these layers, the signals interact with the components of the atmosphere, which changes the speed of propagation of the signals and, as a result, their arrival at the receiver is delayed. This phenomenon, known as total signal delay, is mainly caused by the troposphere and ionosphere layers. Meanwhile, the ionospheric delay, which is caused by the presence of ions and free electrons in this layer, can be eliminated by using observational techniques such as combining observations of the two frequencies L1 and L2.When processing GNSS observations, the tropospheric delay can be estimated along with the coordinate unknowns. This delay is divided into two main parts: the hydrostatic delay and the wet delay, the latter of which is related to the water vapor in the atmosphere. In the process of retrieving precipitable water vapor from the total tropospheric delay using GNSS meteorology, the atmospheric weighted mean temperature (Tm) plays an important role. Bevis et al. (1992) created a mapping model designed to transform wet delay (ZWD) into precipitable water vapor (PWV). This mapping model includes various physical coefficients and Tm to calculate ZWD based on PWV. There are several methods for calculating Tm, among which the use of actual observations of radiosonde atmospheric profiles is considered as the basic method. However, limitations such as very low temporal resolution and poor spatial resolution in most regions of the world, especially in a vast country like Iran, have necessitated the need for an alternative model in the absence of radiosonde observations. Since accurate estimation of Tm requires vertical profiles of atmospheric temperature and humidity, several empirical regional and global models have been developed to estimate it to date. The objective of this research is to assess the precision of several recent global and regional Tm models in Iran, as well as to present a model founded on the SVM algorithm aimed at enhancing the accuracy of Tm estimation within the study area. To develop the model, observations from 11 radiosonde stations collected between 2015 and 2023 were utilized. For evaluation, Tm values derived from data from these stations in 2024 were employed to assess the proposed model alongside other existing models. The results indicated that the proposed model achieved reductions in RMSE of 3.25, 2.43, 1.00, 1.02, 0.58, 0.61, and 0.61 °C compared with the seven selected models: Bevis, hgpt2, gpt2w, gpt3, GTrop, GGNTm, and Rahimi, respectively. Following the model, the GTrop, GGNTm, and Rahimi models demonstrated, on average, better performance than the other models within the study area. Furthermore, the proposed model was evaluated against other models under rainy conditions, and statistical analyses confirmed that it outperformed the alternatives.The Global Navigation Satellite System (GNSS) has been in existence for several decades, serving the purpose of position determination and finding applications in navigation, military operations, mapping, and earth sciences. Nevertheless, for almost thirty years, this system has also been utilized in meteorology, leading to the establishment of a field known as GNSS metrology. Signals transmitted from GNSS satellites must pass through all layers of the atmosphere to reach receivers located on the ground or at higher altitudes. When passing through these layers, the signals interact with the components of the atmosphere, which changes the speed of propagation of the signals and, as a result, their arrival at the receiver is delayed. This phenomenon, known as total signal delay, is mainly caused by the troposphere and ionosphere layers. Meanwhile, the ionospheric delay, which is caused by the presence of ions and free electrons in this layer, can be eliminated by using observational techniques such as combining observations of the two frequencies L1 and L2.When processing GNSS observations, the tropospheric delay can be estimated along with the coordinate unknowns. This delay is divided into two main parts: the hydrostatic delay and the wet delay, the latter of which is related to the water vapor in the atmosphere. In the process of retrieving precipitable water vapor from the total tropospheric delay using GNSS meteorology, the atmospheric weighted mean temperature (Tm) plays an important role. Bevis et al. (1992) created a mapping model designed to transform wet delay (ZWD) into precipitable water vapor (PWV). This mapping model includes various physical coefficients and Tm to calculate ZWD based on PWV. There are several methods for calculating Tm, among which the use of actual observations of radiosonde atmospheric profiles is considered as the basic method. However, limitations such as very low temporal resolution and poor spatial resolution in most regions of the world, especially in a vast country like Iran, have necessitated the need for an alternative model in the absence of radiosonde observations. Since accurate estimation of Tm requires vertical profiles of atmospheric temperature and humidity, several empirical regional and global models have been developed to estimate it to date. The objective of this research is to assess the precision of several recent global and regional Tm models in Iran, as well as to present a model founded on the SVM algorithm aimed at enhancing the accuracy of Tm estimation within the study area. To develop the model, observations from 11 radiosonde stations collected between 2015 and 2023 were utilized. For evaluation, Tm values derived from data from these stations in 2024 were employed to assess the proposed model alongside other existing models. The results indicated that the proposed model achieved reductions in RMSE of 3.25, 2.43, 1.00, 1.02, 0.58, 0.61, and 0.61 °C compared with the seven selected models: Bevis, hgpt2, gpt2w, gpt3, GTrop, GGNTm, and Rahimi, respectively. Following the model, the GTrop, GGNTm, and Rahimi models demonstrated, on average, better performance than the other models within the study area. Furthermore, the proposed model was evaluated against other models under rainy conditions, and statistical analyses confirmed that it outperformed the alternatives.https://jesphys.ut.ac.ir/article_106164_88f5dbf42bc5a7baa843ca898dc0f012.pdfUniversity of Tehran PressJournal of the Earth and Space Physics2538-371X52120260530Investigating Rossby wave breaking in the Northern Hemisphere storm tracks during the period 1958–2023 using the distribution of potential temperature on the isosurfaces of potential vorticityInvestigating Rossby wave breaking in the Northern Hemisphere storm tracks during the period 1958–2023 using the distribution of potential temperature on the isosurfaces of potential vorticity13915910530210.22059/jesphys.2025.402301.1007723FAFatemeh ZahraMotevalli-TaherDepartment of Space Physics, Institute of Geophysics, University of Tehran, Tehran, Iran.0000-0003-0686-9340MohammadMirzaeiDepartment of Space Physics, Institute of Geophysics, University of Tehran, Tehran, Iran.0000-0003-0813-3994Ali RezaMohebalhojehDepartment of Space Physics, Institute of Geophysics, University of Tehran, Tehran, Iran.0000-0002-5906-8486FarhangAhmadi-GiviDepartment of Space Physics, Institute of Geophysics, University of Tehran, Tehran, Iran.0000-0002-9487-4862Journal Article20250917On isentropic surfaces, Rossby Wave Breaking (RWB) is characterized by the rapid and irreversible deformation of potential vorticity (PV) contours. Rossby waves are generated by the meridional PV gradient of the background flow and can be identified by their PV anomaly relative to that background flow. The present study is devoted to evaluating the distribution of RWB based on the method introduced by Pelly and Hoskins (2003; PH), known as the PV-θ method, and its associated index (BI), across the Northern Hemisphere storm tracks during the boreal wintertime over 65-year period from 1958 to 2023, using the JRA-55 reanalysis data with a horizontal resolution of at 00 UTC. Two quantities, the Direction of Breaking (DB) and the Intensity of Breaking (RI), were employed to identify four distinct types of Anticyclonic and Cyclonic Rossby wave breaking: Anticyclonic breaking with warm air intrusion (ABW), Anticyclonic breaking with cold air intrusion (ABC), Cyclonic breaking with warm air intrusion (CBW), and Cyclonic breaking with cold air intrusion (CBC). First, to evaluate the application of the PH method in identifying the occurrence of various wave breaking types, the distribution of BI, DB, and RI, were compared with the distribution of Potential Vorticity (PV) on the 330-K isentropic surface and potential temperature ( ) on the PV isosurface with 2 PVU value, alongside geopotential height at the 500 hPa level and wind speed at the 200 hPa level, for three case studies. The first case study corresponds to March 8, 1959, the second to January 6, 2008, and the third to January 27, 2018. In the next step, the climatological distributions of various types of wave breaking using the above quantities (BI, DB, and RI) for the winter season were examined and analyzed during the study period. Finally, wave breaking was examined in terms of its intensity and categorized into strong, moderate, and weak classes.
The evaluation of the PH method performance indicates that the higher the RI value, the more overturning of contours, and a stronger geopotential height gradient at the 500 hPa level. The climatological distribution of the results demonstrates the predominance of anticyclonic wave breaking events over the eastern Atlantic Ocean, Europe, western Asia, the eastern Pacific Ocean, and western North America. The frequency of ABW is higher than ABC in these regions. The highest frequency of CBW is observed over the eastern Pacific Ocean and western Asia, while the maximum frequency of cyclonic wave breaking events dominated by CBC is found over the western Atlantic Ocean and the western Pacific Ocean. The frequency of CBW event is higher in East Asia than in other regions, while CBC events are more frequent in the western Atlantic and Pacific Oceans. Furthermore, the frequency of strong wave breaking is higher than that of weak wave breaking across all longitudes, though marginally.On isentropic surfaces, Rossby Wave Breaking (RWB) is characterized by the rapid and irreversible deformation of potential vorticity (PV) contours. Rossby waves are generated by the meridional PV gradient of the background flow and can be identified by their PV anomaly relative to that background flow. The present study is devoted to evaluating the distribution of RWB based on the method introduced by Pelly and Hoskins (2003; PH), known as the PV-θ method, and its associated index (BI), across the Northern Hemisphere storm tracks during the boreal wintertime over 65-year period from 1958 to 2023, using the JRA-55 reanalysis data with a horizontal resolution of at 00 UTC. Two quantities, the Direction of Breaking (DB) and the Intensity of Breaking (RI), were employed to identify four distinct types of Anticyclonic and Cyclonic Rossby wave breaking: Anticyclonic breaking with warm air intrusion (ABW), Anticyclonic breaking with cold air intrusion (ABC), Cyclonic breaking with warm air intrusion (CBW), and Cyclonic breaking with cold air intrusion (CBC). First, to evaluate the application of the PH method in identifying the occurrence of various wave breaking types, the distribution of BI, DB, and RI, were compared with the distribution of Potential Vorticity (PV) on the 330-K isentropic surface and potential temperature ( ) on the PV isosurface with 2 PVU value, alongside geopotential height at the 500 hPa level and wind speed at the 200 hPa level, for three case studies. The first case study corresponds to March 8, 1959, the second to January 6, 2008, and the third to January 27, 2018. In the next step, the climatological distributions of various types of wave breaking using the above quantities (BI, DB, and RI) for the winter season were examined and analyzed during the study period. Finally, wave breaking was examined in terms of its intensity and categorized into strong, moderate, and weak classes.
The evaluation of the PH method performance indicates that the higher the RI value, the more overturning of contours, and a stronger geopotential height gradient at the 500 hPa level. The climatological distribution of the results demonstrates the predominance of anticyclonic wave breaking events over the eastern Atlantic Ocean, Europe, western Asia, the eastern Pacific Ocean, and western North America. The frequency of ABW is higher than ABC in these regions. The highest frequency of CBW is observed over the eastern Pacific Ocean and western Asia, while the maximum frequency of cyclonic wave breaking events dominated by CBC is found over the western Atlantic Ocean and the western Pacific Ocean. The frequency of CBW event is higher in East Asia than in other regions, while CBC events are more frequent in the western Atlantic and Pacific Oceans. Furthermore, the frequency of strong wave breaking is higher than that of weak wave breaking across all longitudes, though marginally.https://jesphys.ut.ac.ir/article_105302_0409ff187db9a27a2ce39d68d6d743f1.pdfUniversity of Tehran PressJournal of the Earth and Space Physics2538-371X52120260530Projecting Future Compound Dry and Hot Extremes Events (CDHE) in IranProjecting Future Compound Dry and Hot Extremes Events (CDHE) in Iran16117810530310.22059/jesphys.2025.403681.1007727FAAbbas AliDadashi-RoudbariDepartment of Geography, Faculty of Dr. Ali Shariati Letters and Humanities, Ferdowsi University of Mashhad, Mashhad, Iran.0000-0002-9308-1019Journal Article20251005Climate change has amplified the frequency of weather and climate extreme events, such as heatwaves, heavy precipitation, and droughts. These extreme events are increasingly likely to co-occur, resulting in what are known as Compound Extreme Events. Given its geographical location within the global arid and semi-arid belt and the inherent vulnerability of its ecosystems, Iran is recognized as a critical hotspot for the impacts of climate change. A growing body of scientific evidence indicates that the nation is increasingly affected by such compound events. Compound extremes, particularly the concurrence of heatwaves and drought, pose a significant and escalating threat to societies and ecosystems in vulnerable regions. This study aims to assess and project changes in the frequency and duration of Compound Dry-Hot Extremes (CDHE) across Iran. The data utilized in this research are twofold: observational data from synoptic weather stations and outputs from CMIP6 models. The observational dataset comprises daily maximum temperature and precipitation time series from 98 synoptic stations across the country for the 1990–2014 period. For future projections, this study employed six models from the CMIP6 ensemble. To mitigate the uncertainties inherent in individual models and enhance predictive performance, a Weighted Multi-Model Ensemble (WMME) approach was adopted, based on the Inter-annual Variability Skill (IVS) score method. The analysis involves comparison of a historical period (1990–2014) with near-future (2026–2050), mid-future (2051–2075), and far-future (2076–2100) periods under two shared socio-economic pathways (SSP): SSP2-4.5 and SSP5-8.5. The specific phenomenon investigated is the compound hot-dry event. A CDHE is identified by the simultaneous occurrence of two individual extremes, each defined by specific percentile thresholds. A hot event is defined as a day when the maximum temperature exceeds the 95th percentile of daily values for a given period. Concurrently, a dry event is characterized by daily precipitation falling below the 5th percentile for the same period. Analysis of the historical period reveals a statistically significant increasing trend in both the frequency (0.53 events/decade) and duration (0.86 days/decade) of CDHEs. Future projections indicate a positive anomaly, signifying an increase in CDHE occurrences. Under the SSP2-4.5 scenario, the annual frequency of these events is projected to rise from a historical average of 2.8 events to 8.2 events annually by the end of the century. The increase is substantially more severe under the SSP5-8.5 scenario, with the frequency escalating to 10.4 events per year. Similarly, the duration of these events is expected to lengthen considerably, increasing from a spatially averaged 4.4 days in the historical period to 20.6 days under SSP2-4.5 and a notable 36 days under SSP5-8.5. These results unequivocally highlight a significant intensification of risks associated with compound hot-dry extremes in Iran throughout the 21st century. These findings underscore the urgent necessity for implementing proactive adaptation strategies to address the escalating climate challenges.Climate change has amplified the frequency of weather and climate extreme events, such as heatwaves, heavy precipitation, and droughts. These extreme events are increasingly likely to co-occur, resulting in what are known as Compound Extreme Events. Given its geographical location within the global arid and semi-arid belt and the inherent vulnerability of its ecosystems, Iran is recognized as a critical hotspot for the impacts of climate change. A growing body of scientific evidence indicates that the nation is increasingly affected by such compound events. Compound extremes, particularly the concurrence of heatwaves and drought, pose a significant and escalating threat to societies and ecosystems in vulnerable regions. This study aims to assess and project changes in the frequency and duration of Compound Dry-Hot Extremes (CDHE) across Iran. The data utilized in this research are twofold: observational data from synoptic weather stations and outputs from CMIP6 models. The observational dataset comprises daily maximum temperature and precipitation time series from 98 synoptic stations across the country for the 1990–2014 period. For future projections, this study employed six models from the CMIP6 ensemble. To mitigate the uncertainties inherent in individual models and enhance predictive performance, a Weighted Multi-Model Ensemble (WMME) approach was adopted, based on the Inter-annual Variability Skill (IVS) score method. The analysis involves comparison of a historical period (1990–2014) with near-future (2026–2050), mid-future (2051–2075), and far-future (2076–2100) periods under two shared socio-economic pathways (SSP): SSP2-4.5 and SSP5-8.5. The specific phenomenon investigated is the compound hot-dry event. A CDHE is identified by the simultaneous occurrence of two individual extremes, each defined by specific percentile thresholds. A hot event is defined as a day when the maximum temperature exceeds the 95th percentile of daily values for a given period. Concurrently, a dry event is characterized by daily precipitation falling below the 5th percentile for the same period. Analysis of the historical period reveals a statistically significant increasing trend in both the frequency (0.53 events/decade) and duration (0.86 days/decade) of CDHEs. Future projections indicate a positive anomaly, signifying an increase in CDHE occurrences. Under the SSP2-4.5 scenario, the annual frequency of these events is projected to rise from a historical average of 2.8 events to 8.2 events annually by the end of the century. The increase is substantially more severe under the SSP5-8.5 scenario, with the frequency escalating to 10.4 events per year. Similarly, the duration of these events is expected to lengthen considerably, increasing from a spatially averaged 4.4 days in the historical period to 20.6 days under SSP2-4.5 and a notable 36 days under SSP5-8.5. These results unequivocally highlight a significant intensification of risks associated with compound hot-dry extremes in Iran throughout the 21st century. These findings underscore the urgent necessity for implementing proactive adaptation strategies to address the escalating climate challenges.https://jesphys.ut.ac.ir/article_105303_f2a9c1e60399beceda3281cd36ece5e6.pdfUniversity of Tehran PressJournal of the Earth and Space Physics2538-371X52120260530Identifying the Rainy Season in IranIdentifying the Rainy Season in Iran17919410653010.22059/jesphys.2026.408873.1007746FAMohadesehVaziri MehrDepartment of Geography, Faculty of Human Sciences, University of Zanjan, Zanjan, Iran.0000-0002-0246-9828HosseinAsakerehDepartment of Geography, Faculty of Human Sciences, University of Zanjan, Zanjan, Iran.0000-0001-7699-0547MohsenHamidianpourDepartment of Physical Geography, Faculty of Geography and Environmental Planning, University of Sistan and Baluchestan, Sistan and Baluchestan, Iran.0000-0001-7389-172XJournal Article20260101The rainy season represents a key manifestation of the climate system’s response to atmospheric general circulation, synoptic-scale interactions, and land–atmosphere thermodynamic conditions. The timing of the rainy season—its onset, cessation, and duration—reflects the trajectories of cyclones, the intensity and positioning of westerlies, and variations in moisture flux convergence. Iran, situated within the arid and semi-arid subtropical belt, exhibits high spatiotemporal variability in its rainy season due to its location at the intersection of Mediterranean, Sudanese, Caspian, and Monsoon influences. Consequently, identifying the rainy season using a physically based approach with daily precipitation data is essential for understanding the mechanisms governing regional precipitation. This study aims to extract and analyze the temporal and spatial characteristics of Iran’s rainy season and interpret them within the framework of the governing atmospheric processes.
Daily precipitation data from 72 synoptic stations across Iran were analyzed for the period 1960–2023. Stations were selected to ensure appropriate spatial coverage, topographic diversity, and minimal data gaps. The rainy season was identified using a 30-day moving average applied to daily precipitation series, with the long-term mean over 366 days serving as the reference threshold. Any period of at least 30 consecutive days in which the moving average exceeded the threshold was classified as a rainy season. Transition points from dry to wet and vice versa defined the onset and cessation, respectively. Based on this method, indicators including the onset, cessation, duration of the rainy season, and the proportion of seasonal precipitation relative to annual totals, were extracted. To evaluate the influence of spatial factors on rainy season variability, multivariate regression and Pearson correlation analyses were conducted between geographical variables (latitude, longitude, and altitude) and rainy season characteristics. The analysis revealed that Iran exhibits both a general and a regional rainy season regime. The general rainy season extends across the country, whereas the regional rainy season occurs in the northwest, parts of the Caspian Sea coast, and the southeast of Iran.
The general rainy season initiates in areas along the Caspian Sea coast and gradually extends westward, northwestward, eastward, and southeastward. This pattern aligns with the expansion of the Siberian high, weakening of the subtropical high-pressure system, and Mediterranean cyclone activity. Cessation occurs first along the Caspian Sea coast, followed by the central and southern regions. Maximum rainfall duration is observed in the northwest, while the central and southeastern regions experience the shortest durations. The length of the general rainy season ranges from approximately four to over seven months. The ratio of seasonal to annual precipitation indicates a high concentration of cold-season rainfall (>85%) along the southwest to eastern regions, consistent with large-scale cyclonic dominance. In contrast, the Caspian Sea coast and northwestern coastal areas exhibit greater temporal dispersion of precipitation due to the interaction of synoptic and convective systems.
The regional rainy season is primarily influenced by mesoscale processes in the northwest and monsoon flows in the southeast. Its duration exceeds two months in the northwest but is less than one month in the southeast. Statistical analyses indicate that altitude strongly influences the spatial variability of rainy season onset and cessation, particularly in northern and southern regions, whereas latitude plays a significant role in the east. These findings highlight the combined control of topography and geographic location on precipitation responses to general atmospheric circulation.
This study demonstrates that the rainy season in Iran reflects a structural spatiotemporal organization. Variations in onset, cessation, and duration correspond with Mediterranean cyclone trajectories, moisture flux convergence, the subtropical jet stream, and regional sea surface temperatures. Overall, the rainy season in Iran is not only a climatic phenomenon but also a manifestation of underlying physical changes in the regional atmosphere–ocean system.The rainy season represents a key manifestation of the climate system’s response to atmospheric general circulation, synoptic-scale interactions, and land–atmosphere thermodynamic conditions. The timing of the rainy season—its onset, cessation, and duration—reflects the trajectories of cyclones, the intensity and positioning of westerlies, and variations in moisture flux convergence. Iran, situated within the arid and semi-arid subtropical belt, exhibits high spatiotemporal variability in its rainy season due to its location at the intersection of Mediterranean, Sudanese, Caspian, and Monsoon influences. Consequently, identifying the rainy season using a physically based approach with daily precipitation data is essential for understanding the mechanisms governing regional precipitation. This study aims to extract and analyze the temporal and spatial characteristics of Iran’s rainy season and interpret them within the framework of the governing atmospheric processes.
Daily precipitation data from 72 synoptic stations across Iran were analyzed for the period 1960–2023. Stations were selected to ensure appropriate spatial coverage, topographic diversity, and minimal data gaps. The rainy season was identified using a 30-day moving average applied to daily precipitation series, with the long-term mean over 366 days serving as the reference threshold. Any period of at least 30 consecutive days in which the moving average exceeded the threshold was classified as a rainy season. Transition points from dry to wet and vice versa defined the onset and cessation, respectively. Based on this method, indicators including the onset, cessation, duration of the rainy season, and the proportion of seasonal precipitation relative to annual totals, were extracted. To evaluate the influence of spatial factors on rainy season variability, multivariate regression and Pearson correlation analyses were conducted between geographical variables (latitude, longitude, and altitude) and rainy season characteristics. The analysis revealed that Iran exhibits both a general and a regional rainy season regime. The general rainy season extends across the country, whereas the regional rainy season occurs in the northwest, parts of the Caspian Sea coast, and the southeast of Iran.
The general rainy season initiates in areas along the Caspian Sea coast and gradually extends westward, northwestward, eastward, and southeastward. This pattern aligns with the expansion of the Siberian high, weakening of the subtropical high-pressure system, and Mediterranean cyclone activity. Cessation occurs first along the Caspian Sea coast, followed by the central and southern regions. Maximum rainfall duration is observed in the northwest, while the central and southeastern regions experience the shortest durations. The length of the general rainy season ranges from approximately four to over seven months. The ratio of seasonal to annual precipitation indicates a high concentration of cold-season rainfall (>85%) along the southwest to eastern regions, consistent with large-scale cyclonic dominance. In contrast, the Caspian Sea coast and northwestern coastal areas exhibit greater temporal dispersion of precipitation due to the interaction of synoptic and convective systems.
The regional rainy season is primarily influenced by mesoscale processes in the northwest and monsoon flows in the southeast. Its duration exceeds two months in the northwest but is less than one month in the southeast. Statistical analyses indicate that altitude strongly influences the spatial variability of rainy season onset and cessation, particularly in northern and southern regions, whereas latitude plays a significant role in the east. These findings highlight the combined control of topography and geographic location on precipitation responses to general atmospheric circulation.
This study demonstrates that the rainy season in Iran reflects a structural spatiotemporal organization. Variations in onset, cessation, and duration correspond with Mediterranean cyclone trajectories, moisture flux convergence, the subtropical jet stream, and regional sea surface temperatures. Overall, the rainy season in Iran is not only a climatic phenomenon but also a manifestation of underlying physical changes in the regional atmosphere–ocean system.https://jesphys.ut.ac.ir/article_106530_7e70a898fac0cb7bd502a8314e20741c.pdfUniversity of Tehran PressJournal of the Earth and Space Physics2538-371X52120260530Ensemble lightning forecasting in the southern foothills of the Alborz mountain range using WRF modelEnsemble lightning forecasting in the southern foothills of the Alborz mountain range using WRF model19521010622310.22059/jesphys.2026.408935.1007747FAMojtabaJalali KoutanaeiDepartment of Space Physics, Institute of Geophysics, University of Tehran, Tehran, Iran.0009-0005-8921-4359FarahnazTaghaviDepartment of Space Physics, Institute of Geophysics, University of Tehran, Tehran, Iran.0000-0003-4399-882XAli RezaMohebalhojehDepartment of Space Physics, Institute of Geophysics, University of Tehran, Tehran, Iran.0000-0002-5906-8486MaryamGharaylouDepartment of Space Physics, Institute of Geophysics, University of Tehran, Tehran, Iran.0000-0002-0014-9409SarmadGhaderDepartment of Space Physics, Institute of Geophysics, University of Tehran, Tehran, Iran.0000-0001-9666-5493Journal Article20251230Lightning is a phenomenon of instantaneous electrical discharge over long distances associated with thunderstorms which causes significant human and financial losses worldwide. In this study, the Lightning Potential Index (LPI), which measures the ability to charge within a cloud, was used to predict lightning occurrences on the southern slopes of the Alborz Mountains. This was carried out using a regional WRF model at three nested domains with resolutions of 9, 3, and 1 km. Twelve physics parameterization schemes were utilized in the model with initial and boundary conditions taken from Global Forecast System (GFS), 21 members of Global Ensemble Forecast System (GEFS), and the ECAMWF ERA5 data at a resolution of 0.25 degrees. Additionally, lightning occurrence prediction was enhanced using machine learning methods, including support vector machines (SVM) and random forests (RF). The Earth Networks data was used for the real lightning data. The highest value of the squared correlation R<sup>2</sup>, or coefficient of determination, was 0.41 with a NRMSE (normalized root mean square error: RMSE divided by the standard deviation) of 0.77 for the GEFS input data set using the Goddard microphysics parameterization, Kain–Fritsch (KF) convection, Dudiha for shortwave radiation, RRTM for longwave radiation, MYJ for boundary layer, and Noah LSM surface layer. The lowest value of R<sup>2</sup> was 0.06 with the NRMSE of 0.97 for the GFS input data set and the Morrison–Morr microphysics parameterization, Grell–Devenyi ensemble convection, RRTM for radiation, MYNN boundary layer and NOAH LSM surface layer. In addition to LPI, other quantities related to static instability were also examined for their statistical relation with the number of lightning flashes. The quantities examined included Convective Avaliable Potential Energy (CAPE), Cloud Physics Thunder Parameter (CPTP), K index (KI), Convection Inhibition (CIN) and equivalent reflectivity factor (DBZ). The R<sup>2 </sup>values for the linear regression between the number of flashes and CAPE, CPTP, KI, CIN and DBZ were 0.14, 0.07, 0.02, 0.03 and 0.07, respectively. Therefore, only CAPE exhibited modest statistical relation with the lightning flashes. After weighted matching and machine learning correction with the support vector machine (SVM) method, the R<sup>2</sup> value increased to 0.44 and 0.59, respectively, while the corresponging NRMSE values were 0.75 and 0.65. Given the significant impact of CAPE on the formation of convective clouds, its weight was doubled in applying the SVM. Consequently, R<sup>2 </sup>for the quadratic regression between the LPI and the actual lightning data was increased to 0.63, while NRMSE was decreased slightly to 0.62. Overall, the results suggest that the LPI index is a suitable indicator for predicting lightning occurrence on the southern slopes of the Alborz Mountains, as there is a sufficiently strong statistical relation between the actual lightning data and the LPI index. Moreover, following weighted matching and correction by machine learning, the accuracy of lightning prediction is significantly improved.Lightning is a phenomenon of instantaneous electrical discharge over long distances associated with thunderstorms which causes significant human and financial losses worldwide. In this study, the Lightning Potential Index (LPI), which measures the ability to charge within a cloud, was used to predict lightning occurrences on the southern slopes of the Alborz Mountains. This was carried out using a regional WRF model at three nested domains with resolutions of 9, 3, and 1 km. Twelve physics parameterization schemes were utilized in the model with initial and boundary conditions taken from Global Forecast System (GFS), 21 members of Global Ensemble Forecast System (GEFS), and the ECAMWF ERA5 data at a resolution of 0.25 degrees. Additionally, lightning occurrence prediction was enhanced using machine learning methods, including support vector machines (SVM) and random forests (RF). The Earth Networks data was used for the real lightning data. The highest value of the squared correlation R<sup>2</sup>, or coefficient of determination, was 0.41 with a NRMSE (normalized root mean square error: RMSE divided by the standard deviation) of 0.77 for the GEFS input data set using the Goddard microphysics parameterization, Kain–Fritsch (KF) convection, Dudiha for shortwave radiation, RRTM for longwave radiation, MYJ for boundary layer, and Noah LSM surface layer. The lowest value of R<sup>2</sup> was 0.06 with the NRMSE of 0.97 for the GFS input data set and the Morrison–Morr microphysics parameterization, Grell–Devenyi ensemble convection, RRTM for radiation, MYNN boundary layer and NOAH LSM surface layer. In addition to LPI, other quantities related to static instability were also examined for their statistical relation with the number of lightning flashes. The quantities examined included Convective Avaliable Potential Energy (CAPE), Cloud Physics Thunder Parameter (CPTP), K index (KI), Convection Inhibition (CIN) and equivalent reflectivity factor (DBZ). The R<sup>2 </sup>values for the linear regression between the number of flashes and CAPE, CPTP, KI, CIN and DBZ were 0.14, 0.07, 0.02, 0.03 and 0.07, respectively. Therefore, only CAPE exhibited modest statistical relation with the lightning flashes. After weighted matching and machine learning correction with the support vector machine (SVM) method, the R<sup>2</sup> value increased to 0.44 and 0.59, respectively, while the corresponging NRMSE values were 0.75 and 0.65. Given the significant impact of CAPE on the formation of convective clouds, its weight was doubled in applying the SVM. Consequently, R<sup>2 </sup>for the quadratic regression between the LPI and the actual lightning data was increased to 0.63, while NRMSE was decreased slightly to 0.62. Overall, the results suggest that the LPI index is a suitable indicator for predicting lightning occurrence on the southern slopes of the Alborz Mountains, as there is a sufficiently strong statistical relation between the actual lightning data and the LPI index. Moreover, following weighted matching and correction by machine learning, the accuracy of lightning prediction is significantly improved.https://jesphys.ut.ac.ir/article_106223_11a52a45fff8cf2f58a66ee435a18d14.pdf