mohammadreza azizzadeh

The outputs of general circulation models (GCMs) usually have a bias compared to observational data, and some corrections must be made before using them to develop future climate scenarios. The bias correction methods are the standard statistical methods for processing the output of climate models. In this research, the effect of five bias correction methods on the projected precipitation of the GFDL-ESM4 model in the Lake Urmia basin has been evaluated. The methods used in this research include linear scaling (LS), local intensity scaling (LOCI), power transformation (PT), distribution mapping (DM) and delta change factor (DC). Statistical metrics such as the correlation coefficient, root mean square error (RMSE) and percentage bias (PBias) have been used to evaluate the accuracy of the corrected data in the period of 1990-2014 compared to the observational data and to choose the best method for correcting the data of future scenarios. research results showed that the delta change method significantly improved the raw estimates after correction; Therefore, this method was used to correct the data of scenarios SSP1-2.6, SSP2-4.5 and SSP5-8.5. In addition, the projection of the mean annual precipitation shows a decrease between 2 and 9 percent in SSP1-2.6, between 5 and 17 percent in SSP2-4.5, and between 8 and 26 percent in SSP2-8.5 compared to the observed data.

The purpose of this study is to investigate the trend of temporal and spatial changes in cloud temperature and cloud water fraction in Iran. To achieve this goal, MSG SEVIRI satellite products have been used for the period 2004 to 2017. First, the studied data was set in a regular geographical network with dimensions of 290×380. Then the cloud properties were extracted separately for each month and finally, the time changes of the cloud properties were modeled. To accurately assess the changes in variables, Iran was spatially defined into four separate regions. Based on statistical methods, the trend of time changes was examined through the Mann-Kendall test and Sen’s Slope to reveal the existence of a trend. The results of calculations of indicators showed that water fraction and cloud temperature in Iran, except in May and September, was upward. The highest significant value in the cloud water fraction variable can be seen in June in southern Iran and the lowest in May. Percentage study of the trend showed that the highest significant amount of cloud temperature in June in southern Iran was the lowest in the month. According to the calculations, the lowest amount of cloud water fraction in Iran is located to the north of the country with 25% and the highest amount is located to the west of Iran with 41.6%. Also, concerning high cloud temperatures, southern Iran with 58.3% has the highest amount, and eastern Iran with 25% upward data. The maximum significant percentage of series in the cloud temperature trend in western Iran was 70.83% and the minimum in the south was 45.83%.

In the present study, the meteorological conditions of the plane crash on the Tehran-Urmia route on 01/19/2011 were investigated. The ultimate goal of this study is to detect icing threatening pixels in aircraft. To achieve this goal, using the products of Meteosat satellite, the physical properties of the cloud in the northwest were evaluated. First, cloud products were received in Netcdf4 format in 15 minutes. Then, a regular network of geographical coordinates with a spatial resolution of 101×165 was prepared. After the data networking process, cloud characteristics (cloud cover, cloud type, cloud phase, cloud optical depth and cloud temperature) were extracted for the study day in a period of 15 minutes. Finally, by combining cloud characteristics (temperature cloud less than 273 and cloud liquid phase and optical depth less than one) through FIT algorithm, icing mask was modeled for the study area. Examination of cloud characteristics maps shows that the cloud temperature and the cloud phase (liquid state) have played the most important role in creating icing conditions. According to the Aviation Authorities, there are icing pixels on the flight path and at the crash location. Examination of synoptic maps also showed unstable weather conditions with severe convection at the time of the accident in the study area. Finally, under such conditions and with access to moisture sources in the upper layers of the atmosphere and the strengthening of super-cold water vapor, it has provided icing conditions.

Thunderstorms are one of the climatic phenomena that cause numerous damages in different parts of the world, due to the accompaniment with thunder, high winds, hail and heavy precipitation. In this study, the probability of thunderstorm days in Tabriz in the spring is analyzed using probabilistic rules and Markov chain model. For this purpose, the daily data related to thunderstorms (codes 95 to 99) was used for 65 years (1951-2015). At first, the daily data is classified into the normal days (code 0) and thunderstorm days (code 1). Then the frequency matrix is formed and the probability matrix is created accordingly based on maximum likelihood method. The Markov chain properties such as empirical probability and equilibrium probability, Frequency of occurrence, mean time periods and weather cycle were investigated. Finally, Occurrence Probability and return period of these spells were determined. The results show that the shortest weather cycle is in May, which has the highest frequency of thunderstorms. Conversely, the longest weather cycle is in April, which has the lowest frequency of thunderstorms. Also, in 65 years period, the one-day and two-day stormy sequences have the highest frequency. And For longer sequences, the frequency of thunderstorm days is reduced. The return period of one-day and two-day stormy spells is 1.5 and 5 days. Regression relation between the observed and estimated values of n-step periods of thunderstorm days shows that the considered accuracy and reliability for all months is more than 99%.

  Thunderstorms are formed by overheating the earth's surface in air masses or in the weather fronts especially cold front (Kaviani and Alijani, 1991). These storms are part of climatic destructive Phenomenon that cause irreparable damage as hail, heavy rainfall and thunders to facilities, farms and houses every year, so it is necessary to study this phenomenon. Since climatic phenomena such as thunderstorms, as random phenomena, are not exactly predictable and can be gained useful information by monitoring them, that this is possible through the laws of probability. Markov chain is a common method for modeling random phenomena (Wilks, 2006). This model is one of the statistical methods to examine the temporal relationship between the various climatic phenomena and it is the most common method to determine the frequency of climatic phenomena sequences. In this method, the probability of occurrence of a climate state is projected at time t based on its condition at the time before (t-1) (Alizadeh, 2001). This model is widely used in various fields including atmospheric sciences. In recent decades, climate researchers have used this model in various fields such as rainfall, drought, thunderstorm, wind speed and solar radiation. Thunderstorms is known as one of the most important atmospheric phenomena, due to the obvious climatic function and imposition of natural and human catastrophic effects, and many scholars and researchers pays attention to it in the various branches of science. The aim of this study is to evaluate the occurrence probability and return period of thunderstorm using Markov chain model and its spatial analysis in the Northwest of Iran. In this study, to Spatial-temporal modeling of thunderstorm day's occurrence in the Northwest of Iran, were used data for 19 synoptic stations during the period of (2000-2015) related to specific phenomena and the thunderstorms codes (99-95) were extracted. Markov chain models are including stochastic processes that are widely used in discrete time series modeling. Time dependence of random variables expressed through autocorrelation coefficient or transition probability matrix (Ashgartousi et al., 2003). Transition probability matrix, is a square matrix, depending on its number of states that includes the number of possible n combinations of the transition probability from one state to another. The first order Markov chain, is the main form of the Markov chain which consists of a discrete time series which the behavior of the series in the next time step depends on present not on past time steps.
Result and To examine the sequence of thunderstorm days in the Northwest of Iran, at first the frequency of normal and Thunderstorm days were calculated. The results show that in April, the highest frequency of days with thunderstorms is in center, in May in northwestern and central areas and in June and July, can be seen in the northwest. The lowest frequency also can be seen in the southern parts of the study area. Frequency and transition probability matrix was calculated for time series of thunderstorm days, Then the fit of the two- state first order Markov chain model were studied using χ 2 test and the results showed that the states frequency follow a two-state Markov chain. In order to predict the thunderstorm period, at first, the frequency of n days’ period of thunderstorm days was projected in April, May, June and July for stations, the result show that by increasing the duration of period, the frequency of the dusty days is reduced.
The estimate of n day continuity of thunderstorm days is the abilities of the Markov chain model. In April the occurrence of two-days in the center and west of study area is more possible. In May, there is the highest frequency of thunderstorm days, the probability of two- days increased to 24%. In June, the average probability of two-days is 23%. The probability of three-days in April reduced an average of 6%. In May and June, the probability is about 10%. In these months, the probability of three-days has reached 15% in North and Northeast. In July, the probability of three-days has reduced to; While, in the western part is estimated up to 14%. Return period of one-day in all stations is on average 1.5. This means that every 1.5 days, a one-day will be in the Northwest Iran. For increasing duration of thunderstorm, the return period increases, it means that it takes a long time a five-thunderstorm day happens again. Spatial distribution of occurrence probability of thunderstorm days in the long-term (persistent probability) showed that in April, the lowest probability of thunderstorm is in the Northeast and the highest probability is located in the center. In May and June, the lowest probability is in the southern parts and the highest probability is in the North and Northwest. In July, the lowest probability is in the south and Northeast and the highest probability is located in the West and Northwest. In general, the probability of days with thunderstorm in the south and northeast is less than other areas and center, north and northwest have the highest probability. The present study is conducted aimed to evaluate the occurrence probability of thunderstorm using Markov chain model in the northwest of Iran. The results show that in all stations, frequency of normal days is more than days with thunderstorm. The highest frequency of days with thunderstorms in April is in center, in May in northwestern and central areas and in June and July is in the northwest. The estimate of 1-10 days continuity showed that by increasing the duration of period, frequency of dusty days reduce. The occurrence probability of two and three-days is low in the south and Northeast and high in center and Northwest. Return period of one-day on all stations and in all months is 1.5, but by increasing duration of dust, its return period increases exponentially. The spatial distribution of persistent probability matrix shows that the occurrence of thunderstorm in center, North and Northwest is more

The variability of temperature extremes has been the focus of attention during the past several decades and had a great influence on the hydrologic cycle. A long-term, high-quality daily maximum (TX) and minimum temperature (TN) of seven stations was used to determine the spatial and temporal characteristics of extreme temperature events in Lake Urmia Basin in Iran during 1987 to 2014. The RClimdex was used to calculate 16 extreme temperature indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI) for this study and the Mann-Kendall test was employed to assess the trend. All the temperature-based indices show patterns consistent with a general warming trend. The results revealed statistically significant changes in important temperature indices over the study area during the past three decades. On the whole, cold indices, including cool days, cool nights, ice days, frost days and cold spell duration index significantly decreased by -3.07, -2.27, -1.8, -1.53 and -0.16 days/decade, respectively. In contrast, warm indices such as warm days, warm nights, summer days, tropical nights, warm spell duration index, and growing season length significantly increased by 2.99, 3.34, 3.3, 3.06, 2.63 and 1.79 days/decade. Minimum TX, maximum TX, Minimum TN and maximum TN increased significantly by 1.00, 1.76, 0.36 and 1.01 °C/decade. Furthermore, the magnitudes of the trends in cold/warm days are larger than those in cold/warm nights, which indicate that the trends in minimum temperature extremes are more rapid than in maximum temperature extremes. Strong relationships between the annual mean temperature and the extreme temperature indices were detected in this study.

Satellite rainfall estimate systems can produce data for areas for which other sources are unavailable. Without referring to ground measurements, satellite-based estimates cannot be used directly before bias correction. This study aims to evaluate the performance of the TRMM-3B43 V7 rainfall products in the Northwest of Iran. The evaluation was carried out using monthly data obtained from 21 meteorological stations during 1998–2015. The monthly and annual spatial distributions of the Pearson correlation coefficient between the station and satellite-based observations as well as the statistical error measure were calculated. The results revealed that the correlations between TRMM 3B43 and rain gauge data were high (R> 0.80) in October and small (R 30 mm) and in December to May (>20 mm). On the contrary, an underestimation was found in the Southwest regions where summer season (June, July & August) is generally characterized by small anomalies in terms of R values. Generally, the satellite products applied in this study underestimate higher rainfall values while showing overestimation for lower rainfall records. The measure of Root Mean Square Error (RMSE) showed that a large spatial variability takes place in September, October and November in most of the stations, particularly when rainfall records is less than 50 mm. Meanwhile, the lowest variability occurred in June, July and August with a slight increase in the Northeast. Our findings imply that satellite products have poor performance for estimating higher rainfalls in the Northwest of Iran on a monthly scale.

Drought is one of the most important weather-induced phenomena which may have severe impacts on different areas such as agriculture, economy, energy production, and society. A number of drought indices have been introduced and used in various countries to date. In the current study, four meteorological drought indices including Percent of Normal Precipitation Index (PNPI), Standard Index of Annual Precipitation (SIAP), Rainfall Anomaly Index (RAI), and Standardized Precipitation Index (SPI) are compared and evaluated for monitoring droughts in Lake Urmia Basin in Iran. The comparison of indices was carried out based on drought classes that were monitored in the study area using 40 years of data (1966-2005). Two well-known probability approaches including Runs theory and Markov chain model, were used to estimate the probability of wet and dry periods. The frequency matrix is formed and the transition probability matrix of wet- dry spells is created accordingly based on maximum likelihood method. The equilibrium probability is calculated based on succeed power on probability matrix. The results demonstrated that among the drought indices, PNPI is not an appropriate index in annual estimates and SPI and RAI are better than other indices and their results are nearer to reality. The results indicated the equilibrium probability of very dry, dry, normal, wet and very wet periods is obtained 0.23, 0.27, 0.23, 0.17 and 0.1, respectively.

The Satureja was consist of 14 annual and perennial species in Iran, which Satureja sahendica is one of the endemic species in Iran. This research was carried out to investigate quality and quantity of essential oil in satureja sahendica accessions. Seeds of eight Satureja sahendica accessions were collected from greenhouse in medicinal research farm of Yazd Agricultural and Natural Resources and Education Center. Seedlings were planted in field using a randomized complete block design with three replications in 2010. Samples were harvested at 50% flowering stage during four years. Essential oils were extracted from dried samples by Clevenger apparatus and the oil compounds were analyzed by GC/MS. The results were showed significant differences between accessions for essential oil percentage at 5% level and the thymol and e-caryophylen at 1% level. Cluster analysis were showed two chemotype groups rich for thymol and carvacrol (SS6, SS5,SS4, SS3, SS2, SS1) and rich for para-cymen and γ -terpinene (SS7 and SS8). Regarding to essential oil yield per hectare and essential oil quality, in SS3 accessions from East Azarbayjan province with mean of essential oil (2.13%),oil yield (14.52 Kg/ha) which had carvacrol (%13.57) and thymol (%41.23) , according to SS2 accessions from Kordestan province with mean of essential oil (2.96%),oil yield (13.49 Kg/ha) had carvacrol (%2.78) and thymol (%37.62), both of them were selected as the superior accessions.

In this paper we have focused on one of the important climatic phenomena, namely, the wind chill effect. This feature is created through the combination of climatic elements such as temperature and wind under specific environmental conditions, by which the human welfare is disturbed. Under these conditions, the lower temperatures increases human stress and the possibility of body texture freezing. In fact, the level of temperature that causes freezing of boding limbs, is called Wind Chill index, and so far there has been several modelling standards and indexes in the literature. In this article, we have applied two famous indexes that have been alredy applied worldwide. These include Siple and Passel Indexs, and the second index is widely and practically used by Canadian and American meteorological organizations. After represe nting the oretical background, we have briefly introduced each Index, followed by the application of them for the calcutation of wind chill effect. The tables and standardized equations, and the mixture of climatic elements of temperature and wind have been utilized for Ahar City in north west Iran. A comparison of final models indicates that the equivalent wind chill temperatures of first Index have more difference with the real temperature. Accordingly, we propose the Canadian Meterological Index for the studyins of similar climatic conditions of north west Iran.