Climatology
Saeed jahanbakhsh; Majid Rezaei Banafsheh; Alimohammad Khorshiddoust; Hajar Farahmand
Abstract
In recent years, South-east and east of Iran has become one of the most important hotspots of dust events due to numerous droughts, upstream dams and severe land use changes. In order to evaluate the seasonal variations of dust, 15 synoptic stations were selected during 1980–2015 and then extracted ...
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In recent years, South-east and east of Iran has become one of the most important hotspots of dust events due to numerous droughts, upstream dams and severe land use changes. In order to evaluate the seasonal variations of dust, 15 synoptic stations were selected during 1980–2015 and then extracted from the present weather codes. Additionally, the AOD index of the Terra MODIS satellite sensor and the Aura Satellite Aerial Index of Absorption (AAI) were used during the period 2015–2005. Mann-Kendall nonparametric test was used to investigate the trend of dust days and Spearman correlation method was used for correlation of dust days. The average days of dust in this region are 9 days and maximum days of dusty days are 45 days that occur in Zabol station at summer. Intra-seasonal variations of dust over east and southwestern of Iran have two maximum phases at spring and summer. Dust also has an inverse relationship with altitude and latitude. Climate parameters, drying up of rivers and lakes, and land use changes are three major factors in dust production in eastern and southeastern Iran. Main sources of dust production and emission over the region are (1) Makran coast; (2) Hamoun and Jazmourian dried bed (3) Lut Plain and (4) Border region of Iran, Afghanistan and Pakistan. At most stations except Zabul, Bam and Kerman have an increasing trend of dust events.
Climatology
shahnaz Rashedi; Saeed jahanbakhsh; Ali Khorshiddoust; Gholam Hasan Mohammadi
Abstract
For this purpose, data on the type, amount, and height of different cloud layers and daily precipitation of 36 synoptic stations located on the southern coast of the Caspian Sea were received from the Meteorological Organization. MODIS images were used to investigate the relationship between precipitation ...
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For this purpose, data on the type, amount, and height of different cloud layers and daily precipitation of 36 synoptic stations located on the southern coast of the Caspian Sea were received from the Meteorological Organization. MODIS images were used to investigate the relationship between precipitation and cloud microphysical parameters (CTT, CTH, COT, CER, CWP). ERA5 and NCEP/NCAR data were also used to identify synoptic patterns leading to cloud formation. Finally, HYSPLIT model and regression method were used to identify the path of moisture flow. The results of observational data showed that Caspian clouds were observed in the form of low Stratus clouds and middle clouds of Altocumulus type in the region. So that among the low clouds, the heights of 750 and 900 meters and among the middle clouds, the heights of 2700 meters had the highest frequency. The results of Caspian clouds rainfall showed that in most areas, 1 to 5 mm of precipitation has occurred. Correlation results showed that precipitation was positively correlated with CTH,COT, CER and CWP, and negatively correlated with CTT. Multivariate regression predicted 17% of precipitation by cloud parameters. The results of the study of synoptic maps showed that with the establishment of a 1012 hPa high pressure core in the north of the Caspian Sea, the north-south wind flow along with the transfer of sea moisture to the south shore of the Caspian Sea, ascending the air mass and the formation of clouds and limited rainfall in the region. Vertical profiles showed maximum specific humidity in the lower levels of the atmosphere (1000 to 900 hPa). The results of HYSPLIT model moisture flow path showed that the main source of regional moisture was the Caspian Sea.
Land use Planning
Rahimeh Rostami; Ali Mohammadkhorshidduost
Abstract
Introduction One of the physical factors in the development of Maragheh is the change of utilities and their conversion into residential use, which in turn have a direct impact on the process of physical development. The city of Maragheh is one of the most important cities in East Azerbaijan province, ...
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Introduction One of the physical factors in the development of Maragheh is the change of utilities and their conversion into residential use, which in turn have a direct impact on the process of physical development. The city of Maragheh is one of the most important cities in East Azerbaijan province, which has no exception to the rule of physical development. In this research, the characteristics of the city development process are examined physically. Statistics show the growing population in the city of Maragheh during the years 1966 to 2011. Understanding the factors affecting the physical expansion and development of cities helps understand urban growth and development trends. The study clarifies the factors that cause horizontal and physical growth of cities and, conversely, the repulsive factor of other parts of the city that are left behind by physical growth for any reason, and this explains the need for this research. The causes of urban growth are exactly the same as the origins of urban sprawl. In many cases, no distinction can be made between urban growth and urban sprawl however, it is important to have a clear understanding of the difference between urban sprawl and urban growth. The most imperative problems that have arisen from the uncontrolled growth of city due to physical development are the excessive use of land, the lack of sufficient urban facilities and equipment, the disconnection of the suburbs and thus adding to the urban problems. The purpose of this study is to study environmental and physical factors and their impact on the physical growth and development of Maragheh city and provide the best place for future development of the city. Data and Method In the present study, the criteria and sub-criteria and the relationships between them were first defined by the ANP method using Super Design software, and after obtaining the weight for each criterion and sub-criterion, the sub-criteria were first fuzzy and according to the weight of each of them the main criterion was obtained from the sum of these sub-criteria. Finally, the three main criteria of human, environmental and topographic parameters, considering their weight, have provided the main layer of the possibility of physical development of the city. Choosing a suitable method and approach for modeling a system depends entirely on the complexity of that system and complexity is inversely related to the amount of knowledge and understanding of our system. Fuzzy systems can be well used to model two main types of uncertainty in the phenomena. The first type is uncertainty due to the lack of knowledge and human tools in recognizing the complexities of a phenomenon. Results and Discussion Using the three main layers obtained for topographic, environmental and human criteria and according to the weight of each, the final location map of physical development of Maragheh city was obtained. The development map of Maragheh shows that the south-eastern parts are very unsuitable for physical development, while the north-eastern, north-western and south-western parts are suitable for development in terms of three environmental, human and topographic features. Environmental factors seem to have the greatest impact on the development and expansion of cities. According to our findings, and depicting the map of the city development over different years, the final map was obtained for the development of this city which is almost in line with the expansion of the city during the years 1996 to 2006 and 1976 to 1986. Conclusion With the arrival of the third wave of industrialization in Third World countries since the beginning of the twentieth century, production and income in cities, followed by increased demand for urban services and consequently urbanization has expanded. One of the effects of physical growth is related to the expansion of the outskirts of cities beyond the administrative boundaries of any city. Such urban development goes to areas outside the administrative boundaries and changes in land uses. The city of Maragheh has many limitations in terms of physical expansion due to the gardens around the city. In order to prevent the destruction of gardens and agricultural lands as well as physical expansion in line with natural and human criteria, extensive studies should be conducted. Information and data were applied in this study for the analysis of these parameters.The city of Maragheh needs to expand physically following the increase in population naturally and its increasing expansion goes on due to uncontrolled urban migration. In the present study, three main criteria of human, environmental and topography have been used. From the topographic criteria, the south-western parts are the most suitable places and the north-eastern parts of the city are the most unsuitable parts. In terms of environmental criteria, the western and south-western parts and to some extent parts of the north are suitable for development. Due to being a garden city, it is limited to gardens from the surroundings, which makes it difficult to expand from a human and environmental standards point of view. By combining three layers of environment, human and topography, the best place for the development of this city according to the final map was prepared for the optimal location of the future development the city based on ANP Fuzzy method by which it is more suitable in the west and north-west than in the south and south-west.Paying attention to horizontal expansion preserves the traditional texture of the city and single-storey buildings and prevents its vertical expansion. Although vertical expansion has advantages over horizontal spreading out, the current conditions of Maragheh city and its size, as well as its traditional texture and culture, make the need for horizontal expansion more tangible than vertical expansion.By using the right development model, both the traditional construction of the city can be preserved and the problems caused by sporadic development can be reduced, provided that the horizontal development goes in the direction that environmental and human conditions demand.
Climatology
Naser Jafarbegloo; Ali Mohammad khorshiddoust; majid rezaei banafsheh; Hashem Rostamzadeh
Abstract
INTRODUCTION
Today, pre-risk awareness has become an integral part of the national development management and planning system in many countries (Civiacumar et al., 2005). Agriculture is inherently sensitive to climatic conditions. The minimum temperature, which has been identified as the most vital ...
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INTRODUCTION
Today, pre-risk awareness has become an integral part of the national development management and planning system in many countries (Civiacumar et al., 2005). Agriculture is inherently sensitive to climatic conditions. The minimum temperature, which has been identified as the most vital determining factor in the distribution of plant species on the planet, can be both a limiting factor and a factor in the spread and species distribution (Rodrigo, 2000: 155). Therefore, in this study, we examined the changes in minimum temperatures in the statistical period (1980-2010) and predicted these changes in the 2050s (2065-2046) in the Northwest of the country using the LARS-WG microscale method and model output. Atmospheric pairings of HadCM3 and MPEH5 were addressed. The prediction of minimum temperature variations to determine the extent of its future changes and considering the necessary measures to minimize the adverse effects of climate change on agricultural products were of great importance. In this regard, general atmospheric circulation models (GCMs) are designed that can simulate climatic parameters.
DATA AND METHODS
In the present study, the output data of two HadCM3 and MPEH5 general circulation models based on two scenarios A2 and B1 were analyzed by LARS-WG statistical method in 21 synoptic stations located in the Northwest of the country. The results were monthly and periodic on the base period (1980-1999) and the 2050s (2046-2065), thereby the minimum temperature was evaluated and analyzed. In assessing the LARS-WG model, the observational and simulation error data were evaluated using MSE, RMSE, MAE and R2, and the model was evaluated for the appropriate region. The results showed that the minimum temperature in the future period will increase compared to the base period in the study area. This increase in air temperature at the study area is based on the HadCM3 and MPEH5 models, on average, 1.9 and 1.7 degrees Celsius to 2065 horizons compared to the base period. The north-eastern part of the northwestern region of Iran will have higher temperatures than the semi-southern regions. In fact, the cooler regions of the high latitudes will face more incremental changes in the amount of minimum temperatures. The results and achievements of this research are important for long-term plans for adaptive measures in the management of fruit gardens, agricultural products and water resources management. In order to calibrate and ensure the accuracy of the LARS-WG microscale model, the model was first implemented for the basic statistical period (1980-2010); then the minimum temperature output and its standard deviation were compared with the observational data of the studied stations, which indicated a small difference between the observed and simulated values and also deviated from their criteria.
RESULTS AND DISCUSSION
The results of evaluation of observational and simulated data by LARS-WG microscale model using RMSE, MSE and MAE error measurement indices for the studied stations indicate that there is a significant difference between the simulated values and the values of the observed observations. There is no critical 0.05 significance levels, and Pearson correlation values between simulated and real data are acceptable at the significance level of 0.01. The obtained results show that the accuracy of the model varies in different stations. In general, the results of error measurement indices indicate that the LARS-WG model is of good accuracy for micro-scaling the parameters under study. In order to better represent and ensure the accuracy of the prediction as well as to investigate the uncertainties in the studied models, the simulated values were compared and observations were made on a long-term average during the base period in the studied stations using comparative graphs. As can be seen, the observed and generated values in the base period at all stations are very close to each other and the LARS-WG model has been successful in simulating the studied parameter. After evaluating the LARS-WG model and ensuring its suitability, the data predicted by the model for two scenarios A2 and B1 using HadCM3 and MPEH5 models and were examined on a monthly and long-term basis. The study of the status of minimum temperature changes of the studied stations in the future period (2065-2056) shows that the minimum temperature is based on both scenarios and in all months and stations compared to the period, the base has increased. Due to the large number of study stations, only stations located in provincial centers of this study are listed.
CONCLUSION
Cold and frost are one of the most significant climatic hazards on fruit trees. This type of climate risk affects different parts every year, especially the cold regions of the northwest of the country. Studies show that in recent years, the rate of economic damage to fruit trees in this region has increased, so in this study, the outlook for changes in minimum temperatures in this region using the LARS-WG statistical microscale model and output two HadCM3 global model and MPEH5 were introduced in the 2050s (2065-2046). For accuracy and precision of the models, error measurement indices and coefficients of determination and correlation were used. The results showed that the LARS-WG model has a good ability to simulate the studied variables in the study area. The results of long-term prediction of the studied models show that the minimum temperature values will increase in all study stations, which is based on HadCM3 and MPEH5 models on average. In the 2050s, and it will be 1.9 and 1.7 respectively, compared to the base period. The results of the studies of Kayo et al. (2016), Sharma et al. (2017), Khalil Aghdam et al. (2012), Qaderzadeh (2015), Sobhani et al. (2015) and Khalili et al. (2015) were confirmed. In general, based on the studied scenarios and models, the minimum temperatures are expected to increase in the study area in the future. By increasing it, the yield of some crops that need cold during the growing and productive period would decrease. It can also reduce snowfall, followed by frost on crops and lack of water in dry seasons. Therefore, due to the fact that following the climate changes, the conditions of the agricultural climatology are also changing, it is necessary for the relevant officials and planners in the agricultural sectors to adopt the necessary strategies to reduce the consequences and adapt to the new climate.
Climatology
mehdi asadi; Ali mohammad khorshiddoust; Hassan Haji Mohamadi
Abstract
Introduction
Data and information from the Meteorological Department of India and the Joint Hurricane Warning Center (JHWC) were used to investigate the structural nature of Ashuba tropical storm in the Arabian Sea from June 7 to June 12, 2015. To study the atmospheric structure, the analyzed digital ...
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Introduction
Data and information from the Meteorological Department of India and the Joint Hurricane Warning Center (JHWC) were used to investigate the structural nature of Ashuba tropical storm in the Arabian Sea from June 7 to June 12, 2015. To study the atmospheric structure, the analyzed digital data were taken from the European Center for Medium-Term Forecasts and the Center for Environmental/Atmospheric Forecasts (NCEP/NCAR) for the Arabian Sea and beyond. The study area was the Arabian Sea, located between the Indian subcontinent (eastern part) and the Arabian Peninsula (western part) and northwest of the Indian Ocean. On average, 1-2 tropical cyclones form on the Arabian Sea each year. Even in some tropical regions, strong cyclonic cycles occur at the synoptic scale (Evan & Camargo, 2001: 145). Therefore, from previous years, climatologists have studied the types of storms, due to the increase in tropical cyclones in the last decade; and thereby, this issue is followed with more sensitivity. Consequently, the main purpose of this study was to explore the structural nature of Ashuba tropical storm on the Arabian Sea in order to identify one of the region's main moisture sources.
Materials and Methods
Storm data statistics were obtained from the Meteorological Department of India and the Hawaii Hurricane Warning Center. Analyzed digital data, including; Geopotential altitude (Hgt), orbital (u), meridional wind (v), sea surface pressure (SLP), air temperature and sea water temperature (SST) for standard levels at 17 compression levels with a resolution of average daily geographic degree belonged to the National Center for Environmental Prediction/Atmospheric Science and precipitated networked data were obtained from the European Center for Medium-Term Atmospheric Forecasting (ECMWF) with a resolution of 0.125 degrees Celsius for the Arabian Sea. NASA and MODIS satellite imagery were also used for the visible band for every six days. The CAPE index was applied to evaluate the energy required by the storm supplier.
Findings and Discussion
The results of study displayed that in the middle level of the atmosphere, while forming a low-altitude nucleus with very strong positive rotation, the conditions for the production of tropical storms in the region have been provided. On the other hand, on the surface, low pressure has formed in the southeast of the Arabian Sea with a central pressure of 995 hPa and has started moving westwards towards the coasts of Oman and northern Yemen. Creating a very strong convergence current on the surface and upper divergence caused the storm to reach its maximum strength in the region on June 9. However, the anomalous temperature of the water surface in the range where the storm reached its maximum intensity reaches to over than 5 degrees Celsius. The increase in water surface temperature and the transfer of heat and moisture into the storm has strengthened and, by its nature, caused heavy rainfall in the region. Finally, on June 12, as it approached the east coast of Oman, it began to disappear due to lack of moisture for its dynamic movements, and changed from a tropical storm to a tropical hurricane. Also examining the prepared maps for the amount of precipitation and the flow of the lower levels of the atmosphere, it was determined that on the first day of the storm, a cyclonic current occurred in the east of the Arabian Sea, resulting in the maximum amount of precipitation in the west of the system, which reaches more than 240 mm. On the second day, moving north of the system, the amount of precipitation was concentrated in the south, so that the southern coast of India was not unaffected by precipitation and had about 120 mm of rainfall. On the third day, with the placement of this tropical storm in the north of the Arabian Sea, the maximum precipitation was created in the east of the system, which was more than 160 mm. On the fourth day, the western half of the Indian coast was faced with a rainfall of nearly 110 mm, which was due to its location in the east of the cyclone, which in turn caused the rise of air and the transfer of moisture to the air parcel, floods in the region. On the fifth day, the maximum rainfall was close to the eye of the storm, which was close to 100 mm, and the coastal areas of the Indian subcontinent were still experiencing heavy rainfall. Examination of the 850 hPa pressure system revealed that on the first day, the maximum relative pressure system nucleus formed in the southeastern parts of the Arabian Sea. These conditions have led to very strong convergence in the lower levels. The presence of such strong convergence and amplification of rotation has caused this anomaly to reach its maximum in the region. The strong rotating nucleus then extended to the west coast of India and then moved westward on the third day to the central regions of the Arabian Sea, with a very strong rotating current extending from latitudes 10 to 30 degrees north. As the storm/hurricane approached the west coast of the Arabian Sea, it intensified to more than five pressure system units on the fourth day. On the fifth day, the positive nucleus became independent and formed a very strong rotating closed cell. On the sixth day, with the cyclone remaining on the eastern coast of the Arabian Peninsula, its power had gradually diminished.
Considering the water temperature in the region, which is an average of 6 days, it showed that the water temperature in most parts of the Arabian Sea was high, so that these conditions reached more than 32 degrees Celsius in the coasts of India and the center of the Arabian Sea. These conditions were less only in the northern regions of the sea than in other regions. To understand the water surface temperature, its anomaly was also calculated for six days with the storm. Its output indicated that the eastern, northern, western and southwestern regions of the Arabian Sea were associated with a positive anomaly of 2 to 3° C. Negative anomalies only reached -1.5 degrees Celsius in the north and south of the sea. Occurrence of maximum positive anomalies in the region was one of the main reasons for the intensification of cyclones in the region, so that the western regions of the Arabian Sea had the maximum positive anomalies and on the other hand the maximum area of tropical cyclone activity.
The 12-hour reports from the Indian Meteorological Agency and the Hawaii Hurricane Warning Center were used to route the tropical storm. In these two centers, there were several data methods for routing and the origin of the storm. Geographical coordinate data with a 12-hour separation was used, which from the beginning of the storm to its decline, its characteristics and longitude and latitude were recorded as a text file. The onset of the storm was from the eastern part of the Arabian Sea, which migrated northward to higher elevations and deviated in its path due to the dominance of the Coriolis to the west of the region and disappeared off the coast of Oman.
Conclusion
Ashuba tropical storm/hurricane formed on June 7, 2015 in the Arabian Sea and disappeared on June 12, 2015. This investigation revealed that on the first day, a low-lying cell was formed in the eastern part of the Arabian Sea, during which a positive rotating nucleus or vortex was formed in the mentioned area and strengthened in the following days. The role of the Arabian Sea and abnormal changes in its water surface temperature in the occurrence of hurricanes has been mentioned in the researches of Ghavidel Rahimi (2015: 31) and Lashkari and Kaykhosravi (2010: 19). On June 9, as the subtropical anticyclone expanded further east, the Arabian Sea's low-pressure cell became oval in a circle, contributing to the deepening of the system, creating another bond at the heart of the closed cell with a height of 5,810 geopotential meters. In the last days, as the coasts of Oman and Yemen approach, the intensity of this cell decreases and its extinction stage was reached. On the surface, in parallel with the mentioned period, a low-pressure core with a central pressure of 995 hPa formed on the southeast of the Arabian Sea and the creation of a very strong positive rotation indicates the occurrence of hurricanes in the region. The central pressure of the storm reached less than 993 hPa on days 9 and 10, which was the peak of the storm. As it approached the shores, the intensity of this cyclone was greatly reduced, turning it from a tropical storm into a tropical turbulence. Examination of the water surface temperature showed that the average water surface temperature in these 6 days in most parts of the Arabian Sea was more than 29 degrees Celsius. Inspection of water surface temperature anomalies also disclosed that the maximum positive anomalies corresponded to several places in the sea, including the southern coasts of Pakistan to western India, eastern Oman and a very strong core corresponding to the southwest of the Arabian Sea with an average temperature of more than 5° C. The maximum rainfall inside the cyclone indicated that on the first day of the storm, the maximum rainfall in the southwest was 240 mm. In the following days, with the transfer of this core to the south, southeast and finally to the east, the maximum rainfall would be on the west side of the Indian coast. Only in the last days it was observed that while the maximum rainfall occured in India near the eastern part of the eye of the storm, a maximum precipitation center with an average of 100 mm has been created. In this study, two indicators, CAPE and SWEAT, were used to assess the location of storm formation. The results showed that these two indicators well showed the formation and severity and weakness of the storm during different stages. Thus, on the first day in the south of the Arabian Sea, the amount of CAPE was more than 5000 Jules/kg, which indicates the amount of convective energy available. On the other hand, the values of the SWEAT index have reached more than 380, which specify that the probability of a hurricane in this region is very high. Also, with the increase of water surface temperature in the region and the increase of anomalies in it, the necessary energy is provided for the production of cyclones in the region, which with the increase of energy within the air mass system and the presence of buoyancy energy in it, and on the other hand, instability indicators in monitoring and tracking these types of storms showed that they are a suitable tool for tracking and are able to navigate it while being aware of the intensity of the storm.
Climatology
kobra baharvandi; Ali Mohammad Khorshiddoust; Mojtaba Nassaji Zavareh
Abstract
Introduction The purpose of this study is to analyze the temperature trend in Khorramabad station, and an attempt has been made to provide a suitable method to ensure the accuracy of the data, which is the first time that this station is used. The statistical years (2013-2013) have been that the data ...
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Introduction The purpose of this study is to analyze the temperature trend in Khorramabad station, and an attempt has been made to provide a suitable method to ensure the accuracy of the data, which is the first time that this station is used. The statistical years (2013-2013) have been that the data in these years have been recorded in a coherent and regular manner and this data has been easier to access. In view of the above, this study intends to identify and modify possible inhomogeneity as much as possible in the first stage while examining the accuracy of data homogeneity before analyzing the trend. In the second stage, the analysis evaluates the trend of minimum temperature over 30 years. Data and Method The SNHT (Standard Normal Homogeneity Test) method is one of the most common methods for examining the homogeneity of temperature and precipitation data, which has been used by many researchers around the world. This method has been proposed by various researchers and for more accurate detection of atmospheric fluctuations from heterogeneity by non-atmospheric factors, this test is used by considering the reference series. In this method, the tested time series is based on the stability of the difference of parameter d between the temperature in the tested station and the reference series. Heterogeneity in the test series is revealed by changes in the d series. To reduce the spatial effect on temperature values, the relation (t ˍˍ t is used, where t is the average temperature value and r is the correlation coefficient between the subject and reference station (for example (t io ˍˍ to) and t jr ˍˍ tj)), respectively, temperature values It is in the test station and in each reference station. The parameter d in each time step i for k reference station is calculated based on the following equation. This test is performed by two methods of absolute standard normal homogeneity and relative normal standard homogeneity. Here, considering that only the time series of a station is examined, the absolute standard normal homogeneity method is used. In fact, this method is a necessity for climate research that must be done before any calculations, and after confirming the homogeneity of the data by the test, the rest of the research studies can be continued (Nassaji Zavareh, 1392: 58). Results and Discussion In this study, due to the lack of adjacent stations during the statistical period in the region, the absolute standard normal homogeneity method has been used to examine the homogeneity of the data. This test was used for monthly time series. The test results showed most of the monthly time series were homogeneous. In a number of months, heterogeneity was observed in the time series. Because the type of test used was an absolute test and the metadata did not confirm this heterogeneity, these heterogeneities could be attributed to natural atmospheric fluctuations. This result is consistent with the research of Peterson et al. (1998). Analysis of the plotted graphs shows that there is no heterogeneity based on this test, which is also confirmed by the metadata in Table (4). Because the meteorological station of Khorramabad city has been moved from the city centre to outside the city since 1981. Therefore, the data recorded from 1981 onwards are standard and acceptable. In this study, the length of the statistical period under study begins in 1984 and ends in 2013. Data homogenization results were performed by absolute homogeneity test for each month separately for 30 years. Altogether two results are obtained from the analyses: Two results are obtained: 1- The temperature of the minimum statistical period of thirty years has acceptable homogeneity. 2. Some inhomogeneity observed in April, May, June and July are due to weather conditions. Conclusion 1. The results of the SNHT test on the data showed that a series of heterogeneity is seen in the data process over 30 years, but it is not related to the displacement of the station, and it is related to the weather conditions. 2 - The results of non-parametric I-Kendall test on the data and during the 30 years of the statistical period showed that the value of T-statistic is significant in most months and the trend is also positive. 3- According to the T-statistic of the non-parametric method I-Kendall, the trend of glacial intensity in Khorramabad station is decreasing, i.e. the days we had in this glacial station are decreasing and it shows the fact that the weather in Khorram-abad city has an increasing trend. The results of this study are consistent with the research of other researchers such as Rahimzadeh (2011), and Shiravand et al. (2010). In relation to answering the research questions, it should be stated that this research, according to its title, is an analysis of the trend of minimum temperature and frosty days during 30 years. It is hoped that in other studies, researchers will address this issue in a more comprehensive manner, and these responses have only been proven using the statistical methods studied, if in addition to other atmospheric factors, factors such as The heat island in the city centre, the reduction of green space, the increase of carbon dioxide, etc. have always affected the climate of different regions. Therefore, all factors should be considered in the study of climate change in a region, which in this study, according to its title, is not an opportunity to research and describe the mentioned factors.
Climatology
mehdi asadi; Ali Mohammad Khorshiddoust; Abbas Ali Dadashi Roudbari
Abstract
Introduction As the stations measuring precipitation continuously are not regularly available, the best solution should be to investigate the points without statistics using optimal methods. Among these methods, we can mention geostatistical methods. Geostatistical methods have been approved as appropriate ...
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Introduction As the stations measuring precipitation continuously are not regularly available, the best solution should be to investigate the points without statistics using optimal methods. Among these methods, we can mention geostatistical methods. Geostatistical methods have been approved as appropriate ways for studying precipitation data and estimating precipitation regions. Results of many studies have shown that geostatistical techniques are more accurate than conventional interpolation methods. Statistical context can also be used for precipitation variability. Accurate estimation of the spatial distribution of precipitation requires a dense and regular cell network. The spatio-temporal variation of precipitation is one of the most important issues of applied climatology, so the main purpose of this study is to monitor the spatio-temporal variation of precipitation in Iran in seasonal context by the application of the mentioned techniques. Data and Methods In this study, the common statistics of 125 synoptic stations in the country with the statistical period of 30 years (1980-2010) have been used. Also, the station data were generalized to the 15 km cell spaces using the Kriging interpolation method in ArcGIS 10.2.2 software. To speed up the computational process, the capabilities of GS + software were used to fit the variogram, and ArcGIS software was used to map the precipitation regions of the country. In order to study the pattern of precipitation, spatial autocorrelation techniques (local Moran and global Moran) were used. Also, the skewness coefficient (G1) and the peak degree coefficient (G2) were calculated separately for each of the months studied. Cluster and non-cluster analyses and hot spot method were used to study the patterns and spatio-temporal variations of precipitation. Cluster and non-cluster analysis, also known as Moran local Anselin index is an optimal model for showing the statistical distribution of phenomena in space (Anselin et al, 2009: 74). For cluster and non-cluster analyses for each complication in the layer, the value of the local Moran index score, which represents the significance of the calculated index, was also calculated. Results and Discussion The value of the global Moran index for all 4 studied seasons and the annual total is above 0.95, which indicates the pattern of high clusters of precipitation in the country at the level of 95 and 99%. However, the highest Moran index in the world with a value of 0.970356 is related to the winter. Statistics for each of the five decades studied are high, between 255 and 261. Therefore, based on global trends, it can be inferred that the annual changes in precipitation in the country follow a very high cluster pattern. Consequently, due to the high value and low value, the hypothesis of no spatial autocorrelation between data in each of the five decades can be rejected. If precipitation were to be normally distributed in space for different seasons in the country, the global Moran index would be -0.000139. Moran's spatial autocorrelation only determines the type of pattern. For this reason, to show the spatial distribution of the pattern governing the distribution of precipitation in Iran, local Moran has been used during the studied periods. In winter (36.56%) there was no pattern or in other words it lacked spatial autocorrelation. This amount increased by 1.14% for spring and reached 37.70. This amount has increased significantly in summer, so that it has increased by 47.04% compared to spring. It has reached areas with no spatial autocorrelation in autumn (41.92) and winter (36.56). LL precipitation patterns have been distributed in the five studied periods with values of 36.53, 0, 34.64, 35.31 and 38.29% in the country, respectively, and in the form of nationwide spots in the eastern, southeastern and central regions. Precipitation values with negative spatial correlation in summer had the highest value (84.74%) and the lowest annual average (35.06%). However, values with high rate or positive spatial autocorrelation in all five studied periods were limited to the northern regions of the country, the highlands of Alborz, Zagros and had significant fluctuations in some parts of the country. Local Moran Anselin statistics have been able to well determine the process of precipitation (Masoudian, 1390: 97) and the era of windbreak slopes as well as adjacent areas with climatic contrasts such as north-south slopes of Alborz and slopes of east-west Zagros. Due to the complexity of precipitation patterns in the country, spatial statistics can well explain precipitation patterns. The general results of this statistic (local Anselin Moran) indicate that the amount of rainy areas in the country has been reduced during five study periods. It should be noted that most of these reductions were related to the Zagros region, the southeast of the country and the northern regions of Khorasan. Conclusions Iran has special conditions in terms of precipitation due to its vastness with respect to latitude and longitude, the configuration of unevenness and exposure to air masses. The general structure of precipitation in Iran is affected by latitude, altitude and air masses, so that with the change of any of these factors, precipitation will also change. In other words, the general conditions of precipitation are a function of latitude and altitude, and other factors such as water areas and land cover, which are referred to as local factors, play a role in the formation of Iranian precipitation. In the present study, spatio-temporal analysis of Iranian precipitation has been done using a new method of spatial statistics. For this purpose, high and low clustering methods, local and global Moran, hot spots and cluster and non-cluster analyses have been used. The present study focuses on the assumption that precipitation in Iran follows a cluster pattern and the pattern of precipitation distribution is itself a function of internal and external conditions. To achieve this goal, the average seasonal and annual precipitation statistics of 125 synoptic stations in the country during the statistical period of 1980-2010 were used. Then, to apply the methods used in this research, the capabilities of GIS were used. The results of the global Moran method and the K-function of some distances showed that the annual changes in precipitation in Iran follow the pattern of high clusters. According to spatial autocorrelation analyses, the areas with negative spatial autocorrelation in all studied periods are related to the southeast, the coasts of the Oman Sea to Abadan and parts of the northeast of the country. Areas with positive spatial autocorrelation were often located on the southern shores of the Caspian Sea and the Zagros strip. In all the studied periods, less than one quarter of the country's area lacked a significant spatial autocorrelation pattern. Spatial analyses showed that Iran's precipitation patterns are divided into two precipitation spots of southern tabs (low precipitation spot LL), and Caspian coasts west and northwest (precipitation spot HH). The results also indicated that during the period under study, low precipitation spots (negative spatial autocorrelation) had much more frequency than precipitation spots.
Climatology
HABIBEH NAGHIZADEH; ali mohammad KHorshiddoust; Rashid Saeidabadi; MohammadSaeid najafi
Abstract
Introduction Today, one of the most important issues in the field of climatology is air pollution and its relationship to the general circulation of the atmosphere. The atmosphere around the planet Earth is made up of gases called fixed atmosphere gases. Humans and all living things are accustomed to ...
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Introduction Today, one of the most important issues in the field of climatology is air pollution and its relationship to the general circulation of the atmosphere. The atmosphere around the planet Earth is made up of gases called fixed atmosphere gases. Humans and all living things are accustomed to this composition of the atmosphere and have adapted to it. Any changes in the quality and quantity of these elements can be considered as air pollution. Therefore, since the main cause of all changes in the characteristics of the human environment is related to changes in atmospheric pressure, so in all climate-related studies, the first step is to identify patterns of air masses. Anti-cyclonic circulation patterns, both at the Earth's surface and in the upper atmosphere, create sunny weather, leading to temperature inversion and subsequent air pollution, especially in densely populated and industrial cities. In winter, when these inversions are stronger, hot air on the cold air acts like a cap that prevents air mixing. Thus, urban areas have a strong potential to face serious problems of air pollution as a result of a combination of limited conditioning of air and emission of pollutants from high atmospheric levels. Atmosphere in terms of temperature inversion is associated with minimum air mixture and stable conditions. So the highest density in the direction of the wind extends from the source of diffusion. Methodology For the recognition and extraction of the synoptic patterns affecting the temperature inversion in Tabriz city, we initially prepared the data records on the temperature inversion for the time period of 2001-2010 by the use of upper atmosphere station data. This was followed by the utilization of digital data on sea surface pressure as daily mean from the reanalyzed data series of NCEP/NCAR in the eastern longitudes of 10°-60° and the latitudes of 10°-90° in 651 pixels of 2.5/2.5 degrees. With the PCA analysis on the data of sea surface data pressure in the days having temperature inversion, we reduced their volume and carrying out cluster analysis on the obtained components we recognized the most important atmospheric patterns and through which the map of each pattern was drawn. Results and discussion Based on the results of cluster analysis on the matrix of factor scores in this study, the occurrence of temperature inversion in the city of Tabriz is due to the domination of four consecutive patterns. The general characteristics of these patterns are as follows. 1- In general, in the hot period of the year, the high-pressure pattern of Migrant Europe is the most important system in the formation of temperature inversions. In this pattern, languages from the highlands to the western shores of the Caspian Sea are advancing, and due to the presence of a mid-level ridge, it is possible to strengthen the anticyclone core at sea level and thus create a stable atmosphere. With the dominance of the downward process of air, the stability of the earth's surface air and the possibility of inversion formation in the warm period of the year intensify. Two summer patterns, which have been associated with the establishment of a high-pressure pattern on the northwest and in some cases with a low pressure on the Persian Gulf, have caused the upheavals of this period of the year.2 - In other patterns that have occurred more in the cold season, the surface stable layer due to the penetration of the tabs of Anti-cyclonic systems including high-pressure Siberian and European Migrant Europe high-pressure is done alone or in combination and in some cases with high-pressure Migrant Europe. North pressure is also present on the map, which is exacerbated by the Convection of cold weather. Despite the process of air fall due to the dominance of the convergence region of the mid-level convergence creates deep inversions and sometimes double-layer. In these patterns, the thickness of the inversion layer is low and the temperature difference between the peak and the base is high, which indicates the acute conditions of inversion to create air pollution. This phenomenon is likely to occur in any season. But its severity, which depends on synoptic factors. Conclusion The most important factor in causing temperature inversion in most cases is how to arrange the dominant pressure patterns, In this Patterns the cold weather due to the presence high pressure system expanded in the surface with the establishment Left side of a deep trough over the region, the cold air has diffused from higher latitudes on Tabriz and strong sustainability has been created in vertical column of the atmosphere. In cases of being cause the Northern low pressure along with pressure-immigrant Europe for the spread of a cold into the region. The warm air of lower latitudes has been placed over the cold air of ground by domination of a deep ridge over the region. Therefore the intensity of stability increased and severe temperature inversion into the air near the surface formed.
Climatology
Ali Mohammad khorshiddoust; Kaveh Mohammadpour; Seyed Asaad Hosseini
Abstract
Introduction Prediction of hospital admissions related to climatic parameters is discussed matters that in recent decades in result from climate change, urbanization and air pollution has triggered widespread in many societies. Fluctuations in climatic parameters, in turn, can have a significant impact ...
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Introduction Prediction of hospital admissions related to climatic parameters is discussed matters that in recent decades in result from climate change, urbanization and air pollution has triggered widespread in many societies. Fluctuations in climatic parameters, in turn, can have a significant impact on mortality and mortality, and the use of predictive models can be used to identify fluctuations in climatic parameters affecting disease and their prevalence and planning and Compatibility with the environment to be effective. Methodology Using of predictive models can be consider as an effective tool in managing and controlling the diseases, reducing mortality and planning. Recent study used from Artificial Neural Networks and Logistic Regression models as an effective tool in the prediction of nonlinear processes to predict the rate of asthma admissions related to Climatic parameters in Sanandaj/Sine city. Used data during period of 8-years (2001-2008) collected from synoptic station and Toheid and Beasat hospitals in the Sanandaj/Sine city. Then, the climatic parameters and rate of asthma admissions considered as an input and output data of models, respectively. Result and Discussion The results of the output of two nonlinear models of artificial neural network and Logit in examining the effect of climatic parameters on the number of the asthma patients in Sanandaj/Sine showed that the monthly average parameters with high coefficient of determination (R2=0.98) of temperature (average, minimum, maximum) and QFE pressure in the artificial neural network model and The monthly average minimum temperature, QFF pressure and wind speed (in Knot) in the Logit model have had the greatest impact on the rate of asthma admissions in the city. As the wind speed in the Logit model is more effective than other climatic parameters, that it is clear with the logarithmic superiority (-0.977) and the Wald coefficient (85.616). In general, air pressure, temperature and wind speed are the most effective climatic parameters on the number of asthma patients visiting the hospital. Therefore, depending on the accuracy of the models, the above argument means that among the parameters examined, the elements are more important than other parameters in the city. As the climatic elements have a more effective role in the admission patients to the hospital, and their fluctuations will be more significant in patients' fluctuations. The effects of environmental parameters (climate and pollutants) on diseases have previously been investigated as well, so that the results of previous logistic regression have display a increase respiratory disease, vulnerability of children to asthma and an increase in allergies; In the present study, the results of Logit model (69.5%) also indicate that decrease in the average minimum temperature lead to a decrease in the number of the asthma patients, it means that the rate of asthma is more less in temperatures close to zero or higher and vice versa, the admission more higher in the colder temperature (below zero); in the other words, the more balanced the temperature has the lower the rate, and in the colder the ambient temperature has the highest the number of asthma patients. Thus, comparison the present results and previous studies show that admissions change depending on climate, geographic position and the fluctuation of the elements and then the specific geographical location and the different climatic types of a region will play a decisive role in the number of asthma visitors to hospital. Conclusion The results indicated that Artificial Neural Network model predicted the asthma admissions related to monthly minimum, maximum and average temperatures with considerable accuracy, so that the correlation between actual and predicted data is significant with 0.01 coefficient and 0.99 confidence. Also, Input parameters in the Logit method shows that the rate of asthma admissions affected by parameters of average minimum temperature, average pressure QFF and average wind speed (in knot). In other words, the logarithmic ratio of each of cited parameters is significant with β-coefficients (-0.517), (-0.734) and (-0.977), respectively, that throughout of studied parameters is wind element of effective in asthma admissions then others to the hospital. In general, Artificial Neural Network model showed more sufficiency and accuracy than Logit model. As a result, both Logistic Regression and the Artificial Neural Network methods show that climatic parameters have a greater than 50% effect on the number of asthma patients referred to the hospital (the accuracy models: 69.5 and 98, respectively). In the Artificial Neural Network model, the most accurate possible result shows the more effective role of climatic parameters of temperature and air pressure on the asthma patients. Also, filtering the parameters examined at the output of the Logistic model showed the most possible coefficients for minimum temperature, QFF air pressure and wind speed (knot), among which wind speed was the most important element. Finally, the accuracy of the models showed that the Artificial Neural Network model has a higher accuracy depending on the coefficient of determination and highest correlation. Thus, Artificial Neural Network and Logit as nonlinear methods could well predict the relationship between climatic parameters and the number of the asthma patients. Also, according to the appropriate selection of input parameters and determination of different structures in the neural network is possible to design different models with the highest efficiency and can be considered as an effective and powerful tool in estimating similar studies. Introduction Prediction of hospital admissions related to climatic parameters is discussed matters that in recent decades in result from climate change, urbanization and air pollution has triggered widespread in many societies. Fluctuations in climatic parameters, in turn, can have a significant impact on mortality and mortality, and the use of predictive models can be used to identify fluctuations in climatic parameters affecting disease and their prevalence and planning and Compatibility with the environment to be effective. Methodology Using of predictive models can be consider as an effective tool in managing and controlling the diseases, reducing mortality and planning. Recent study used from Artificial Neural Networks and Logistic Regression modelsasan effective toolinthe prediction ofnonlinearprocessesto predict the rate of asthma admissions related to Climatic parameters in Sanandaj/Sine city. Used data during period of 8-years (2001-2008) collected from synoptic station and Toheid and Beasat hospitals in the Sanandaj/Sine city. Then, the climatic parameters and rate of asthma admissions considered as an input and output data of models, respectively. Result and Discussion The results of the output of two nonlinear models of artificial neural network and Logit in examining the effect of climatic parameters on the number of the asthma patients in Sanandaj/Sine showed that the monthly average parameters with high coefficient of determination (R2=0.98) of temperature (average, minimum, maximum) and QFE pressure in the artificial neural network model and The monthly average minimum temperature, QFF pressure and wind speed (in Knot) in the Logit model have had the greatest impact on the rate of asthma admissions in the city. As the wind speed in the Logit model is more effective than other climatic parameters, that it is clear with the logarithmic superiority (-0.977) and the Wald coefficient (85.616). In general, air pressure, temperature and wind speed are the most effective climatic parameters on the number of asthma patients visiting the hospital. Therefore, depending on the accuracy of the models, the above argument means that among the parameters examined, the elements are more important than other parameters in the city. As the climatic elements have a more effective role in the admission patients to the hospital, and their fluctuations will be more significant in patients' fluctuations. The effects of environmental parameters (climate and pollutants) on diseases have previously been investigated as well, so that the results of previous logistic regression have display a increase respiratory disease, vulnerability of children to asthma and an increase in allergies; In the present study, the results of Logit model (69.5%) also indicate that decrease in the average minimum temperature lead to a decrease in the number of the asthma patients, it means that the rate of asthma is more less in temperatures close to zero or higher and vice versa, the admission more higher in the colder temperature (below zero); in the other words, the more balanced the temperature has the lower the rate, and in the colder the ambient temperature has the highest the number of asthma patients. Thus, comparison the present results and previous studies show that admissions change depending on climate, geographic position and the fluctuationof the elements and then the specific geographical location and the different climatic types of a region will play a decisive role in the number of asthma visitors to hospital. Conclusion The results indicated that Artificial Neural Network model predicted the asthma admissions related to monthly minimum, maximum and average temperatures with considerable accuracy, so that the correlation between actual and predicted data is significant with 0.01coefficient and0.99 confidence.Also, Input parameters in the Logit method shows that the rate of asthma admissions affected by parameters of average minimum temperature, average pressure QFF and average wind speed (in knot). In other words, the logarithmicratio ofeach of citedparametersissignificant with β-coefficients (-0.517), (-0.734)and(-0.977), respectively, thatthroughoutofstudied parametersis windelement of effective in asthma admissionsthen others to thehospital. In general, ArtificialNeural Networkmodelshowed more sufficiencyandaccuracy than Logitmodel. As a result, both Logistic Regression and the Artificial Neural Network methods show that climatic parameters have a greater than 50% effect on the number of asthma patients referred to the hospital (the accuracy models: 69.5 and 98, respectively). In the Artificial Neural Network model, the most accurate possible result shows the more effective role of climatic parameters of temperature and air pressure on the asthma patients. Also, filtering the parameters examined at the output of the Logistic model showed the most possible coefficients for minimum temperature, QFF air pressure and wind speed (knot), among which wind speed was the most important element. Finally, the accuracy of the models showed that the Artificial Neural Network model has a higher accuracy depending on the coefficient of determination and highest correlation. Thus, Artificial Neural Network and Logit as nonlinear methods could well predict the relationship between climatic parameters and the number of the asthma patients. Also, according to the appropriate selection of input parameters and determination of different structures in the neural network is possible to design different models with the highest efficiency and can be considered as an effective and powerful tool in estimating similar studies.
Climatology
Ali Mohammad Khrshieddoust; Hamid Mirhashemi; Mousa Nazari
Volume 23, Issue 68 , September 2019, , Pages 71-90
Abstract
Evaporation is one of the important factors in the hydrological cycle and is one of the determinants of energy equilibrium at ground level and water balance, which is required in various areas such as hydrology, hydrology, agriculture, forest management, and management of water resources (Sanei Nejad ...
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Evaporation is one of the important factors in the hydrological cycle and is one of the determinants of energy equilibrium at ground level and water balance, which is required in various areas such as hydrology, hydrology, agriculture, forest management, and management of water resources (Sanei Nejad et al., 2011). In this regard, one of the basic data in designing irrigation and drainage networks is the amount of evaporation power in each region. Because the design of transmission networks, such as drainage or drainage channels, as well as other parts of water design, depends on the amount of water required by the evaporation phenomenon (Jahanbakhsh et al., 1380). In general, evaporation hydrology is generally referred to as the phenomenon of water It simply turns steam into a physical process.
Geomorphology
Somayeh Khaleghi; Shahram Roostayee; Ali Mohammad Khorshiddoost; Mohammad Hossein Rezaee Moghaddam; Mhammad ali Ghorbani
Abstract
Catchments and river systems altered in response to changes of internal and external factors. Hence, several techniques have been proposed to simulate these changes and Evolution of the river systems. Cellular Automaton is one of the newest river cellular models that define the catchment landscape with ...
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Catchments and river systems altered in response to changes of internal and external factors. Hence, several techniques have been proposed to simulate these changes and Evolution of the river systems. Cellular Automaton is one of the newest river cellular models that define the catchment landscape with a grid of cells, and development of this landscape is determined by the interactions between cells (for example fluxes of water and sediment) using rules based on simplifications of the governing physics.This method is used for simulation of Lighvan catchment with 20 m cell size and 10 years precipitation data (1380 to 89). Simulation results evaluated in two qualitative and quantitative methods, So that the relative changes in the catchment and spatial distribution of erosion and aggradation value in the entire catchment and each cell was identified on Digital Elevation Model map and also, values of different particle size distribution in different discharges showed that with the increasing discharge, and amounts of sediment increased and among this coarse sand have the highest value and very fine sand, clay and silt particles have the lowest value. Also investigation of longitudinal and latitude profile show that Lighvan river is in mature stage and Lighvan channel has been underwent aggradation due to climate changes and increasing catchment precipitation in last decade that causes hillslope erosion and channel aggradation. Finally, Since certainty of Cellular Automata results is difficult and CAESAR is sensitive to input parameters but comparing the results with previous investigation and field observation shows that Cellular Automata has acceptable results.
Ali mohammad Khorshiddoust; Ali asghar Shirzad
Volume 18, Issue 49 , November 2014, , Pages 101-118
Abstract
In this research we used multivariable statistical methods (cluster and discriminative analysis) with the purpose of the recognition of spatio-temporal differences of precipitation in similar areas. We used monthly precipitation of 35 synoptic, climatic, and rain-gauging station data records of Northern ...
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In this research we used multivariable statistical methods (cluster and discriminative analysis) with the purpose of the recognition of spatio-temporal differences of precipitation in similar areas. We used monthly precipitation of 35 synoptic, climatic, and rain-gauging station data records of Northern Iran including three provinces of Golestan, Guilan, and Mazandaran for 1995-2007 periods. For grouping and homogenizing the stations, we initially applied Ward cluster analysis method. Then we used discriminative analysis and Wilk’s Lambda for finding out the validity of cluster analysis calculations. Results obtained from cluster analysis with Euclid interval method indicated that 4 major clusters can be drawn according to the amount and the location of the precipitation in the study area. Discriminate analysis showed that 82.3% of the clusters in our analysis were valid and about 17.7% were incorrect. The Wilk’s Lambda method also proved the differences between the means.
Ali Mohammad Khorshiddoust; Gholam Hasan Mohammadi; Atefeh Hosseini Sadr; Khadijeh Javan; Abolfazl Jamali
Volume 17, Issue 46 , February 2014, , Pages 47-66
Abstract
The effective synoptic systems on dust events was investigated in the west of Iran based on observed dusty days at 50 meteorological stations using principal component analysis (PCA) and geographical information system (GIS). Results showed that the first 5 components explained 69.11% of spatial variability ...
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The effective synoptic systems on dust events was investigated in the west of Iran based on observed dusty days at 50 meteorological stations using principal component analysis (PCA) and geographical information system (GIS). Results showed that the first 5 components explained 69.11% of spatial variability of dust event variance. For detecting of affective synoptic systems, the computed correlation coefficient of stations with each component (Rotated Component Matrix) was transferred to the GIS environment, and synoptic patterns that affected spatial variability of dust events were simulated through Kriging interpolation method. It was shown that Azores high pressure system has the most effective role in frequency of days with dust in the west of Iran through the creation of surface thermal low pressures.
