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
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
mehdi asadi; Ali Mohammad Khorshiddoust
Volume 23, Issue 70 , March 2020, , Pages 101-122
Abstract
Limited fossil energy source and increase of energy use is always pushed man to replace the energy source. In this case the winds have always had a special place in the new generation of energy sources. East Azarbaijan province because of the topographical and relativity situation is one of the best ...
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Limited fossil energy source and increase of energy use is always pushed man to replace the energy source. In this case the winds have always had a special place in the new generation of energy sources. East Azarbaijan province because of the topographical and relativity situation is one of the best places for building a wind farm. therefore this research have been done to find out the best places for building wind farms in East Azarbaijan province, to find this places different criteria and sub criteria have been used. Given the importance of information fusion, analytic hierarchy process (AHP) were selected for weighting the layers and were implemented by the help of Expert choice software. For special analyses and overlapping of layers the Arc GIS program have been used and after the analysis of information, according to the capacity of building wind farms, province of East Azarbaijan have been divided to four parts, great, good, normal, weak. At last, final conclusions represent that Geographic Information System as a Supportive Decision making system can be practical both in preparing of data and designing the priorities and expert's ideas dealing with different factors and also help the designers to select the proper location to found the wind farms. In this research,15 regions have been determined, considering priority of, overlay, limitation of land and places, survey of priority area, climate condition and personal observation have been determined that in sequence this places are Tabriz, Sahand, Osko, Azarshahr, Bostanabad, Shabestar, Jolfa, Haris, Miyane, Bonab, Marageh, Sarab, Ahar, Charayomagh and Hashtrod.
Climatology
Golam Abbas Fallah Galharei; mehdi asadi
Volume 22, Issue 64 , September 2018, , Pages 229-246
Abstract
This study aims to identify the spatial autocorrelation and spatial variation of sunshine hours in Iran. For this purpose, the sunshine hours to form a network database have been made in Iran. The data from the base of a 30-year period, the daily period from 1/01/1982 to 12/31/2012 AD to the present ...
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This study aims to identify the spatial autocorrelation and spatial variation of sunshine hours in Iran. For this purpose, the sunshine hours to form a network database have been made in Iran. The data from the base of a 30-year period, the daily period from 1/01/1982 to 12/31/2012 AD to the present study, and intercellular dimensions of 15 × 15 km area stretching is studied. In order to achieve the sunshine hourly changes within a year, the sunshine of the Iran of spatial statistical methods, such as spatial autocorrelation global Moran, Moran's index of local Insulin, and hot spots was used by using the programming environment GIS. The results of this study showed that the spatial and temporal variation in sunshine hours in Iran is High-cluster pattern. In the meantime, based on local Moran and hot spots, South, South East and Central synoptic stations representing the provinces of Sistan and Baluchistan, Kerman, Shiraz, Isfahan and Yazd have positive spatial autocorrelation pattern, full sun pattern, and parts of North, North East and North West representing synoptic stations in Tabriz, Mazandaran, Mashhad and Semnan have a negative spatial autocorrelation, low sun pattern. In the study period, in most cases, a large part of the Iran, almost half of the total area, has had no significant pattern or spatial autocorrelation
All other Geographic fields of studies , Interdisciplinary
Saeed Jahanbakhsh Asl; Mehdi Asad; Elaheh Akbari
Volume 20, Issue 56 , August 2016, , Pages 55-72
Abstract
In this study, for potential survey construction of wind power plants in the provinces of Khorasan Razavi and Northern different criteria and sub-criteria have been considered. To become fuzzy criteria are based on expert opinions and investigation done researches, control point and fuzzy function for ...
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In this study, for potential survey construction of wind power plants in the provinces of Khorasan Razavi and Northern different criteria and sub-criteria have been considered. To become fuzzy criteria are based on expert opinions and investigation done researches, control point and fuzzy function for each of the layers based on their membership gradation range of zero and one was determined in the IDRISI software. Then, according to the importance of integrating information, Analytical Hierarchy Process (AHP) for layers weighting was implemented by Expert choice software. Then, the software ArcGIS, was used to spatial analysis and overlapping layers, and after the analysis of information, Razavi Khorasan and North Khorasan province, in terms of capability the wind power plants building, divided into four levels: excellent, good, fair and poor. Finally, the results indicated that excellent are as for the construction of wind power plantsin the study area are locatedin thesoutheast ofthe study area at Torbatjam station with an area exceeding 222565.97 hectares (0.018 percent). In addition, good areas are located around the Taybad and Khaaf, Golmakan, Sarakhs, Roshtkhar, Bardaskan, Neyshaboor, Sabzevar, Bojnurd, Ferdows and Jajarm stations with an area exceeding 1817573.81 hectares (0.17 percent). One cans that renewable energy of wind without any pollution could be utilized by the construction of wind power plants in the replaces ultimately.
Climatology
Gholam abbas Fallah Ghalhari; Mehdi Asadi; Alireza Entezari
Volume 19, Issue 54 , February 2016, , Pages 235-251
Abstract
Climate classification and identifying the most effective factors and elements of each area is one way of understanding identity of the climate zones. Therefore, to identify Guilan climate mapping new methods such as factor analysis and hierarchical cluster were performed. For this, we used 20 climate ...
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Climate classification and identifying the most effective factors and elements of each area is one way of understanding identity of the climate zones. Therefore, to identify Guilan climate mapping new methods such as factor analysis and hierarchical cluster were performed. For this, we used 20 climate variables of the 16weather stations in the study area. Then, using interpolation method, a matrix with dimensions of 20×106 data was obtained. Climate mapping of the province with factor analysis showed that the climate of the province is made up of two factors. These two factors are: humid-rain–wind and temperature–cloudy factors. Results also indicated that these two factors explain 99.44 percent of the variance of the primary variables. The contribution of each factor was 64.49, 34.95 percent respectively. Finally, cluster analysis on two climatic factors identified three climatic regions in the provinces. These three regions are: moderate and humid, mountainous, semi humid and cold.
