JOURNAL OF CLIMATE RESEARCH
Year:2018 | Volume:9 | Issue:33-34 |Papers Count:6

One of the most important issues that human societies are currently facing is the phenomenon of climate change that attracted the attention of many researchers around the world. One of the effects of climate change is the increase in the frequency and severity of occurrence of extreme weather events, such as frost temperatures and extreme rainfall. The effects of climate change on changes in cold-wave temperatures in recent years, as well as the effects of cold waves on various aspects of human societies, have always been the concern of various scholars in most parts of the world, including cold weather, and atmospheric hazards. Several definitions of cold waves are presented. In general, scientists used cold weather or cold weather for at least 2 days or more at temperatures below zero degrees Celsius. In general, the characteristics of the cold wave can be summarized in three characteristics: 1. the intensity and velocity of the temperature drop 2. the numerical value of the drop or deviation in the daily temperature value 3. the duration of the continuity (time continuity) of the cold wave. Materials and methods For the purpose of this research, data from the upper levels of the atmosphere were used to analyze the data of the 20th century. The data listed on the website was https: //www. esrl. noaa. gov/psd/data/gridded/data. 20thC_ReanV2. html and was used. NASA's Twentieth Century Data has a good place in comparison with the normal data from time separation, and has a better and more accurate accuracy than it is. The data used are 142 years old from 1871 to 2012. The data are 6 hours and 4 daily observations (0, 6, 12 and 18 Zulu) with spatial resolution of 2 degrees longitude at 2 degrees latitude and 24 pressure levels from 1000 to 10 hPa. For the purpose of this study, at first, the cold waves with a minimum temperature of-15 ° C and continuity of time for at least 2 days in 142 years of statistical period were extracted from a minimum of two-meter surface temperature data. Then the relevant data was categorized in terms of frequency, severity, duration and spatial extent. In order to validate the extracted data, they were controlled to the point where they met the ground stations and ensured the accuracy of the data. For synoptic analysis, the coldest waves occurred in each of the months. For this purpose, combinational maps of ground pressure, geopotential heights and wind current of 500 hPa, bar thickness map, temperature map, and a minimum temperature of two meters of ground level were used. Results and discussion The results of the synoptic analysis of the selected days indicate the dominance of the high-pressure system on the surface of the earth and the collapse of the block, followed by the cavern and stack at the upper levels of the atmosphere. In most of the study days, the high pressure (Siberian high-pressure migratory force) system in the Earth has been flowing on the Earth's surface, which has intensified its intensity. The meridian flow formed by the flood-blocking blocks has led to a very cold, northwest northern part of Iran's cold air and, on the other hand, has increased the duration of the deployment of these cold waves. Conclusion According to the results, 44 cold currents at-15 ° C and colder in the north east of the country were identified during the studied time period. The waves are mostly due to January, February and December. Which is very good for the influence of Siberian cold pressures, shows such waves. A noticeable decrease has occurred in these waves from 1907 onwards. The frequency of these waves has decreased over the years, but has been added to the severity and duration of its deployment. According to the results of the studies, it can be said that changes in the process of cold weather are not related to global warming and climate change. This cold wave is formed in most cases when the Siberian high-pressure earth is merged with the Western Immigrant High. On the other hand, in the upper levels of the atmosphere, due to the high surface pressure of the western winds, they collide with blocking barriers and convert the orbital flow of the western wind to a meridian stream. The process mentioned above leads to the formation of a heap over Eastern Europe to the middle of the Red Sea and the formation of a ravine over the Mediterranean and Turkey. As explained above, it leads to the very cold weather flowing north to the northern part of Iran. Also, the thinness of barley thickness over the atmosphere of northeastern Iran formed as a result of a very cold, semi-arctic flow; the very cold weather has dominated the northern latitudes for several days. The study of temperature patterns indicates the domination of the northern and northwest winds, and also indicates an increase in the cold intensity of the waves occurring with the increase of the latitude of the source. Very cool temperatures occur when the source of cold air is from latitudes above 60 degrees.

This paper is aimed at climatological study of low level clouds in four seasons and accordance to the long-term pattern for operational cloud seeding over Iran. Clouds are observed in different shapes and heights in the atmosphere. Regarding the classification of clouds there are various ideas. Based on the Howard classification 10 types of clouds are classified in 4 groups based on the height of cloud base from surface includes low with height of less than 6500 ft, middle with height of 6500-16000 ft, high with 16000-18000 ft, and vertical development. Cloud climatology is main part of weather modification program for cloud seeding site selection. In this study, frequency of occurrences of low level cloud including Stratocumulus (Sc. ), Stratus (St), Cumulus (Cu) occurrence have been collected from 44 synoptic stations of Iranian Meteorological Organization (IRIMO) for 30 years (1981-2010) for day time including 06, 09, 12, and 15 UTC and CFSR[1] data with 0. 5 degree resolution have been used for deriving of long term seasonal pattern. Seasonal mean of geopotential height, temperature, horizontal wind, vertical wind at 500 hPa, surface pressure and precipitable water (PW) in (500-1000) hPa layer have been derived. Investigations have been carried out based on location of province and watershed basins. The most important investigating results show that low level clouds occurrence with less than %30, have not been observed in selected stations in cold season including autumn and winter. The Northwest, North, and Northeast provinces and basins have high (>%70) low level clouds occurrence in all months which have suitable condition for operational cloud seeding with respect to this index. Low clouds occurrences in warm season including spring and summer show that southern part of Iran have low (<%30) to medium (%30-%70) but were high (>%70) for other stations which detail information of Southern provinces are as following; provinces including Hormozgan, Bushehr, Sistan-Baluchestan and Khuzestan, Kerman, Fars, and South Khorasan are between %30-%70. Maximum of low cloud occurrence belongs to spring, autumn, winter, and summer respectively. Maximum of cloudiness belongs to March and April in spring. The percent of low level cloud occurrence in southern basins in most months were low (<%30) which achieved very low score for operational cloud seeding with respect to this index. Long term atmospheric pattern shows suitable condition for amplification of cloudiness in clod seasons which accompanied with falling of cold weather Siberian high pressure and amplification of the warm Southern flow provide suitable condition for enhancement of humidity specially for west part of country. Formation of thermal low pressure in summer season, coverage of level cloud and precipitable water reduces over region. In cold seasons (roughly autumn and winter) maximum of low cloud occurs at Bandar-Anzali station in Gilan Province with max of 93. 03% and Ramsar station in Mazandaran province with max of 94. 34% which both occur in October. In the warm season (roughly spring and summer) is related to Ramsar station with Max of 99. 10% and Noshahr station with 98. 72 % which both occur in September. The minimum of low cloud occurrence is related to Zabol station in Sistan-Baluchestan province with 32. 88% and Iranshahr station in Sistan-Baluchestan province with 37. 93% in clod season which both occur in October. In warm season Bushehr station in Bushehr province and Abadan station in Khuzestan province have 15. 79% and 16. 34% respectively which both occur in September. Due to location of Caspian Sea in North of Iran and Alborz Mountain in southern part of it, therefore the most low level clouds occur in moist and rainy climate. Long term surface pressure pattern show formation of suitable condition for amplification of cloudiness amount in cold season which with falling of cold weather in North and Northwest associated with Siberian high pressure and amplification of warm and humid weather provide suitable condition for increasing of humidity specially in Midwest of country. Formation of thermal low pressure at the surface causes decreasing of coverage of low cloud and precipitable water over the region in summer. Although application of satellite data for investigating of clouds is prevalent due to their expand coverage, but the accuracy and importance of surface observation is not deniable. However, in order to achieve more accurate mapping of low level cloud over Iran, due to the shortage of number of synoptic stations with long term observation using satellite data is necessary.

Abstract Introduction: Digital soil mapping is achieved using different data and methods in integrated methods. These methods determine the spatial and temporal distribution of soil features. Digital soil mapping can fill the gaps in the knowledge and data of soil in recent years. In early 2000, some effective factors have led to the more successes of the digital soil mapping including: 1) increasing the access to the spatial data such as satellite images, Digital elevation models (DEM), and increasing the number of available maps, 2) increasing the computational abilities in data processing, and 3) development of the data mining tools and Geographic Information Systems (GIS). Moreover, increasing the global demand to reduce uncertainties in the spatial data, repairing existing maps, and the help of global organizations in development of methods are among the other effective factors. Materials and methods: Theoretical framework of digital soil mapping was introduced in a large number of researches. In this research, some aspects of digital soil mapping were represented including the structure, history, and explanation of some of its parts. In this regard, two main categories exist including sensing the soil properties close to the soil and soil remote sensing. In the latter one, the most important method is soil spectroscopy. However, soil spectroscopy encountered some problems such as complexity of the spectrum of soil components and overlapping the spectrum of those components. Results and discussion: The results showed that the digital soil mapping requires three fundamental parts including: 1) the costs in the form of field campaigns and laboratory observation methods, 2) the process of utilizing spatial and non-spatial inference systems of the soil, and 3) output parts in the form of soil geospatial information systems which include outputs in the form of forecasting rasters along with their uncertainties. Conclusion: The results indicate the success of the proposed methods. Moreover, the developments of digital soil mapping is composed of the access to digital spatial data, abilities in bulk data processing, prevalence of data mining tools, improving the GIS systems, and utilizing geostatistic methods. Furthermore, extracted data from remote sensing data such Landsat, Spot, MODIS, NOAA AVHRR, and IKONOS images can be helpful. However, calibrating satellite data require laboratory measurement such as soil spectrometry.

Introduction Wind is known as an intermittent event because of its rapid change in direction and value. Various effects of storm on civil aviation, besides of its danger to the urban, industrial and agricultural areas, make it very important to forecast wind in appreciate lead time. Direct effect of wind on many industries, specially its role in energy generation and increasing share of wind energy in the market, made it very important. High penetration of wind power in the electricity system provides many challenges to the power system operators, mainly due to the unpredictability and variability of wind power generation. Material and methods Different kind of observation systems including in-situ devices and remote sensing devices are useful to measure wind, and different methods are useful to detect and estimate probability of extreme events as well as forecast the wind speed. Different methods for detection and forecasting of wind have been invented and several works were done for comparing and improving them. In-situ measuring devices include, cup anemometer, ultrasonic anemometer and hotwire anemometer, while remote sensing measuring devices include, SODAR, LiDAR and radar. SODAR, LiDAR and radar operate in a similar manner except that they use different kind of pulses for transition. Generally, both the intensity and the Doppler frequency shift of the return signal are analyzed to determine wind speed, wind direction and turbulence. In spite of in-situ measuring instruments which measure the wind at a single point, remote sensing devices measure the wind in several points or a limited area. Each measuring device has its advantage and limitations witch has been listed in the paper. Wind farm deployment is moving from flat to complex terrains because of the availability of stronger winds there. The cost of site assessment through local sensing techniques is also growing due to the increasing height of meteorological masts. The maintenance required after installing the setup makes this approach even more expensive. On the other hand, remote sensing technologies are cheaper solutions, but their accuracy in complex terrains is still questionable. Turbulence also needs to be considered when measuring the wind. Turbulence is caused by (i) friction with the earth’ s surface, that is flow disturbances caused by the topographical features and (ii) thermal effects that can cause air masses to move vertically as a result of variations in temperature. Turbulent flow is chaotic with a variable pattern over a short time frame but it has a relatively constant average over longer time periods. Wind turbulence is the rapid disturbances or irregularities in the wind speed, direction, and vertical component. The most common indicator of turbulence is the standard deviation (σ ) of wind speed. σ normalized with the average wind speed gives the Turbulence Intensity (TI) of a site. Results and discussion Various methods classified according to time-scales or methodology, are available for wind forecasting. According to the time-scales, wind forecasting methods can be divided into 4 categories. (i) ultra-short-term forecasting: from few minutes to 1 hour ahead, (ii) Short-term forecasting: from 1 hour to several hours ahead, (iii) medium-term forecasting: from several hours to 1 week ahead and (iv)long-term forecasting: from 1 week to 1 year or more ahead. Each category has its own application in industry. The rapid increase in numbers of connectable devices, the expansion of networks, the implementation of new networks, and the requirement for field workers to be completely mobile but always connected (with laptops, smart tablets, smart phones), makes even more imperative the implementation of some form of Unified Communications. Otherwise it takes too long to adapt to changes. Under this paradigm the communications medium from the central server to a remote station, and around the remote station may still be varied (fibre, cable, cellular, satellite, ADSL, Radio, Microwave, WiFi, Ethernet etc. ). However the interconnection method between the different medium link modules is all the same-Ethernet, with Power over Ethernet (PoE) where practicable. New frameworks in observation systems like IOT (Internet Of Things), make a revolution in measuring methods along with data transfer. In IOT, all of the data sources (sensors), end user devices (displays, databases), and even a data source and sink (an actuator, smart phone) are connected to the Internet and have two ways communication. Conclusion This paper review the wind measuring devices along with the new frameworks of measuring methods like IOT and then presents a comparison between different wind forecasting methods. Spatial correlation method has been depicted by use of measured data of two ultrasonic wind sensors of IKIA (Imam Khomeini International Airport) in March 31st 2015. Results show strong dependencies of the observed data of two sites, and wind speed and direction in second site, follow the first site with a delay. Comparison between wind measurement by radiosonde and VVP and CAPPI products of S-band weather radar in Ahwaz shows good consistency at higher at elevation.

Introduction: The climate of the summer season, dry and low rainfall region of the Middle East, has not been considered by atmosphere scientists for unclear reasons. The claim for the need to update the definition of seasons is due to the fact that the weather conditions are not considered in the meteorological and astronomical definitions. Almost all of the definitions given to identify the characteristics of the summer season follow three physical, dynamic and synthetic approaches. The physical index considers days with an average temperature above threshold as a summer day. Dynamically, more heat transfer in the summer leads to an increase in altitude and a reduction in the pressure of the tropopause. Synoptically, scholars have outlined the beginning of the summer to overcome the Persian trough over the region. In addition, with the sudden disappearance of the Persian trough in the middle of September, the summer season ends. With the assumption that global warming has accelerated the course of the summer period and significantly increased its intensity over the past decades, researchers have been reviewing the summer atmosphere in the Middle East, and especially in Iran, by designing new indicators. Materials and methods, In this study, using the reanalyzed data of temperature, tropopause pressure and geopotential height of the NCEP/NCAR database during 1948-2016, the characteristics of the summer season, including its start, peak, durability and ending in Iran has been evaluated. By using the script written in the GRADS software, the daily values of the data were extracted in the desired ranges. Then, in order to estimate the summer period based on the threshold of each indicator, the daily average of the data was calculated. The thresholds for physical, dynamical and synoptic indicators for estimating the start and end of the summer season and the number of summer days, respectively, were daily temperatures higher than 25 degrees, tropopause pressure less than 120, and geopotential heights less than 50 meters. The transit of daily data values from the threshold and their 10-day continuity indicates the beginning and end of the summer period. Finally, the summer weather variations in Iran were also studied based on the Kendal’ s tau tests and the graphically Man-Kendal test. Results and discussion, the results of temperature-based index (physical index) with a threshold of 25 degrees Celsius and a 10-day durability shown that the beginning of the summer season in Iran is 1 June and its end is 27 September. Based on the variations of the tropopause level pressure (dynamic index) with a pressure threshold of less than 120hp and ten day duration, the beginning of the summer season is 4 June and the its end 1 October. The results of the assessment based on the index of geopotential height changes in 1000hp level (Synoptic index; Persian Trough Formation) with a threshold of 50 meters and ten days duration showed the beginning and end of the summer season in the Iranian region, 20 May and 15 September, respectively. The average annual summer season based on physical, dynamic and synoptic indexes was estimated, respectively, 134, 120 and 119 days. The results of trend analysis indicate that the frequency of summer days on the Iran based on the physical index has a significant upward trend. Accordingly, the summer atmosphere has a tendency towards early starters and late end-time. Dynamic and especially Synoptic indices the opposite of the physical index show the declining trend of summer atmosphere. Conclusion, The time series process of the temperature-based physical index indicates the upward trend in the number of summer days during the study period. The correlation coefficient of τ =0. 39 and a significant trend in the time series of the mean time average of the summer temperatures of the 69-year period, also proving the hypothesis that climate change was happened. In terms of the physical index (temperature) over the last seven decades, the summer tended to start earlier and more sustained, while the synoptic index was opposite. In contrast, the time series of the synoptically index based on the period of the Persian trough formation over the Persian Gulf region indicates a decline in the number of annual summer days. Because of the differences in the results of the physical index and the similarity of the dynamical and synoptic indexes with previous studies, the authors emphasize the need for further research in this regard.

Introduction Evapotranspiration is significantly affected by global climatic changes as an essential component of both climate and hydrological cycles. Comprehensive analyses of the spatiotemporal changes of ETo enhance the understanding of hydrological processes and improve water resource management. The main objective of this study are to investigate and predict the temporal trend and spatial distributions of the reference evapotranspiration (ET0) during 1961-2014, 2021-2050 and 2051-2080 over Razavi Khorasan Province using observed grided dataset named CRU and four GCMs outputs. Data and methods 2. 1. study area Razavi Khorasan Province is located in northeastern Iran. This province is located within the longitude and latitude of 56° 19” to 61° 16” E and 33° 52” to 37° 42” N. The climate of this area can be characterized as arid and semiarid with a long term annual precipitation of about 207. 5 mm and a long term annual maximum and minimum temperture of 18. 3° C and 9. 3° C, respectivily. 2. 2. data Monthly maximum and minimum temperature data were collected from updated highr esolution (0. 5° × 0. 5° ) gridded dataset of CRU initially released in 1999 by New et al., total 42 grid points in Razavi Khorasan province. After assessing the accuracy of the CRU data using 11 synoptic stations over this province, the monthly ETo values were calculated using Hargrives-Samani method to study the spatiotemporal variations in this variable. The impact of climate change on future spatiotemporal Eto, evaluated using four coupled atmosphere-ocean general circulation models (AOGCMs) under RCP8. 5 scenarios. The parametric t-test and nonparametric Mann-Kendall test methods were used to analyze the temporal characteristics of annual ET during 1961-2005, 2021-2050 and 2051-2080. Results and Conclusions The results were as follows: (i) generally, ETo increased from north to south across the province (ii) from 1961 to 2014, annual ET exhibited an increasing continuous trend across the area under study (iii) ETo also displayed a significantly increasing temporal trend during two future periods across Razavi Khorasan Province. (v). The difference between mean annual ETo values of two periods was statistically significant in all grid points covering this province. The results showed that these increases may lead to the increase in crop water requirements and aggravate the water shortage in this area in view of the increase in ET0 in response to ongoing climate change. ETo play an important role in the agricultural and water resources management, and its accurate prediction will signify better planning and management of the water, agriculture and other sectors, hence, using outputs of GCMs can facilitate the sectors by reliably predicting the future climate change impact on ETo in this province.