https://www.irimo.ir/Journal of Climate Research2228-504013963120170923Quantifying the possible impacts of climate change on temperature-humidity index, (THI) under RCP scenarios across IranQuantifying the possible impacts of climate change on temperature-humidity index, (THI) under RCP scenarios across Iran11868577FASajad AsgariAsgari1. MSc graduate, University of TehranNOZARGHAHREMANUniversity of tehran0000-0002-9442-8870ImanBabaeianAssitant Professor, National Climatological Institute, Mashhad, IranJournal Article20180903The temperature humidity index (THI) is one of the measures which describe the climatic discomfort as a result of combined effects of temperature and humidity. This index provides a rapid and accurate evaluation of heat stress effect on animal’s health and productions. Considering the sequences of global warming and climate change, it is expected that THI values would vary due to changes in temperature and humidity in coming decades. As the soft computing skills increased in recent decades, more number of climate models has been developed for weather and climate predictions which have significantly improved the quality and resolution of projections. This remarkable increase in number of climate models has enabled the scientists to estimate a wide range of main climate variables such as humidity and temperature in fine temporal and spatial resolution. Upon release of new scenarios based on Radiative Forcing which are known as Representative Concentration Pathway scenarios (RCP scenarios), by Intergovernmental panel on climate change (IPCC) in fifth assessment report (AR5), a new set of climate models (GCMs) have been proposed for future climate projections. The main aim of this study is to investigate the temporal and spatial variations of THI values in selected weather stations across Iran as a result of climate change under RCP climatic scenarios proposed in fifth assessment report (AR5) of IPCC.The temperature humidity index (THI) is one of the measures which describe the climatic discomfort as a result of combined effects of temperature and humidity. This index provides a rapid and accurate evaluation of heat stress effect on animal’s health and productions. Considering the sequences of global warming and climate change, it is expected that THI values would vary due to changes in temperature and humidity in coming decades. As the soft computing skills increased in recent decades, more number of climate models has been developed for weather and climate predictions which have significantly improved the quality and resolution of projections. This remarkable increase in number of climate models has enabled the scientists to estimate a wide range of main climate variables such as humidity and temperature in fine temporal and spatial resolution. Upon release of new scenarios based on Radiative Forcing which are known as Representative Concentration Pathway scenarios (RCP scenarios), by Intergovernmental panel on climate change (IPCC) in fifth assessment report (AR5), a new set of climate models (GCMs) have been proposed for future climate projections. The main aim of this study is to investigate the temporal and spatial variations of THI values in selected weather stations across Iran as a result of climate change under RCP climatic scenarios proposed in fifth assessment report (AR5) of IPCC.https://clima.irimo.ir/article_68577_144288547b7e555d22ee8519b7a6280c.pdfhttps://www.irimo.ir/Journal of Climate Research2228-504013963120170923Dust storms simulation using WRF-Chem and EURAD models in the summertime 2015 (case study)Dust storms simulation using WRF-Chem and EURAD models in the summertime 2015 (case study)193668779FANasimHossein HamzehPhD. Student of Meteorology of Meteorological Institute, Tehran,0000-0003-2089-311XEbrahimFattahiAssociation Professor: Faculty of Meteorological Institute, Tehran,0009-0004-4871-2004AbbasRanjbar0000-0003-4040-8783MojtabaZoljodiAssistant Professor: Iranian National Institute for Oceanography and Atmospheric Science, TehranParvinGhafarianAssistant Professor: Iranian National Institute for Oceanography and Atmospheric Science, TehranJournal Article20180906Satellite images show that WRF/CHEM model has simulated emission, source, diffusion as well as the extent of the areas covered with dust very well. Comparing PM10 concentration of the model outputs with Environment Organization data, show that WRF/CHEM model forecast daily changes very well. But model underestimate significantly in quantity of concentration. These errors may be are due to a model considering only dust quantity but other pollutants have affects on visibility. EURAD model has wrong prediction in dust storm sources and it simulates it in central of our country. In general, it can be said that in this event, dust concentration has been underestimated by WRF/CHEM model especially in maximum amount of PM10 concentration and EURAD model does not simulate dust concentration correctly in amounts and dispersion patterns.Satellite images show that WRF/CHEM model has simulated emission, source, diffusion as well as the extent of the areas covered with dust very well. Comparing PM10 concentration of the model outputs with Environment Organization data, show that WRF/CHEM model forecast daily changes very well. But model underestimate significantly in quantity of concentration. These errors may be are due to a model considering only dust quantity but other pollutants have affects on visibility. EURAD model has wrong prediction in dust storm sources and it simulates it in central of our country. In general, it can be said that in this event, dust concentration has been underestimated by WRF/CHEM model especially in maximum amount of PM10 concentration and EURAD model does not simulate dust concentration correctly in amounts and dispersion patterns.https://clima.irimo.ir/article_68779_81d88d4f508d4f0d8acdf98c2b7ddbf3.pdfhttps://www.irimo.ir/Journal of Climate Research2228-504013963120170923Identification of Southern Low Pressure SystemsIdentification of Southern Low Pressure Systems375468871FAMahboobehPouratashiPh. D. Candidate of Meteorology, Atmospheric Science and Meteorological Research Center (ASMERC)MohammadMoradiAssistant Professor of Atmospheric Science and Meteorological Research Center (ASMERC)EbrahimFattahiAssociate Professor of Atmospheric Science and Meteorological Research Center (ASMERC)Journal Article20180910In this study, using the theoretical basis, an algorithm was designed to identify southern system for the period of 1986-2015 which led to identification of 158 southern low pressure systems. Then two of them were selected for synoptic and dynamic analysis. <br />According to the results, the designed algorithm has ability to identify southern systems. The results showed that formation and structure of southern low pressure systems were very similar to mid-latitude weather systems and differed from thermal low pressure systems. Strong baroclinicity, strong temperature advection in low levels, cold pool in mid-levels and cyclonic circulation in air columns are the most important characteristics of southern low pressure systems that are basically different from thermal low pressure systems.In this study, using the theoretical basis, an algorithm was designed to identify southern system for the period of 1986-2015 which led to identification of 158 southern low pressure systems. Then two of them were selected for synoptic and dynamic analysis. <br />According to the results, the designed algorithm has ability to identify southern systems. The results showed that formation and structure of southern low pressure systems were very similar to mid-latitude weather systems and differed from thermal low pressure systems. Strong baroclinicity, strong temperature advection in low levels, cold pool in mid-levels and cyclonic circulation in air columns are the most important characteristics of southern low pressure systems that are basically different from thermal low pressure systems.https://clima.irimo.ir/article_68871_2bad3b25b993455f9c56a2fc868ca9cb.pdfhttps://www.irimo.ir/Journal of Climate Research2228-504013963120170923Assessment of different downscaling techniques for low flow indices under climate change effectsAssessment of different downscaling techniques for low flow indices under climate change effects577268874FAMohammad RezaGodarziDoctorate in Civil Engineering, Assistant Prof., Dept. of Civil Engineering, University of Ayatollah Borujerdi, Borujerd, IranAlirezaFarajiM.Sc. Graduated. Dept. of Water Engineering and Hydraulic Structures, University of Ayatollah Borujerdi, Borujerd, IranJournal Article20180910<strong>Introduction: </strong>In the next decades, climate change is likely to have become a serious challenge that will alter flow regimes such as torrential flows (floods), low flows as well as the existing water resources. Climate change impact is increasingly becoming a subject matter for researchers, but the effect of this phenomena on the low flow is still not widely understood. The consequences of climate change have led the international community to further studies, which will affect the changes in the natural resources, ecosystems, and the population. Global climate cycle has been found to be intensified due to climate change, as well as it has led frequent, severe, long-term, and stable droughts in many regions. Changes in the precipitation and temperature as two major variables in the hydrological cycle will unbalance the rainfall. Also, climate change in the future decades will be very effective on high flows and low flows so that it will have a significant impact on the results of the intensity and recurrence Interval of low flows. The aim of this study is to evaluate the effects of climate change on the low flows using HEC-HMS model, as well as changes in the flow regime such as low flow frequency, the probability, and the recurrence Interval of low flow in the future period (2040-2069). Also, the uncertainties related to the downscaling methods are considered by using two statistical methods (SDSM model) and the change factor as well as it has utilized the outputs of the General Circulation Models, which are resulted from the fifth report of the IPCC. <br /><strong>Material and methods</strong> : The Gharesou Sub-basin is located in the northwest of Karkheh basin. It covers an area of 5354 km<sup>2</sup> and its maximum and minimum height is 3364 and 1180 m, respectively. The average annual precipitation varies between 300 and 800 mm. Three major rivers of Merck, Gharesou, and Razavar are flowing into this basin. This study has measured the project of climate change in the Gharesou basin with CANESN2 and RCP scenarios and by using SDSM downscaling model as well as using change factor downscaling. Also, the indices of low flow frequency and FL method on the flows of the future period were analyzed. GCMs are the first tool for understanding past climate variations and predicting the projection for future climate conditions. GCMs demonstrate the atmosphere and ocean in a grid of 1 to 4 latitude degrees and in a longitude from 10 to more than 200 layers vertical in each fluid. Because of the spatial and temporal scale mismatch between the GCMs’ ability and the need of hydrological modelers, the outputs of these models cannot be explicitly used in climate change studies. Various methods of downscaling are used to overcome the problem, which dynamic, statistical, and change factor are three types of downscaling. The low flow selection is done by two different methods that are 7Q10 and FL methods. In fact, FL method is one of the five groups, which has been used in low flows and/or management balances of St. Johns River in Florida. Minimum Flows and/or Levels are low flows or levels that are needed to avoid serious damages to water resources caused by water withdrawals and they are divided into five following groups of flows. The first step in selecting the low flow is done by providing long-term flow data using FL method and then drawing the FDC curve. A low flow is obtained by drawing an FDC curve. FL method was chosen in order to select the low flows on the basis of the following reasons: 1. 7Q10 method for the extreme conditions of low flow, which is appropriate between 95 to 99% of the flow duration curve time. This condition usually occurs during a short-term severe drought and a very high recurrence Interval. 2. FL method is within the low flow selection domain and FL groups are clearly determined the effects of low flow on the floodplain, animal and plant life and ecosystems. <br /><strong>Results and Discussion</strong>: The results of statistical method showed that basin flow has been reduced in all of the scenarios during the dry season, including July, August, and September. In addition, the results of the change factor suggested an increase in flow in the November, December, January and February and its decrease in the spring and summer. It is critical to note that both of downscaling methods indicated flow reduction in the low flow period. The value of the 7Q10 index in the statistical method and change factor has reached respectively, about 0.008 m<sup>3</sup>/s and zero in the future period while this value in the corresponding historic period was respectively 0.724 and 1.429 m<sup>3</sup>/s. Also, the Q80 index has reached the value between 0.1 and 0.3 m<sup>3</sup>/s using both of downscaling methods. Generally, the results indicate a severe low flow condition in the future period.<strong>Introduction: </strong>In the next decades, climate change is likely to have become a serious challenge that will alter flow regimes such as torrential flows (floods), low flows as well as the existing water resources. Climate change impact is increasingly becoming a subject matter for researchers, but the effect of this phenomena on the low flow is still not widely understood. The consequences of climate change have led the international community to further studies, which will affect the changes in the natural resources, ecosystems, and the population. Global climate cycle has been found to be intensified due to climate change, as well as it has led frequent, severe, long-term, and stable droughts in many regions. Changes in the precipitation and temperature as two major variables in the hydrological cycle will unbalance the rainfall. Also, climate change in the future decades will be very effective on high flows and low flows so that it will have a significant impact on the results of the intensity and recurrence Interval of low flows. The aim of this study is to evaluate the effects of climate change on the low flows using HEC-HMS model, as well as changes in the flow regime such as low flow frequency, the probability, and the recurrence Interval of low flow in the future period (2040-2069). Also, the uncertainties related to the downscaling methods are considered by using two statistical methods (SDSM model) and the change factor as well as it has utilized the outputs of the General Circulation Models, which are resulted from the fifth report of the IPCC. <br /><strong>Material and methods</strong> : The Gharesou Sub-basin is located in the northwest of Karkheh basin. It covers an area of 5354 km<sup>2</sup> and its maximum and minimum height is 3364 and 1180 m, respectively. The average annual precipitation varies between 300 and 800 mm. Three major rivers of Merck, Gharesou, and Razavar are flowing into this basin. This study has measured the project of climate change in the Gharesou basin with CANESN2 and RCP scenarios and by using SDSM downscaling model as well as using change factor downscaling. Also, the indices of low flow frequency and FL method on the flows of the future period were analyzed. GCMs are the first tool for understanding past climate variations and predicting the projection for future climate conditions. GCMs demonstrate the atmosphere and ocean in a grid of 1 to 4 latitude degrees and in a longitude from 10 to more than 200 layers vertical in each fluid. Because of the spatial and temporal scale mismatch between the GCMs’ ability and the need of hydrological modelers, the outputs of these models cannot be explicitly used in climate change studies. Various methods of downscaling are used to overcome the problem, which dynamic, statistical, and change factor are three types of downscaling. The low flow selection is done by two different methods that are 7Q10 and FL methods. In fact, FL method is one of the five groups, which has been used in low flows and/or management balances of St. Johns River in Florida. Minimum Flows and/or Levels are low flows or levels that are needed to avoid serious damages to water resources caused by water withdrawals and they are divided into five following groups of flows. The first step in selecting the low flow is done by providing long-term flow data using FL method and then drawing the FDC curve. A low flow is obtained by drawing an FDC curve. FL method was chosen in order to select the low flows on the basis of the following reasons: 1. 7Q10 method for the extreme conditions of low flow, which is appropriate between 95 to 99% of the flow duration curve time. This condition usually occurs during a short-term severe drought and a very high recurrence Interval. 2. FL method is within the low flow selection domain and FL groups are clearly determined the effects of low flow on the floodplain, animal and plant life and ecosystems. <br /><strong>Results and Discussion</strong>: The results of statistical method showed that basin flow has been reduced in all of the scenarios during the dry season, including July, August, and September. In addition, the results of the change factor suggested an increase in flow in the November, December, January and February and its decrease in the spring and summer. It is critical to note that both of downscaling methods indicated flow reduction in the low flow period. The value of the 7Q10 index in the statistical method and change factor has reached respectively, about 0.008 m<sup>3</sup>/s and zero in the future period while this value in the corresponding historic period was respectively 0.724 and 1.429 m<sup>3</sup>/s. Also, the Q80 index has reached the value between 0.1 and 0.3 m<sup>3</sup>/s using both of downscaling methods. Generally, the results indicate a severe low flow condition in the future period.https://clima.irimo.ir/article_68874_be0f7bc1d8fda065e5f8b576af2fcdbc.pdfhttps://www.irimo.ir/Journal of Climate Research2228-504013963120170923PhD student of Climatology, Razi University and expert of Hamedan Meteorological OrganizationPhD student of Climatology, Razi University and expert of Hamedan Meteorological Organization738868879FAAliHanafiAssistant Professor of Climatology, Department of Geography, University of Imam AliFakhrodinIranpourPhD student of Climatology, Razi University and expert of Hamedan Meteorological OrganizationJournal Article20180910<strong>Introduction</strong>
Wind has long been considered and used by humans as the oldest source of energy. The conversion of wind energy and the energy extraction have been carried by humans since the ancient times, and since about 5000 BC, wind power has been used to navigate the Nile. At present, over 83 percent of wind power is used in only five countries of the world including Germany, the United States, Denmark, India and Spain. Therefore, most of the wind energy knowledge can be found in these countries, yet the use of wind energy technology is expanding rapidly to other areas of the world, and the information needed is extended throughout the world (Patel et al., 1999). The energy crisis, in particular that of oil, and air pollution, are the issues that will require the continuation and strengthening of meteorological and climatological studies in search of new sources of clean energies. The most important of these sources are solar and wind energy. Of course, in the design of energy conversion tools, there should also be considered the type of use and storing it. Today, energy is being developed through electric grids, static generating motors (diesel, gasoline or hybrid), hydropower, fossil fuels, and gas-powered plants, generators and wind turbines, and other newly developed technologies. In a paper titled Wind Turbines and Wind Power Potential Assessment in Iran, while studying the types of wind turbines and the amount of wind energy and the speed required to produce wind power, using the five- year wind data (1981 to 1985) in the country's synoptic stations, Kaviani (1995), evaluated the potential of wind energy in Iran and concluded that in the whole country, Zabol station (Sistan region) has the best conditions for the construction of wind farms. Ghahreman and Ghareh Khani (2010), also investigated the process of time variation of wind speed in 40 synoptic stations of Iran during 1975-2005 with two nonparametric methods of Man Kendal and Spearman coefficient and two parametric methods of regression and Pearson correlation coefficient, in annual and seasonal scale. Bakhtiari et al. (2013) examined the wind energy potential based on short-term 10-minute data in Kerman, Jiroft, and Bam areas using probability distribution and density function calculations. Roshan et al. (2014) investigated the potential of wind energy at selected Iranian stations and concluded that Zabol, Ardebil and Kish stations had the highest wind power at 3042, 1675 and 1092 watts per square meter, respectively, among stations studied.
Wan and et al. (2010) examined the wind speed near the ground level of 117 stations in Canada; the results showed that the wind speed in the western parts of Canada and most of its southern parts was decreasing in all seasons, but in the central and northern parts in all seasons and in marine areas, there is an increasing trend in the spring and autumn seasons. Bilal et al. (2013) examined the feasibility of using wind energy to generate electricity on the coast of western Senegal, and estimated wind power density in the area to be between 30 and 120 watts per square meter.
In this research, we tried to identify suitable locations for wind turbines in the country by studying the wind variable in the country. Therefore, this study aimed to identify the windy regions of the country and the potential of wind energy.
<strong>Materials and methods</strong>
In this study, the three- hour wind speed and direction in synoptic stations of the country were used to study the wind energy in the country. At first, the wind speed and direction statistics were surveyed for 183 stations in the country at three –hour frequency (hours 00, 03, 06, 09, 12, 15, 18, and 21, based on GMT), and according to the required time period (twenty years from January 1, 1991 to December 31, 2010,) 145 stations were selected with full-time statistics for the entire period, and then the average wind speed and wind power potential and probability of suitable speed winds for wind power usage (About 8 knots or 4 m / s and more) were calculated. In order to identify windy regions in the country for wind power generation, cluster analysis was used in SPSS software. Finally, the potential wind energy in the windy regions of the country was calculated according to the following equation:
<strong>Results and discussion</strong>
According to a cluster analysis on wind speed data in 145 synoptic stations of the country, the stations in the country were classified according to the wind speed into five main categories.
The first group consists of three stations, Manjil, Nehbandan and Zabol, which are very suitable for wind power use to produce electrical energy in most days and months in that the average annual wind speed at these stations is about 10.55 knots.
The second group includes stations of Abadan, Aligudarz, Ardebil, Ardestan, Bandar Dir, Bandarlangeh, Mahshahr, Bijar, Bostan, Borujerd, Qorveh, Jask, Qeshm, Kish, Kahnuj, Khoramdareh, Nain, Sabzevar, Torbat Jam, Zahedan, and Zarina Obato. Which also have the potential of wind energy to produce electric energy in some months of the year or at some times of the day. This makes it possible to place them in the appropriate category. The average annual wind speed in these areas is about 7.2 knots.
The third group includes the stations of Ahar, Baft, Bam, Bandar Abbas, Biarjamand, Birjand, Bushehr, Chahbahar, Nojeh Hamedan, Kangan Cham, Kerman, Khash, Khodabandeh, Malayer, Marahtepeh, Ravansar, Rabat Pashtbadam, Saravan, Sardasht, Shahrbabak, Reza shahr, Tehran, Shoshtar, Tabriz and Yazd, which are not very suitable for wind power generation in terms of wind potential, and the annual average wind speed in these areas is about 5.6 knots.
The fourth and fifth groups include stations with an average annual wind speed of less than 4 knots in these areas, not having the potential to produce electric energy from wind.
Regarding the importance of wind speed continuity to move wind turbines, the percentage of wind speed frequency in windy stations of the country was also studied and ranked in five categories. The first rank includes stations where the frequency of winds of more than 4 m / s over the year is well above 40%, which only includes the Manjil station. The second rank relates to stations where the frequency of winds at speeds of more than 4 m / s during the year is between 30 and 40%, including Zabol, Torbat Jam and Bandar Dir stations. The third rank goes for the stations with frequency of winds of more than 4 m / s between 20% and 30%, while the fourth rank belongs to the stations with an frequency of winds of more than 4 m / s between 10% and 20%, and the fifth rank is allocated to the station with frequency of wind speed exceeding 4 m/s less than 10%. Finally, based on the calculations performed on wind data, it was found out that the highest wind energy potential in the country with a value of over 1500 J / m 2 is in the region of Manjil and Zabol. Followed by these stations is Nehbandan area with values between 1,000 and 1,500 J per square meter, ranking the second highest wind power potential in the country to produce wind energy. The third rank in terms of the potential of wind energy in the country with the values of 500 to 1,000 J / m2 is related to the stations of Mahshahr, Bijar, Torbat Jam, Jask, Kish, Khoramdareh, Aligudarz, Ardestan and Ardabil stations, which also have the potential for launching wind turbines to produce wind energy.
<strong>Conclusion</strong>
Iran is rich in renewable and non-renewable energy sources. The geographic location of Iran has caused a huge source of solar and wind to exist all over the year. These two renewable energy sources are free and environmentally friendly, and wind energy can produce a significant amount of electrical energy. The eastern part of Iran, from the south east to the northeast, is the windiest region of the country, and in most cases it has wind power production capacity. Thereafter, Khuzestan plain, islands and coasts of the Persian Gulf and Oman Sea, the western regions of the country in the provinces of Kurdistan, Kermanshah, Lorestan and Hamedan, and small and scattered areas including: Manjil, Ardebil, Firouzkooh, Rafsanjan and Ardestan, posses the capacity of wind energy production in many months of the year. Wind power. Most of the country's windy regions are deprived, arid and less developed regions, with low levels of electrical energy production, and the cost of bringing electricity to these areas is also of high costs. Therefore, creating wind farms in these areas is a necessity. Most of the energy consumption in Iran is in the summer in order to cope with the warmth, and the windiest time in Iran is summer.<strong>Introduction</strong>
Wind has long been considered and used by humans as the oldest source of energy. The conversion of wind energy and the energy extraction have been carried by humans since the ancient times, and since about 5000 BC, wind power has been used to navigate the Nile. At present, over 83 percent of wind power is used in only five countries of the world including Germany, the United States, Denmark, India and Spain. Therefore, most of the wind energy knowledge can be found in these countries, yet the use of wind energy technology is expanding rapidly to other areas of the world, and the information needed is extended throughout the world (Patel et al., 1999). The energy crisis, in particular that of oil, and air pollution, are the issues that will require the continuation and strengthening of meteorological and climatological studies in search of new sources of clean energies. The most important of these sources are solar and wind energy. Of course, in the design of energy conversion tools, there should also be considered the type of use and storing it. Today, energy is being developed through electric grids, static generating motors (diesel, gasoline or hybrid), hydropower, fossil fuels, and gas-powered plants, generators and wind turbines, and other newly developed technologies. In a paper titled Wind Turbines and Wind Power Potential Assessment in Iran, while studying the types of wind turbines and the amount of wind energy and the speed required to produce wind power, using the five- year wind data (1981 to 1985) in the country's synoptic stations, Kaviani (1995), evaluated the potential of wind energy in Iran and concluded that in the whole country, Zabol station (Sistan region) has the best conditions for the construction of wind farms. Ghahreman and Ghareh Khani (2010), also investigated the process of time variation of wind speed in 40 synoptic stations of Iran during 1975-2005 with two nonparametric methods of Man Kendal and Spearman coefficient and two parametric methods of regression and Pearson correlation coefficient, in annual and seasonal scale. Bakhtiari et al. (2013) examined the wind energy potential based on short-term 10-minute data in Kerman, Jiroft, and Bam areas using probability distribution and density function calculations. Roshan et al. (2014) investigated the potential of wind energy at selected Iranian stations and concluded that Zabol, Ardebil and Kish stations had the highest wind power at 3042, 1675 and 1092 watts per square meter, respectively, among stations studied.
Wan and et al. (2010) examined the wind speed near the ground level of 117 stations in Canada; the results showed that the wind speed in the western parts of Canada and most of its southern parts was decreasing in all seasons, but in the central and northern parts in all seasons and in marine areas, there is an increasing trend in the spring and autumn seasons. Bilal et al. (2013) examined the feasibility of using wind energy to generate electricity on the coast of western Senegal, and estimated wind power density in the area to be between 30 and 120 watts per square meter.
In this research, we tried to identify suitable locations for wind turbines in the country by studying the wind variable in the country. Therefore, this study aimed to identify the windy regions of the country and the potential of wind energy.
<strong>Materials and methods</strong>
In this study, the three- hour wind speed and direction in synoptic stations of the country were used to study the wind energy in the country. At first, the wind speed and direction statistics were surveyed for 183 stations in the country at three –hour frequency (hours 00, 03, 06, 09, 12, 15, 18, and 21, based on GMT), and according to the required time period (twenty years from January 1, 1991 to December 31, 2010,) 145 stations were selected with full-time statistics for the entire period, and then the average wind speed and wind power potential and probability of suitable speed winds for wind power usage (About 8 knots or 4 m / s and more) were calculated. In order to identify windy regions in the country for wind power generation, cluster analysis was used in SPSS software. Finally, the potential wind energy in the windy regions of the country was calculated according to the following equation:
<strong>Results and discussion</strong>
According to a cluster analysis on wind speed data in 145 synoptic stations of the country, the stations in the country were classified according to the wind speed into five main categories.
The first group consists of three stations, Manjil, Nehbandan and Zabol, which are very suitable for wind power use to produce electrical energy in most days and months in that the average annual wind speed at these stations is about 10.55 knots.
The second group includes stations of Abadan, Aligudarz, Ardebil, Ardestan, Bandar Dir, Bandarlangeh, Mahshahr, Bijar, Bostan, Borujerd, Qorveh, Jask, Qeshm, Kish, Kahnuj, Khoramdareh, Nain, Sabzevar, Torbat Jam, Zahedan, and Zarina Obato. Which also have the potential of wind energy to produce electric energy in some months of the year or at some times of the day. This makes it possible to place them in the appropriate category. The average annual wind speed in these areas is about 7.2 knots.
The third group includes the stations of Ahar, Baft, Bam, Bandar Abbas, Biarjamand, Birjand, Bushehr, Chahbahar, Nojeh Hamedan, Kangan Cham, Kerman, Khash, Khodabandeh, Malayer, Marahtepeh, Ravansar, Rabat Pashtbadam, Saravan, Sardasht, Shahrbabak, Reza shahr, Tehran, Shoshtar, Tabriz and Yazd, which are not very suitable for wind power generation in terms of wind potential, and the annual average wind speed in these areas is about 5.6 knots.
The fourth and fifth groups include stations with an average annual wind speed of less than 4 knots in these areas, not having the potential to produce electric energy from wind.
Regarding the importance of wind speed continuity to move wind turbines, the percentage of wind speed frequency in windy stations of the country was also studied and ranked in five categories. The first rank includes stations where the frequency of winds of more than 4 m / s over the year is well above 40%, which only includes the Manjil station. The second rank relates to stations where the frequency of winds at speeds of more than 4 m / s during the year is between 30 and 40%, including Zabol, Torbat Jam and Bandar Dir stations. The third rank goes for the stations with frequency of winds of more than 4 m / s between 20% and 30%, while the fourth rank belongs to the stations with an frequency of winds of more than 4 m / s between 10% and 20%, and the fifth rank is allocated to the station with frequency of wind speed exceeding 4 m/s less than 10%. Finally, based on the calculations performed on wind data, it was found out that the highest wind energy potential in the country with a value of over 1500 J / m 2 is in the region of Manjil and Zabol. Followed by these stations is Nehbandan area with values between 1,000 and 1,500 J per square meter, ranking the second highest wind power potential in the country to produce wind energy. The third rank in terms of the potential of wind energy in the country with the values of 500 to 1,000 J / m2 is related to the stations of Mahshahr, Bijar, Torbat Jam, Jask, Kish, Khoramdareh, Aligudarz, Ardestan and Ardabil stations, which also have the potential for launching wind turbines to produce wind energy.
<strong>Conclusion</strong>
Iran is rich in renewable and non-renewable energy sources. The geographic location of Iran has caused a huge source of solar and wind to exist all over the year. These two renewable energy sources are free and environmentally friendly, and wind energy can produce a significant amount of electrical energy. The eastern part of Iran, from the south east to the northeast, is the windiest region of the country, and in most cases it has wind power production capacity. Thereafter, Khuzestan plain, islands and coasts of the Persian Gulf and Oman Sea, the western regions of the country in the provinces of Kurdistan, Kermanshah, Lorestan and Hamedan, and small and scattered areas including: Manjil, Ardebil, Firouzkooh, Rafsanjan and Ardestan, posses the capacity of wind energy production in many months of the year. Wind power. Most of the country's windy regions are deprived, arid and less developed regions, with low levels of electrical energy production, and the cost of bringing electricity to these areas is also of high costs. Therefore, creating wind farms in these areas is a necessity. Most of the energy consumption in Iran is in the summer in order to cope with the warmth, and the windiest time in Iran is summer.https://clima.irimo.ir/article_68879_620c94ced213766ab3cf11cd439f4582.pdfhttps://www.irimo.ir/Journal of Climate Research2228-504013963120170923A case study on upper level vertical wind shearA case study on upper level vertical wind shear8910268900FAMohammadMoradiAssistant Professor of Atmospheric Science and Meteorological Research Center(ASMERC), Tehran, Iran.MehrnoshSalighazadehMaster student of Meteorology, Islamic Azad university, North branchForozanArkianAssistant Professor of Islamic Azad university, North branchJournal Article20180910In this article for two case studies mean sea level pressure data, height and wind at standard pressure levels were extracted from NCEP/NCAR archives relatedto a regular network, on the next step divergence quantities, vertical motion, upper levels vertical wind shear and thermal winds in different days were calculated by means of formulated equtions.
The results of analysis of upper level vertical wind shear and thermal wind indicated that the maximum values of these two quantities coincide with jet stream axis. This result expresses the direct correlation between these two quantities and all energy producing interactions within the atmosphere. Moreover, this study showed that the maximum values of upper levels vertical wind shear were located between the trough line and the ridge line mid level altitude and their values at high latitudes were fewer than those of low latitudes.In this article for two case studies mean sea level pressure data, height and wind at standard pressure levels were extracted from NCEP/NCAR archives relatedto a regular network, on the next step divergence quantities, vertical motion, upper levels vertical wind shear and thermal winds in different days were calculated by means of formulated equtions.
The results of analysis of upper level vertical wind shear and thermal wind indicated that the maximum values of these two quantities coincide with jet stream axis. This result expresses the direct correlation between these two quantities and all energy producing interactions within the atmosphere. Moreover, this study showed that the maximum values of upper levels vertical wind shear were located between the trough line and the ridge line mid level altitude and their values at high latitudes were fewer than those of low latitudes.https://clima.irimo.ir/article_68900_9f96f0c23a6a55dbd2e9427729e24266.pdfhttps://www.irimo.ir/Journal of Climate Research2228-504013963120170923Pattern Analysis of Dust storms in the West and Southwest Provinces of Iran and its Relation to the Middle East Dust StormsPattern Analysis of Dust storms in the West and Southwest Provinces of Iran and its Relation to the Middle East Dust Storms10312268904FASoodabehNamdariPhD in Climatology, Forests, Range and Watershed Management OrganizationNeamat KarimiKarimiAssistant Professor, Department of Water Resources Research, Water Research Institute (WRI), Tehran, IranGholamhHasanMohammadiPhD in climatology/ East Azarbayjan Meteorological OrganizationJournal Article20180910In recent years, by intensified desertification in Iraq, Saudi Arabia and Syria, dust storms have affected different aspects of people's lives over the West and southwest of Iran. Atmospheric aerosols affect public health, air quality, energy balance of the Earth and the hydrological cycle. Thus, knowledge of the spatio-temporal dust storm's distribution characteristics is extremely important to quantify these effects. By increasing in dust storms in the West and southwest of Iran much discussion about the various causes of this phenomenon has been increasing. <br /><strong>Materials and methods</strong> <br />Aerosol optical depth (AOD) is an important remote sensing parameter that serves as a columnar proxy variable representing aerosol abundance. In this study, monthly mean AOD from Moderate Resolution Imaging Spectroradiometer (MODIS) are used to investigate the spatio-temporal distribution of dust storms in these affected areas for the period between 2000 and 2015. Monthly AOD data from the MOD08 Level 3 product (http://ladsweb.nascom.nasa.gov/data) at a resolution of 1° × 1° are used. This study focuses on AOD at 550 nm over land, as this is close to the peak of the solar spectrum and is, therefore, associated with major radiative effects. Synoptic data are examined to identify how well geopotential height, temperature, and wind field at various levels (i.e., sea level, /500/250 hPa isobaric levels) are related to AOD and to dust activity. Meteorological data from the ECMWF archived operational initialized analyses at a resolution of 2.5° × 2.5° (Bou Karam et al. 2010; http://apps.ecmwf.int/datasets/data/interim-full-moda/levtype=sfc) is used. <br /><strong>Results, discussion </strong> <br />MODIS monthly mean AOD validation results using the Pearson correlation test to determine the correlation between monthly mean AOD and monthly visibility data indicated high efficiency of AOD products in the dust storm intensity study. To distinguish between the Rub’ al Khali and the influence of factors such as dams in other areas, the study domain is divided in two sections consisting of desert areas in (i) Saudi Arabia and (ii) Iraq-Syria. <br />AOD plot of the provinces of the west and southwestern of Iran showed provinces of Khuzestan, Ilam, Kermanshah and Kohgiluyeh in order have experienced the highest dust storms from 2000 to 2015. <br />As the dust storms in all months of the year are not active, to evaluate the spatial distribution of mean AOD per year, the average for the months in which the dust storms are active evaluated (named as annual AOD). Annual and monthly average plots showed that the trend of the amount of AOD in many cases in the deserts of Iraq and Syria desert was similar to deserts of Saudi Arabia. <br />Considering the wide range of climatic conditions affecting the occurrence of dusty storms, the best way to find out the effect of circulation on the formation of dusty storms is to compare the synoptic state of a special month in two active and inactive periods. To achieve this goal, the monthly anomaly maps (monthly average of 30 years (1981-2010)) were extracted at surface level, 250 and 500 Hp. Two months as representative the active and passive dust storm period was selected to study atmospheric circulation associated with dust. To investigate the impact of synoptic systems on factors influencing dust in the study region, precipitation, temperature a case study analysis of July 2009 and July 2014 was conducted. <br />Also analysis the map of the average annual distribution of dust in each region shows that the trend of AOD value in many cases in the deserts of the Saudi Arabian desert was similar to Iraq and Syria desert, but AOD value in which years the dust in deserts of Iraq and Syria were still down is high in East and South East Arabian Peninsula. <br /><strong>Conclusion</strong> <br />In this study, changes in the behavior of dust storms in the west and southwest of Iran were investigated using quantitative monthly and annual AOD data from 2000 to 2015. The result of the Pearson correlation test with the aim of determining the correlation between the monthly average visibility and mean monthly AOD and indicates the high ability of the AOD monthly data to study dust storms. <br />Trends of AOD show that from 2006 to 2012 there was increasing trend in February, March, April, May, June and July with a significant slope. The gradient of the monthly AOD slope of Kermanshah province was similar to Iraq and Syrian deserts and also Khuzestan province comply Saudi desert or desert of Iraq-Syria in different cases. These differences can only be caused by differences in atmospheric circulation in different dates. <br />By comparing the synoptic maps of active and passive anomaly, it seems at sea level pressure existence of Pakistan's low pressure center and increasing pressure on the Mediterranean Sea exacerbated atmospheric turbulence and wind speed in the Middle East play an important role on the intensification of dust storm activities. At the middle and upper levels of the atmosphere, the sovereignty of the subtropical high pressure over the Arabian Peninsula, south of Iraq, the Persian Gulf and the southern part of Iran, also increasing the activity of the Mediterranean waves, increasing the mean wind speed at the upper levels of the atmosphere, as well as the flow of jet stream core has led to an intensification of dust storms in July 2009. That's why limited dust storms have been created in central and eastern regions of the Arabian Peninsula.In recent years, by intensified desertification in Iraq, Saudi Arabia and Syria, dust storms have affected different aspects of people's lives over the West and southwest of Iran. Atmospheric aerosols affect public health, air quality, energy balance of the Earth and the hydrological cycle. Thus, knowledge of the spatio-temporal dust storm's distribution characteristics is extremely important to quantify these effects. By increasing in dust storms in the West and southwest of Iran much discussion about the various causes of this phenomenon has been increasing. <br /><strong>Materials and methods</strong> <br />Aerosol optical depth (AOD) is an important remote sensing parameter that serves as a columnar proxy variable representing aerosol abundance. In this study, monthly mean AOD from Moderate Resolution Imaging Spectroradiometer (MODIS) are used to investigate the spatio-temporal distribution of dust storms in these affected areas for the period between 2000 and 2015. Monthly AOD data from the MOD08 Level 3 product (http://ladsweb.nascom.nasa.gov/data) at a resolution of 1° × 1° are used. This study focuses on AOD at 550 nm over land, as this is close to the peak of the solar spectrum and is, therefore, associated with major radiative effects. Synoptic data are examined to identify how well geopotential height, temperature, and wind field at various levels (i.e., sea level, /500/250 hPa isobaric levels) are related to AOD and to dust activity. Meteorological data from the ECMWF archived operational initialized analyses at a resolution of 2.5° × 2.5° (Bou Karam et al. 2010; http://apps.ecmwf.int/datasets/data/interim-full-moda/levtype=sfc) is used. <br /><strong>Results, discussion </strong> <br />MODIS monthly mean AOD validation results using the Pearson correlation test to determine the correlation between monthly mean AOD and monthly visibility data indicated high efficiency of AOD products in the dust storm intensity study. To distinguish between the Rub’ al Khali and the influence of factors such as dams in other areas, the study domain is divided in two sections consisting of desert areas in (i) Saudi Arabia and (ii) Iraq-Syria. <br />AOD plot of the provinces of the west and southwestern of Iran showed provinces of Khuzestan, Ilam, Kermanshah and Kohgiluyeh in order have experienced the highest dust storms from 2000 to 2015. <br />As the dust storms in all months of the year are not active, to evaluate the spatial distribution of mean AOD per year, the average for the months in which the dust storms are active evaluated (named as annual AOD). Annual and monthly average plots showed that the trend of the amount of AOD in many cases in the deserts of Iraq and Syria desert was similar to deserts of Saudi Arabia. <br />Considering the wide range of climatic conditions affecting the occurrence of dusty storms, the best way to find out the effect of circulation on the formation of dusty storms is to compare the synoptic state of a special month in two active and inactive periods. To achieve this goal, the monthly anomaly maps (monthly average of 30 years (1981-2010)) were extracted at surface level, 250 and 500 Hp. Two months as representative the active and passive dust storm period was selected to study atmospheric circulation associated with dust. To investigate the impact of synoptic systems on factors influencing dust in the study region, precipitation, temperature a case study analysis of July 2009 and July 2014 was conducted. <br />Also analysis the map of the average annual distribution of dust in each region shows that the trend of AOD value in many cases in the deserts of the Saudi Arabian desert was similar to Iraq and Syria desert, but AOD value in which years the dust in deserts of Iraq and Syria were still down is high in East and South East Arabian Peninsula. <br /><strong>Conclusion</strong> <br />In this study, changes in the behavior of dust storms in the west and southwest of Iran were investigated using quantitative monthly and annual AOD data from 2000 to 2015. The result of the Pearson correlation test with the aim of determining the correlation between the monthly average visibility and mean monthly AOD and indicates the high ability of the AOD monthly data to study dust storms. <br />Trends of AOD show that from 2006 to 2012 there was increasing trend in February, March, April, May, June and July with a significant slope. The gradient of the monthly AOD slope of Kermanshah province was similar to Iraq and Syrian deserts and also Khuzestan province comply Saudi desert or desert of Iraq-Syria in different cases. These differences can only be caused by differences in atmospheric circulation in different dates. <br />By comparing the synoptic maps of active and passive anomaly, it seems at sea level pressure existence of Pakistan's low pressure center and increasing pressure on the Mediterranean Sea exacerbated atmospheric turbulence and wind speed in the Middle East play an important role on the intensification of dust storm activities. At the middle and upper levels of the atmosphere, the sovereignty of the subtropical high pressure over the Arabian Peninsula, south of Iraq, the Persian Gulf and the southern part of Iran, also increasing the activity of the Mediterranean waves, increasing the mean wind speed at the upper levels of the atmosphere, as well as the flow of jet stream core has led to an intensification of dust storms in July 2009. That's why limited dust storms have been created in central and eastern regions of the Arabian Peninsula.https://clima.irimo.ir/article_68904_832cb5843d4b85b3afb7d9a659cf9c41.pdfhttps://www.irimo.ir/Journal of Climate Research2228-504013963120170923Compare the goodness of fit probability distributions trend with increasing sample size Case Study: 119 annuals rainfall of MashhadCompare the goodness of fit probability distributions trend with increasing sample size Case Study: 119 annuals rainfall of Mashhad12313669162FAMahbobehFarzandiPhd student, Ferdowsi University of MashhadHojjatRezaee-PazhandInstructor, Islamic Azad University, Mashhad BranchJournal Article20180915<strong>Introduction</strong> Annual precipitation is a major meteorological variable. The occurrence of this variable follows a particular law. This rule of law is precisely defined that All rain events occur during the climatic period. But This is impossible. Usually a small sample of this variable is available. Matching several competing laws to data fit and the best with statistical tests of choice, which is an approximate successor to real law. Increasing the sample size increases the accuracy of the test and closer to the actual law. This is more prominent in arid and semi-arid regions such as Iran. The acceptable statistical period for arid and semi-arid regions (such as Mashhad) is at least 70 years old. If this length is more than 100 years old then statistical analysis of the data can be assured relative. Also, if the return period is a phenomenon more than one-fifth of the length of the data, then the estimation of this phenomenon is not accurate. Statistical analysis of precipitation is one of the important requirements of meteorology, climatology, hydrology, drought forecasting and so on. The Sample size is important in results of the statistical analysis of rainfall. The duration of rainfall stations in Iran are often short and the maximum durations of a few stations are 61 years. This study analyzes the frequency of data after collecting and repairing the long-term data of Mashhad. Comparison of the effect of sample size variation on the results of statistical analysis is necessary in order to evaluate the trust of existing data.<strong>Introduction</strong> Annual precipitation is a major meteorological variable. The occurrence of this variable follows a particular law. This rule of law is precisely defined that All rain events occur during the climatic period. But This is impossible. Usually a small sample of this variable is available. Matching several competing laws to data fit and the best with statistical tests of choice, which is an approximate successor to real law. Increasing the sample size increases the accuracy of the test and closer to the actual law. This is more prominent in arid and semi-arid regions such as Iran. The acceptable statistical period for arid and semi-arid regions (such as Mashhad) is at least 70 years old. If this length is more than 100 years old then statistical analysis of the data can be assured relative. Also, if the return period is a phenomenon more than one-fifth of the length of the data, then the estimation of this phenomenon is not accurate. Statistical analysis of precipitation is one of the important requirements of meteorology, climatology, hydrology, drought forecasting and so on. The Sample size is important in results of the statistical analysis of rainfall. The duration of rainfall stations in Iran are often short and the maximum durations of a few stations are 61 years. This study analyzes the frequency of data after collecting and repairing the long-term data of Mashhad. Comparison of the effect of sample size variation on the results of statistical analysis is necessary in order to evaluate the trust of existing data.https://clima.irimo.ir/article_69162_eb1f5534442828c4c704999fabd5ffd4.pdfhttps://www.irimo.ir/Journal of Climate Research2228-504013963120170923Climatic Zoning of Kohgiluyeh & BoyerAhmad Province Using Factor and Cluster AnalysisClimatic Zoning of Kohgiluyeh & BoyerAhmad Province Using Factor and Cluster Analysis13714969163FAHassanSalehiPhD Student, Ferdowsi University of Mashhad0000-0002-9660-8111ZolfagharRezapoorM.Sc. in Climatology, Kohgiluyeh and Boyer-Ahmad Meteorology BureauKeyvanNamjooJournal Article20180915The main objective of Climate Zoning is to obtain a simple and also comprehensive framework of climatic elements in a region. Classic or traditional methods that have been pervasive in the past decades are not able to explain the reality of the climate of a geographic area. With the advancement of science and appearance of computer on the field of science studies, as well as the emergence of geographic information systems, applying multivariate methods (factor analysis, principal component analysis, cluster analysis, etc.) has been rampantly used on the zoning of climatic regions in the last two decades. These methods are comprehensive and more accurate than classical methods and also are easily applicable. <br />After reviewing the principal components analysis on 48 climatic elements, five components that have the most significant role in Kohgiluyeh and Boyer-Ahmad Province, had been identified. These factors, in order of importance, are: temperature component, cloud-wind component, moisture component, wind component, and dust component. Between all the climate elements, temperature and cloud-wind factors, with 61% and 19% of total variance respectively, have the major role in determining the climate diversity of the province. So that in the western half of the province, due to lower elevation and proximity to the hot deserts and lack of rainfall, the role of temperature is highlighted. While by moving to the eastern half of the province, the rainfall is increasing significantly. Regional classification of province’s climate from two to eight climate zones, can be considered and help in development decisions.The main objective of Climate Zoning is to obtain a simple and also comprehensive framework of climatic elements in a region. Classic or traditional methods that have been pervasive in the past decades are not able to explain the reality of the climate of a geographic area. With the advancement of science and appearance of computer on the field of science studies, as well as the emergence of geographic information systems, applying multivariate methods (factor analysis, principal component analysis, cluster analysis, etc.) has been rampantly used on the zoning of climatic regions in the last two decades. These methods are comprehensive and more accurate than classical methods and also are easily applicable. <br />After reviewing the principal components analysis on 48 climatic elements, five components that have the most significant role in Kohgiluyeh and Boyer-Ahmad Province, had been identified. These factors, in order of importance, are: temperature component, cloud-wind component, moisture component, wind component, and dust component. Between all the climate elements, temperature and cloud-wind factors, with 61% and 19% of total variance respectively, have the major role in determining the climate diversity of the province. So that in the western half of the province, due to lower elevation and proximity to the hot deserts and lack of rainfall, the role of temperature is highlighted. While by moving to the eastern half of the province, the rainfall is increasing significantly. Regional classification of province’s climate from two to eight climate zones, can be considered and help in development decisions.https://clima.irimo.ir/article_69163_1f493358d4c4e85e516a05e6b70409e7.pdf