Journal of Earth and Space Physics
The Journal of Earth and Space Physics (JESP) is a quarterly Journal (three issues in Persian with English summaries and one in English) that was started in 1971 with the aim of publishing articles in all research areas of Geophysical Sciences. JESP accepts papers in all fields of Geophysics including: Earthquake Seismology, Engineering Seismology, Exploration Seismology, Geomagnetism, Gravimetry, Satellite Gravimetry, Geodesy, Geoelectric, Meteorology, Atmospheric Physics, Air Pollution, Tide, Luminescence Dating, Physical Oceanography, Solar Physics, Astronomy, Astrophysics, Space Sciences and other related areas, for publication.
Supervisors: Dr. Aliakbari-Bidokhti, A. A. (Editor-in-Chief), Dr. Nabi-Bidhendi, M. (Editorial Board), Dr. Ebrahimzadeh Ardestani, V. (Editorial Board), Dr. Ahmadi-Givi, F. (Editorial Board), Dr. Tatar, M. (Editorial Board), Dr. Javaherian, A. (Editorial Board), Dr. Roshandel Kahoo, A. (Editorial Board), Dr. Zawar-Reza, P. (Editorial Board), Dr. Alavi, S. A. (Editorial Board), Dr. Ghorashi, M. (Editorial Board), Dr. Gheitanchi, M. R. (Editorial Board), Dr. Kalaee, M. J. (Editorial Board), Dr. Kamali, M. R. (Editorial Board), Dr. Mohebalhojeh, A. R. (Editorial Board), Dr. Mirzaei, N. (Editorial Board), and Dr. Najafi-Alamdari, M. (Editorial Board)
Supervisors: Dr. Aliakbari-Bidokhti, A. A. (Editor-in-Chief), Dr. Nabi-Bidhendi, M. (Editorial Board), Dr. Ebrahimzadeh Ardestani, V. (Editorial Board), Dr. Ahmadi-Givi, F. (Editorial Board), Dr. Tatar, M. (Editorial Board), Dr. Javaherian, A. (Editorial Board), Dr. Roshandel Kahoo, A. (Editorial Board), Dr. Zawar-Reza, P. (Editorial Board), Dr. Alavi, S. A. (Editorial Board), Dr. Ghorashi, M. (Editorial Board), Dr. Gheitanchi, M. R. (Editorial Board), Dr. Kalaee, M. J. (Editorial Board), Dr. Kamali, M. R. (Editorial Board), Dr. Mohebalhojeh, A. R. (Editorial Board), Dr. Mirzaei, N. (Editorial Board), and Dr. Najafi-Alamdari, M. (Editorial Board)
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In most ensemble post-processing approaches, it is implicitly assumed that there is statistical independence between different forecast margins, such as lead time, location, and meteorological variables. However, this assumption is not valid for realistic forecast application scenarios. End users may be interested in scenarios such as total hydrological basin precipitation, temporal evolution of precipitation, or the interaction of precipitation and temperature, especially when temperatures are close to zero degrees Celsius. Important examples include hydrological applications, air traffic management, and energy forecasting. Such dependencies exist in raw ensemble forecasts, but these dependencies are ignored if standard univariate post-processing methods are applied separately to each margin.
In recent years, various multivariate post-processing methods have been proposed. These methods can be categorized into two approaches. The goal of the first approach is to directly model the joint distribution by fitting a specific multivariate probability distribution. This approach is mainly used in low-dimensional problems or when a specific structure is chosen for the application at hand. For example, multivariate models for temperature across space, for wind vectors, and joint models for temperature and wind speed.
The second approach is a two-step approach. In the first step, univariate post-processing methods are applied independently to all dimensions, and samples are generated from the resulting probability distributions. In the second step, the multivariate dependencies are recovered by reordering the univariate sample values according to the ranking order structure of a specific multivariate dependence pattern. Mathematically, this is equivalent to using a copula (parametric or nonparametric). Examples include ensemble copula coupling (ECC), Schaake Shuffle, and the Gaussian copula approach.
This paper presents multivariate ensemble post-processing of temperature, two meter above ground using the ECC approach. The EMOS method is used for univariate post-processing. The performance of the raw ensemble, EMOS post-processed ensemble, and ECC systems is evaluated using energy score (ES) and variogram score (VS). The ECMWF 51-member ensemble system is used as raw data for the period from January 1, 2018 to December 31, 2023.
The results showed that in addition to eliminating the bias of the raw ensemble forecast, the ECC method also preserved the dependence structure between the ensemble members. In contrast, the EMOS method only eliminated the biases without considering the dependence between the ensemble members. Because of its ability to preserve the dependence structure, the ECC method was able to achieve significantly better results than the EMOS method on a variety of metrics, including energy scores and variogram score. This suggests that the ECC method is a valuable tool for ensemble post-processing, and that it should be considered for a wide range of applications.
The VWSH was calculated across three layers at altitudes of 1000, 3000 and 6000 meters from the surface. Investigations were conducted on daily, monthly, seasonal and long-term time scales. On a monthly scale, the minimum (maximum) root mean square errors (RMSE) for VWSH-1000, VWSH-3000, and VWSH-6000 were approximately 3 (8.5), 3.36 (9.84), and 4 (20) m/s, respectively. The results showed that the ERA5 reanalysis data consistently underestimated the value of VWSH-1000 across all stations (except Ahvaz station in recent years). The estimation of VWSH-3000 and VWSH-6000 parameters exhibited both overestimation and underestimation in different months. Notably, the highest error in ERA5 data for VWSH-6000 occurred during January. Across most stations, the largest errors were observed during cold months (particularly for the VWSH-6000 parameter), while the smallest errors occurred during warm months. In conclusion, the results suggest that as the height of the investigated layer increases, the performance of ERA5 in generating the considered VWSH parameters at the stations improves, especially in recent years.
A comparison between reanalysis-LI and observational-LI indicatedes that the highest (lowest) error occurs during warm (cold) months of the year. Throughout the study period, the reanalysis data produced an error of at least 10 K (at Zahedan station) and up to 15 K (at Tehran station) in LI estimation. Except for Ahvaz station, LI was consistently underestimated across all stations. The monthly mean of reanalysis-LI reflected more unstable conditions, whereas the observed values indicated a more stable atmosphere. Consequently, reanalysis-LI may not be a suitable metric for distinguishing stability and instability in the considered stations. However, in Mashhad and Tehran stations, there were consistencies between the trend of annual average values from reanalysis and observational data. In other stations, this agreement becomes evident in recent years. However, in some stations, the annual average value of reanalysis LI has overcome the observations, while in others, it is the opposite.
To improve our ability to gain a clear understanding of the mutual coupling between temperature and pressure changes on intra-annual to inter-decadal scales, research is required to describe the possible relationship between these quantities from different points of view. This relationship can be complex on some time scales. Hence, in the current research, with using time series of monthly reanalysis data, the relationship between surface temperature and pressure at sea level has been investigated in different time scales over Iran. Based on the analysis of the linear trend of the raw data in the statistical period of 1948-2020, a tendency of increasing temperature and surface pressure was observed in all regions of Iran. The highest increase in temperature has occurred in the north-east (1.6 °C to 1.8 °C), and the lowest increase in temperature has been found in the extreme northwest of Iran (0.4 °C to 0.6 °C), which can be attributed to the effects of global warming. In addition, the highest increase in pressure (more than 4.5 hPa) was observed in the extreme northwest of the country, and the lowest increase in pressure (less than 1.5 hPa) was found in the southern coast of the country. Based on correlation and regression analyses, the inter-relationships between pressure and temperature were investigated for the raw data as well as the filtered data (in different time scales). The results demonstrate that the annual component has a large impact on the correlation pattern between the raw temperature and pressure data. A phase difference of 5-6 months was observed between the common annual periodic component of the temperature and pressure. For the intra-annual components, there is a negative correlation (-0.5 to -0.6) in the northeast at the lag = 0 and in the southeast at the lag = 3 months. In addition, the highest positive correlation (+0.5 to +0.6) was revealed in the lag = 6 months in the southeast of Iran. For the inter-annual plus inter-decadal components of the temperature and pressure at the lag of zero, the highest negative correlation coefficient (-0.3 to -0.4) was observed in the east and northeast of Iran. In larger time lags, the correlation coefficient gradually becomes positive, which reaches more than +0.4 in a large part of the country at the lag = 200 months. Therefore, with a phase (time) difference of several years (2 to 16 years), the correlation pattern tends to increase over a large area of Iran, which in turn indicates the presence of common inter-decadal quasi-periodic components (but with a specific phase difference) in the time series of surface pressure and temperature. Scatter plots and regression modeling for the selected stations and for different scales included in the time series display unique patterns for the relationship between the pressure and temperature so that these patterns can change over Iran depending on the latitude of the selected station.
Satellite data were validated using daily soil temperature data from 174 meteorological stations. The accuracy of satellite data showed that the area average values of root mean square error (RMSE) and mean bias error (MBE) compared with station data are 1.7℃ and 1.39℃ respectively. Also, the PBias statistics results show that the underestimation prevails in the night LST data compared to the soil temperature. In total, the MODIS sensor estimates LST in most regions of the country with an error of less than 10%, which indicates the high efficiency of the MYD11A1 product in assessing Iran's nighttime LST.
The findings showed that the combination of altitude and higher latitude has significant importance in the seasonal changes of nighttime minimum LST in Iran. So, the combination of these two factors plays an important role in the occurrence of the lowest night temperatures in the country in the middle parts of central Alborz and the highlands of Azerbaijan. Thus, the longest period of dominance of low temperatures in Iran is related to 38°N and 36°N latitudes, respectively. The results also indicate that the prevailing temperature zones in Iran fluctuate from the minimum temperature in January to the maximum temperature in July. In the transitional seasons, only April and October have distinct and independent temperature identities, and the rest of the months of the transitional seasons are either connected to winter or connected to summer according to the pattern. Examining the course of intra-annual temperature changes shows that the highest intra-annual temperature concentration and the highest homogeneity are observed in the summer season. On the other hand, the highest level of heterogeneity and the lowest level of temperature concentration are related to the autumn season. Examining the average nighttime temperature difference in Iran also shows that the highest values of the earth surface temperature difference occur in the winter season and the lowest values in the summer season. In a general view, the temperature difference curve fluctuates between a maximum of 56.6℃ in January and a minimum of 46.5℃ in July. Spatial analysis of nighttime temperature difference showed that the Lut desert experiences the highest average values of nighttime temperature difference throughout the year. In general, lack of moisture, very low percentage of cloud cover, lack of vegetation cover, and very low soil moisture seem to have led to high nighttime temperature fluctuations in the Lut desert. On the other hand, a narrow strip along the southern coastline of the Caspian Sea experiences the lowest nighttime temperature difference in the country. Clearly, the high humidity of the southern coastline of the Caspian Sea plays the main role in reducing the temperature fluctuations of this region. The geographical analysis of cold spots indicates that the coldest spots of Iran are observed in the high peaks, located in high latitudes. Also, the highest geographical concentration of cold spots in Iran is related to the summer season. Meanwhile, the largest spatial distribution of Iran's cold spots can be seen in the winter season, especially in December. The findings also indicate the existence of a regular annual cycle in the spatial arrangement of cold spots in Iran. In this way, in the warm period of the year, the cold spots of Iran are concentrated only in the middle part of the central Alborz. With the beginning of the cold period of the year, we are witnessing a northwest shift of cold spots toward the highlands of Azerbaijan. So that a bimodal pattern replaces the summer concentrated pattern. The bimodal pattern continues at the height of winter with the dispersion of cold spots in Alborz and Azerbaijan, but with the arrival of the warm period of the year, a southeast-ward shift of cold spots towards the Alborz Mountains begins, with the cold spots completely concentrated on the Damavand peak, this annual cycle ends in the summer season.
The weakness of the sensitivity of quartz sediment grains in Iran to produce the OSL signal and also the bad behavior of the resulting luminescence signal to produce the signal growth curve (in terms of dose) has been repeatedly seen by the second author and has sometimes been published (Fattahi 2015, Appendix; Fattahi et al., 2019 Figure 3). As, this weakness can cause the inability of the luminescence method to determine the reliable age of young samples, investigating and solving this problem of quartz OSL characteristic in Iran is of great importance. However, in many studies, feldspar signal has been used due to the lack of quartz or weak sensitivity or bad behavior of quartz signal (Fattahi et al., 2007).
In order to solve this problem, comprehensive research was conducted and this article presents part of its results. In this study, following extracting quartz grains from 3 kinds of sedimentary quartz their luminescence characteristics were investigated. The results of designed experiments show that by increasing the intensity of the stimulating source (blue-470 nm) while sample is hold at 125°C, the intensity and the decay rate of the OSL decay curves increase. It also shows that by increasing the laboratory dose a more accurate growth curve (luminescence vs dose) can be created and the specified laboratory dose can be restored. These finding confirm the finding of previous workers on the quartz OSL characteristic from other part of the world and will provide the potential for dating young samples.
In addition to reviewing aerial photographs and field survey, studying satellite images is one of the practical methods for identifying the trend of obvious faults and preparing maps of the fault system of different regions. In recent years, preparing airborne geophysical maps for hidden fault structures has become common. On the other hand, one of the most common methods for detecting hidden structures, including faults, is aerial magnetic studies, the interpretation and modeling of which has helped researchers in identifying subsurface faults or possible buried faults.
It is worth mentioning that in some cases the boundary of the structures may not be associated with a fault. Also, there is a possibility that a fault structure does not have a noticeable magnetic signal. Therefore, the results of satellite images or aerial magnetometry do not necessarily lead to the identification of all hidden faults. In this research, it has been tried to process the aerial magnetometer data of Tehran province by different methods (e.g. reduction to the pole, directional derivatives, upward continuation, analytical signal, and horizontal gradient). Then put it on the fault map of the area and comparing the results, the degree of concordance of the trends of the faults in the region with the magnetic anomalies, magnetic bedrock type faults are identified. In the final stage, by placing a new layer of the seismicity map of the region, those active bedrock faults can be identified.
The general results obtained in this research confirm that some of the active faults in the Tehran region are of the basement type, that the ability of these faults to cause large earthquakes is not far from expected, and this result is consistent with other recent seismological studies conducted by Soltani-Moghadam (2016), Ahmadzadeh et al. (2019) and Azqandi et al. (2023) and there is in very good agreement with their finalings.
x_t=∑_(n=1)^∞▒〖a_n ϕ_n (A_n t^2+B_n t+C_n e^(-D_n (t/T)^2 ) ) 〗+〖Cϵ〗_t t∈T
It provides an error prediction that will effectively modify the model prediction. This machine is compact in terms of computing. The value of the standard deviation of the statistical population of the maximum temperature during the period was 10 celsius, which shows a significant improvement from the value of 9.5 to 10.01 by the tracking machine. Also, the standard deviation of the minimum temperature was about 8.5 degrees Celsius, which was improved by the machine from 7.7 to 8.4 degrees Celsius. In this research, we use the skill score criterion, whose value will show that the skill score of the model for short-term maximum temperature has grown from a negative value with a leap to more than 0.8, which shows the significant impact of the machine in improving forecasting. The minimum temperature prediction skill score of the model will show an increase in the way of improving the prediction. The comparison of the obtained results shows that the skill score and RMSE of predicting the maximum and minimum temperature of the modification of the output of the model have increased significantly compared to the model. Also, the monthly change in the skill score indicates the effect of the chasing car on the ability to correct the forecast, especially for the short-term maximum temperature. Investigations will show that the modification of the model has a uniform overfitting in the studied period. In addition, a powerful index independent of the concept of accuracy size will be introduced and used as a method to check the reliability of the model and tracking machine outputs, which indicates the level of confidence that can be had in the model and machine outputs. In this case, the reliability of the maximum and minimum temperature predictions and the significant growth of the index have shown stability in providing the output. After bias correction, the variability of the skill score has been significantly reduced, and by reducing the amount of forecasting error, the reliability of the model forecasts has increased from 60% to more than 85%. Depending on the location and time, the WRF model's forecasting performance is different, but after bias correction, this dependence is removed, and forecasting in all regions and times has almost the same performance.
In most ensemble post-processing approaches, it is implicitly assumed that there is statistical independence between different forecast margins, such as lead time, location, and meteorological variables. However, this assumption is not valid for realistic forecast application scenarios. End users may be interested in scenarios such as total hydrological basin precipitation, temporal evolution of precipitation, or the interaction of precipitation and temperature, especially when temperatures are close to zero degrees Celsius. Important examples include hydrological applications, air traffic management, and energy forecasting. Such dependencies exist in raw ensemble forecasts, but these dependencies are ignored if standard univariate post-processing methods are applied separately to each margin.
In recent years, various multivariate post-processing methods have been proposed. These methods can be categorized into two approaches. The goal of the first approach is to directly model the joint distribution by fitting a specific multivariate probability distribution. This approach is mainly used in low-dimensional problems or when a specific structure is chosen for the application at hand. For example, multivariate models for temperature across space, for wind vectors, and joint models for temperature and wind speed.
The second approach is a two-step approach. In the first step, univariate post-processing methods are applied independently to all dimensions, and samples are generated from the resulting probability distributions. In the second step, the multivariate dependencies are recovered by reordering the univariate sample values according to the ranking order structure of a specific multivariate dependence pattern. Mathematically, this is equivalent to using a copula (parametric or nonparametric). Examples include ensemble copula coupling (ECC), Schaake Shuffle, and the Gaussian copula approach.
This paper presents multivariate ensemble post-processing of temperature, two meter above ground using the ECC approach. The EMOS method is used for univariate post-processing. The performance of the raw ensemble, EMOS post-processed ensemble, and ECC systems is evaluated using energy score (ES) and variogram score (VS). The ECMWF 51-member ensemble system is used as raw data for the period from January 1, 2018 to December 31, 2023.
The results showed that in addition to eliminating the bias of the raw ensemble forecast, the ECC method also preserved the dependence structure between the ensemble members. In contrast, the EMOS method only eliminated the biases without considering the dependence between the ensemble members. Because of its ability to preserve the dependence structure, the ECC method was able to achieve significantly better results than the EMOS method on a variety of metrics, including energy scores and variogram score. This suggests that the ECC method is a valuable tool for ensemble post-processing, and that it should be considered for a wide range of applications.
The VWSH was calculated across three layers at altitudes of 1000, 3000 and 6000 meters from the surface. Investigations were conducted on daily, monthly, seasonal and long-term time scales. On a monthly scale, the minimum (maximum) root mean square errors (RMSE) for VWSH-1000, VWSH-3000, and VWSH-6000 were approximately 3 (8.5), 3.36 (9.84), and 4 (20) m/s, respectively. The results showed that the ERA5 reanalysis data consistently underestimated the value of VWSH-1000 across all stations (except Ahvaz station in recent years). The estimation of VWSH-3000 and VWSH-6000 parameters exhibited both overestimation and underestimation in different months. Notably, the highest error in ERA5 data for VWSH-6000 occurred during January. Across most stations, the largest errors were observed during cold months (particularly for the VWSH-6000 parameter), while the smallest errors occurred during warm months. In conclusion, the results suggest that as the height of the investigated layer increases, the performance of ERA5 in generating the considered VWSH parameters at the stations improves, especially in recent years.
A comparison between reanalysis-LI and observational-LI indicatedes that the highest (lowest) error occurs during warm (cold) months of the year. Throughout the study period, the reanalysis data produced an error of at least 10 K (at Zahedan station) and up to 15 K (at Tehran station) in LI estimation. Except for Ahvaz station, LI was consistently underestimated across all stations. The monthly mean of reanalysis-LI reflected more unstable conditions, whereas the observed values indicated a more stable atmosphere. Consequently, reanalysis-LI may not be a suitable metric for distinguishing stability and instability in the considered stations. However, in Mashhad and Tehran stations, there were consistencies between the trend of annual average values from reanalysis and observational data. In other stations, this agreement becomes evident in recent years. However, in some stations, the annual average value of reanalysis LI has overcome the observations, while in others, it is the opposite.
To improve our ability to gain a clear understanding of the mutual coupling between temperature and pressure changes on intra-annual to inter-decadal scales, research is required to describe the possible relationship between these quantities from different points of view. This relationship can be complex on some time scales. Hence, in the current research, with using time series of monthly reanalysis data, the relationship between surface temperature and pressure at sea level has been investigated in different time scales over Iran. Based on the analysis of the linear trend of the raw data in the statistical period of 1948-2020, a tendency of increasing temperature and surface pressure was observed in all regions of Iran. The highest increase in temperature has occurred in the north-east (1.6 °C to 1.8 °C), and the lowest increase in temperature has been found in the extreme northwest of Iran (0.4 °C to 0.6 °C), which can be attributed to the effects of global warming. In addition, the highest increase in pressure (more than 4.5 hPa) was observed in the extreme northwest of the country, and the lowest increase in pressure (less than 1.5 hPa) was found in the southern coast of the country. Based on correlation and regression analyses, the inter-relationships between pressure and temperature were investigated for the raw data as well as the filtered data (in different time scales). The results demonstrate that the annual component has a large impact on the correlation pattern between the raw temperature and pressure data. A phase difference of 5-6 months was observed between the common annual periodic component of the temperature and pressure. For the intra-annual components, there is a negative correlation (-0.5 to -0.6) in the northeast at the lag = 0 and in the southeast at the lag = 3 months. In addition, the highest positive correlation (+0.5 to +0.6) was revealed in the lag = 6 months in the southeast of Iran. For the inter-annual plus inter-decadal components of the temperature and pressure at the lag of zero, the highest negative correlation coefficient (-0.3 to -0.4) was observed in the east and northeast of Iran. In larger time lags, the correlation coefficient gradually becomes positive, which reaches more than +0.4 in a large part of the country at the lag = 200 months. Therefore, with a phase (time) difference of several years (2 to 16 years), the correlation pattern tends to increase over a large area of Iran, which in turn indicates the presence of common inter-decadal quasi-periodic components (but with a specific phase difference) in the time series of surface pressure and temperature. Scatter plots and regression modeling for the selected stations and for different scales included in the time series display unique patterns for the relationship between the pressure and temperature so that these patterns can change over Iran depending on the latitude of the selected station.
Satellite data were validated using daily soil temperature data from 174 meteorological stations. The accuracy of satellite data showed that the area average values of root mean square error (RMSE) and mean bias error (MBE) compared with station data are 1.7℃ and 1.39℃ respectively. Also, the PBias statistics results show that the underestimation prevails in the night LST data compared to the soil temperature. In total, the MODIS sensor estimates LST in most regions of the country with an error of less than 10%, which indicates the high efficiency of the MYD11A1 product in assessing Iran's nighttime LST.
The findings showed that the combination of altitude and higher latitude has significant importance in the seasonal changes of nighttime minimum LST in Iran. So, the combination of these two factors plays an important role in the occurrence of the lowest night temperatures in the country in the middle parts of central Alborz and the highlands of Azerbaijan. Thus, the longest period of dominance of low temperatures in Iran is related to 38°N and 36°N latitudes, respectively. The results also indicate that the prevailing temperature zones in Iran fluctuate from the minimum temperature in January to the maximum temperature in July. In the transitional seasons, only April and October have distinct and independent temperature identities, and the rest of the months of the transitional seasons are either connected to winter or connected to summer according to the pattern. Examining the course of intra-annual temperature changes shows that the highest intra-annual temperature concentration and the highest homogeneity are observed in the summer season. On the other hand, the highest level of heterogeneity and the lowest level of temperature concentration are related to the autumn season. Examining the average nighttime temperature difference in Iran also shows that the highest values of the earth surface temperature difference occur in the winter season and the lowest values in the summer season. In a general view, the temperature difference curve fluctuates between a maximum of 56.6℃ in January and a minimum of 46.5℃ in July. Spatial analysis of nighttime temperature difference showed that the Lut desert experiences the highest average values of nighttime temperature difference throughout the year. In general, lack of moisture, very low percentage of cloud cover, lack of vegetation cover, and very low soil moisture seem to have led to high nighttime temperature fluctuations in the Lut desert. On the other hand, a narrow strip along the southern coastline of the Caspian Sea experiences the lowest nighttime temperature difference in the country. Clearly, the high humidity of the southern coastline of the Caspian Sea plays the main role in reducing the temperature fluctuations of this region. The geographical analysis of cold spots indicates that the coldest spots of Iran are observed in the high peaks, located in high latitudes. Also, the highest geographical concentration of cold spots in Iran is related to the summer season. Meanwhile, the largest spatial distribution of Iran's cold spots can be seen in the winter season, especially in December. The findings also indicate the existence of a regular annual cycle in the spatial arrangement of cold spots in Iran. In this way, in the warm period of the year, the cold spots of Iran are concentrated only in the middle part of the central Alborz. With the beginning of the cold period of the year, we are witnessing a northwest shift of cold spots toward the highlands of Azerbaijan. So that a bimodal pattern replaces the summer concentrated pattern. The bimodal pattern continues at the height of winter with the dispersion of cold spots in Alborz and Azerbaijan, but with the arrival of the warm period of the year, a southeast-ward shift of cold spots towards the Alborz Mountains begins, with the cold spots completely concentrated on the Damavand peak, this annual cycle ends in the summer season.
The weakness of the sensitivity of quartz sediment grains in Iran to produce the OSL signal and also the bad behavior of the resulting luminescence signal to produce the signal growth curve (in terms of dose) has been repeatedly seen by the second author and has sometimes been published (Fattahi 2015, Appendix; Fattahi et al., 2019 Figure 3). As, this weakness can cause the inability of the luminescence method to determine the reliable age of young samples, investigating and solving this problem of quartz OSL characteristic in Iran is of great importance. However, in many studies, feldspar signal has been used due to the lack of quartz or weak sensitivity or bad behavior of quartz signal (Fattahi et al., 2007).
In order to solve this problem, comprehensive research was conducted and this article presents part of its results. In this study, following extracting quartz grains from 3 kinds of sedimentary quartz their luminescence characteristics were investigated. The results of designed experiments show that by increasing the intensity of the stimulating source (blue-470 nm) while sample is hold at 125°C, the intensity and the decay rate of the OSL decay curves increase. It also shows that by increasing the laboratory dose a more accurate growth curve (luminescence vs dose) can be created and the specified laboratory dose can be restored. These finding confirm the finding of previous workers on the quartz OSL characteristic from other part of the world and will provide the potential for dating young samples.
In addition to reviewing aerial photographs and field survey, studying satellite images is one of the practical methods for identifying the trend of obvious faults and preparing maps of the fault system of different regions. In recent years, preparing airborne geophysical maps for hidden fault structures has become common. On the other hand, one of the most common methods for detecting hidden structures, including faults, is aerial magnetic studies, the interpretation and modeling of which has helped researchers in identifying subsurface faults or possible buried faults.
It is worth mentioning that in some cases the boundary of the structures may not be associated with a fault. Also, there is a possibility that a fault structure does not have a noticeable magnetic signal. Therefore, the results of satellite images or aerial magnetometry do not necessarily lead to the identification of all hidden faults. In this research, it has been tried to process the aerial magnetometer data of Tehran province by different methods (e.g. reduction to the pole, directional derivatives, upward continuation, analytical signal, and horizontal gradient). Then put it on the fault map of the area and comparing the results, the degree of concordance of the trends of the faults in the region with the magnetic anomalies, magnetic bedrock type faults are identified. In the final stage, by placing a new layer of the seismicity map of the region, those active bedrock faults can be identified.
The general results obtained in this research confirm that some of the active faults in the Tehran region are of the basement type, that the ability of these faults to cause large earthquakes is not far from expected, and this result is consistent with other recent seismological studies conducted by Soltani-Moghadam (2016), Ahmadzadeh et al. (2019) and Azqandi et al. (2023) and there is in very good agreement with their finalings.
x_t=∑_(n=1)^∞▒〖a_n ϕ_n (A_n t^2+B_n t+C_n e^(-D_n (t/T)^2 ) ) 〗+〖Cϵ〗_t t∈T
It provides an error prediction that will effectively modify the model prediction. This machine is compact in terms of computing. The value of the standard deviation of the statistical population of the maximum temperature during the period was 10 celsius, which shows a significant improvement from the value of 9.5 to 10.01 by the tracking machine. Also, the standard deviation of the minimum temperature was about 8.5 degrees Celsius, which was improved by the machine from 7.7 to 8.4 degrees Celsius. In this research, we use the skill score criterion, whose value will show that the skill score of the model for short-term maximum temperature has grown from a negative value with a leap to more than 0.8, which shows the significant impact of the machine in improving forecasting. The minimum temperature prediction skill score of the model will show an increase in the way of improving the prediction. The comparison of the obtained results shows that the skill score and RMSE of predicting the maximum and minimum temperature of the modification of the output of the model have increased significantly compared to the model. Also, the monthly change in the skill score indicates the effect of the chasing car on the ability to correct the forecast, especially for the short-term maximum temperature. Investigations will show that the modification of the model has a uniform overfitting in the studied period. In addition, a powerful index independent of the concept of accuracy size will be introduced and used as a method to check the reliability of the model and tracking machine outputs, which indicates the level of confidence that can be had in the model and machine outputs. In this case, the reliability of the maximum and minimum temperature predictions and the significant growth of the index have shown stability in providing the output. After bias correction, the variability of the skill score has been significantly reduced, and by reducing the amount of forecasting error, the reliability of the model forecasts has increased from 60% to more than 85%. Depending on the location and time, the WRF model's forecasting performance is different, but after bias correction, this dependence is removed, and forecasting in all regions and times has almost the same performance.